Characterization of In-Pixel Buried-Channel Source Follower with Optimized
Materials Characterization
Materials Characterization Materials characterization is a fundamental aspect of materials science and engineering, encompassing a wide range of techniques employed to investigate the structure, properties, and performance of materials. These techniques providecrucial insights into the nature of materials, enabling scientists and engineersto understand their behavior and tailor their properties for specific applications. One prominent category of materials characterization techniques is spectroscopy, which involves the interaction of matter with electromagnetic radiation.Techniques like X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) utilize X-rays to probe the crystal structure and elemental composition of materials, respectively. Infrared (IR) spectroscopy, on the other hand, employs infrared radiation to identify functional groups and molecular vibrations within materials. Another significant category is microscopy, which allows for the visualization of materials at different length scales. Optical microscopy uses visible light to magnify and observe the surface features of materials, while electron microscopy utilizes electrons to achieve much higher resolutions,enabling the visualization of nanoscale features. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are widely employed techniquesfor examining the morphology and microstructure of materials. Mechanical testing methods assess the response of materials to applied forces. Tensile testing measures the strength and ductility of materials under tension, while hardness testing determines the resistance to indentation. These techniques providevaluable information about the mechanical properties of materials, crucial for structural applications and material selection. Thermal analysis techniques investigate the behavior of materials as a function of temperature. Differential scanning calorimetry (DSC) measures the heat flow associated with phasetransitions and chemical reactions, while thermogravimetric analysis (TGA)monitors the weight change of materials upon heating. These techniques provide insights into the thermal stability and decomposition behavior of materials. In conclusion, materials characterization encompasses a diverse array of techniques, each providing unique information about the structure, properties, and performance of materials. These techniques are indispensable tools for materials scientistsand engineers, enabling them to understand, design, and optimize materials for a wide range of applications, from advanced electronics to aerospace and biomedical engineering. The ongoing development of novel characterization techniques continues to push the boundaries of materials science, paving the way for the discovery and development of innovative materials with unprecedented properties and functionalities.。
Sensors and Actuators APhysical
Sensors and Actuators A:Physical1.IntroductionAt present several wireless capsule endoscopy systems areavailable on the market(Given Imaging,Olympus EndoCapsule)[1].Although appealing to the patient for comfort reasons,they lack three major properties:adequate image resolution (256×256 pixels),sufficient frame rate(2–7 frames per second (fps)),and the ability to move around in a controlled way through the GI tract.These shortcomings hamper their breakthrough with gastro-enterologists,who still prefer the traditional endoscopes[2].These limitations are a direct consequence of the finite energy supply available in these capsular endoscopes.All of them being battery powered,their lifetime is limited between 6 and 8 h,consuming 25 mW.The tight energy limit does not exist in the case of inductive powering[3,25].Without the energy constraint,a higher resolution sensor and higher frame rates become possible.However,in order to get the mass of image data outside the body to the receiver,a high speed data transmitter is required.2.Requirements2.1.Data rateModerm wired endoscopes are already equipped with High-Definition(HD)CCD cameras, providing up to 30 fps at 1920×1080 pixels per frame[4].This resolution would requirea raw Bayer data rate of 78 MByte/D image sensors are not suitable for use in capsular endoscopes because of their high power consumption compared to their CMOS equivalent.HD resolution in wireless endoscopy,even highly compressed,sounds like a fairy tale,for the simple reason that high data rate and low power is hard to combine.It is a questionable prognosis if the big advantage of patient comfort will surpass the need for HD image resolution.Compared to the presently used 256×256 pixel resolution,a big improvement in image quality can already be obtained by using a 640×480 pixel(VGA)image sensor.For 10 fps,which is a major step ahead of the current 2 fps,a raw Bayer data rate of 3.84 MByte/s is required.Still being too high for low power transmission,appropriate compression algorithms have to be used to reduce the raw data rates to acceptable levels suitable for low power transmission.pressionImage compression basically removes visually redundant information from a picture or video stream,without exaggerated loss of detail or introduction of compression artifacts. The (lossy) compression algorithms are either based on removal of high frequency image content(e.g.JPEG)or on removing redundancy from the image colors[5].A20-fold compression can easily be reached without disturbing artifacts or visual image degradation.This will relax the data rate requirement to a more feasible 1.5 Mbps.The choice of the compression engine is important at system level design:depending on the type of compression more bulky and power consuming RAM is needed.2.3.Carrier frequency and modulationLittle research has been done on the choice of the carrier frequency for through-body wireless near-field transmission.The paper of Johnson and Guy describes the attenuation of electromagnetic(EM)waves through the human body[6].This paper suggests to choose a relatively low(<200 MHz)carrier,as the attenuation increases exponentially with the carrier frequency.The reduced attenuation would lead to less required transmitted power.It is currently not known if the same behavior can be expected for near fields,so it is hard to draw a definitive conclusion on the choice of frequency.The work of Chirwa et al.suggests using a carrier between 450 and 900 MHz for maximum radiation[7].This work is based on finite-difference time-domain(FDTD)simulations on a human body model,and does not include antenna loading effects.The same antenna model was used for all frequencies,which partly accounts for the lower radiation intensities at lower frequencies.This work confirms the rapidly increasing absorption above 500 MHz,both for near-and far-fields,as described by Johnson[6].The experiments of Wang et al.describe the in-vitro characterization of ingestible capsules for 30 MHz and 868 MHz[8].They conclude that the low frequency capsule is less influenced by surrounding tissues,shows less orientation-dependent fading and a higher signal to noise(S/N)ratio for a certain power consumption.The research suggests to use a carrier below 500 MHz,although there must exist a trade off between antenna size and wavelength.For far fields,a higher carrier frequency would require a smaller antenna,so less occupied space inside the capsule.As we are working in,or close to the near-field region(the transmission and reception antenna distance is smaller or equal to the wavelength),it is not sure whether this trade-off is still valid.Near-field simulations and experiments will lead to an optimal choice of the carrier.Governmental(FCC and ERO)regulations will eventually define which frequency band can be used[9,10].Candidates could be ISM(433.05–434.79 MHz)or MICS(402–405 MHz),although the defined bandwidth limits the maximum data rate.Currently the 2 m amateur band(144 MHz)is used for data transmission,as this limits the interference with other important bands.FSK is chosen as modulation type,for its simplicity in modulation and demodulation,and its inherent insensitivity to systemnon-linearities.2.4.AntennasThe space around the transmitter antenna can be divided into two main regions as illustrated in Fig.1:far field and near field.In the far field,electric and magnetic fields propagate outward as an electromagnetic wave and are perpendicular to each other and to the direction of propagation.The angular field distribution does not depend on r,the distance from the antenna.The fields are uniquely related to each other viafree-space impedance and decay as 1/r.An the near field,the field components have different angular and radial dependence(e.g.1/r3).The near-field region includes two sub-regions:radiating,where the angular field distribution is dependent on the distance,and reactive,where the energy is stored but not radiated.For antennas whose size is comparable to wavelength(as used in UHF RFID),the approximate boundary between the far-field and the near-field region is commonly given as r=2*D2/λ,where D is the maximum antenna dimension and λis the wavelength.For electrically small antennas(as used in LF/HF RFID and in this application),the radiating near-field region is small and the boundary between the far-field and the near-field regions is commonly given as r=/2π.When the receiver antenna is located in the near field of the transmitter antenna,the coupling between the antennas affects the impedance of both antennas as well as the field distribution around them.The equivalent antenna performance parameters (gain and impedance)can no longer be specified independently from each other and become position and orientation-dependent [11].In general,to calculate the power received by the receiver in such a situation,one needs to perform a three-dimensional electromagnetic simulation of the near-field problem except when the transmitter is small and does not perturb the field of the receiving antenna.The near field of a transmitter antenna can have several tangential and radial electric and magnetic field components which can all contribute to coupling.Two ultimate cases are magnetic (inductive) coupling and electric (capacitive) coupling.In magnetic RFID systems,both receiver and transmitter antennas are coils,inductively coupled to each other like in a transformer.If the transmitter antenna is small,the coupling coefficient is proportional to[12]:where f is the frequency,N is the number of turns in transmitter coil,S is the cross-section area of the coil,B is magnetic field at the transmitter and?is the coil misalignment loss.The primary coupling mechanism in near-field transmission can be either magnetic(inductive)or electric(capacitive).Depending on the environment,the field distribution can be affected by the presence of various objects.Inductive coupled systems,where most reactive energy is stored in the magnetic field,are mostly affected by objects with high magnetic permeability.The magnetic permeability of biological tissue is practically equal to the magnetic permeability of air.On the other hand,capacitive coupling systems,where most reactive energy is stored in electric field,are affected by objects with high dielectric permittivity and loss.Since the body has high epsilon,it seems that inductive coupling is much more efficient for this kind of applications.3.Design strategy3.1.TransmitterThe first work on endoscopic telemetric capsules dates from 1957,where Mackay used a single transistor Hartley oscillator as transmitter[13].The capsule was used to measure gastric pressures,where a pressure sensitive membrane connected to an iron core modulated the oscillator inductance and thereby the oscillation frequency.The operating frequency of this device was in the 100 kHz range.The transmitter developed in earlier work[14]was redesigned such that the power consumption dropped with 66%while doubling the data rate.The transmitter consists of a single transistor Colpitts oscillator as shown in Fig.2.The circuit is built up as a common collector circuit,with the antenna connected to the collector side.The presented topology operates the transistor with a unique double function:(1)it provides gain to the feedback loop to sustain the oscillation and(2)it provides acascode function at the collector.During the capsule GI tract transit,the parasitic loads seen by the antenna change continuously.The cascode limits the antenna load detuning of the LC tank,which greatly improves frequency stability,and therefore reception.FSK modulation is achieved through modulation of the transistor base current.A change in the base current causes a change in base-emitter voltage,which influences the depletion layer width of the base-emitter junction.In this way the base-emitter capacitance is modulated,controlling the tuning of the LC tank.The maximal data rate of this transmitter is limited by the RC time constant of the R data resistor and the capacitance seen at the base.It is clear that from a frequency higher than 1/(R data*C base),the modulation index decreases,because the injected base current is shorted in the base capacitance.Although the occupied bandwidth decreases,the S/N ratio decreases too,and robust demodulation becomes more difficult at faster modulation rates.From experiments,the limit was found to be at 2 Mbps.As stated earlier in the requirements,it is more beneficial to use inductive near-field coupling for the transmitter–receiver system. For that purpose a coil transmitter antenna was developed,which was tuned to resonance around the transmitter frequency,using a trimmable capacitor.The tuned regime(for maximum power output)was measured using a spectrum analyzer.The antenna was designed as a proof of concept,but not simulated or optimized.For that reason further optimization regarding coupling,coil layout and matching is required in the future,when dedicated antennas will be designed and simulated.3.2.ReceiverThe receiver of[14]was reused,which is based on a SA639 IC application note.It consists of an IF mixing system used in DECT receivers.The FSK modulated carrier is converted back to a serial data stream by mixing the IF carrier with a phase-shifted version of itself.This results in a DC component containing the data and a component at twice the IF frequency,which is attenuated using a low pass filter.A2 m low noise amplifier(LNA)was built and included in the system,to improve the noise figure(NF)of the receiving system.The SA639 receiver has a NF of 11 dB,where the LNA has NF of3 dB with a gain of 20 dB.By including the LNA,the overall NF decreased with 6 dB.A tuned loop was used as receiving antenna,designed as a proof of concept for near-field reception.The received data is resynchronized in a hardware developed data recovery system,written in VHDL and implemented in a Xilinx Spartan XC3S200.When a synchronization pattern for a newimage frame is detected in the data stream,the data is buffered in a FIFO and sent through USB to a PC.On the PC the image data is visualized on the screen.The receiver setup is depicted in Fig.3,showing from left to right the USB interface+data recovery,the FSK demodulation board with the PLL below,and the LNA+loop receiver antenna.4.SimulationsThe transmitter circuit was simulated using ELDO[23],in order to:•optimize the component values to maximize the output power,•optimize the component values for the correct carrier frequency,•check for stable and fast start-up behavior,•check the influence of parasitic(capacitive)load changes on the output,•optimize the frequency deviation depending on the DA TA input.The sweep simulation depicted in Fig.4 shows the output frequency at DC modulation.The typical frequency deviation between DA TA=0 and DA TA=1 is approximately 180 kHz.The simulation results in Fig.5 shows the modulated frequency of the transmitter with the DA TA input toggling at 2 Mbps.The short nanosecond spikes are a result of the frequency measurement algorithm.Note the exponential curves at each data edge.This is the consequence of the base capacitance being(dis)charged through the R data resistor.This RC time constant puts a limit on the maximum achievable data rate.An important advantage of this transmitter topology is the insensitivity of the frequency vs.load capacitance variations(Fig.6).The change in frequency is<0.15%for a change in capacitive load of 20 pF.This is a huge improvement compared to[13],where the output frequency is directly proportional to the output capacitive load.5.MeasurementsThe transmitter–receiver system functionality was initially characterized using the setup as described in[24].This setup showed an unmodulated output power of -18 dBm at 50,for a supply voltage of 1.8 V.The power consumption is 2 mW at a modulation rate of 2 Mbps,being equivalent to 15–20 VGA frames/s,using appropriate compression.This results in a FOM of 1 nJ/bit,which is lower than most state-of-the-art transmitter circuits,see Table 1.Fig.7 depicts the data output of the receiver together with the data from the pseudorandom generator,showing that we are able to transmit and receive a data stream a 2 Mbps.A more qualitative test setup was conceived in the meantime, depicted in Figs.8 and 9.The transmitted data is generated from a Micron MT9V013 VGA image sensor(Fig.8 top board+sensor were provided by V ector partner Neuricam).As 30 VGA frames per second are continuously provided,and maximally 2 Mbps can be handled by the transmitter,only one out of 30 frames is buffered in a DRAM,and then slowly released from the buffer(Fig.8,bottom board).This leads to a data rate of 1.5 Mbps,or about 0.5 VGA fps which is fed to the transmitter.To avoid ground loops,or influence/crosstalk between the transmitter and the data generation board,a galvanic isolation between both is mandatory.A TOSLINK optical transmitter and receiver(TOTX147 and TORX147)were used for this purpose,allowing complete galvanic isolation between the data source and the transmitter.Fig.9 depicts the functional transmitter/receiver setup,together with the GUI showing the received picture.Transmitter and receiver are at a distance of about 20 cm.The big difference between[24] and this setup is the use of real VGA image data,as well as the use of near field instead of far-field antennas.For these tests,the transmitter was battery powered by a 3 V coin cell.The transmitter measures only 0.42 cm2 which easily enablesintegration into an endoscopic capsule.Fig.10 depicts the assembled FSK transmitter.6.ConclusionA simple,high data rate and low power transmitter is designed,fabricated and measured.It enables a 4–5 times higher image resolution(VGA instead of QVGA)and 15–17 fps for inductively powered endoscopic capsules.This development greatly improves the acceptation level by the surgeon,and will help to achieve correct diagnosis for GI tract diseases.Future improvements and measurements consist of improving the Tx-Rx antenna system,full integration with a wireless powering module and quantitative tests of the transmitter module,like Bit Error Rate(BER)vs.distance and BER vs.transmission power.More tests need be done on the functional integration of an inductive powering module and transmitter,especially with regards to harmonics of the inductive power link disturbing the transmitted FSK spectrum.AcknowledgmentsThis work is supported by the European Community,within the 6th Framework Programme, through the V ector project(contract number 0339970).The authors wish to thank the project partners and the funding organization.。
A Discriminatively Trained, Multiscale, Deformable Part Model
A Discriminatively Trained,Multiscale,Deformable Part ModelPedro Felzenszwalb University of Chicago pff@David McAllesterToyota Technological Institute at Chicagomcallester@Deva RamananUC Irvinedramanan@AbstractThis paper describes a discriminatively trained,multi-scale,deformable part model for object detection.Our sys-tem achieves a two-fold improvement in average precision over the best performance in the2006PASCAL person de-tection challenge.It also outperforms the best results in the 2007challenge in ten out of twenty categories.The system relies heavily on deformable parts.While deformable part models have become quite popular,their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge.Our system also relies heavily on new methods for discriminative training.We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM.A latent SVM,like a hid-den CRF,leads to a non-convex training problem.How-ever,a latent SVM is semi-convex and the training prob-lem becomes convex once latent information is specified for the positive examples.We believe that our training meth-ods will eventually make possible the effective use of more latent information such as hierarchical(grammar)models and models involving latent three dimensional pose.1.IntroductionWe consider the problem of detecting and localizing ob-jects of a generic category,such as people or cars,in static images.We have developed a new multiscale deformable part model for solving this problem.The models are trained using a discriminative procedure that only requires bound-ing box labels for the positive ing these mod-els we implemented a detection system that is both highly efficient and accurate,processing an image in about2sec-onds and achieving recognition rates that are significantly better than previous systems.Our system achieves a two-fold improvement in average precision over the winning system[5]in the2006PASCAL person detection challenge.The system also outperforms the best results in the2007challenge in ten out of twenty This material is based upon work supported by the National Science Foundation under Grant No.0534820and0535174.Figure1.Example detection obtained with the person model.The model is defined by a coarse template,several higher resolution part templates and a spatial model for the location of each part. object categories.Figure1shows an example detection ob-tained with our person model.The notion that objects can be modeled by parts in a de-formable configuration provides an elegant framework for representing object categories[1–3,6,10,12,13,15,16,22]. While these models are appealing from a conceptual point of view,it has been difficult to establish their value in prac-tice.On difficult datasets,deformable models are often out-performed by“conceptually weaker”models such as rigid templates[5]or bag-of-features[23].One of our main goals is to address this performance gap.Our models include both a coarse global template cov-ering an entire object and higher resolution part templates. The templates represent histogram of gradient features[5]. As in[14,19,21],we train models discriminatively.How-ever,our system is semi-supervised,trained with a max-margin framework,and does not rely on feature detection. We also describe a simple and effective strategy for learn-ing parts from weakly-labeled data.In contrast to computa-tionally demanding approaches such as[4],we can learn a model in3hours on a single CPU.Another contribution of our work is a new methodology for discriminative training.We generalize SVMs for han-dling latent variables such as part positions,and introduce a new method for data mining“hard negative”examples dur-ing training.We believe that handling partially labeled data is a significant issue in machine learning for computer vi-sion.For example,the PASCAL dataset only specifies abounding box for each positive example of an object.We treat the position of each object part as a latent variable.We also treat the exact location of the object as a latent vari-able,requiring only that our classifier select a window that has large overlap with the labeled bounding box.A latent SVM,like a hidden CRF[19],leads to a non-convex training problem.However,unlike a hidden CRF, a latent SVM is semi-convex and the training problem be-comes convex once latent information is specified for thepositive training examples.This leads to a general coordi-nate descent algorithm for latent SVMs.System Overview Our system uses a scanning window approach.A model for an object consists of a global“root”filter and several part models.Each part model specifies a spatial model and a partfilter.The spatial model defines a set of allowed placements for a part relative to a detection window,and a deformation cost for each placement.The score of a detection window is the score of the root filter on the window plus the sum over parts,of the maxi-mum over placements of that part,of the partfilter score on the resulting subwindow minus the deformation cost.This is similar to classical part-based models[10,13].Both root and partfilters are scored by computing the dot product be-tween a set of weights and histogram of gradient(HOG) features within a window.The rootfilter is equivalent to a Dalal-Triggs model[5].The features for the partfilters are computed at twice the spatial resolution of the rootfilter. Our model is defined at afixed scale,and we detect objects by searching over an image pyramid.In training we are given a set of images annotated with bounding boxes around each instance of an object.We re-duce the detection problem to a binary classification prob-lem.Each example x is scored by a function of the form, fβ(x)=max zβ·Φ(x,z).Hereβis a vector of model pa-rameters and z are latent values(e.g.the part placements). To learn a model we define a generalization of SVMs that we call latent variable SVM(LSVM).An important prop-erty of LSVMs is that the training problem becomes convex if wefix the latent values for positive examples.This can be used in a coordinate descent algorithm.In practice we iteratively apply classical SVM training to triples( x1,z1,y1 ,..., x n,z n,y n )where z i is selected to be the best scoring latent label for x i under the model learned in the previous iteration.An initial rootfilter is generated from the bounding boxes in the PASCAL dataset. The parts are initialized from this rootfilter.2.ModelThe underlying building blocks for our models are the Histogram of Oriented Gradient(HOG)features from[5]. We represent HOG features at two different scales.Coarse features are captured by a rigid template covering anentireImage pyramidFigure2.The HOG feature pyramid and an object hypothesis de-fined in terms of a placement of the rootfilter(near the top of the pyramid)and the partfilters(near the bottom of the pyramid). detection window.Finer scale features are captured by part templates that can be moved with respect to the detection window.The spatial model for the part locations is equiv-alent to a star graph or1-fan[3]where the coarse template serves as a reference position.2.1.HOG RepresentationWe follow the construction in[5]to define a dense repre-sentation of an image at a particular resolution.The image isfirst divided into8x8non-overlapping pixel regions,or cells.For each cell we accumulate a1D histogram of gra-dient orientations over pixels in that cell.These histograms capture local shape properties but are also somewhat invari-ant to small deformations.The gradient at each pixel is discretized into one of nine orientation bins,and each pixel“votes”for the orientation of its gradient,with a strength that depends on the gradient magnitude.For color images,we compute the gradient of each color channel and pick the channel with highest gradi-ent magnitude at each pixel.Finally,the histogram of each cell is normalized with respect to the gradient energy in a neighborhood around it.We look at the four2×2blocks of cells that contain a particular cell and normalize the his-togram of the given cell with respect to the total energy in each of these blocks.This leads to a vector of length9×4 representing the local gradient information inside a cell.We define a HOG feature pyramid by computing HOG features of each level of a standard image pyramid(see Fig-ure2).Features at the top of this pyramid capture coarse gradients histogrammed over fairly large areas of the input image while features at the bottom of the pyramid capture finer gradients histogrammed over small areas.2.2.FiltersFilters are rectangular templates specifying weights for subwindows of a HOG pyramid.A w by hfilter F is a vector with w×h×9×4weights.The score of afilter is defined by taking the dot product of the weight vector and the features in a w×h subwindow of a HOG pyramid.The system in[5]uses a singlefilter to define an object model.That system detects objects from a particular class by scoring every w×h subwindow of a HOG pyramid and thresholding the scores.Let H be a HOG pyramid and p=(x,y,l)be a cell in the l-th level of the pyramid.Letφ(H,p,w,h)denote the vector obtained by concatenating the HOG features in the w×h subwindow of H with top-left corner at p.The score of F on this detection window is F·φ(H,p,w,h).Below we useφ(H,p)to denoteφ(H,p,w,h)when the dimensions are clear from context.2.3.Deformable PartsHere we consider models defined by a coarse rootfilter that covers the entire object and higher resolution partfilters covering smaller parts of the object.Figure2illustrates a placement of such a model in a HOG pyramid.The rootfil-ter location defines the detection window(the pixels inside the cells covered by thefilter).The partfilters are placed several levels down in the pyramid,so the HOG cells at that level have half the size of cells in the rootfilter level.We have found that using higher resolution features for defining partfilters is essential for obtaining high recogni-tion performance.With this approach the partfilters repre-sentfiner resolution edges that are localized to greater ac-curacy when compared to the edges represented in the root filter.For example,consider building a model for a face. The rootfilter could capture coarse resolution edges such as the face boundary while the partfilters could capture details such as eyes,nose and mouth.The model for an object with n parts is formally defined by a rootfilter F0and a set of part models(P1,...,P n) where P i=(F i,v i,s i,a i,b i).Here F i is afilter for the i-th part,v i is a two-dimensional vector specifying the center for a box of possible positions for part i relative to the root po-sition,s i gives the size of this box,while a i and b i are two-dimensional vectors specifying coefficients of a quadratic function measuring a score for each possible placement of the i-th part.Figure1illustrates a person model.A placement of a model in a HOG pyramid is given by z=(p0,...,p n),where p i=(x i,y i,l i)is the location of the rootfilter when i=0and the location of the i-th part when i>0.We assume the level of each part is such that a HOG cell at that level has half the size of a HOG cell at the root level.The score of a placement is given by the scores of eachfilter(the data term)plus a score of the placement of each part relative to the root(the spatial term), ni=0F i·φ(H,p i)+ni=1a i·(˜x i,˜y i)+b i·(˜x2i,˜y2i),(1)where(˜x i,˜y i)=((x i,y i)−2(x,y)+v i)/s i gives the lo-cation of the i-th part relative to the root location.Both˜x i and˜y i should be between−1and1.There is a large(exponential)number of placements for a model in a HOG pyramid.We use dynamic programming and distance transforms techniques[9,10]to compute the best location for the parts of a model as a function of the root location.This takes O(nk)time,where n is the number of parts in the model and k is the number of cells in the HOG pyramid.To detect objects in an image we score root locations according to the best possible placement of the parts and threshold this score.The score of a placement z can be expressed in terms of the dot product,β·ψ(H,z),between a vector of model parametersβand a vectorψ(H,z),β=(F0,...,F n,a1,b1...,a n,b n).ψ(H,z)=(φ(H,p0),φ(H,p1),...φ(H,p n),˜x1,˜y1,˜x21,˜y21,...,˜x n,˜y n,˜x2n,˜y2n,). We use this representation for learning the model parame-ters as it makes a connection between our deformable mod-els and linear classifiers.On interesting aspect of the spatial models defined here is that we allow for the coefficients(a i,b i)to be negative. This is more general than the quadratic“spring”cost that has been used in previous work.3.LearningThe PASCAL training data consists of a large set of im-ages with bounding boxes around each instance of an ob-ject.We reduce the problem of learning a deformable part model with this data to a binary classification problem.Let D=( x1,y1 ,..., x n,y n )be a set of labeled exam-ples where y i∈{−1,1}and x i specifies a HOG pyramid, H(x i),together with a range,Z(x i),of valid placements for the root and partfilters.We construct a positive exam-ple from each bounding box in the training set.For these ex-amples we define Z(x i)so the rootfilter must be placed to overlap the bounding box by at least50%.Negative exam-ples come from images that do not contain the target object. Each placement of the rootfilter in such an image yields a negative training example.Note that for the positive examples we treat both the part locations and the exact location of the rootfilter as latent variables.We have found that allowing uncertainty in the root location during training significantly improves the per-formance of the system(see Section4).tent SVMsA latent SVM is defined as follows.We assume that each example x is scored by a function of the form,fβ(x)=maxz∈Z(x)β·Φ(x,z),(2)whereβis a vector of model parameters and z is a set of latent values.For our deformable models we define Φ(x,z)=ψ(H(x),z)so thatβ·Φ(x,z)is the score of placing the model according to z.In analogy to classical SVMs we would like to trainβfrom labeled examples D=( x1,y1 ,..., x n,y n )by optimizing the following objective function,β∗(D)=argminβλ||β||2+ni=1max(0,1−y i fβ(x i)).(3)By restricting the latent domains Z(x i)to a single choice, fβbecomes linear inβ,and we obtain linear SVMs as a special case of latent tent SVMs are instances of the general class of energy-based models[18].3.2.Semi-ConvexityNote that fβ(x)as defined in(2)is a maximum of func-tions each of which is linear inβ.Hence fβ(x)is convex inβ.This implies that the hinge loss max(0,1−y i fβ(x i)) is convex inβwhen y i=−1.That is,the loss function is convex inβfor negative examples.We call this property of the loss function semi-convexity.Consider an LSVM where the latent domains Z(x i)for the positive examples are restricted to a single choice.The loss due to each positive example is now bined with the semi-convexity property,(3)becomes convex inβ.If the labels for the positive examples are notfixed we can compute a local optimum of(3)using a coordinate de-scent algorithm:1.Holdingβfixed,optimize the latent values for the pos-itive examples z i=argmax z∈Z(xi )β·Φ(x,z).2.Holding{z i}fixed for positive examples,optimizeβby solving the convex problem defined above.It can be shown that both steps always improve or maintain the value of the objective function in(3).If both steps main-tain the value we have a strong local optimum of(3),in the sense that Step1searches over an exponentially large space of latent labels for positive examples while Step2simulta-neously searches over weight vectors and an exponentially large space of latent labels for negative examples.3.3.Data Mining Hard NegativesIn object detection the vast majority of training exam-ples are negative.This makes it infeasible to consider all negative examples at a time.Instead,it is common to con-struct training data consisting of the positive instances and “hard negative”instances,where the hard negatives are data mined from the very large set of possible negative examples.Here we describe a general method for data mining ex-amples for SVMs and latent SVMs.The method iteratively solves subproblems using only hard instances.The innova-tion of our approach is a theoretical guarantee that it leads to the exact solution of the training problem defined using the complete training set.Our results require the use of a margin-sensitive definition of hard examples.The results described here apply both to classical SVMs and to the problem defined by Step2of the coordinate de-scent algorithm for latent SVMs.We omit the proofs of the theorems due to lack of space.These results are related to working set methods[17].We define the hard instances of D relative toβas,M(β,D)={ x,y ∈D|yfβ(x)≤1}.(4)That is,M(β,D)are training examples that are incorrectly classified or near the margin of the classifier defined byβ. We can show thatβ∗(D)only depends on hard instances. Theorem1.Let C be a subset of the examples in D.If M(β∗(D),D)⊆C thenβ∗(C)=β∗(D).This implies that in principle we could train a model us-ing a small set of examples.However,this set is defined in terms of the optimal modelβ∗(D).Given afixedβwe can use M(β,D)to approximate M(β∗(D),D).This suggests an iterative algorithm where we repeatedly compute a model from the hard instances de-fined by the model from the last iteration.This is further justified by the followingfixed-point theorem.Theorem2.Ifβ∗(M(β,D))=βthenβ=β∗(D).Let C be an initial“cache”of examples.In practice we can take the positive examples together with random nega-tive examples.Consider the following iterative algorithm: 1.Letβ:=β∗(C).2.Shrink C by letting C:=M(β,C).3.Grow C by adding examples from M(β,D)up to amemory limit L.Theorem3.If|C|<L after each iteration of Step2,the algorithm will converge toβ=β∗(D)infinite time.3.4.Implementation detailsMany of the ideas discussed here are only approximately implemented in our current system.In practice,when train-ing a latent SVM we iteratively apply classical SVM train-ing to triples x1,z1,y1 ,..., x n,z n,y n where z i is se-lected to be the best scoring latent label for x i under themodel trained in the previous iteration.Each of these triples leads to an example Φ(x i,z i),y i for training a linear clas-sifier.This allows us to use a highly optimized SVM pack-age(SVMLight[17]).On a single CPU,the entire training process takes3to4hours per object class in the PASCAL datasets,including initialization of the parts.Root Filter Initialization:For each category,we auto-matically select the dimensions of the rootfilter by looking at statistics of the bounding boxes in the training data.1We train an initial rootfilter F0using an SVM with no latent variables.The positive examples are constructed from the unoccluded training examples(as labeled in the PASCAL data).These examples are anisotropically scaled to the size and aspect ratio of thefilter.We use random subwindows from negative images to generate negative examples.Root Filter Update:Given the initial rootfilter trained as above,for each bounding box in the training set wefind the best-scoring placement for thefilter that significantly overlaps with the bounding box.We do this using the orig-inal,un-scaled images.We retrain F0with the new positive set and the original random negative set,iterating twice.Part Initialization:We employ a simple heuristic to ini-tialize six parts from the rootfilter trained above.First,we select an area a such that6a equals80%of the area of the rootfilter.We greedily select the rectangular region of area a from the rootfilter that has the most positive energy.We zero out the weights in this region and repeat until six parts are selected.The partfilters are initialized from the rootfil-ter values in the subwindow selected for the part,butfilled in to handle the higher spatial resolution of the part.The initial deformation costs measure the squared norm of a dis-placement with a i=(0,0)and b i=−(1,1).Model Update:To update a model we construct new training data triples.For each positive bounding box in the training data,we apply the existing detector at all positions and scales with at least a50%overlap with the given bound-ing box.Among these we select the highest scoring place-ment as the positive example corresponding to this training bounding box(Figure3).Negative examples are selected byfinding high scoring detections in images not containing the target object.We add negative examples to a cache un-til we encounterfile size limits.A new model is trained by running SVMLight on the positive and negative examples, each labeled with part placements.We update the model10 times using the cache scheme described above.In each it-eration we keep the hard instances from the previous cache and add as many new hard instances as possible within the memory limit.Toward thefinal iterations,we are able to include all hard instances,M(β,D),in the cache.1We picked a simple heuristic by cross-validating over5object classes. We set the model aspect to be the most common(mode)aspect in the data. We set the model size to be the largest size not larger than80%of thedata.Figure3.The image on the left shows the optimization of the la-tent variables for a positive example.The dotted box is the bound-ing box label provided in the PASCAL training set.The large solid box shows the placement of the detection window while the smaller solid boxes show the placements of the parts.The image on the right shows a hard-negative example.4.ResultsWe evaluated our system using the PASCAL VOC2006 and2007comp3challenge datasets and protocol.We refer to[7,8]for details,but emphasize that both challenges are widely acknowledged as difficult testbeds for object detec-tion.Each dataset contains several thousand images of real-world scenes.The datasets specify ground-truth bounding boxes for several object classes,and a detection is consid-ered correct when it overlaps more than50%with a ground-truth bounding box.One scores a system by the average precision(AP)of its precision-recall curve across a testset.Recent work in pedestrian detection has tended to report detection rates versus false positives per window,measured with cropped positive examples and negative images with-out objects of interest.These scores are tied to the reso-lution of the scanning window search and ignore effects of non-maximum suppression,making it difficult to compare different systems.We believe the PASCAL scoring method gives a more reliable measure of performance.The2007challenge has20object categories.We entered a preliminary version of our system in the official competi-tion,and obtained the best score in6categories.Our current system obtains the highest score in10categories,and the second highest score in6categories.Table1summarizes the results.Our system performs well on rigid objects such as cars and sofas as well as highly deformable objects such as per-sons and horses.We also note that our system is successful when given a large or small amount of training data.There are roughly4700positive training examples in the person category but only250in the sofa category.Figure4shows some of the models we learned.Figure5shows some ex-ample detections.We evaluated different components of our system on the longer-established2006person dataset.The top AP scoreaero bike bird boat bottle bus car cat chair cow table dog horse mbike person plant sheep sofa train tvOur rank 31211224111422112141Our score .180.411.092.098.249.349.396.110.155.165.110.062.301.337.267.140.141.156.206.336Darmstadt .301INRIA Normal .092.246.012.002.068.197.265.018.097.039.017.016.225.153.121.093.002.102.157.242INRIA Plus.136.287.041.025.077.279.294.132.106.127.067.071.335.249.092.072.011.092.242.275IRISA .281.318.026.097.119.289.227.221.175.253MPI Center .060.110.028.031.000.164.172.208.002.044.049.141.198.170.091.004.091.034.237.051MPI ESSOL.152.157.098.016.001.186.120.240.007.061.098.162.034.208.117.002.046.147.110.054Oxford .262.409.393.432.375.334TKK .186.078.043.072.002.116.184.050.028.100.086.126.186.135.061.019.036.058.067.090Table 1.PASCAL VOC 2007results.Average precision scores of our system and other systems that entered the competition [7].Empty boxes indicate that a method was not tested in the corresponding class.The best score in each class is shown in bold.Our current system ranks first in 10out of 20classes.A preliminary version of our system ranked first in 6classes in the official competition.BottleCarBicycleSofaFigure 4.Some models learned from the PASCAL VOC 2007dataset.We show the total energy in each orientation of the HOG cells in the root and part filters,with the part filters placed at the center of the allowable displacements.We also show the spatial model for each part,where bright values represent “cheap”placements,and dark values represent “expensive”placements.in the PASCAL competition was .16,obtained using a rigid template model of HOG features [5].The best previous re-sult of.19adds a segmentation-based verification step [20].Figure 6summarizes the performance of several models we trained.Our root-only model is equivalent to the model from [5]and it scores slightly higher at .18.Performance jumps to .24when the model is trained with a LSVM that selects a latent position and scale for each positive example.This suggests LSVMs are useful even for rigid templates because they allow for self-adjustment of the detection win-dow in the training examples.Adding deformable parts in-creases performance to .34AP —a factor of two above the best previous score.Finally,we trained a model with partsbut no root filter and obtained .29AP.This illustrates the advantage of using a multiscale representation.We also investigated the effect of the spatial model and allowable deformations on the 2006person dataset.Recall that s i is the allowable displacement of a part,measured in HOG cells.We trained a rigid model with high-resolution parts by setting s i to 0.This model outperforms the root-only system by .27to .24.If we increase the amount of allowable displacements without using a deformation cost,we start to approach a bag-of-features.Performance peaks at s i =1,suggesting it is useful to constrain the part dis-placements.The optimal strategy allows for larger displace-ments while using an explicit deformation cost.The follow-Figure 5.Some results from the PASCAL 2007dataset.Each row shows detections using a model for a specific class (Person,Bottle,Car,Sofa,Bicycle,Horse).The first three columns show correct detections while the last column shows false positives.Our system is able to detect objects over a wide range of scales (such as the cars)and poses (such as the horses).The system can also detect partially occluded objects such as a person behind a bush.Note how the false detections are often quite reasonable,for example detecting a bus with the car model,a bicycle sign with the bicycle model,or a dog with the horse model.In general the part filters represent meaningful object parts that are well localized in each detection such as the head in the person model.Figure6.Evaluation of our system on the PASCAL VOC2006 person dataset.Root uses only a rootfilter and no latent place-ment of the detection windows on positive examples.Root+Latent uses a rootfilter with latent placement of the detection windows. Parts+Latent is a part-based system with latent detection windows but no rootfilter.Root+Parts+Latent includes both root and part filters,and latent placement of the detection windows.ing table shows AP as a function of freely allowable defor-mation in thefirst three columns.The last column gives the performance when using a quadratic deformation cost and an allowable displacement of2HOG cells.s i01232+quadratic costAP.27.33.31.31.345.DiscussionWe introduced a general framework for training SVMs with latent structure.We used it to build a recognition sys-tem based on multiscale,deformable models.Experimental results on difficult benchmark data suggests our system is the current state-of-the-art in object detection.LSVMs allow for exploration of additional latent struc-ture for recognition.One can consider deeper part hierar-chies(parts with parts),mixture models(frontal vs.side cars),and three-dimensional pose.We would like to train and detect multiple classes together using a shared vocab-ulary of parts(perhaps visual words).We also plan to use A*search[11]to efficiently search over latent parameters during detection.References[1]Y.Amit and A.Trouve.POP:Patchwork of parts models forobject recognition.IJCV,75(2):267–282,November2007.[2]M.Burl,M.Weber,and P.Perona.A probabilistic approachto object recognition using local photometry and global ge-ometry.In ECCV,pages II:628–641,1998.[3] D.Crandall,P.Felzenszwalb,and D.Huttenlocher.Spatialpriors for part-based recognition using statistical models.In CVPR,pages10–17,2005.[4] D.Crandall and D.Huttenlocher.Weakly supervised learn-ing of part-based spatial models for visual object recognition.In ECCV,pages I:16–29,2006.[5]N.Dalal and B.Triggs.Histograms of oriented gradients forhuman detection.In CVPR,pages I:886–893,2005.[6] B.Epshtein and S.Ullman.Semantic hierarchies for recog-nizing objects and parts.In CVPR,2007.[7]M.Everingham,L.Van Gool,C.K.I.Williams,J.Winn,and A.Zisserman.The PASCAL Visual Object Classes Challenge2007(VOC2007)Results./challenges/VOC/voc2007/workshop.[8]M.Everingham, A.Zisserman, C.K.I.Williams,andL.Van Gool.The PASCAL Visual Object Classes Challenge2006(VOC2006)Results./challenges/VOC/voc2006/results.pdf.[9]P.Felzenszwalb and D.Huttenlocher.Distance transformsof sampled functions.Cornell Computing and Information Science Technical Report TR2004-1963,September2004.[10]P.Felzenszwalb and D.Huttenlocher.Pictorial structures forobject recognition.IJCV,61(1),2005.[11]P.Felzenszwalb and D.McAllester.The generalized A*ar-chitecture.JAIR,29:153–190,2007.[12]R.Fergus,P.Perona,and A.Zisserman.Object class recog-nition by unsupervised scale-invariant learning.In CVPR, 2003.[13]M.Fischler and R.Elschlager.The representation andmatching of pictorial structures.IEEE Transactions on Com-puter,22(1):67–92,January1973.[14] A.Holub and P.Perona.A discriminative framework formodelling object classes.In CVPR,pages I:664–671,2005.[15]S.Ioffe and D.Forsyth.Probabilistic methods forfindingpeople.IJCV,43(1):45–68,June2001.[16]Y.Jin and S.Geman.Context and hierarchy in a probabilisticimage model.In CVPR,pages II:2145–2152,2006.[17]T.Joachims.Making large-scale svm learning practical.InB.Sch¨o lkopf,C.Burges,and A.Smola,editors,Advances inKernel Methods-Support Vector Learning.MIT Press,1999.[18]Y.LeCun,S.Chopra,R.Hadsell,R.Marc’Aurelio,andF.Huang.A tutorial on energy-based learning.InG.Bakir,T.Hofman,B.Sch¨o lkopf,A.Smola,and B.Taskar,editors, Predicting Structured Data.MIT Press,2006.[19] A.Quattoni,S.Wang,L.Morency,M.Collins,and T.Dar-rell.Hidden conditional randomfields.PAMI,29(10):1848–1852,October2007.[20] ing segmentation to verify object hypothe-ses.In CVPR,pages1–8,2007.[21] D.Ramanan and C.Sminchisescu.Training deformablemodels for localization.In CVPR,pages I:206–213,2006.[22]H.Schneiderman and T.Kanade.Object detection using thestatistics of parts.IJCV,56(3):151–177,February2004. [23]J.Zhang,M.Marszalek,zebnik,and C.Schmid.Localfeatures and kernels for classification of texture and object categories:A comprehensive study.IJCV,73(2):213–238, June2007.。
晶圆混合键合工艺优化研究
2 实验方案
本文研究了金属铜键合垫和氧化层相对高度对 hybrid bonding 工 艺 空 洞 的 影 响 。 应 该 看 到 ,在 bonding 前晶圆表面平坦化处理过程中,由于氧化层 的研磨速率和金属铜的研磨速率存在较大的差异, 所以在最终形成的晶圆表面上,金属铜键合垫和 SiO2 很难完全保持在同一水平面上。基此,我们通 过实验,调整了金属铜键合垫和氧化层相对高度,分 为如以下 2 种情况,研究了金属铜键合垫和氧化层 相对高度对 hybrid bonding 空洞的影响。具体表现:
http://
2021·7· (总第 266 期)65
封装
CIC 中国集成电路
China lntegrated Circult
0 引言
一是,晶圆在完成前段器件形成以及后段金属 互联工艺之后,将两片晶圆表面分别做平坦化处理,
日益增长的消费类电子产品市场不断推动着半 导体技术飞速发展,各种应用对芯片的集成度要求 不断提高,芯片尺寸不断减小,促使了各种新技术进 步都可在 CMOS 工艺中获得了应用,包括有多重光 刻图形化、新的应变增强材料和金属氧化物栅介质 等。目前集成电路工艺技术节点已经实现了 5nm 工 艺的量产,继续缩小晶体管尺寸使技术复杂度变得 越来越困难,而且继续缩小尺寸已经不能降低单位 晶体管的成本,所以越来越难以找到一种解决方案 来满足在增加器件性能的同时又能降低成本的要 求。
第一种情况。如图 3(a)所示,在,其是通过加大 对 Cu 的研磨量,使 Cu 金属键合垫的高度低于二氧 化硅氧化层;
第二种情况。如图 3(b)所示,其是通过加大对 SiO2 的研磨量,使最终晶圆上金属铜键合垫的高度 略高于二氧化硅氧化层。
图 2 键合空洞 C-SAM 图片 http://
美国洛克希德·马丁公司将研制地球同步碳循环观测任务有效载荷
图3碲镉汞薄膜截面的SBM 测试结果4结论采用扫描电镜分别测试了碲镉汞薄膜经过 粗磨和经过细磨后的表面形貌像和解理面形貌 像,得到了经过两种不同减薄工艺后的碲镉汞 薄膜损伤层信息,获得了非常有价值的实验结 果。
实验结果显示,采用细磨的方式对碲镉汞薄 膜进行减薄产生的损伤层的最大深度比采用粗 磨方式要小得多。
通过扫描电镜对减薄后的碲 镉汞薄膜损伤层进行研究,认识并掌握了碲镉汞 薄膜的损伤层信息,这为后续碲镉汞薄膜减薄 工艺方法和参数的优化与改进提供了重的参 考依据和指导意义。
这也表明采用扫描电镜测 试碲镉汞薄膜的损伤层是评价碲镉汞薄膜减薄 后损伤层的一种非常有效的检测方法。
参考文献[1] 郎艳菊.GSP 晶体加工表面/亚表茴损伤研究p].大连:大连理工大学,2008.[2] 许秀娟,田震.碲镉汞薄膜减薄工艺损伤层的评价方法及应用[J].激光与红外,2〇15, 45(3): 235-239.[3] 康俊勇,黄启圣,王家库,等.HgCdTe 晶片研磨和拋光表面的扫描电镜观察[J].红外技术,1999, 21⑷: 24-27.[4] Li Y , Yi X J, Cai L P. Study on Surface Oxidative Characterization of LPE HgCdTe Epilayer by X-ray Photoelectron Spectroscopy [J]. International Journal of Infrared and Millimeter Waves, 2000, 21(1): 31-37.[5] Madejczyk P, Piotrowski A, Klos K. Surface Smoothness Improvement of HgCdTe Layers Grown by MOCVD [J]. Bulletin of the Polish Academy of Sciences, Technical Sciences, 2009, 57(2): 139-146.[6] Farrell S, Mulpuri V, Rao G, et al. Comparison of the Schaake and Benson Etches to Delineate Dislocations in HgCdTe Layers [J]. Journal of Electronic Materials, 2013, 42(11): 3097-3102.[7] Mollard L, Destefanis G, Rothman J, et al. HgCdTe FPAs Made by Arsenic-ion Implantation [C]. SPIE, 2008, 6940: 69400F.[8] Mollard L, Destefanis G, Bourgeois G, et al. State of p-on-n Arsenic-implanted HgCdTe Technologies [J]. Journal of Electronic Materials, 2011, 40(8): 18301839.新闻动态News美国洛克希德•马丁公司将研制地球同步碳循环观测任务有效载荷据 www.lockheedm 网站报道,美国洛克希德.马丁公司将为美国国家航空航 天局(NASA )的地球同步碳循环观濒(GeoCARB ) 任务研制一台搭载于商业地球同步轨道卫星的 先进红外光谱仪,以帮助科学家们更好地了解 地球的碳循环i 和植被健康状况,相关人员表示,该公司在红外探测和搭载 有教载#方面具有丰富'经验,他们也将与儀克 拉荷马大学、N _A S A 以及科罗拉多.:州立大学合# 完成此次任务.洛克希德•马丁公司位于柏洛阿尔托的先 进技术中心(A TC )将基于詹姆斯.韦伯望远镜 (JWST )的遮..紅外相机(N IR C 爾)谁计来欄这 个搭载有效载荷《与深空探溯:不同,预计于2022 年升空的G eoC A R B 红外光谱仪将用于澜量地球 大气中的二氧化碳、一氧化碳和甲規以及太阳 光诱导..荧光敷据。
基于混沌掩码虚拟光学成像系统的图像加密
第35卷,增刊红外与激光工程2006年10月v01.35Suppl em cntI睹ared and hcr E ngi I l eer i I l g oct.2006基于混沌掩码虚拟光学成像系统的图像加密朱从旭,陈志刚(中南大学信息科学与工程学院,湖南长沙410083)擅要:提出了一种基于混沌随机掩码虚拟光学成像系统新颖的图像加密算法,详细描述了利用该方法进行数字图像加密解密的过程。
首先,引入了经混沌随机掩码的虚拟光学成像系统模型;然后描述了利用该模型进行图像加密解密的算法。
利用混沌系统的特性,提高了算法的安全性。
数值仿真实验证明了该方法的有效性,表明该加密算法对参数具有强敏感性。
关键词:图像处理;加密/解密;混沌掩码;虚拟光学;成像系统中圈分类号:T P309.7文l-I标识码:A文章编号:1007.2276(2006)增D.0073—06T J●●』l1J●■1●●』■J●l m age enC r V D U0n W l U l C na onC m aSK e nC0dl ng V I r t U a l-oD n岱i m agi ng s yst emZ H U C ong—xu,C H EN Zhj—gang(Sch ool of hf0咖at如咀Sci∞∞锄d如gi n。
砒l g,C cnn缸sout I l uni V硎t y,ch柚gsha410083,Q Ii n丑)A bs t r act:AnoV el i Ina ge encr yp t i on al gor i m m bas ed o n chaot i c m as k eI l codi ng V i咖a1一叩恤s i m agi ng syst em is pr opos e d.T he di gi t a l i m a ge encr y pt i on aI l d decr y pt i on pr oced ur es a r edesc曲ed i n det a i l.Fi rs t,m e r no del of V i nI l a lopt i ca l i I I l a gi ng sys t em w i t l l a chaot i c姗dom m as ken codi ng is pr op os ed.Then t l le i m a ge encr)7碑on and decr yp t i onal gori t l l m s a r e depi ct ed.B y ut i l i zi I l g m e s pe ci al c ha ra ct er i s t i cs of chaot i c s ys t em s,廿l e s ecuri t y l eV el is i ncr e as ed.N um e dca l exper i m ent s pr o V e m e e伍巴ct i V e nes s of nl e II l et l l od.P{l r am e t er se nsi t i vi t i e s by us i ng su ch a胁cr y砸on al gor i m m ar e a l s o dem ons仃at ed.K ey、V o“l s:Im age proc e ss i ng;Encr ypdon/decr y砸on;chao dcm嬲k%coding;V删optics;I I】均gi ng syst emO引言随着信息时代网络通信技术的飞速发展和广泛应用,信息安全问题已成为人们广泛关注的焦点。
MCD12Q1_的user guide用户手册或manual说明
User G uide f or t he M ODIS L and C over T ype P roduct (MCD12Q1)Last U pdated: A ug 8, 20121. IntroductionLand c over p lays a m ajor r ole i n t he c limate a nd b iogeochemistry o f t he E arth system. A n i mportant u se o f g lobal l and c over d ata i s t he i nference o f p arameters that i nfluence b iogeochemical a nd e nergy e xchanges b etween t he a tmosphere a nd the l and s urface f or u se i n m odels a nd o ther g lobal c hange s cience a pplications. Examples o f s uch p arameters i nclude l eaf a rea i ndex, r oughness l ength, s urface resistance t o e vapotranspiration, c anopy g reenness f raction, v egetation d ensity, root d istribution, a nd t he f raction o f p hotosynthetically-‐active r adiation a bsorbed. The M ODIS L and C over T ype P roduct p rovides a s uite o f l and c over t ypes t hat support g lobal c hange s cience b y m apping g lobal l and c over u sing s pectral a nd temporal i nformation d erived f rom M ODIS. T he o bjective o f t his d ocument i s t o provide i nformation r elated t o t he C ollection 5 M ODIS L and C over T ype P roduct (MCD12Q1). I t i s n ot d esigned t o b e a s cientific d ocument. R ather, i t p rovides t hree main t ypes o f i nformation:1.An o verview o f t he M CD12Q1 a lgorithm a nd p roduct, a long w ith r eferencesto p ublished l iterature w here m ore d etails c an b e f ound.2.Guidance a nd i nformation r elated t o d ata a ccess a nd d ata f ormats, t o h elpusers a ccess a nd u se t he d ata.3.Contact i nformation f or u sers w ith q uestions t hat c annot b e a ddressedthrough i nformation o r w ebsites p rovided i n t his d ocument.2. O verview o f t he M CD12Q1 L and C over T ype P roductThe M ODIS L and C over T ype P roduct i s p roduced u sing a s upervised c lassification algorithm t hat i s e stimated u sing a d atabase o f h igh q uality l and c over t raining sites. T he t raining s ite d atabase w as d eveloped u sing h igh-‐resolution i magery i n conjunction w ith a ncillary d ata (Muchoney e t a l., 1999). T he s ite d atabase i s a “living” d atabase t hat r equires o n-‐going a ugmentation a nd m aintenance t o i mprove the t raining d ata a nd d etect m islabeled s ites o r s ites t hat h ave c hanged o ver t ime. MODIS d ata u sed i n t he c lassification i nclude a f ull y ear o f c omposited 8-‐day M ODIS observations. S pecific i nputs i nclude N ormalized B RDF-‐Adjusted R eflectance (NBAR; S chaaf e t a l., 2002) a nd M ODIS L and S urface T emperature (LST; W an e t a l., 2002) d ata. T hese f eatures a re p rovided t o t he c lassifier a s m onthly c omposites a nd annual m etrics (see F riedl e t a l., 2002; 2010).The c lassification i s p roduced u sing a d ecision t ree c lassification a lgorithm (C4.5; Quinlan 1993) i n c onjunction w ith a t echnique f or i mproving c lassification accuracies k nown a s b oosting (Freund 1995). B oosting i mproves c lassification accuracies b y i teratively e stimating a d ecision t ree w hile s ystematically v arying t he training s ample. A t e ach i teration t he t raining s ample i s m odified t o f ocus t he classification a lgorithm o n t he m ost d ifficult e xamples. T he b oosted c lassifier'sprediction i s t hen b ased u pon a n a ccuracy-‐weighted v ote a cross t he e stimatedclassifiers. T he i mplementation u sed h ere i s A daboost.M1 (Freund a nd S chapire, 1997), w hich i s t he s implest m ulti-‐class b oosting m ethod. B oosting h as b een s hownto b e a f orm o f a dditive l ogistic r egression (Friedman e t a l. 2000). A s a r esult, probabilities o f c lass m embership c an b e o btained f rom b oosting. T hese probabilities p rovide a m eans o f a ssessing t he c onfidence o f t he c lassification results a s w ell a s a m eans o f i ncorporating a ncillary i nformation i n t he f orm o f p rior probabilities t o i mproved d iscrimination o f c over t ypes t hat a re d ifficult t o s eparate in t he s pectral f eature s pace.Using t his a pproach, t he M ODIS L and C over T ype a lgorithm i ngests M ODIS t raining data f or a ll s ites i n t he t raining d atabase, e stimates b oosted d ecision t rees b ased o n those d ata, a nd t hen c lassifies t he l and c over a t e ach M ODIS l and p ixel. F ollowing the c lassification a s et o f p ost-‐processing s teps i ncorporate p rior p robability knowledge a nd a djust s pecific c lasses b ased o n a ncillary i nformation. F or m ore specific i nformation a nd c omplete d etails r elated t o t he M ODIS L and C over T ype algorithm, t he r eader i s r eferred t o t he f ollowing k ey r eferences:•Friedl e t a l. (1997)•Friedl e t a l. (1999)•McIver a nd F riedl (2001)•McIver a nd F riedl (2002)•Friedl e t a l. (2002)•Friedl e t a l. (2010)Full c itations t o e ach o f t hese p apers a re p rovided b elow.3. P roduct O verview a nd S cience D ata S etsThe M ODIS L and C over T ype P roduct s upplies g lobal m aps o f l and c over a t a nnualtime s teps a nd 500-‐m s patial r esolution f or 2001-‐present. T he p rimary l and c overscheme i s p rovided b y a n I GBP l and c over c lassification (Belward e t a l., 1999; Scepan, 1999; F riedl e t a l., 2002; F riedl e t a l., 2010). F or e ase o f u se b y t he community, a n umber o f o ther c lassification s chemes a re a lso p rovided, i ncluding the U niversity o f M aryland c lassification s cheme (Hansen e t a l., 2000), t he B iome classification s cheme d escribed b y R unning e t a l. (1994), t he L AI/fPAR B iome scheme d escribed b y M yneni e t a l. (1997), a nd t he p lant f unctional t ype s cheme described b y B onan e t a l. (2002). I n a ddition, a n a ssessment o f t he r elative classification q uality (scaled f rom 0-‐100) i s p rovided a t e ach p ixel, a long w ith quality a ssurance i nformation a nd a n e mbedded l and/water m ask.The m ost r ecent v ersion o f t he M ODIS L and C over T ype P roduct i s C ollection 5.1,which i ncludes a djustments f or s ignificant e rrors t hat w ere d etected i n C ollection 5 of t he M CD12Q1 p roduct. T his v ersion i s a vailable o n t he L and P rocesses D AAC a nd is t he r ecommended v ersion f or u sers. E ssential i nformation r equired f or a ccessing and u sing t hese d ata i nclude t he f ollowing:•Overview o f d ata s et c haracteristics (temporal c overage, s patial r esolution, image s ize, d ata t ypes, e tc.).•Science d ata s ets i ncluded i n t he M ODIS L and C over T ype P roduct, a nd t heir associated d efinitions.•Information a nd s pecifications r elated t o t he M ODIS L and C over T ype Q A Science d ata s et.Up-‐to-‐date i nformation r elated t o e ach o f t hese t opics i ncluding s cience d ata s ets, data f ormats, a nd q uality i nformation a re a vailable f rom t he L and P rocesses D AAC at t he f ollowing U RL:https:///products/modis_products_table/mcd12q13.1. D ata F ormats a nd P rojectionMODIS d ata a re p rovided a s t iles t hat a re a pproximately 10° x 10° a t t he E quator using a s inusoidal g rid i n H DF4 f ile f ormat. I nformation r elated t o t he M ODIS sinusoidal p rojection a nd t he H DF4 f ile f ormat c an b e f ound a t:•MODIS t ile g rid: h ttp:///MODLAND_grid.html•MODIS H DF4: h ttp:///products/hdf4/3.2. A ccessing a nd A cquiring D ataMCD12Q1 d ata c an b e a cquired f rom t he L and P rocesses D istributed A ctive A rchive Center (https:///get_data). T here a re m ultiple p ortals f or downloading t he d ata. R everb i s t he e asiest t o u se a nd d oes n ot r equire a u ser account, b ut y ou o nly h ave t he o ption t o d ownload t he d ata i n i ts o riginal p rojection and H DF f ormat. T he M RTWeb p ortal e nables m ore a dvanced o ptions s uch a s reprojection, s ubsetting, a nd r eformatting b ut d oes r equire a u ser a ccount.4. C ontact I nformationProduct P I: M ark F riedl (friedl@)Associate t eam m ember a nd c ontact f or u sers: D amien S ulla-‐Menashe(dsm@)5. R eferences C ited1.Belward, A. S., E stes, J. E., & K line, K. D. (1999). T he I GBP-‐DIS G lobal 1-‐km L and-‐Cover D ata S et D ISCover: A P roject O verview. P hotogrammetric E ngineering a nd Remote S ensing, 65, 1013-‐1020.2.Bonan, G. B., O leson, K. W., V ertenstein, M., L evis, S., Z eng, X. B., & D ai, Y. (2002).The l and s urface c limatology o f t he c ommunity l and m odel c oupled t o t he N CAR community l and m odel. J ournal o f C limate, 15, 3123-‐3149.3.Freund, Y. (1995). B oosting a w eak l earning a lgorithm b y m ajority. I nformationand C omputation, 121(2), 256-‐285.4.Freund, Y., & S chapire, R. E. (1997). A d ecision-‐theoretic g eneralization o f o n-‐linelearning a nd a n a pplication t o b oosting. J ournal o f C omputer a nd S ystem S ciences, 5(1), 119-‐139.5.Friedl, M.A., & B rodley, C.E. (1997). D ecision t ree c lassification o f l and c overfrom r emotely s ensed d ata. R emote S ensing o f E nvironment, 61, 399-‐409.6.Friedl, M.A., B rodley, C.E., & S trahler, A.H. (1999). M aximizing l and c overclassification a ccuracies a t c ontinental t o g lobal s cales. I EEE T ransactions o nGeoscience a nd R emote S ensing, 37, 969-‐977.7.Friedl, M. A., M cIver, D. K., H odges, J. C. F., Z hang, X. Y., M uchoney, D., S trahler, A.H., W oodcock, C. E., G opal, S., S chneider, A., C ooper, A., B accini, A., G ao, F., &Schaaf, C. (2002). G lobal l and c over m apping f rom M ODIS: a lgorithms a nd e arly results. R emote S ensing o f E nvironment, 83, 287-‐302.8.Friedl, M. A., S ulla-‐Menashe, D., T an, B., S chneider, A., R amankutty, N., S ibley, A.,& H uang, X. (2010). M ODIS C ollection 5 g lobal l and c over: A lgorithm r efinements and c haracterization o f n ew d atasets. R emote S ensing o f E nvironment, 114, 168-‐182.9.Friedman, J., H astie, T., & T ibshirani, R. (2000). A dditive l ogistic r egression: Astatistical v iew o f b oosting. T he A nnals o f S tatistics, 28(2), 337-‐374.10.Hansen, M. C., D eFries, R. S., T ownshend, J. R. G., & S ohlberg, R. (2000). G loballand c over c lassification a t t he 1km s patial r esolution u sing a c lassification t ree approach. I nternational J ournal o f R emote S ensing, 21, 1331-‐1364.11.Muchoney, D., S trahler, A., H odges, J., & L oCastro, J. (1999). T he I GBP D ISCoverConfidence S ites a nd t he S ystem f or T errestrial E cosystem P arameterization: Tools f or V alidating G lobal L and C over D ata. P hotogrammetric E ngineering a nd Remote S ensing, 65(9), 1061-‐1067.12.McIver, D. K., & F riedl, M. A. (2001). E stimating p ixel-‐scale l and c overclassification c onfidence u sing n on-‐parametric m achine l earning m ethods. I EEE Transactions o n G eoscience a nd R emote S ensing, 39(9), 1959-‐1968.13.Mciver, D. K., & F riedl, M. A. (2002). U sing p rior p robabilities i n d ecision-‐treeclassification o f r emotely s ensed d ata. R emote S ensing o f E nvironment, 81, 253-‐261.14.Myneni, R. B., N emani, R. R., & R unning, S. W. (1997). E stimation o f g lobal l eafarea i ndex a nd a bsorbed P AR u sing r adiative t ransfer m odel. I EEE T ransactions on G eoscience a nd R emote S ensing, 35, 1380-‐1393.15.Quinlan, J. R. (1993). C4.5: P rograms f or M achine L earning. S an M ateo, C A:Morgan K aufmann.16.Running, S. W., L oveland, T. R., & P ierce, L. L. (1994). A v egetation c lassificationlogic b ased o n r emote s ensing f or u se i n g lobal s cale b iogeochemical m odels, Ambio, 23, 77-‐81.17.Scepan, J. 1999. T hematic V alidation o f H igh-‐Resolution G lobal L and-‐Cover D ataSets, P hotogrammetric E ngineering a nd R emote S ensing, 65, 1051-‐1060.18.Schaaf, C.B., G ao, F., S trahler, A. H., L ucht, W., L i, X., T sang, T., S trugnell, N. C.,Zhang, X., J in, Y., M uller, J. P., L ewis, P., B arnsley, M., H obson, P., D isney, M.,Roberts, G., D underdale, M., D oll, C., d’Entremont, R. P., H u, B., L iang, S., P rivette, J.L., & R oy, D. (2002). F irst o perational B RDF, a lbedo n adir r eflectance p roducts from M ODIS. R emote S ensing o f E nvironment, 83, 135-‐148.19.Wan, Z. M., Z hang, Y. L., Z hang, Q. C., a nd L i, Z. L. (2002). V alidation o f t he l and-‐surface t emperature p roducts r etrieved f rom T erra M oderate R esolutionImaging S pectroradiometer d ata. R emote S ensing o f E nvironment, 83, 163-‐180.。
计算机网络(第四版)课后习题(英文)+习题答案(中英文)
ANDREW S. TANENBAUM 秒,约533 msec.----- COMPUTER NETWORKS FOURTH EDITION PROBLEM SOLUTIONS 8. A collection of five routers is to be conn ected in a poi nt-to-poi nt sub net.Collected and Modified By Yan Zhe nXing, Mail To: Betwee n each pair of routers, the desig ners may put a high-speed line, aClassify: E aEasy, M ^Middle, H Hard , DaDeleteGree n: Importa nt Red: Master Blue: VI Others:Know Grey:—Unnecessary ----------------------------------------------------------------------------------------------ML V Chapter 1 In troductio nProblems2. An alter native to a LAN is simply a big timeshari ng system with termi nals forall users. Give two adva ntages of a clie nt-server system using a LAN.(M)使用局域网模型可以容易地增加节点。
如果局域网只是一条长的电缆,且不会因个别的失效而崩溃(例如采用镜像服务-------------------------------------------器)的情况下,使用局域网模型会更便宜。
transformer 提取图像特征流程
英文回复:The Transformer model is an in—depth learning model applied to serial data processing, with notable achievements in the field of natural language processing andputer visualization。
Image characterization as an important task in theputer visual field, for which the Transformer model can be used。
It captures dependencies of different locations in the input series through a self—directional mechanism and shows good resultsin processing sequence data。
In the characterization of images,we can use the attention mechanism of the Transformer modelto capture the dependency between different positions of pixels in the images, and thus to obtain the characterization of the images。
This will be followed by a presentation on how to use the Transformer model to extract image features。
Transformer模型是一种应用于序列数据处理的深度学习模型,在自然语言处理和计算机视觉领域取得了显著的成就。
结合像素交换与菱形编码的图像隐写
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© 2012 Baker Hughes Incorporated. All Rights Reserved.
High Definition Electrical Imaging ★ StarTrak
High Definition Electrical Imaging
Resistivity Gamma & Imaging Porosity & Imaging Acoustics Formation Pressure Testing LWD Magnetic Resonance
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© 2012 Baker Hughes Incorporated. All Rights Reserved.
Structural (natural fault & fracture) and sedimentological interpretation using high
definition electrical images (High Resolution=0.25”)
图像边缘检测算法英文文献翻译中英文翻译
image edge examination algorithmAbstractDigital image processing took a relative quite young discipline, is following the computer technology rapid development, day by day obtains the widespread edge took the image one kind of basic characteristic, in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widespread edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develops the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainly has Robert, Laplacian, Sobel, Canny, operators and so on LOG。
First as a whole introduced digital image processing and the edge detection survey, has enumerated several kind of at present commonly used edge detection technology and the algorithm, and selects two kinds to use Visual the C language programming realization, through withdraws the image result to two algorithms the comparison, the research discusses their good and bad points.ForewordIn image processing, as a basic characteristic, the edge of the image, which is widely used in the recognition, segmentation,intensification and compress of the image, is often applied to high-level are many kinds of ways to detect the edge. Anyway, there are two main techniques: one is classic method based on the gray grade of every pixel; the other one is based on wavelet and its multi-scale characteristic. The first method, which is got the longest research,get the edge according to the variety of the pixel gray. The main techniques are Robert, Laplace, Sobel, Canny and LOG algorithm.The second method, which is based on wavelet transform, utilizes the Lipschitz exponent characterization of the noise and singular signal and then achieve the goal of removing noise and distilling the real edge lines. In recent years, a new kind of detection method, which based on the phase information of the pixel, is developed. We need hypothesize nothing about images in advance. The edge is easy to find in frequency domain. It’s a reliable method.In chapter one, we give an overview of the image edge. And in chapter two, some classic detection algorithms are introduced. The cause of positional error is analyzed, and then discussed a more precision method in edge orientation. In chapter three, wavelet theory is introduced. The detection methods based on sampling wavelet transform, which can extract maim edge of the image effectively, and non-sampling wavelet transform, which can remain the optimum spatial information, are recommended respectively. In the last chapter of this thesis, the algorithm based on phase information is introduced. Using the log Gabor wavelet, two-dimension filter is constructed, many kinds of edges are detected, including Mach Band, which indicates it is a outstanding and bio-simulation method。
ICDAR2011
Text Detection and Character Recognition in Scene Imageswith Unsupervised Feature LearningAdam Coates,Blake Carpenter,Carl Case,Sanjeev Satheesh,Bipin Suresh,Tao Wang,David J.Wu,Andrew Y.NgComputer Science DepartmentStanford University353Serra MallStanford,CA94305USA{acoates,blakec,cbcase,ssanjeev,bipins,twangcat,dwu4,ang}@Abstract—Reading text from photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are(i)text detection from images and(ii)character recognition,and many recent methods have been proposed to design better feature representations and models for both.In this paper,we apply methods recently developed in machine learning–specifically,large-scale algo-rithms for learning the features automatically from unlabeled data–and show that they allow us to construct highly effective classifiers for both detection and recognition to be used in a high accuracy end-to-end system.Keywords-Robust reading,character recognition,feature learning,photo OCRI.I NTRODUCTIONDetection of text and identification of characters in scene images is a challenging visual recognition problem.As in much of computer vision,the challenges posed by the complexity of these images have been combated with hand-designed features[1],[2],[3]and models that incorporate various pieces of high-level prior knowledge[4],[5].In this paper,we produce results from a system that attempts to learn the necessary features directly from the data as an alternative to using purpose-built,text-specific features or models.Among our results,we achieve performance among the best known on the ICDAR2003character recognition dataset.In contrast to more classical OCR problems,where the characters are typically monotone onfixed backgrounds, character recognition in scene images is potentially far more complicated due to the many possible variations in background,lighting,texture and font.As a result,build-ing complete systems for these scenarios requires us to invent representations that account for all of these types of variations.Indeed,significant effort has gone into creating such systems,with top performers integrating dozens of cleverly combined features and processing stages[5].Recent work in machine learning,however,has sought to create algorithms that can learn higher level representations of data automatically for many tasks.Such systems might be particularly valuable where specialized features are needed but not easily created by hand.Another potential strength of these approaches is that we can easily generate large numbers of features that enable higher performance to be achieved by classification algorithms.In this paper,we’ll apply one such feature learning system to determine to what extent these algorithms may be useful in scene text detection and character recognition.Feature learning algorithms have enjoyed a string of successes in otherfields(for instance,achieving high perfor-mance in visual recognition[6]and audio recognition[7]). Unfortunately,one caveat is that these systems have often been too computationally expensive,especially for applica-tion to large images.To apply these algorithms to scene text applications,we will thus use a more scalable feature learning system.Specifically,we use a variant of K-means clustering to train a bank of features,similarly to the system in[8].Armed with this tool,we will produce results showing the effect on recognition performance as we increase the number of learned features.Our results will show that it’s possible to do quite well simply by learning many features from the data.Our approach contrasts with much prior work in scene text applications,as none of the features used here have been explicitly built for the application at hand.Indeed, the system follows closely the one proposed in[8].This paper is organized as follows.We willfirst survey some related work in scene text recognition,as well as the machine learning and vision results that inform our basic approach in Section II.We’ll then describe the learning architecture used in our experiments in Section III,and present our experimental results in Section IV followed by our conclusions.II.R ELATED W ORKScene text recognition has generated significant interest from many branches of research.While it is now possible to achieve extremely high performance on tasks such as digit recognition in controlled settings[9],the task of detecting and labeling characters in complex scenes remains an active research topic.However,many of the methods used for scene text detection and character recognition arepredicated on cleverly engineered systems specific to the new task.For text detection,for instance,solutions have ranged from simple off-the-shelf classifiers trained on hand-coded features[10]to multi-stage pipelines combining many different algorithms[11],[5].Common features include edge features,texture descriptors,and shape contexts[1]. Meanwhile,variousflavors of probabilistic model have also been applied[4],[12],[13],folding many forms of prior knowledge into the detection and recognition system.On the other hand,some systems with highlyflexible learning schemes attempt to learn all necessary information from labeled data with minimal prior knowledge.For in-stance,multi-layered neural network architectures have been applied to character recognition and are competitive with other leading methods[14].This mirrors the success of such approaches in more traditional document and hand-written text recognition systems[15].Indeed,the method used in our system is related to convolutional neural networks.The primary difference is that the training method used here is unsupervised,and uses a much more scalable training algorithm that can rapidly train many features.Feature learning methods in general are currently the focus of much research,particularly applied to computer vision problems.As a result,a wide variety of algorithms are now available to learn features from unlabeled data[16], [17],[18],[19],[20].Many results obtained with feature learning systems have also shown that higher performance in recognition tasks could be achieved through larger scale representations,such as could be generated by a scalable feature learning system.For instance,Van Gemert et al.[21] showed that performance can grow with larger numbers of low-level features,and Li et al.[22]have provided evidence of a similar phenomenon for high-level features like objects and parts.In this work,we focus on training low-level features,but more sophisticated feature learning methods are capable of learning higher level constructs that might be even more effective[23],[7],[17],[6].III.L EARNING A RCHITECTUREWe now describe the architecture used to learn the feature representations and train the classifiers used for our detection and character recognition systems.The basic setup is closely related to a convolutional neural network[15],but due to its training method can be used to rapidly construct extremely large sets of features with minimal tuning.Our system proceeds in several stages:1)Apply an unsupervised feature learning algorithm to aset of image patches harvested from the training data to learn a bank of image features.2)Evaluate the features convolutionally over the trainingimages.Reduce the number of features using spatial pooling[15].3)Train a linear classifier for either text detection orcharacter recognition.We will now describe each of these stages in more detail.A.Feature learningThe key component of our system is the application of an unsupervised learning algorithm to generate the features used for classification.Many choices of unsupervised learn-ing algorithm are available for this purpose,such as auto-encoders[19],RBMs[16],and sparse coding[24].Here, however,we use a variant of K-means clustering that has been shown to yield results comparable to other methods while also being much simpler and faster.Like many feature learning schemes,our system works by applying a common recipe:1)Collect a set of small image patches,˜x(i)from trainingdata.In our case,we use8x8grayscale1patches,so˜x(i)∈R64.2)Apply simple statistical pre-processing(e.g.,whiten-ing)to the patches of the input to yield a new dataset x(i).3)Run an unsupervised learning algorithm on the x(i)tobuild a mapping from input patches to a feature vector, z(i)=f(x(i)).The particular system we employ is similar to the one presented in[8].First,given a set of training images,we extract a set of m8-by-8pixel patches to yield vectors of pixels˜x(i)∈R64,i∈{1,...,m}.Each vector is brightness and contrast normalized.2We then whiten the˜x(i)using ZCA3whitening[25]to yield x(i).Given this whitened bank of input vectors,we are now ready to learn a set of features that can be evaluated on such patches.For the unsupervised learning stage,we use a variant of K-means clustering.K-means can be modified so that it yields a dictionary D∈R64×d of normalized basis vectors.Specifically,instead of learning“centroids”based on Euclidean distance,we learn a set of normalized vectors D(j),j∈{1,...,d}to form the columns of D,using inner products as the similarity metric.That is,we solveminD,s(i)i||Ds(i)−x(i)||2(1)s.t.||s(i)||1=||s(i)||∞,∀i(2)||D(j)||2=1,∀j(3) where x(i)are the input examples and s(i)are the corre-sponding“one hot”encodings4of the examples.Like K-means,the optimization is done by alternating minimization over D and the s(i).Here,the optimal solution for s(i)given 1All of our experiments use grayscale images,though the methods here are equally applicable to color patches.2We subtract out the mean and divide by the standard deviation of all the pixel values.3ZCA whitening is like PCA whitening,except that it rotates the data back to the same axes as the original input.4The constraint||s(i)||1=||s(i)||∞means that s(i)may have only1 non-zero value,though its magnitude is unconstrained.Figure1.A small subset of the dictionary elements learned from grayscale, 8-by-8pixel image patches extracted from the ICDAR2003dataset.D is to set s(i)k=D(k) x(i)for k=arg max j D(j) x(i), and set s(i)j=0for all other j=k.Then,holding all s(i)fixed,it is easy to solve for D(in closed-form for each column)followed by renormalizing the columns.Shown in Figure1are a set of dictionary elements (columns of D)resulting from this algorithm when applied to whitened patches extracted from small images of char-acters.These are visibly similar tofilters learned by other algorithms(e.g.,[24],[25],[16]),even though the method we use is quite simple and very fast.Note that the features are specialized to the data—some elements correspond to short,curved strokes rather than simply to edges.Once we have our trained dictionary,D,we can then define the feature representation for a single new8-by-8patch.Given a new input patch˜x,wefirst apply the normalization and whitening transform used above to yield x,then map it to a new representation z∈R d by taking the inner product with each dictionary element(column of D) and applying a scalar nonlinear function.In this work,we use the following mapping,which we have found to work well in other applications:z=max{0,|Dx|−α}whereαis a hyper-parameter to be chosen.(We typically useα=0.5.)B.Feature extractionBoth our detector and character classifier consider32-by-32pixel images.To compute the feature representation of the 32-by-32image,we compute the representation described above for every8-by-8sub-patch of the input,yielding a25-by-25-by-d representation.Formally,we will let z(ij)∈R d be the representation of the8-by-8patch located at position i,j within the input image.At this stage,it is necessary to reduce the dimensionality of the representation before classification.A common way to do this is with spatial pooling[26]where we combine the responses of a feature at multiple locations into a single feature.In our system, we use average pooling:we sum up the vectors z(ij)over 9blocks in a3-by-3grid over the image,yielding afinal feature vector with9d features for this image.C.Text detector trainingFor text detection,we train a binary classifier that aims to distinguish32-by-32windows that contain text from windows that do not.We build a training set for thisclassifier(a)Distorted ICDAR ex-amples(b)Synthetic examplesFigure2.Augmented training examples.by extracting32-by-32windows from the ICDAR2003 training dataset,using the word bounding boxes to decide whether a window is text or non-text.5With this procedure, we harvest a set of6000032-by-32windows for training (30000positive,30000negative).We then use the feature extraction method described above to convert each image into a9d-dimensional feature vector.These feature vectors and the ground-truth“text”and“not text”labels acquired from the bounding boxes are then used to train a linear SVM.We will later use our feature extractor and the trained classifier for detection in the usual“sliding window”fashion.D.Character classifier trainingFor character classification,we also use afixed-sized input image of32-by-32pixels,which is applied to the character images in a set of labeled train and test datasets.6 However,since we can produce large numbers of features using the feature learning approach above,over-fitting be-comes a serious problem when training from the(relatively) small character datasets currently in use.To help mitigate this problem,we have combined data from multiple sources. In particular,we have compiled our training data from the ICDAR2003training images[27],Weinman et al.’s sign reading dataset[4],and the English subset of the Chars74k dataset[1].Our combined training set contains approximately12400labeled character images.With large numbers of features,it is useful to have even more data.To satisfy these needs,we have also experimented with synthetic augmentations of these datasets.In particular, we have added synthetic examples that are copies of the ICDAR training samples with random distortions and image filters applied(see Figure2(a)),as well as artificial examples of rendered characters blended with random scenery images 5We define a window as“text”if80%of the window’s area is within a text region,and the window’s width or height is within30%of the width or height(respectively)of the ground-truth region.The latter condition ensures that the detector tends to detector characters of size similar to the window. 6Typically,input images from public datasets are already cropped to the boundaries of the character.Since our classifier uses afixed-sized window, we re-cropped characters from the original images using an enclosing window of the proper size.Figure3.Precision-Recall curves for detectors with varying numbers of features.(Figure2(b)).With these examples included,our dataset includes a total of49200images.IV.E XPERIMENTSWe now present experimental results achieved with the system described above,demonstrating the impact of being able to train increasing numbers of features.Specifically, for detection and character recognition,we trained our classifiers with increasing numbers of learned features and in each case evaluated the results on the ICDAR2003test sets for text detection and character recognition.A.DetectionTo evaluate our detector over a large input image,we take the classifier trained as in Section III-C and compute the features and classifier output for each32-by-32window of the image.We perform this process at multiple scales and then,for each location in the original image assign it a score equal to the maximum classifier output achieved at any scale.By this mechanism,we label each pixel with a score according to whether that pixel is part of a block of text.These scores are then thresholded to yield binary decisions at each pixel.By varying the threshold and using the ICDAR bounding boxes as per-pixel labels,we sweep out a precision-recall curve for the detector and report the area under this curve(AUC)as ourfinal performance measure.Figure3plots the precision-recall curves for our detector for varying numbers of features.It is seen there that perfor-mance improves consistently as we increase the number of features.Our detector’s performance(area under each curve) improves from0.5AUC,to0.62AUC simply by including more features.While our performance is not yet comparable to top performing systems it is notable that our approach included virtually no prior knowledge.In contrast,Pan et al.’s recent state-of-the-art system[5]involvesmultiple(a)ICDAR testimage(b)Text detectorscores(c)ICDAR testimage(d)Text detector scoresFigure4.Example text detection classifier outputs.Figure5.Character classification accuracy(62-way)on ICDAR2003test set as a function of the number of learned features.highly tuned processing stages incorporating several sets of expert-chosen features.Note that these numbers are per-pixel accuracies(i.e., the performance of the detector in identifying,for a single window,whether it is text or non-text).In practice,the predicted labels of adjacent windows are highly correlated and thus the outputs include large contiguous“clumps”of positively and negatively labeled windows that could be passed on for more processing.A typical result generated by our detector is shown in Figure4.B.Character RecognitionAs with the detectors,we trained our character classifiers with varying numbers of features on the combined training set described in Section III.We then tested this classifier on the ICDAR2003test set,which contains5198test characters 7Achieved without pre-segmented characters.Table IT EST RECOGNITION ACCURACY ON ICDAR2003CHARACTER SETS.(D ATASET-C LASSES)Algorithm Test-62Sample-62Sample-36 Neumann and Matas,2010[28]67.0%7--Yokobayashi et al.,2006[2]-81.4%-Saidane and Garcia,2007[14]--84.5% This paper81.7%81.4%85.5% from62classes(10digits,26upper-and26lower-case letters).The average classification accuracy on the ICDAR test set for increasing numbers of features is plotted in Figure5.Again,we see that accuracy climbs as a function of the number of features.Note that the accuracy for the largest system(1500features)is the highest,at81.7%for the62-way classification problem.This is comparable or superior to other(purpose-built)systems tested on the same problem. For instance,the system in[2],achieves81.4%on the smaller ICDAR“sample”set where we,too,achieve81.4%. The authors of[14],employing a supervised convolutional network,achieve84.5%on this dataset when it is collapsed to a36-way problem(removing case sensitivity).In that scenario,our system achieves85.5%with1500features. These results are summarized in comparison to other work in Table I.V.C ONCLUSIONIn this paper we have produced a text detection and recognition system based on a scalable feature learning algorithm and applied it to images of text in natural scenes. We demonstrated that with larger banks of features we are able to achieve increasing accuracy with top performance comparable to other systems,similar to results observed in other areas of computer vision and machine learning.Thus, while much research has focused on developing by hand the models and features used in scene-text applications,our results point out that it may be possible to achieve high performance using a more automated and scalable solution. With more scalable and sophisticated feature learning al-gorithms currently being developed by machine learning researchers,it is possible that the approaches pursued here might achieve performance well beyond what is possible through other methods that rely heavily on hand-coded prior knowledge.A CKNOWLEDGMENTAdam Coates is supported by a Stanford Graduate Fel-lowship.R EFERENCES[1]T. E.de Campos, B.R.Babu,and M.Varma,“Charac-ter recognition in natural images,”in Proceedings of the International Conference on Computer Vision Theory and Applications,Lisbon,Portugal,February2009.[2]M.Yokobayashi and T.Wakahara,“Binarization and recog-nition of degraded characters using a maximum separability axis in color space and gat correlation,”in International Conference on Pattern Recognition,vol.2,2006,pp.885–888.[3]J.J.Weinman,“Typographical features for scene text recog-nition,”in Proc.IAPR International Conference on Pattern Recognition,Aug.2010,pp.3987–3990.[4]J.Weinman,E.Learned-Miller,and A.R.Hanson,“Scenetext recognition using similarity and a lexicon with sparse belief propagation,”in Transactions on Pattern Analysis and Machine Intelligence,vol.31,no.10,2009.[5]Y.Pan,X.Hou,and C.Liu,“Text localization in natural sceneimages based on conditional randomfield,”in International Conference on Document Analysis and Recognition,2009.[6]J.Yang,K.Yu,Y.Gong,and T.S.Huang,“Linear spatialpyramid matching using sparse coding for image classifica-tion.”in Computer Vision and Pattern Recognition,2009. [7]H.Lee,R.Grosse,R.Ranganath,and A.Y.Ng,“Convolu-tional deep belief networks for scalable unsupervised learning of hierarchical representations,”in International Conference on Machine Learning,2009.[8] A.Coates,H.Lee,and A.Y.Ng,“An analysis of single-layernetworks in unsupervised feature learning,”in International Conference on Artificial Intelligence and Statistics,2011. [9]M.Ranzato,Y.Boureau,and Y.LeCun,“Sparse featurelearning for deep belief networks,”in Neural Information Processing Systems,2007.[10]X.Chen and A.Yuille,“Detecting and reading text in naturalscenes,”in Computer Vision and Pattern Recognition,vol.2, 2004.[11]Y.Pan,X.Hou,and C.Liu,“A robust system to detectand localize texts in natural scene images,”in International Workshop on Document Analysis Systems,2008.[12]J.J.Weinman,E.Learned-Miller,and A.R.Hanson,“A dis-criminative semi-markov model for robust scene text recog-nition,”in Proc.IAPR International Conference on Pattern Recognition,Dec.2008.[13]X.Fan and G.Fan,“Graphical Models for Joint Segmentationand Recognition of License Plate Characters,”IEEE Signal Processing Letters,vol.16,no.1,2009.[14]Z.Saidane and C.Garcia,“Automatic scene text recogni-tion using a convolutional neural network,”in Workshop on Camera-Based Document Analysis and Recognition,2007.[15]Y.LeCun, B.Boser,J.S.Denker, D.Henderson,R. E.Howard,W.Hubbard,and L.D.Jackel,“Backpropagation applied to handwritten zip code recognition,”Neural Compu-tation,vol.1,pp.541–551,1989.[16]G.Hinton,S.Osindero,and Y.Teh,“A fast learning algorithmfor deep belief nets,”Neural Computation,vol.18,no.7,pp.1527–1554,2006.[17]R.Salakhutdinov and G.E.Hinton,“Deep Boltzmann Ma-chines,”in12th International Conference on AI and Statistics, 2009.[18]M.Ranzato,A.Krizhevsky,and G.E.Hinton,“Factored3-way Restricted Boltzmann Machines for Modeling Natural Images,”in13th International Conference on AI and Statis-tics,2010.[19]Y.Bengio,mblin, D.Popovici,and rochelle,“Greedy layer-wise training of deep networks,”in Neural Information Processing Systems,2006.[20]R.Raina,A.Battle,H.Lee,B.Packer,and A.Ng,“Self-taught learning:transfer learning from unlabeled data,”in 24th International Conference on Machine learning,2007.[21]J.C.van Gemert,J.M.Geusebroek,C.J.Veenman,andA.W.M.Smeulders,“Kernel codebooks for scene catego-rization,”in European Conference on Computer Vision,2008.[22]L.-J.Li,H.Su, E.Xing,and L.Fei-Fei,“Object bank:A high-level image representation for scene classificationand semantic feature sparsification,”in Advances in Neural Information Processing Systems,2010.[23]K.Kavukcuoglu,P.Sermanet,Y.Boureau,K.Gregor,M.Mathieu,and Y.LeCun,“Learning convolutional feature hierarchies for visual recognition,”in Advances in Neural Information Processing Systems,2010.[24] B.A.Olshausen and D.J.Field,“Emergence of simple-cellreceptivefield properties by learning a sparse code for natural images,”Nature,vol.381,no.6583,pp.607–609,1996. [25] A.Hyvarinen and E.Oja,“Independent component analysis:algorithms and applications,”Neural networks,vol.13,no.4-5,pp.411–430,2000.[26]Y.Boureau,F.Bach,Y.LeCun,and J.Ponce,“Learningmid-level features for recognition,”in Computer Vision and Pattern Recognition,2010.[27]S.Lucas,A.Panaretos,L.Sosa,A.Tang,S.Wong,andR.Young,“ICDAR2003robust reading competitions,”Inter-national Conference on Document Analysis and Recognition, 2003.[28]L.Neumann and J.Matas,“A method for text localizationand recognition in real-world images,”in Asian Conference on Computer Vision,2010.。
倒装焊工艺简介
随着超导量子比特技术的进一步发展,实验上需要可以表面误码差校正以及更复杂的高保真量子电路[1-2]。
相关报道介绍了一些平面二维阵列的设计[3-5],但是这几种现行的设计方案中控制布线和读出电路往往使得量子比特数目与器件高保真度这二者与不能同时兼顾。
例如,二维阵列的X mon单量子位就需要利用电容耦合到四个最近量子比特和读出谐振腔,此外还要考虑XY驱动线的设计[6]。
多层膜加工工艺是解决这个问题的一种直观的方案[7],但是该方案中制备的量子比特基片上制备的绝缘层会造成额外的退相干,从而影响量子比特的特性[8]。
目前国际上解决上述困难的主流方法是通过将器件分离成两部分,其中一部分是密集的布线基片并在该基片上制备绝缘层,另一部分上制备量子比特而不生长大面积的绝缘层,随后将这两种基片通过倒装焊工艺结合起来形成一种同时满足多量子比特数目与高保真度的量子比特器件。
该种工艺已经在半导体工业中得到了广泛的应用,从手机到大型强子对撞机都有应用[9]。
而在低温技术中的应用还较少。
该工艺对两部分基片的连接部分提出了如下的要求:1.连接材料应是常规的量子比特制备工艺中常用的并可以与现有的量子比特制备工艺兼容。
2.谐振腔的可加工数量与质量必须达到高要求。
(在布线基片上需加工数百个高Q值的谐振腔)。
3.在极低温的条件下可以保证两部分的联通。
4.可以在不高的温度与大气压下进行两部分基片的连接,以避免退火改变约瑟夫森结临界电流[10]。
5.两部分基片的连接部分必须在测量条件下进入超导态,以保证芯片之间的无损连接与避免局部生热破坏测量条件。
6.相互连接偏置线临界电流应大于5nA,以保证可以进行实验测量。
铟的临界温度相对较高为3.4K,室温铟焊接工艺也是一种在半导体工业中较为成熟的技术[11],而在量子比特的加工工艺中高纯度的铟可以通过常用的热蒸发工艺生长在指定的位置,因此基片之间连接材料可选高纯铟。
但是,由于量子比特的基底金属常用铝,而铝和铟接触层会形成交叠层[12]影响量子比特的性能,因此热蒸发的时候必须在铝基底上生长氮化钛介质缓冲层以防止上述现象的出现,氮化钛的临界温度高达5.64K,并且是一种高相干性能的量子比特材料[13,14]。
混合像元
Geometrical MESMA
MVSA/SISAL
Minimum volume algorithms
Threat: when no pure pixels are present in the data, the spectral signatures derived may be unrealistic.
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高光谱图像混合像元分析及其进展
S k1 arg min f (S, Ak ) S 0
Ak1 arg min f (S k1, A) A0
梯度最速下降算法
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高光谱图像混合像元分析及其进展
端元提取
采用拟合的方法能同时较好的提取前两种条件下的端元。 但却很难准确的估计第三种条件下的端元。
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高光谱图像混合像元分析及其进展
端元提取
IEEE
15
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高光谱图像混合像元分析及其进展
端元提取
美国Nevada的Cuprite地区露头良好,矿物组合多样。主要的 地物类型为Alunite、Kaolinite、Calcite、Chalcedony等。
图像中约含11种端元光谱
NFINDR 提取结果
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高光谱图像混合像元分析及其进展
端元提取
8
Earth Science Workshop, vol. 95, pp. 23-26, 1995.
高光谱图像混合像元分析
端元提取
N-FINDR 算法通过寻找具有最大内接体积的单形体而自动 获取图像中的所有端元(以端元为顶点的单形体的体积是所有 由观测数据构成的单形体中最大的)。
M. E. Winter, “N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data,” 3753, pp. 266–270, Oct. 1999.
DS证据理论在SAR图像边缘检测中的应用
第33卷第1期2008年1月武汉大学学报・信息科学版G eomatics and Information Science of Wuhan University Vol.33No.1Jan.2008收稿日期:2007210220。
项目来源:国家自然科学基金资助项目(50505045)。
文章编号:167128860(2008)0120105204文献标志码:ADS 证据理论在SAR 图像边缘检测中的应用张 浩1 蔡晋辉1 周泽魁1(1 浙江大学工业控制技术国家重点实验室,杭州市余杭塘路388号,310027)摘 要:提出了一种新的基于DS 证据理论的SAR 图像多尺度边缘检测方法。
该方法利用DS 证据理论融合多级尺度ROA 算子输出响应,在融合过程中引入检测不确定性。
并通过分析ROA 算子检测门限与虚警率的关系设计基本可信度分配函数(BPA F ),使得检测不确定性在门限处达到最大值。
实验通过检测结果比较以及列表分析,表明本文算法能够有效降低边缘检测不确定性。
关键词:SAR 图像;边缘检测;DS 证据理论;数据融合中图法分类号:P237.3 SAR (合成孔径雷达)图像边缘是SAR 图像分析的基础。
但由于SAR 图像每一个分辨单元(像素)的灰度值是由照射场景中的许多回波叠加得到的,回波具有很强的干涉效果,所以SA R 图像中存在较强的乘性干涉斑点噪声,使得基于微分算子的传统边缘检测方法,如Sobel 、Robert s 、Canny 等算子[123]均失效。
近年来,人们提出了多种针对SAR 图像的边缘检测方法,如基于恒虚警率(CFAR )的局部均值比[4]和最大似然[5]边缘检测方法。
多尺度边缘检测算法[628]被证明是非常有效的,但对于各尺度间信息的融合却没有统一有效的方法。
人们已提出了多种数据融合方法,其中DS 证据理论[9,10]能够很好地表达推理过程中的不确定性,且推广形式简单,因而在目标识别和许多融合系统中得到广泛应用。
New Perspectives on the Structure of Graphitic Carbons
Critical Reviews in Solid State and Materials Sciences,30:235–253,2005 Copyright c Taylor and Francis Inc.ISSN:1040-8436printDOI:10.1080/10408430500406265New Perspectives on the Structure of Graphitic CarbonsPeter J.F.Harris∗Centre for Advanced Microscopy,University of Reading,Whiteknights,Reading,RG66AF,UKGraphitic forms of carbon are important in a wide variety of applications,ranging from pollutioncontrol to composite materials,yet the structure of these carbons at the molecular level ispoorly understood.The discovery of fullerenes and fullerene-related structures such as carbonnanotubes has given a new perspective on the structure of solid carbon.This review aims toshow how the new knowledge gained as a result of research on fullerene-related carbons canbe applied to well-known forms of carbon such as microporous carbon,glassy carbon,carbonfibers,and carbon black.Keywords fullerenes,carbon nanotubes,carbon nanoparticles,non-graphitizing carbons,microporous carbon,glassy carbon,carbon black,carbonfibers.Table of Contents INTRODUCTION (235)FULLERENES,CARBON NANOTUBES,AND CARBON NANOPARTICLES (236)MICROPOROUS(NON-GRAPHITIZING)CARBONS (239)Background (239)Early Models (241)Evidence for Fullerene-Like Structures in Microporous Carbons (242)New Models for the Structure of Microporous Carbons (242)Carbonization and the Structural Evolution of Microporous Carbon (243)GLASSY CARBON (244)CARBON FIBERS (245)CARBON BLACK (248)Background (248)Structure of Carbon Black Particles (249)Effect of High-Temperature Heat Treatment on Carbon Black Structure (250)CONCLUSIONS (250)ACKNOWLEDGMENTS (251)REFERENCES (251)INTRODUCTIONUntil quite recently we knew for certain of just two allotropes of carbon:diamond and graphite.The vast range of carbon ma-∗E-mail:p.j.f.harris@ terials,both natural and synthetic,which have more disordered structures have traditionally been considered as variants of one or other of these two allotropes.Because the great majority of these materials contain sp2carbon rather than sp3carbon,their struc-tures have been thought of as being made up from tiny fragments235236P.J.F.HARRISFI G.1.(a)Model of PAN-derived carbon fibres from the work of Crawford and Johnson,1(b)model of a non-graphitizing carbon by Ban and colleagues.2of crystalline graphite.Examples of models for the structures of carbons in which the basic elements are graphitic are reproduced in Figure 1.The structure shown in Figure 1(a)is a model for the structure of carbon fibers suggested by Crawford and Johnson in 1971,1whereas 1(b)shows a model for non-graphitizing car-bon given by Ban and colleagues in 1975.2Both structures are constructed from bent or curved sheets of graphite,containing exclusively hexagonal rings.Although these models probably provide a good first approximation of the structures of these car-bons,in many cases they fail to explain fully the properties of the materials.Consider the example of non-graphitizing carbons.As the name suggests,these cannot be transformed into crystalline graphite even at temperatures of 3000◦C and above.I nstead,high temperature heat treatments transform them into structures with a high degree of porosity but no long-range crystalline order.I n the model proposed by Ban et al.(Figure 1(b)),the structure is made up of ribbon-like sheets enclosing randomly shaped voids.It is most unlikely that such a structure could retain its poros-ity when subjected to high temperature heat treatment—surface energy would force the voids to collapse.The shortcomings of this and other “conventional”models are discussed more fully later in the article.The discovery of the fullerenes 3−5and subsequently of re-lated structures such as carbon nanotubes,6−8nanohorns,9,10and nanoparticles,11has given us a new paradigm for solid car-bon structures.We now know that carbons containing pentago-nal rings,as well as other non-six-membered rings,among the hexagonal sp 2carbon network,can be highly stable.This new perspective has prompted a number of groups to take a fresh look at well-known forms of carbon,to see whether any evidence can be found for the presence of fullerene-like structures.12−14The aim of this article is to review this new work on the structure of graphitic carbons,to assess whether models that incorporate fullerene-like elements could provide a better basis for under-standing these materials than the conventional models,and to point out areas where further work is needed.The carbon ma-terials considered include non-graphitizing carbon,glassy car-bon,carbon fibers,and carbon black.The article begins with an outline of the main structural features of fullerenes,carbon nanotubes,and carbon nanoparticles,together with a brief dis-cussion of their stability.FULLERENES,CARBON NANOTUBES,AND CARBON NANOPARTICLESThe structure of C 60,the archetypal fullerene,is shown in Figure 2(a).The structure consists of twelve pentagonal rings and twenty hexagons in an icosahedral arrangement.I t will be noted that all the pentagons are isolated from each other.This is important,because adjacent pentagonal rings form an unstable bonding arrangement.All other closed-cage isomers of C 60,and all smaller fullerenes,are less stable than buck-minsterfullerene because they have adjacent pentagons.For higher fullerenes,the number of structures with isolated pen-tagonal rings increases rapidly with size.For example,C 100has 450isolated-pentagon isomers.16Most of these higher fullerenes have low symmetry;only a very small number of them have the icosahedral symmetry of C 60.An example of a giant fullerene that can have icosahedral symmetry is C 540,as shown in Figure 2(b).There have been many studies of the stability of fullerenes as a function of size (e.g.,Refs.17,18).These show that,in general,stability increases with size.Experimentally,there is evidence that C 60is unstable with respect to large,multiwalled fullerenes.This was demonstrated by Mochida and colleagues,who heated C 60and C 70in a sublimation-limiting furnace.19They showed that the cage structure broke down at 900◦C–1000◦C,although at 2400◦C fullerene-like “hollow spheres”with diameters in the range 10–20nm were formed.We now consider fullerene-related carbon nanotubes,which were discovered by Iijima in 1991.6These consist of cylinders of graphite,closed at each end with caps that contain precisely six pentagonal rings.We can illustrate their structure by considering the two “archetypal”carbon nanotubes that can be formed by cutting a C 60molecule in half and placing a graphene cylinder between the two halves.Dividing C 60parallel to one of the three-fold axes results in the zig-zag nanotube shown in Figure 3(a),whereas bisecting C 60along one of the fivefold axes produces the armchair nanotube shown in Figure 3(b).The terms “zig-zag”and “armchair”refer to the arrangement of hexagons around the circumference.There is a third class of structure in which the hexagons are arranged helically around the tube axis.Ex-perimentally,the tubes are generally much less perfect than the idealized versions shown in Figure 3,and may be eitherNEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE237FI G.2.The structure of (a)C 60,(b)icosahedral C 540.15multilayered or single-layered.Figure 4shows a high resolu-tion TEM image of multilayered nanotubes.The multilayered tubes range in length from a few tens of nm to several microns,and in outer diameter from about 2.5nm to 30nm.The end-caps of the tubes are sometimes symmetrical in shape,but more often asymmetric.Conical structures of the kind shown in Fig-ure 5(a)are commonly observed.This type of structure is be-lieved to result from the presence of a single pentagon at the position indicated by the arrow,with five further pentagons at the apex of the cone.Also quite common are complex cap struc-tures displaying a “bill-like”morphology such as thatshownFI G.3.Drawings of the two nanotubes that can be capped by one half of a C 60molecule.(a)Zig-zag (9,0)structure,(b)armchair (5,5)structure.20in Figure 5(b).21This structure results from the presence of a single pentagon at point “X”and a heptagon at point “Y .”The heptagon results in a saddle-point,or region of negative curvature.The nanotubes first reported by Iijima were prepared by va-porizing graphite in a carbon arc under an atmosphere of helium.Nanotubes produced in this way are invariably accompanied by other material,notably carbon nanoparticles.These can be thought of as giant,multilayered fullerenes,and range in size from ∼5nm to ∼15nm.A high-resolution image of a nanopar-ticle attached to a nanotube is shown in Figure 6(a).22In this238P.J.F.HARRISFI G.4.TEM image of multiwalled nanotubes.case,the particle consists of three concentric fullerene shells.A more typical nanoparticle,with many more layers,is shown in Figure 6(b).These larger particles are probably relatively im-perfect instructure.FI G.5.I mages of typical multiwalled nanotube caps.(a)cap with asymmetric cone structure,(b)cap with bill-like structure.21Single-walled nanotubes were first prepared in 1993using a variant of the arc-evaporation technique.23,24These are quite different from multilayered nanotubes in that they generally have very small diameters (typically ∼1nm),and tend to be curledNEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE239FI G.6.I mages of carbon nanoparticles.(a)small nanoparticle with three concentric layers on nanotube surface,22(b)larger multilayered nanoparticle.and looped rather than straight.They will not be considered further here because they have no parallel among well-known forms of carbon discussed in this article.The stability of multilayered carbon nanotubes and nanopar-ticles has not been studied in detail experimentally.However,we know that they are formed at the center of graphite electrodes during arcing,where temperatures probably approach 3000◦C.I t is reasonable to assume,therefore,that nanotubes and nanopar-ticles could withstand being re-heated to such temperatures (in an inert atmosphere)without significant change.MICROPOROUS (NON-GRAPHITIZING)CARBONS BackgroundIt was demonstrated many years ago by Franklin 25,26that carbons produced by the solid-phase pyrolysis of organic ma-terials fall into two distinct classes.The so-called graphitizing carbons tend to be soft and non-porous,with relatively high den-sities,and can be readily transformed into crystalline graphite by heating at temperatures in the range 2200◦C–3000◦C.I n con-trast,“non-graphitizing”carbons are hard,low-densitymateri-FI G.7.(a)High resolution TEM image of carbon prepared by pyrolysis of sucrose in nitrogen at 1000◦C,(b)carbon prepared bypyrolysis of anthracene at 1000◦C.I nsets show selected area diffraction patterns.30als that cannot be transformed into crystalline graphite even at temperatures of 3000◦C and above.The low density of non-graphitizing carbons is a consequence of a microporous struc-ture,which gives these materials an exceptionally high internal surface area.This high surface area can be enhanced further by activation,that is,mild oxidation with a gas or chemical pro-cessing,and the resulting “activated carbons”are of enormous commercial importance,primarily as adsorbents.27−29The distinction between graphitizing and non-graphitizing carbons can be illustrated most clearly using transmission elec-tron microscopy (TEM).Figure 7(a)shows a TEM image of a typical non-graphitizing carbon prepared by the pyrolysis of sucrose in an inert atmosphere at 1000◦C.30The inset shows a diffraction pattern recorded from an area approximately 0.25µm in diameter.The image shows the structure to be disordered and isotropic,consisting of tightly curled single carbon layers,with no obvious graphitization.The diffraction pattern shows symmetrical rings,confirming the isotropic structure.The ap-pearance of graphitizing carbons,on the other hand,approxi-mates much more closely to that of graphite.This can be seen in the TEM micrograph of a carbon prepared from anthracene,240P.J.F.HARRI Swhich is shown in Figure 7(b).Here,the structure contains small,approximately flat carbon layers,packed tightly together with a high degree of alignment.The fragments can be considered as rather imperfect graphene sheets.The diffraction pattern for the graphitizing carbon consists of arcs rather than symmetrical rings,confirming that the layers are preferentially aligned along a particular direction.The bright,narrow arcs in this pattern correspond to the interlayer {0002}spacings,whereas the other reflections appear as broader,less intense arcs.Transmission electron micrographs showing the effect of high-temperature heat treatments on the structure of non-graphitizing and graphitizing carbons are shown in Figure 8(note that the magnification here is much lower than for Figure 7).I n the case of the non-graphitizing carbon,heating at 2300◦C in an inert atmosphere produces the disordered,porous material shown in Figure 8(a).This structure is made up of curved and faceted graphitic layer planes,typically 1–2nm thick and 5–15nm in length,enclosing randomly shaped pores.A few somewhat larger graphite crystallites are present,but there is no macroscopic graphitization.n contrast,heat treatment of the anthracene-derived carbon produces large crystals of highly or-dered graphite,as shown in Figure 8(b).Other physical measurements also demonstrate sharp dif-ferences between graphitizing and non-graphitizing carbons.Table 1shows the effect of preparation temperature on the sur-face areas and densities of a typical graphitizing carbon prepared from polyvinyl chloride,and a non-graphitizing carbon prepared from cellulose.31It can be seen that the graphitizing carbon pre-pared at 700◦C has a very low surface area,which changes lit-tle for carbons prepared at higher temperatures,up to 3000◦C.The density of the carbons increases steadily as thepreparationFI G.8.Micrographs of (a)sucrose carbon and (b)anthracene carbon following heat treatment at 2300◦C.TABLE 1Effect of temperature on surface areas and densities of carbonsprepared from polyvinyl chloride and cellulose 31(a)Surface areas Specific surface area (m 2/g)for carbons prepared at:Starting material 700◦C 1500◦C 2000◦C 2700◦C 3000◦C PVC 0.580.210.210.710.56Cellulose 4081.601.172.232.25(b)Densities Helium density (g/cm 3)for carbons prepared at:Starting material 700◦C 1500◦C 2000◦C 2700◦C 3000◦C PVC 1.85 2.09 2.14 2.21 2.26Cellulose1.901.471.431.561.70temperature is increased,reaching a value of 2.26g/cm 3,which is the density of pure graphite,at 3000◦C.The effect of prepara-tion temperature on the non-graphitizing carbon is very different.A high surface area is observed for the carbon prepared at 700◦C (408m 2/g),which falls rapidly as the preparation temperature is increased.Despite this reduction in surface area,however,the density of the non-graphitizing carbon actually falls when the temperature of preparation is increased.This indicates that a high proportion of “closed porosity”is present in the heat-treated carbon.NEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE241FI G.9.Franklin’s representations of(a)non-graphitizing and(b)graphitizing carbons.25Early ModelsThefirst attempt to develop structural models of graphitizingand non-graphitizing carbons was made by Franklin in her1951paper.25In these models,the basic units are small graphitic crys-tallites containing a few layer planes,which are joined togetherby crosslinks.The precise nature of the crosslinks is not speci-fied.An illustration of Franklin’s models is shown in Figure9.Using these models,she put forward an explanation of whynon-graphitizing carbons cannot be converted by heat treatmentinto graphite,and this will now be summarized.During car-bonization the incipient stacking of the graphene sheets in thenon-graphitizing carbon is largely prevented.At this stage thepresence of crosslinks,internal hydrogen,and the viscosity ofthe material is crucial.The resulting structure of the carbon(at ∼1000◦C)consists of randomly ordered crystallites,held to-gether by residual crosslinks and van der Waals forces,as inFigure9(a).During high-temperature treatment,even thoughthese crosslinks may be broken,the activation energy for themotion of entire crystallites,required for achieving the struc-ture of graphite,is too high and graphite is not formed.Onthe other hand,the structural units in a graphitizing carbon areapproximately parallel to each other,as in Figure9(b),and thetransformation of such a structure into crystalline graphite wouldbe expected to be relatively facile.Although Franklin’s ideason graphitizing and non-graphitizing carbons may be broadlycorrect,they are in some regards incomplete.For example,thenature of the crosslinks between the graphitic fragments is notspecified,so the reasons for the sharply differing properties ofgraphitizing and non-graphitizing carbons is not explained.The advent of high-resolution transmission electron mi-croscopy in the early1970s enabled the structure of non-graphitizing carbons to be imaged directly.n a typical study,Ban,Crawford,and Marsh2examined carbons prepared frompolyvinylidene chloride(PVDC)following heat treatments attemperatures in the range530◦C–2700◦C.I mages of these car-bons apparently showed the presence of curved and twistedgraphite sheets,typically two or three layer planes thick,enclos-ing voids.These images led Ban et al.to suggest that heat-treatednon-graphitizing carbons have a ribbon-like structure,as shownin Figure1(b).This structure corresponds to a PVDC carbonheat treated at1950◦C.This ribbon-like model is rather similar to an earlier model of glassy carbon proposed by Jenkins andKawamura.32However,models of this kind,which are intendedto represent the structure of non-graphitizing carbons follow-ing high-temperature heat treatment,have serious weaknesses,as noted earlier.Such models consist of curved and twistedgraphene sheets enclosing irregularly shaped pores.However,graphene sheets are known to be highlyflexible,and wouldtherefore be expected to become ever more closely folded to-gether at high temperatures,in order to reduce surface energy.Indeed,tightly folded graphene sheets are quite frequently seenin carbons that have been exposed to extreme conditions.33Thus,structures like the one shown in Figure1(b)would be unlikelyto be stable at very high temperatures.It has also been pointed out by Oberlin34,35that the modelsput forward by Jenkins,Ban,and their colleagues were basedon a questionable interpretation of the electron micrographs.In most micrographs of partially graphitized carbons,only the {0002}fringes are resolved,and these are only visible when they are approximately parallel to the electron beam.Therefore,such images tend to have a ribbon-like appearance.However,because only a part of the structure is being imaged,this appear-ance can be misleading,and the true three-dimensional structuremay be more cagelike than ribbon-like.This is a very importantpoint,and must always be borne in mind when analyzing imagesof graphitic carbons.Oberlin herself believes that all graphiticcarbons are built up from basic structural units,which comprisesmall groups of planar aromatic structures,35but does not appearto have given a detailed explanation for the non-graphitizabilityof certain carbons.The models of non-graphitizing carbons described so farhave assumed that the carbon atoms are exclusively sp2and arebonded in hexagonal rings.Some authors have suggested thatsp3-bonded atoms may be present in these carbons(e.g.,Refs.36,37),basing their arguments on an analysis of X-ray diffrac-tion patterns.The presence of diamond-like domains would beconsistent with the hardness of non-graphitizing carbons,andmight also explain their extreme resistance to graphitization.Aserious problem with these models is that sp3carbon is unsta-ble at high temperatures.Diamond is converted to graphite at1700◦C,whereas tetrahedrally bonded carbon atoms in amor-phousfilms are unstable above about700◦C.Therefore,the242P.J.F.HARRI Spresence of sp 3atoms in a carbon cannot explain the resistance of the carbon to graphitization at high temperatures.I t should also be noted that more recent diffraction studies of non-graphitizing carbons have suggested that sp 3-bonded atoms are not present,as discussed further in what follows.Evidence for Fullerene-Like Structures in Microporous CarbonsThe evidence that microporous carbons might have fullerene-related structures has come mainly from high-resolution TEM studies.The present author and colleagues initiated a series of studies of typical non-graphitizing microporous carbons using this technique in the mid 1990s.30,38,39The first such study in-volved examining carbons prepared from PVDC and sucrose,after heat treatments at temperatures in the range 2100◦C–2600◦C.38The carbons subjected to very high temperatures had rather disordered structures similar to that shown in Figure 8(a).Careful examination of the heated carbons showed that they often contained closed nanoparticles;examples can be seen in Figure 10.The particles were usually faceted,and often hexagonal or pentagonal in shape.Sometimes,faceted layer planes enclosed two or more of the nanoparticles,as shown in Figure 10(b).Here,the arrows indicate two saddle-points,similar to that shown in Figure 5(b).The closed nature of the nanoparticles,their hexagonal or pentagonal shapes,and other features such as the saddle-points strongly suggest that the parti-cles have fullerene-like structures.I ndeed,in many cases the par-ticles resemble those produced by arc-evaporation in a fullerene generator (see Figure 6)although in the latter case the particles usually contain many more layers.The observation of fullerene-related nanoparticles in the heat treated carbons suggested that the original,freshly prepared car-bons may also have had fullerene-related structures (see next section).However,obtaining direct evidence for this is diffi-cult.High resolution electron micrographs of freshly prepared carbons,such as that shown in Figure 7(a),are usuallyratherFI G.10.(a)Micrograph showing closed structure in PVDC-derived carbon heated at 2600◦C,(b)another micrograph of same sample,with arrows showing regions of negative curvature.38featureless,and do not reveal the detailed structure.Occasion-ally,however,very small closed particles can be found in the carbons.30The presence of such particles provides circumstan-tial evidence that the surrounding carbon may have a fullerene-related structure.Direct imaging of pentagonal rings by HRTEM has not yet been achieved,but recent developments in TEM imaging techniques suggest that this may soon be possible,as discussed in the Conclusions.As well as high-resolution TEM,diffraction methods have been widely applied to microporous and activated carbons (e.g.,Refs.40–44).However,the interpretation of diffraction data from these highly disordered materials is not straightforward.As already mentioned,some early X-ray diffraction studies were interpreted as providing evidence for the presence of sp 3-bonded atoms.More recent neutron diffraction studies have suggested that non-graphitizing carbons consist entirely of sp 2atoms.40It is less clear whether diffraction methods can establish whether the atoms are bonded in pentagonal or hexagonal rings.Both Petkov et al .42and Zetterstrom and colleagues 43have interpreted neutron diffraction data from nanoporous carbons in terms of a structure containing non-hexagonal rings,but other interpreta-tions may also be possible.Raman spectroscopy is another valuable technique for the study of carbons.45Burian and Dore have used this method to analyze carbons prepared from sucrose,heat treated at tem-peratures from 1000◦C–2300◦C.46The Raman spectra showed clear evidence for the presence of fullerene-and nanotube-like elements in the carbons.There was also some evidence for fullerene-like structures in graphitizing carbons prepared from anthracene,but these formed at higher temperatures and in much lower proportions than in the non-graphitizing carbons.New Models for the Structure of Microporous Carbons Prompted by the observations described in the previous section,the present author and colleagues proposed a model for the structure of non-graphitizing carbons that consists ofNEW PERSPECTIVES ON GRAPHITIC CARBONS STRUCTURE243FI G.11.Schematic illustration of a model for the structure of non-graphitizing carbons based on fullerene-like elements.discrete fragments of curved carbon sheets,in which pentagons and heptagons are dispersed randomly throughout networks of hexagons,as illustrated in Figure11.38,39The size of the micropores in this model would be of the order of0.5–1.0nm, which is similar to the pore sizes observed in typical microp-orous carbons.The structure has some similarities to the“ran-dom schwarzite”network put forward by Townsend and col-leagues in1992,47although this was not proposed as a model for non-graphitizing carbons.I f the model we have proposed for non-graphitizing carbons is correct it suggests that these carbons are very similar in structure to fullerene soot,the low-density, disordered material that forms on walls of the arc-evaporation vessel and from which C60and other fullerenes may be ex-tracted.Fullerene soot is known to be microporous,with a sur-face area,after activation with carbon dioxide,of approximately 700m2g−1,48and detailed analysis of high resolution TEM mi-crographs of fullerene soot has shown that these are consis-tent with a structure in which pentagons and heptagons are dis-tributed randomly throughout a network of hexagons.49,50It is significant that high-temperature heat treatments can transform fullerene soot into nanoparticles very similar to those observed in heated microporous carbon.51,52Carbonization and the Structural Evolutionof Microporous CarbonThe process whereby organic materials are transformed into carbon by heat treatment is not well understood at the atomic level.53,54In particular,the very basic question of why some organic materials produce graphitizing carbons and others yield non-graphitizing carbons has not been satisfactorily answered. It is known,however,that both the chemistry and physical prop-erties of the precursors are important in determining the class of carbon formed.Thus,non-graphitizing carbons are formed, in general,from substances containing less hydrogen and more oxygen than graphitizing carbons.As far as physical properties are concerned,materials that yield graphitizing carbons usu-ally form a liquid on heating to temperatures around400◦C–500◦C,whereas those that yield non-graphitizing carbons gen-erally form solid chars without melting.The liquid phase pro-duced on heating graphitizing carbons is believed to provide the mobility necessary to form oriented regions.However,this may not be a complete explanation,because some precursors form non-graphitizing carbons despite passing through a liquid phase.The idea that non-graphitizing carbons contain pentagons and other non-six-membered rings,whereas graphitizing car-bons consist entirely of hexagonal rings may help in understand-ing more fully the mechanism of carbonization.Recently Kumar et al.have used Monte Carlo(MC)simulations to model the evo-lution of a polymer structure into a microporous carbon structure containing non-hexagonal rings.55They chose polyfurfuryl al-cohol,a well-known precursor for non-graphitizing carbon,as the starting material.The polymer was represented as a cubic lattice decorated with the repeat units,as shown in Figure12(a). All the non-carbon atoms(i.e.,hydrogen and oxygen)were then removed from the polymer and this network was used in the。
基于DWT的图像内容半脆弱水印认证算法
收稿日期: 2016-07-19 作者简介: 张劲松 ( 1987) , 男, 汉, 在读硕士, 助理研究员, 研究方向:数据挖掘, 网络编程。 E-mail: xiahuaxianyou@。
第6期
张劲松等 . 基于 DWT 的图像内容半脆弱水印认证算法
LL2 HL2 HL1 LH2 的中 2ˑ2 子块 HH1
摘
要: 半脆弱水印因为在多媒体内容认证方面的重要作用而受到人们密切的关注。为了能够区分偶
然攻击与恶意篡改, 半脆弱水印需要对一般的内容保护图像操作有一定的鲁棒性。本文提出了一种新 的基于 DWT 变换的半脆弱水印算法, 该算法思想首先对图像进行一层 DWT 小波变换, 再把图像变换 出的高频系数部分 (LH1 和 HL1) 进行分块, 然后分别算出每小块的能量值, 根据能量值大小关系嵌入 水印。仿真实验结果表明, 该算法特别在 JPEG 压缩方面有较好的鲁棒性, 另外算法具有精确的定位检 测能力, 算法整体性能较好。 关键词: 半脆弱水印; DWT; JPEG; 鲁棒性; 小波变换 中图分类号: TP301.6 文献标识码: A 文章编号: 1001-7119 (2017) 06-0192-04
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Characterization of In-Pixel Buried-Channel Source Follower with OptimizedRow Selector in CMOS Image SensorsYue Chen, Xinyang Wang, Adri J. Mierop and Albert J.P. TheuwissenElectronic Instrumentation Lab., Delft University of Technology,Mekelweg 4, 2628 CD, Delft, the NetherlandsAbstract This paper presents a CMOS image sensor with pinned-photodiode 5T active pixels which use in-pixel buried channel source follower (BSF) and optimized row selector (RS). According to our previous work [1], using in-pixel BSFs can achieve significant pixel dark random noise reduction, specially for in-pixel random telegraph signal (RTS) noise, but due to the positively-shifted threshold voltage of the nMOS BSF, it will also introduce a fundamental trade-off between the maximum pixel output and image lag. Also due to this trade-off, our previous results were only measured in dark [1]. Therefore further optimization is necessary. To solve this trade-off but without any influences of the noise improvement from the BSF, a further optimized pixel structure, i.e. in-pixel BSF with optimized RS, is proposed and implemented in 0.18 µm CMOS process.I INTRODUCTIONDuring the past few years, a lot of efforts have been made on reducing the random noise in CMOS imagers, which is mainly composed out of 1/f and the so called Random Telegraph Signal (RTS) noise [2]. Research has revealed that the dominated random noise sources in CMOS image sensors (CIS) are due to the lattice defects at Si-SiO2 interface of the in-pixel source follower (SF) transistor [3, 4]. As CMOS processes scale down, the gate area of the transistors becomes so small that it easily happens to have only one active interface trap underneath the transistor’s gate, which will induce the RTS noise. Because of this single electron trapping and de-trapping during the transistor operation, the RTS appears in pixels which have only one active interface defect and dominates the pixel temporal noise, which limits the imaging quality under low-light conditions [5]. Therefore, as long as a perfect clean gate interface can not be guaranteed, the 1/f or RTS noise will stay dominant in the random noise in pixels.Our previous work [1] has revealed that taking the conducting carriers away from the Si-SiO2interface by creating a buried-channel nMOS SF transistor in a modern CMOS imager process can reduce the dark random noise within pixels dramatically. In this paper, the CIS pixels based on buried-channel nMOS SF transistors with further enhanced performance, i.e. improved output swing and high dynamic range, is introduced. Moreover, because of the drastically improved output swing of the pixels by the buried-channel source follower (BSF) transistor together with an optimized row selector (RS), “digital” transistors with reduced power supply voltages can be used in the pixel without limiting the pixel’s output swing, saturation level and dynamic range.II WORKING PRINCIPLEIn principle, the buried-channel transistors stand for transistors of which the majority of their conducting carriers flow far beneath the gate Si-SiO2interface during operation. Actually in modern CMOS processes, the p-type MOS transistors are naturally buried channel devices because of the threshold voltage (V t) adjust doping process during fabrication. Therefore, the expected structure of a buried-channel nMOS transistor is very straightforward, i.e. a total region reversing of a pMOS transistor, as shown in Fig. 1. The desired operation modes for such a device are shown in Fig. 2. It was simulated from an “ideal” CMOS process, which means all parameters and process flow can be adjusted freely. The dashed lines stand for the boundaries of the depletion regions. As shown in Fig. 2, during switch off, the gate interface region is fully depleted and no current flows from the drain to the source. While during the linear operation, the two depletion regions are separated from each other, which allows current to flow. In the saturation region, the channel is pinched off near the drain side.Because of the buried-channel doping, the V t of this nMOS transistor is shifted towards a negativevalue. This will help to increase the pixel outputswing.Figure 1. Cross section of a buried-channel Nmos transistorFigure 2. Expected operation modes of a buried-channelnMOS transistorIII SENSOR DESIGNAs mentioned, the maximum pixel output swing can be significantly improved because of the negative V t of the BSF transistor. However, such improvement is limited by the row select switch, which is normally realized by a standard nMOS transistor. The maximum voltage which can pass through the row select switch is then determined by the gate voltage and threshold voltage of this row select transistor. Therefore, the floating diffusion (FD) node reset voltage is expected to be reasonable low in order to ensure that the video signal can be properly readout by the row select switch. However, reducing the FD reset voltage brings a potential risk of incomplete charge transfer from the photodiode to the FD region, thus introducing image lag. Therefore, this trade-off between the noise reduction and improvement of the output swing (the possibility of introducing image lag) is limiting the feasibility and performance of the BSFs pixels. A solution to this issue is proposed and applied to the followingdesign, i.e. a test sensor with in-pixel BSFs and optimized row select switches.The test sensor was fabricated in a 0.18 µm 1P4M CMOS process by TSMC. The chip micrograph with several fundamental functional blocks of the prototype chip is shown in Fig. 3. The pixel array is 200 rows × 150 columns with 10 µm pixel pitches. The Pixels are implemented with pinned photodiode 4T structures with BSFs and optimizedRSs, 5 columns of which are still withconventional SSFs for noise comparison. Theschematic of the pixel is shown in Fig. 4, in whicha transmission gate is implemented as the row selector. The system clock frequency is 10MHz. The front-end read timing is supplied by an external FPGA. For the noise measurement, the CDS time interval and the charge transfer period are 1.5 µs and 1 µs, respectively. The outputs of the imager are analog signals, being converted into digital by an off-chip image processor with 12bit ADC. The test imager was successfully fabricated and tested. The measurement results are presentedand discussed in the following section.Figure 3. Chip micrograph of the test imagerFigure 4. Schematic of pixels of the new test imagerIV MEASUREMENT RESULTSCompared to [1], the sensor characterization is done not only in dark but also with light injection, such that more detailed performances of pixels with BSF can be revealed accordingly, e.g. conversion gain and dynamic range.The pixel output swing is measured. The measurement results are shown in Fig. 5. During the measurement, the reset transistor (RT) gate is tied to the highest voltage of the pixel, i.e. performing hard reset, and the transfer gate is grounded. Therefore, the FD voltage equals the reset transistor power supply. The column biascurrent remains constant for all pixels.Figure 5. Pixel output swing measurement with differentimplantation doping and bias currentsAs shown, the output swing of the BSF pixels is about 2 V, gaining almost 100% improvement compared that of SSF pixels. If the bias current is reduced while the implantation dose remains the same, the pixel output further approaches or even exceeds the line of V FD =V out , which indicates that the channel is buried deeper into the silicon. If the bias current remains constant, increasing the implantation dose also pushes the channel deeper. Therefore, in principle, the pixel read-out noise level, if dominated by the interface trap related noise, will be smaller in case of a smaller bias current or higher implantation dose of the buried-channel source follower. Furthermore, it can be seen that regardless the bias current and implantation dose, all output swing curves tend towards V out > V FD at a lower FD voltage, i.e. the source follower operates further into the buried mode. And therefore, the pixel read-out noise is expected to be reduced as well. The measured voltage gain of the source follower is improvedfrom 0.83 of the surface-mode devices to about 0.92~0.95 of a buried-channel transistor. As a conclusion, both the pixel output swing and the source follower voltage gain are improved by using the BSFs inside the pixel.The dark random noise of BSF and SSF pixels is measured with the sensor. Both the BSFs and SSFs are biaes with 6 µA current and 3.3 V power supply. The random noise of each pixel is taken by calculating the standard deviation of pixel outputs through 20 frames. In order to exclude the contribution of the photon shot noise from the total noise floor, all the noise measurements are carried out in complete darkness. The transistor dimension for both BSFs and SSFs are W/L=0.42 µm/0.5 µm. The measurements were processed with an analog sensor gain of 10, at 17fps and with a 12bit board-level ADC. The CDS interval is 1.5 µs with transfer gate (TX) transistor grounded. Hard reset iss performed both on the new BSF pixels with optimized RS and SSF pixels. For the new BSF pixels with optimized RS (10 µm pixel pitch), the fill factor is 33% and the conversion gain is 41 µV/e -.As shown in Fig. 6, the symmetric distribution of the pixels around the peak of the SSFs pixel curve indicates the dominance of the 1/f and RTS noise of the SSFs. The average dark random noise of the BSF pixels is about 5 e -, reduced around 50% comparing with the SSF pixels, and the noise histogram of the BSF pixels closely approximates a true Gaussian distribution with significantlyreduced noise spread.Figure 6. Histograms of the dark random noise for BSF andSSF pixelsThe measurement result of the average dark random noise as a function of different FD voltages is shown in Fig. 7. Apparently, the relationship between pixel random noise and source-follower channel depth is confirmed by the results. The channel is buried deeper by lower FD voltages; therefore, the measured random noise is smaller for lower FD voltages. However, a low FD voltage is not recommended because of the incompletecharge transfer introduced image lag.Figure 7. Dark random noise measurement with different FDvoltagesPhoton transfer curve (PTC) measurement for the BSF pixels with optimized RS is implemented with a DC current controlled monochromic light source. During the measurements, the light intensity is varied and the exposure time is kept constant, i.e. 200 line times (1 line time=0.3 ms). The reset voltage on floating diffusion (FD) node was set to be 3.3 V, i.e. performing hard reset. The measurement is processed under a room temperature. The system analog gain is set to beunit.Figure 8. PTC of pixels with BSF and optimized RSBy calculating from Fig.8, the full well of pixels with BSF and optimized RS is 19500 e -, while read noise is around 7 e -. The dynamic range achieved is 68 dB.VCONCLUSIONSA CMOS image sensor with an in-pixel buried-channel source follower and an optimized RS is presented. The results show that compared to a regular imager with the standard nMOS transistor SSF, the new pixel structure improves output swing by almost 100% without any conflicts to the signal readout operation of the pixels and still can reduce dark random noise by 50%. Moreover, with optimized RS, hard reset on the pixel FD node can be operated, together with the output swing and noise improvement, it finally achieves 68 dB dynamic range. As a conclusion, the new pixel structure is able to not only drastically minimize in-pixel RTS noise but also improve output swing and dynamic range.VI ACKNOWLEDGEMENTThe authors would like to thank B. Mheen for his contribution to the new sensor, M. F. Snoeij for his contribution to the image test board, and, especially thank F. K. Chen, S. G. Wu, V. Hsu and M. Li of TSMC for their support in modifying the process flow.REFERENCES[1] X. Wang, et.al., “A CMOS Image Sensor with a Buried-Channel Source Follower”, in Technical Digest ISSCC , San Francisco, US, Feb. 2008, pp. 62-63[2] M. Cohen, et.al., “Fully Optimized Cu Based Process with Dedicated Cavity Etch for 1.75µm and 1.45µm Pixel Pitch CMOS Image Sensors,” in IEDM Tech. Dig., Dec. 2006, pp.127-130[3] B. Pain, et.al., “Excess Noise and Dark Current Mechanism in CMOS Imagers,” presented at IEEE Workshop on CCD’s and Advanced Image Sensors , Karuizawa, Nagano, Japan, Jun.2005, pp145-148[4] J. Y. Kim, et.al., “Characterization and Improvement of Random Noise in 1/3.2” UXGA CMOS Image Sensor with 2.8um Pixel using 0.13um-Technology,” presented at IEEE Workshop on CCD’s and Advanced Image Sensors , Karuizawa, Nagano, Japan, Jun. 2005, pp. 149-152.[5] C. 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