Noise-induced synchronization transitions

合集下载

脉冲激励混响室测试区域电磁场均匀性

脉冲激励混响室测试区域电磁场均匀性

103220-1第27卷第10期强激光与粒子束V o l .27,N o .10 2015年10月H I G H P OW E R L A S E R A N D P A R T I C L E B E AM S O c t .,2015脉冲激励混响室测试区域电磁场均匀性*贾 锐, 曾勇虎, 王川川(电子信息系统复杂电磁环境效应国家重点实验室,河南洛阳471003) 摘 要: 研究了电磁脉冲激励混响室内电磁波传播规律,通过仿真建模和实验两方面对脉冲激励混响室测试区域内电磁场均匀性进行了研究㊂提出了混响室时域均匀性的概念及其计算方法,并利用此方法计算了仿真和实验结果㊂结果表明:脉冲激励混响室内电磁环境无论在时域上还是频域上,均满足国际标准规定的均匀性要求;在一定空间内,混响室内也可以形成统计平均的脉冲场电磁环境;在混响室内进行电磁脉冲场的电磁敏感度测试是可行的㊂关键词: 混响室; 电磁脉冲; 时域; 均匀性; 测试区域中图分类号: T N 97; O 441 文献标志码: A d o i :10.11884/H P L P B 201527.103220混响室自20世纪60年代应用于电磁兼容领域以来,由于其统计特性,混响室内电磁环境研究主要集中在频域,即在单频点处利用连续平面波进行激励,从而研究其腔室内部电磁环境分布规律㊂然而,电磁脉冲是日常生活中的主要电磁干扰源㊂尤其是复杂战场电磁环境中,可以导致许多电子设备失效或潜在性失效,其在电磁兼容领域占有非常大的比重[1-7]㊂I E C 61000-4-21[8]混响室标准中也提到了脉冲激励混响室的可行性,但由于电磁脉冲的上升时间和持续时间多为n s 级,远小于混响室的时间常数㊂即在脉冲激励混响室内还未建立起稳定的电磁场时,脉冲激励源可能就已经衰减至零㊂这使得电磁脉冲一直没有作为混响室的激励源得到研究和使用㊂近年来,国际上也开始对脉冲激励混响室进行相关研究,但其研究内容主要集中于通信中的K 因子㊁时间参数及脉冲峰值等[9-10],并没有对混响室内测试区域的电磁场场均匀性进行研究㊂本文选用常见的高斯脉冲作为激励源,利用电磁仿真软件C S T ,构建与真实混响室1:1模型,从仿真和实验两方面研究了电磁脉冲在混响室内的传播规律及脉冲激励混响室电磁场的均匀性㊂为开展重复性强的脉冲辐照敏感度测试探索性研究提供参考,并且可拓展混响室的测试范围㊂1 模型分析F i g .1 M o d e l a d o p t e d i nC S Ts i m u l a t i o n 图1 仿真用混响室模型利用C S T 电磁仿真软件构建混响室模型,尺寸为10.5mˑ8.0mˑ4.3m ,如图1所示㊂激励天线采用对数周期天线,搅拌器包括垂直和水平两种类型㊂在混响室模型内矩形工作区域的8个顶点处添加三维探针,记录脉冲激励后一定时间内混响室工作区域内的场环境㊂发射天线采用宽频对数周期天线,其工作频率为80MH z ~2G H z ㊂在混响室内工作区域的8个顶点处,分别设置电场探头用于接收脉冲场电磁环境㊂混响室工作区域的选择首先依据国际标准中距离墙壁1/4波长的距离㊂由于脉冲频率成分的多样性,这里选择混响室最低可用频率的1/4波长㊂再根据8个顶点处的均匀性情况,逐渐扩大测试区域与混响室墙壁的距离,进一步缩小混响室工作体积,直至其电磁场均匀性满足国际标准需求㊂仿真及试验过程中,混响室都工作于步进搅拌模式㊂为保证足够的模数,按照新版国际标准,搅拌器旋转一周过程中存在12个独立步进搅拌位置(边界条件)㊂由于模型及实验用混响室中配备的都是两个搅拌器,因此垂直搅拌器选取4个独立搅拌位置,水平搅拌器选取3个独立搅拌位置㊂这样的步进模式选择可使得混响室腔室内电磁场边界条件最大不相关化㊂*收稿日期:2015-05-27; 修订日期:2015-09-10基金项目:国家自然科学基金项目(51277180)作者简介:贾 锐(1986 ),男,博士,从事电磁环境模拟及评估技术研究;ji a r u i 315@163.c o m ㊂103220-2 2 脉冲传播规律首先,采用频段为80~200MH z 调制高斯脉冲作为激励源(如图2(a )所示)㊂图2(b)给出了高斯脉冲激励混响室的矩形工作区域某顶点处在单一边界条件下的电场传播规律㊂图中所示电场强度存在负值,是因为此时电场方向与设置探针方向相反㊂由图可知,高斯脉冲在混响室内的传播规律为震荡式衰减,其传播时间达5000n s ,远长于其本身的激励脉冲宽度,这是由于混响室的高储能效应所造成的,且通过多次反射叠加,其脉冲幅值也高于其激励脉冲幅值㊂F i g .2 E x c i t e dG a u s s i a n p u l s e a n dn o r m a l i z e d e l e c t r i c f i e l da t a t e s t p o i n t u n d e r a n i n d e pe n d e n t t u n e r c o n d i t i o n 图2 激励高斯脉冲及混响室工作区域某顶点处单一搅拌位置下归一化场强对采集到的时域电磁场分量进行傅里叶变换,获取并分析其频域信息,图3分别给出了某一搅拌位置下时域电磁场分量的频域实部和虚部信息㊂由图可知,频域能量主要集中于150~200MH z 之间㊂这是由于混响室腔体自有的最低可用频率造成其具有一定的选频特性㊂F i g .3 F r e q u e n c y r e s po n s e s o f e l e c t r i c f i e l d 图3 某一搅拌位置下时域电场分量的频域实信息3 脉冲场均匀性评价上节中分析了脉冲激励混响室的时域传播规律㊂但是,脉冲激励混响室是否具有混响室内所特有的统计均匀特性,仍然需要进行系统研究㊂国内外标准和文献中给出了判断混响室内频域电磁场均匀性的方法,如国际电工委员会I E C 61000-4-21标准中给出了混响室工作区域内标准偏差的计算方法以及限值㊂但标准中的方法主要是针对于连续波频域信号,并不适用于脉冲激励而产生的时域信号㊂本节我们参考国际电工委员会制定的频域信号标准偏差,计算脉冲信号激发后的一段时间窗口内的时域场强标准偏差,即可判断混响室时域均匀性㊂定义混响室矩形工作区域8个顶点在12个独立搅拌位置下的场强最大值为E n t ,()p =m a x (E (t ,p 1 p 12))(1)式中:p 1~p 12为12个独立搅拌位置;t 为选取的时间窗口;n =1,2, ,8为混响室矩形工作区域的8个顶点㊂需要注意的是,式(1)定义的电场实际上是一个系综场的峰值包络曲线,是满足一定条件的场的集合㊂计算混响室工作区域内8个顶点处在12个独立搅拌位置下的系纵峰值包络标准差,作为混响室时域标准偏差㊂强激光与粒子束103220-3 利用国际电工委员会标准判断计算得出的标准偏差是否符合混响室场均匀性要求㊂时间窗口内时域信号标准偏差计算方法如下所示㊂式(1)中所示的各个顶点的最大值包含3个正交的方向x ,y ,z ,如式(2)给出㊂混响室工作区域8个顶点在3个正交方向上的平均电场强度分别由式(3)~(5)给出,在3个正交方向上的总的平均电场强度由式(6)给出㊂则每个方向和总的联合标准偏差由式(7),(8)分别给出,转为分贝表达式由式(9)给出㊂其中,m =1,2,3分别为x ,y ,z 三个方向㊂E n t ,()p =E 2n x t ,()p +E 2n y t ,()p +E 2n z t ,()p (2)<E x t ,()p >8=<ð8n =1E n x t ,()p >8(3)<E y t ,()p >8=<ð8n =1E n y t ,()p >8(4)<E z t ,()p >8=<ð8n =1E n z t ,()p >8(5)<Et ,()p >24=<ð3m =1ð8n =1E m ,n t ,()p >24(6)σm =ð8n =1E n m t ,()p -<E m t ,()p >()828-1(7)σx ,y ,z =ð3m =1ð8n =1E m ,n t ,()p -<Et ,()p >()24224-1(8)σx ,y ,z (d B )=20l g (σx ,y ,z +<E x ,y ,z t ,()p >8<E x ,y ,z t ,()p >8)(9) 首先选取工作区域距离混响室墙壁λ/4距离时(工作体积1),计算混响室矩形工作区域8个顶点在12个独立搅拌位置下的场强最大值㊂这里需计算脉冲激励混响室内时域及频域电磁场的均匀性㊂图4(a )给出了脉冲激励混响室内时域标准偏差,图4(b )给出了其频域标准偏差㊂图4(a )中的时域标准偏差呈上升趋势的原因是图2中的时域电磁场随时间的增加而衰减,较小的数值差异就能产生较大的标准偏差㊂F i g .4 T o t a l s t a n d a r dd e v i a t i o n s i n t i m e a n d f r e q u e n c y d o m a i n s a t 8v e r t i c e s i nw o r k i n g vo l u m e 1图4 脉冲激励混响室工作体积1内时域及频域总标准偏差由图4(a ),(b )可知,距离混响室墙壁λ/4距离的工作区域边界上场强的大部分标准偏差,无论时域还是频域都大于4d B ㊂这说明此时混响室工作区域内的电场均匀性不满足理想混响室内场均匀性要求㊂猜想若是进一步缩小混响室工作体积,将测试体积边界距离混响室墙壁距离增大至λ/2(工作体积2),研究此体积内贾 锐等:脉冲激励混响室测试区域电磁场均匀性103220-4F i g .5 T w ow o r k i n g v o l u m s o f r e v e r b e r a t i o nc h a m b e r 图5 混响室工作区域示意图的电磁场均匀性(如图5所示)㊂图6(a ),(b )给出了脉冲激励混响室工作体积2内的时域和频域电场标准偏差,由图可知,无论时域还是频域,当缩小了工作体积,脉冲激励混响室内电场均匀性满足标准要求㊂这说明脉冲激励混响室内电磁环境能够满足国际电工委员会标准I E C 61000-4-21中的规定㊂需要注意的是,该混响室模型的激励源为高斯脉冲信号㊂频率范围为80~200MH z 的调制高斯脉冲的持续时间约为50n s ㊂而混响室中一个很重要的参数就是时间常数,定义为τ=Q /2πf ㊂时间常数与混响室品质因子及工作频率有关㊂由于电磁脉冲的多样性,国际电工委员会I E C61000-4-21标准规定脉冲激励混响室,激励电磁脉冲持续时间至少大于2.5倍的混响室时间常数㊂所用混响室模型的品质因子参数为3000左右,因此在频率为100MH z 处其时间常数为τ=2μs ,远大于激励脉冲的持续时间㊂因此激励脉冲源的持续时间小于混响室的时间常数,这说明脉冲激励混响室内电磁场还未达到稳定状态时,便已经开始衰减㊂本章中研究脉冲激励混响室内的电磁环境特征,实际上是在研究混响室内不稳定电磁场的电磁环境特征,得到混响室内处于不稳定状态下的电磁环境的均匀性及分布规律,这是非常有意义的㊂F i g .6 T o t a l s t a n d a r dd e v i a t i o n s i n t i m e a n d f r e q u e n c y d o m a i n s a t 8v e r t i c e s i nw o r k i n g v o l u m e 2图6 脉冲激励混响室工作体积2内时域及频域总标准偏差4 实验验证为了验证数值模拟中脉冲在混响室中的传播规律,在真实混响室中进行实验验证㊂真实混响室尺寸为10.5mˑ8.0mˑ4.3m ,内部配置垂直和水平两个搅拌器,为提高搅拌效率,垂直搅拌器扇叶边缘开有不规则尺寸V 型槽㊂实验中,混响室激励源采用矩形脉冲激励,脉宽选择50n s ,发射天线选择宽带对数周期天线,接收天线选择鸭嘴型宽带天线㊂实验过程中,选用混响室的步进搅拌工作模式,参考国际电工委员会I E C 61000-4-21的步进搅拌模式采样要求,选用搅拌器12个独立搅拌位置㊂水平搅拌器3个搅拌位置,垂直搅拌器4个搅拌位置㊂实验过程中,考虑到接收天线的尺寸,混响室工作区域选择1.0mˑ1.6mˑ1.0m 的长方体区域,分别将鸭嘴天线放置于混响室测试区域8个顶点处,测试工作区域边界的时域场强信号,再通过傅里叶变换,将时域信号转换至频域,研究脉冲激励混响室在时域㊁频域上的电磁场分布规律及均匀性㊂实验中,激发信号采用单个激发,图7(a )给出其中某一顶点处在12个边界条件下x 方向上的接收场强信号㊂电场幅度出现负值是因为电场传播方向与天线方向相反㊂实验过程中,8个工作区域顶点,12个步进搅拌位置,3个相互正交方向,其系综最大值一共记录了288组数据(图7(b )给出了其中一组的系综最大值)㊂强激光与粒子束103220-5F i g .7 R e c e i v e de l e c t r i c f i e l d s a l o n g x di r e c t i o na n d t h em a x i m u me l e c t r i c f i e l d i n t i m e d o m a i n 图7 某一顶点处x 方向上接收时域场强值及最大值将288组数据代入式(1)~(9)计算,得出其工作区域边界时域场强的标准偏差㊂图8(a )给出了工作区域边界顶点处三个正交方向总的时域场强标准偏差,为了更直观的观察,图8(b )给出了工作区域边界顶点处三个正交方向总标准偏差的累积概率密度曲线㊂由图8(b )可以看出,超过95%的标准偏差都小于3d B ㊂因此可用确定脉冲激励混响室在时域内满足均匀性要求㊂F i g .8 S t a n d a r dd e v i a t i o n i n t i m e d o m a i n i n t h r e e o r t h o go n a l d i r e c t i o n s a t 8t e s t p o i n t s a n d i t s c u m u l a t i v e p r o b a b i l i t y d e n s i t y图8 工作区域边界顶点处三个正交方向场强的时域标准偏差及其累积概率密度曲线将图7(a)中的时域脉冲电磁场信号通过傅里叶变换转换至频域,如图9所示㊂由图可知,脉冲激励电磁场频域能量主要集中在80MH z 及150MH z 附近㊂根据国际标准中规定的计算方法研究其工作区域8个顶点处的频域均匀性,其标准偏差计算结果如图10所示㊂由图可知,除了某几个频率处,绝大部分电场标准偏差均小于3d B ㊂这与上节仿真结果一致,也就从实验角度证明了脉冲激励混响室测试区域内的电磁场均匀性是符合I E C 标准要求的㊂F i g .9 R e c e i v e d e l e c t r i c f i e l d s i n f r e q u e n c y d o m a i n 图9 某一顶点处在12个边界条件下x方向频域场强值F i g .10 T o t a l s t a n d a r dd e v i a t i o n s i n f r e q u e n c yd o m a i na t 8te s t p o i n t s图10 工作区域边界顶点处总场强的频域标准偏差贾 锐等:脉冲激励混响室测试区域电磁场均匀性强激光与粒子束5结论参考国际电工委员会制定的频域信号标准偏差计算方法,选取脉冲信号激发后的一段时间,提出了一定时间窗口内时域场强标准偏差的计算方法㊂构建与真实混响室1ʒ1模型,研究脉冲激励混响室内部电磁场的传播规律及场均匀性㊂脉冲激励混响室内时域电磁环境的场均匀性满足标准要求㊂且将时域信号通过傅里叶变换转换至频域,研究其频域场均匀性,并与实验结果进行对比㊂结果表明,无论时域还是频域其电磁场均匀性均满足I E C标准要求㊂参考文献:[1] D j o k i c S,D e s m e t J,V a n a l m eG,e t a l.S e n s i t i v i t y o f p e r s o n a l c o m p u t e r s t o v o l t a g e s a g s a n d s h o r t i n t e r r u p t i o n s[J].I E E ET r a n s o nP o w e rD e l i v e r y,2005,20(1):375-383.[2]丁坚进,沙斐.E M C混响室电磁场模态研究[J].电波科学学报,2005,20(5):557-560.(D i n g J i a n j i n,S h a F e i.A n a l y s i s o f e l e c t r o m a g n e t i cm o d e-s t a t e s i na nE M Cr e v e r b e r a t i o n c h a m b e r[J].C h i n e s e J o u r n a l o f R a d i oS c i e n c e,2005,20(5):557-560)[3]刘逸飞,陈永光,王庆国,等.混响室复合搅拌方式的搅拌效率分析[J].强激光与粒子束,2013,25(4):963-967.(L i uY i f e i,C h e nY o n g-g u a n g,W a n g Q i n g g u o,e t a l.A n a l y s i s o f s t i r r i n g e f f i c i e n c y i n r e v e r b e r a t i o n c h a m b e rw i t h c o m b i n e d s t i r r i n g.H i g hP o w e rL a s e r a n dP a r t i-c l eB e a m s,2013,25(4):963-967)[4]刘逸飞,陈永光,王庆国,等.基于线性扫频的频率搅拌混响室搅拌参数选取[J].电波科学学报,2014,29(2):363-368.(L i uY i f e i,C h e nY o n g g u a n g,W a n g Q i n g g u o,e t a l.S t i r r i n gp a r a m e t e r ss e l e c t i o nf o r f r e q u e n c y-s t i r r e dr e v e r b e r a t i o nc h a m b e rb a s eo n l i n e a r s w e e p m o d e.C h i n e s e J o u r n a l o f R a d i oS c i e n c e,2014,29(2):363-368)[5]姜林,王庆国,程二威.机械搅拌混响室独立样本数建模及实验[J].强激光与粒子束,2013,25(11):3050-3054.(J i a n g L i n,W a n g Q i n g-g u o,C h e n g E r w e i.M o d e l l i n g a n d e x p e r i m e n t a l s t u d y o f t h en u m b e r o f i n d e p e n d e n t s a m p l e s i n r e v e r b e r a t i o nc h a m b e rw i t hm e c h a n i c a l s t i r-r i n g.H i g hP o w e rL a s e r a n dP a r t i c l eB e a m s,2013,25(11):3050-3054)[6]张华彬,赵翔,周海京,等.混响室的概率统计分析方法及其蒙特卡罗模拟[J].强激光与粒子束,2011,23(9):2475-2480.(Z h a n g H u a-b i n,Z h a oX i a n g,Z h o uH a i j i n g,e t a l.P r o b a b i l i s t ic a nd s t a t i s t i c a l a n a l y s i s o fm o de s t i r r e d r e v e r b e r a t i o n c h a m b e r a n d i t sM o n t eC a r l o s i m u l a-t i o n.H i g hP o w e rL a s e r a n dP a r t i c l eB e a m s,2011,23(9):2475-2480)[7]耿利飞,曾勇虎,王福志,等.接大地的双绞线负载响应简化模型[J].强激光与粒子束,2014,26:023203.(G e n g L i f e i,Z e n g Y o n g h u,W a n g F u z h i,e t a l.S i m p l i f i e dm o d e l f o r l o a d r e s p o n s e o f t w i s t e d-w i r e p a i r c o n n e c t e dw i t h g r o u n d.H i g hP o w e rL a s e r a n dP a r t i c l eB e a m s, 2014,26:023203)[8]61000-4-21I E C.E l e c t r o m a g n e t i c c o m p a t i b i l i t y(E M C)-P a r t4-21:T e s t i n g a n dm e a s u r e m e n t t e c h n i q u e s-R e v e r b e r a t i o nc h a m b e r t e s tm e t h-o d s[S].2011.[9] A r d r i e sMI,b e s n i e rP,L e m o i n eC.E s t i m a t i n g K-f a c t o r a n d t i m e s p r e a d p a r a m e t e r s f r o ma t r a n s i e n t r e s p o n s eo f a p u l s em o d u l a t e ds i n ew a v e i n r e v e r b e r a t i o nc h a m b e r[J].I E E ET r a n s o nA n t e n n a s a n dP r o p a g a t i o n,2013,61(1):380-389.[10] A r t zT,H i r s c hH.P u l s e ds i g n a l s i nr e v e r b e r a t i o nc h a m b e r s:E x p e r i m e n t a l a n a l y s i so f t r a n s i e n t p e a k s[C]//P r o c e e d i n g so f I n t e r n a t i o n a lS y m p o s i u mo nE l e c t r o m a g n e t i cC o m p a t i b i l i t y,2013.U n i f o r m i t y o f e l e c t r o m a g n e t i c f i e l d i na p u l s e e x c i t e d r e v e r b e r a t i o n c h a m b e rJ i aR u i, Z e n g Y o n g h u, W a n g C h u a n c h u a n(S t a t eK e y L a b o r a t o r y o f C o m p l e xE l e c t r o m a g n e t i cE n v i r o n m e n tE f f e c t so nE l e c t r o n i c s a n dI n f o r m a t i o nS y s t e m,L u o y a n g471003,C h i n a)A b s t r a c t: T h e f i e l du n i f o r m i t y o f e l e c t r o m a g n e t i c i na p u l s e e x c i t e dr e v e r b e r a t i o nc h a m b e rw a s i n v e s t i g a t e d i ns i m u l a t i o n s a n de x p e r i m e n t s.T h e d e f i n i t i o n a n d c a l c u l a t i o nm e t h o dw e r e p r o p o s e d a n d t h e r e s u l t s o f b o t h a s p e c t sw e r e a n a l y z e d.T h e r e s u l t s s h o w e d t h a t t h e f i e l du n i f o r m i t y o f p u l s e e x c i t e d r e v e r b e r a t i o nc h a m b e r i s f u l f i l l e dw i t ht h e r e q u i r e m e n t o f I E Cs t a n d a r db o t h i n t i m e a n d f r e q u e n c y d o m a i n s.T h e f a c t t h a t t h e s t a t i s t i c a l u n i f o r m i t y f i e l d c a nb e b u i l t i n p u l s e e x c i t e d r e v e r b e r a t i o n c h a m b e r s u g-g e s t s t h a t t h e s u s c e p t i v e e x p e r i m e n t su n d e r e l e c t r o m a g n e t i c p u l s e f i e l d c a nb e p e r f o r m e d i n r e v e r b e r a t i o n c h a m b e r.K e y w o r d s:r e v e r b e r a t i o n c h a m b e r;e l e c t r o m a g n e t i c p u l s e;t i m e d o m a i n;u n i f o r m i t y;t e s t a r e aP A C S:41.20.J b;41.20.G z;41.20.C v;41.20.-q103220-6。

在集体环境噪声下两个二能级原子的纠缠演化

在集体环境噪声下两个二能级原子的纠缠演化

avr e e l nt l t ea ie .I i so nta f ia ye t g ds t , eet ge e tm aue y eygnr ia s t r g n t s hw to i t l n nl t e t n n m n( esrdb a i i a e v h r n il a e a s h al cn urne a ee e ym nt i o c a n a.F rnt l rd c s t ,h nage e t n ac・ oc r c )m yb i r o o nc r si t g y o ia ypo ut t e teet l m n e hn e e h t b o o li w i il s a n
21 0 0年 3月
湖 南 师 范大 学 自然科 学 学 报
J u n l f tr lS in e o ma r lU ie st o r a u a ce c fHt n Noma n v ri o Na y
V0 . 3 N . 13 o 1
M a., 0 r 201
me ta d d ah C l o c rat r a iey f rfn t i . n n e t al c u l n t l i ie t e v o me
K yw r s cl c v o y etn e n et; ak va poi t n e o d o et eni ;n g met a M ro p r mao l i s al d h x i
adD pr et f hs sH nnN r a U i r t, hnsa40 8 , h a n eat n yi , ua om l nvs y C agh 10 1 C i ) m oP c ei n

我国揭示锯齿形边缘石墨烯纳米带中的电声子耦合效应

我国揭示锯齿形边缘石墨烯纳米带中的电声子耦合效应

^ A
l、 _ _
2 1 年 第 8 第6 ( 第 4 期 ) 0 1 )
_


_ ’ 。-

l ≤l

(o) V c和光 电转 换 效率(1相 比前者 分 别提 高 了 r )
96 .%和4 . 42 %,表 面 钝 化使 得 短 波长 和 长 波 长 光 的 响 应 明 显 提 高 。为 了使A , 化 效 果 充 I 钝 O
离 子 刻 蚀 技 术 , 首 次 实 现 了可 控 的石 墨烯 面 内各 向异 性 刻 蚀 技 术 ; 并 结 合 人 工 缺 陷 工 程 ,首 次 实现 了对 石 墨 烯 纳 米 结构 的精 确 加 工 和 剪 裁 ,制 备 出 了尺 寸 可 控 ( 小 线 宽达 最 5 米 以下 )、边 缘 可 控 ( 有 原 子级 平 整 的 纳 具 锯 齿 形边 缘 结构 )的石 墨烯 纳米 结构 。 最近 ,张广 宇研 究组 的杨 蓉博 士等在 先前 的 工 作 基 础 上 ,利 用 拉 曼 散 射 光 谱 技 术 ,研 究 了具 有 锯 齿 形 边 缘 结 构 的石 墨 烯 纳 米 带 的 电声 子 耦 合 特 性 。他 们 首 次 在 这 种 结构 中观 察 到 了G 的 劈裂 ( 一1 8 软化 的E g 式 ; 峰 G 53 2模 G+ 1 9 本 征 的E g 式 ) 。这 种 非 应 力 效 应 一5 4 2模 导致 的G 劈 裂可 归因于 锯 齿 形边 缘 结构 独特 峰
1 扁置 场 。
’ ■ 《 I ●
纳 米 尺 度 的Al 膜 ,并 在 一 定 的气 氛和 温 ,薄 O
度 下 进 行激 活 处 理 ,形 成 良好 的 复合 钝 化 薄 膜 结构 。 系 统 实验 表 明 ,不 使 用A , 使 用 1 和 O A , 电池 差 别 十 分 明 显 , 后 者 的 开 路 电 压 l 的 O

NORMA 4000 5000 Power Analyzer 用户说明手册说明书

NORMA 4000 5000 Power Analyzer 用户说明手册说明书

Since some countries or states do not allow limitation of the term of an implied warranty, or exclusion or limitation of incidental or consequential damages, the limitations and exclusions of this warranty may not apply to every buyer. If any provision of this Warranty is held invalid or unenforceable by a court or other decision-maker of competent jurisdiction, such holding will not affect the validity or enforceability of any other provision.
BEGRENZTE GEWÄHRLEISTUNG UND HAFTUNGSBESCHRÄNKUNG
Fluke gewährleistet, daß jedes Fluke-Produkt unter normalem Gebrauch und Service frei von Material- und Fertigungsdefekten ist. Die Garantiedauer beträgt 2 Jahre ab Versanddatum. Die Garantiedauer für Teile, Produktreparaturen und Service beträgt 90 Tage. Diese Garantie wird ausschließlich dem Erster

基于声学指数的神农架国家公园声音多样性动态变化

基于声学指数的神农架国家公园声音多样性动态变化

果显示 ACI 指数不能很好地反映日变化趋势,但 BI 指数和 NDSI 指数具有明显的日变化趋势,且变化趋势符合
物种黎明/ 黄昏合唱的习性;声学指数随海拔梯度的空间变化结果表明,ACI、BI 指数在中海拔区域具有最大值,
且 ACI 指数与海拔相关性较强,NDSI 指数没有显著的变化趋势。【结论】BI、NDSI 指数能较好地反映动物声音
:【 】 Abstract Objective The study aims to evaluate the response of acoustic indices to the dynamic changes of animal , sound diversity further to explore the characteristics of the variation of animal sound diversity in Shennongjia National , , 【 】 Park China in order to provide a quantitative basis for the local ecological protection. Method We deployed nine , sound recording equipments in nine sampling sites in Shennongjia National Park and sound recording data from May to ( ), July 2021 were obtained. A time series of ecoacosutic indices including acoustic complexity index ACI bioacoustic ( ), ( ) index BI normalized difference soundscape index NDSI were extracted from the recording data after noise

三态噪声诱导的无穷耦合粒子形成的系统随机共振现象

三态噪声诱导的无穷耦合粒子形成的系统随机共振现象
/ t +C P ( ) s o . d =b X+( +Z f + i t ) nr () 3
此方面. 但是, 双态噪声和三态噪声在模拟 自然涨 落时都是非常有用的; 而且三态噪声更复杂, 它包
z t是 三值 噪声, ( ) 它在 Z :dZ = ,s 一 ,2 0Z = d ( 0 三值之 间发生跃迁 , 迁概 率为 . > ) 跃 假设,
1 Ⅳ
() 1
式中,6 , 都为常量, ,代表 系统参量;S , C C 是平
均 , 的 义 为 = ;代 全 场它 定 式 寺 ( 已表 域 )
耦合系数,当粒 子数 Ⅳ o时,每个粒 子的变化 。
态系统 中的随机共振 . us a等人研究了非线性 B lr a 无限耦合粒子点阵[】 1 及局域耦合粒 子点阵【] 1的随 3 机共振 . 对于一种特殊类型的耦合( 平均场耦合) 研 究中【】 l也观察到随机共振. 此外,由乘性双态噪声 和周期信号驱动的单粒子[】 M及无限个耦合粒子[] 1 5
在人们传统观念 中, 噪声是令人厌烦 的. 然而, 色噪声的平坦系数 :3 对称性双态噪声 的平坦 , 当噪声与非线性系统 、 线性系统相结合, 在适当的 系数 =1 相比较, 三态噪声的平坦系数 可 以是 条件下, 系统会 出现一些非常奇妙的现象, 例如噪 声诱导的相变 、 输运 、 共振激活等等n 在这些现 . 象中随机共振就是一例【 . 4 随机拱振 概念是 18 _ 91 年, ez B ni等人在研究古气象冰川演化问题时首次 提出来 的[ 】此 后, 4. 随机共振 的理 论和 实验 研究 都引起了人们极大 的兴趣 . 在短短 的二十多年间, 被发现具有随机共振现象 、 利用随机共振现象的系 统, 不单单出现在物理学领域里, 而且已经延伸到

磁流变阻尼器的神经网络建模及在半主动控制中的应用

磁流变阻尼器的神经网络建模及在半主动控制中的应用

型 应 用于 14车悬挂模 型 中, 行 半主动 控制 的仿 真 分析 . 果表 明 , 建 立和 优 化 的逆 向模 型可 / 进 结 所
以较好 地预 测所 需 电流指令 , 用于半主 动控 制 中的效 果明 显 . 应 关键 词 : 流 变阻尼 器 ; 经 网络 ; 向模 型 ; 磁 神 逆 半主动控 制 中图分 类号 : 2 3 3 TP 7 . 文献标 志 码 : A
b v n e g M a q a d t o n tma Br i u g o t o sn a a g n r td fo t e y a Le e b r — r u r tme h d a d Op i l an S r e n me h d u i g d t e e a e r m h
Ne r ln t r o e i f a m a n t r e l g c l u a e wo k m d lng o g e 0 h 0 0 ia
d m p r a d a p i a i n i e ia tv o t o a e n p lc to n s m - c i e c n r l
2 S ho o iiE gneig B i ̄ Jatn i rt , eig10 4 , hn ) .col f vl nier , e i i ogUnv sy B i 0 0 4 C i C n j o ei j n a
Ab ta tAn iv remo e fma n tr e lgc l a e t u p tn uo s n u p tn uo s sr c : es d l g eo h oo ia mp rwi f ri u e rn .o eo t u e rn n o d ho n a df te e r n h id nly ri b i nt i a e .Tr iiga dp u igo h d lsd n n i e n n uo si t ehd e e ul i hsp p r f n a s t an n n r n n f emo e o e t i

基于磁流变阻尼器的自适应神经模糊振动控制

基于磁流变阻尼器的自适应神经模糊振动控制

中图分类号 : 1 . ; 3 . TU3 1 3O2 1 3
文献标识码 : A
文章编号 :6 358 (0 0 0—3 60 17 7 12 1 )30 0—3
近年来 , 土木工程 领域新兴 一种新 型的抗震 方 在
式一 结构振动控 制 , 即对 结构施 加控 制机 构 , 由控制 机
数 的形式 , 以得 到相 应 的参 数 集 , 为条件 参数 , 可 称 如

钟型隶属函 () F 数: 一 ]

()第 2层 。实 现 条 件 部 分 模 糊 集 的 运 算 输 2 出对 应 规 则 的 适 用 度 , 常 采 用 乘 法 一 W 通 —
U / ) B ( ) A( “ 。
种 在 土 木 工 程 中 很 有 发 展 前 途 的 智 能 控 制 技
()第 1 。输入 变量 模 糊化 , 出对应 模糊 集 1 层 输 的隶属度 , 中一个 节点 的传递 函数 可以表示为 O 一 其 /
Hi ) 其 中, A( , U为隶 属 函数 。根据 所选 择 的隶 属度 函
适合 已有数据模 型 的模 糊推理模 块 , 其工 作原理 和过
程 与典型模糊 推理 系统 相 同 , 过 输入 值模 糊 化 、 经 模 糊推 理和反模 糊 化输 出进行 控 制[ 。 自适 应模 糊 系 _ 4 ]
收稿 日期 :0 00 —1 2 1-33
()第 3层 。对 各条 规 则 的适 用 度 进行 归 一 化 3
能力来提高控制的快速性和自适应性。文章采用 该方法对磁流变阻尼结构 实施振动控制仿真分析 , 仿真结果表明 , 振动控制效果
明显, 自适 应 神经 网络 模 糊控 制 是 一种 很 好 的 控 制方 法 。 关 键 词 : 流 变 阻尼 器 ; 磁 自适 应 神 经模 糊 系 统 ; 糊控 制 ; 动控 制 模 振

Simultaneous Switching Noise

Simultaneous Switching Noise
Input transition time Capacitive load The device transconductance
17
On-chip (Capacitive Load)
Capacitive load [6]
18
On-chip (Circuit-level constraints)
5
Outline
Main issues Theory and Modeling
External (I/O drivers/buffers) Internal (on-chip)
How to reduce References
6
I/O buffers
Dependences
Type of package Location of the buffers Number of buffers Slew-rate and arrival time of the input signals Buffer sizes Load on the outputs
3
Main Issues (cont.)
CMOS logic gates switch
Draw current from the power bus Inject current into the ground bus
Lots of gates switch in a small time interval Voltage variations
Dependences
Capacitive load Circuit-Level constraints Layout-Level constraints
14
On-chip [4]
15

On the Synchronization Techniques for Wireless OFDM Systems

On the Synchronization Techniques for Wireless OFDM Systems

On the Synchronization Techniques for Wireless OFDM SystemsBo Ai,Member,IEEE,Zhi-xing Yang,Chang-yong Pan,Jian-hua Ge,Yong Wang,Member,IEEE,and Zhen LuAbstract—The latest research works on the synchronization scheme for either continuous transmission mode or burst packet transmission mode for the wireless OFDM communications are overviewed in this paper.The typical algorithms dealing with the symbol timing synchronization,the carrier frequency syn-chronization as well as the sampling clock synchronization are briefly introduced and analyzed.Three improved methods for the fine symbol timing synchronization in frequency domain are also proposed,with several key issues on the synchronization for the OFDM systems discussed.Index Terms—Carrier frequency synchronization,continuous mode and burst packet mode transmission systems,OFDM, sampling clock synchronization,symbol timing synchronization.I.I NTRODUCTIONO FDM,associated with other related technologies have found its wide applications in many scientific areas due to its high spectrum efficiency,its robustness against both multi-path and pulse noises,its highly reliable transmission speed under serious channel conditions,adaptive modulation for each sub-carrier according to the channel conditions, and etc.It has become fundamental technology in the future 4G-multimedia mobile communications systems[1].Many digital transmission systems have adopted OFDM as the modulation technique such as digital video broadcasting terrestrial TV(DVB-T)[2],digital audio broadcasting(DAB), terrestrial integrated services digital broadcasting(ISDB-T), digital subscriber line(xDSL),WLAN systems based on the IEEE802.11(a)[3]or Hiperlan2,multimedia mobile access communications(MMAC),and thefixed wireless access(FW A) system in IEEE802.16.3standard.OFDM has also found its application in Cable TV systems.Technologies fundamentally based on OFDM,such as vector OFDM(V-OFDM),wide-band OFDM(W-OFDM),flash OFDM(F-OFDM)have also shown their great advantages in certain application areas.There are some disadvantages,however,appeared in the OFDM systems,for example,the large Peak-to Average Power Ratio(PAPR)as well as high sensitivity to the synchronization errors.Synchronization issues are of great importance in allManuscript received April26,2005;revised October27,2005.This work was supported in part by the National Natural Science Funds in China(Nos. 50177001,60372007,and60332030)and by the Ministry of Information Industry Foundation under Grant no.2002291.B.Ai is with the Dept.of E&E Tsinghua University,State Key Lab.on Microwave and Digital Communications,China(100084).He is also with the Engineering College of Armed Police Force,Xi’an,China(710086)(e-mail: abeffort_apple@).Z.Yang and C.Pan are with the Dept.of E&E Tsinghua University,State Key Lab.on Microwave and Digital Communications,China(100084).J.Ge and Y.Wang are with the National key Lab.of Integrated Service Net-works,Xidian Univ.,Xi’an,China(710071).Z.Lu is with the Dept.of Electronic Engineering in Shanghai Jiaotong Uni-versity,China(200052).Digital Object Identifier10.1109/TBC.2006.872990digital communications systems,especially in the OFDM systems.Synchronization errors not only cause inter-symbol interference(ISI)but also introduce inter-carrier interference (ICI)due to the loss of orthogonality among all sub-carriers. In this paper,we focus on the synchronization schemes in the OFDM systems.Fundamental theory for the synchronization is briefly described in Section II and in Section III,the symbol timing scheme and three improved methods for thefine symbol timing in frequency domain are proposed.We then conduct the analysis on the carrier frequency recovery as well as the sampling clock synchronization methods in Sections IV and V respectively.In Section VI,joint estimation of all the synchro-nization errors including timing,frequency and phase offsets is simply described.Technical forecast is made in Section VII with conclusions drawn in Section VIII.II.O VERVIEW FOR THE S YNCHRONIZATION IN OFDM S YSTEMS Synchronization is of great importance for all digital com-munication systems.OFDM systems are very sensitive to both timing and carrier frequency offset,especially,when combined with other multi-access techniques such as FDMA,TDMA,and CDMA.Therefore,synchronization is extremely crucial to the OFDM systems.A.Three Synchronization Issues in the OFDM Systems There are three major synchronization issues in the OFDM systems:a.The symbol timing synchronization,which is to deter-mine the correct symbol start position before the FFT de-modulation at the receiver end.b.The carrier frequency synchronization(i.e.,carrier fre-quency recovery technique),which is utilized to eliminate the carrier frequency offset caused by the mismatch from the local oscillators between the transmitter and the re-ceiver,nonlinear characteristic of the wireless channel as well as the Doppler shift.c.The sampling clock synchronization,which is to miti-gate the sampling clock errors due to the mismatch of the crystal oscillators.All these synchronization errors will significantly degrade system performance[4],[5].B.Synchronization Technologies in the Continuous Mode and Burst Packet Mode Transmission SystemsAccurate synchronization is indispensable to suppress the negative impact of the synchronization errors in the commu-nication systems no matter,whether it is in continuous or burst packet mode transmission systems.However,these two different modes require different synchronization schemes:0018-9316/$20.00©2006IEEEa.In the burst packet mode,synchronization ought to beestablished at any time because when data streams are ready to transmit is unknown The duration of the training symbols used for synchronization in this mode is rela-tively short and synchronization should be done within a single training symbol time for the systems such as IEEE 802.11(a)[3]and HiperLan/2to avoid the reduction of the system capacity.It is inappropriate to do averaging over many symbols or pilots because of the stringent re-quirement on synchronization time and the less number of sub-carriers.It is also important for the systems in this mode to establish the synchronization in time domain and this will greatly reduce the acquisition time since it avoids the feedback from frequency domain.b.In the continuous mode such as DAB,DVB-T[2]sys-tems,averaging method can be used to improve the es-timation accuracy because there is no stringent require-ment on the acquisition time.In this mode,large numbers of sub-carriers has been utilized and,it is appropriate to apply the cyclic prefix(CP)or pilots to these synchroniza-tion methods.III.S YMBOL T IMING S YNCHRONIZATIONWhen signals are transmitted through severe channel con-ditions of multi-path fading,pulse noise disturbance and the Doppler Shift,it is important to solve symbol timing synchro-nization problemfirst during the design process of an OFDM receiver.The symbol timing error can not only disturb the amplitude as well as the phase of the received signal,but also introduce ISI. In order to perform the FFT demodulation correctly,the symbol timing synchronization must be done to determine the starting point(i.e.FFT window)of the OFDM symbol.The cyclic prefix (CP,or Guard Interval,GIB)can be removed afterwards.The concept of the GIB wasfirst proposed by A.Peled[6],which can prevent OFDM symbols from ISI disturbance and keeps the orthogonality among all the sub-carriers.Fig.1shows the vari-ation of the signal constellation due to the symbol timing errors. Fig.1(a)and(1b)represent the symbol starting point within GIB(case1)and outside ISI-Free region(case2)respectively. It clearly shows how bad the signal constellation could be due to the symbol timing errors.Accurate and steady symbol timing synchronization can be realized through the coarse symbol timing,thefine symbol timing as well as the symbol timing control structure combined together.The coarse symbol timing synchronization isfirst executed in time domain and then,thefine symbol timing in frequency domain is done to ensure a more accurate estimation. The symbol timing control structure is utilized to coordinate the operations of the coarse and thefine symbol timing.A.The Coarse Symbol Timing Algorithms in Continuous Mode The conventional algorithms for the coarse symbol timing synchronization in time domain are MLE(Maximum Like-lihood Estimation)utilizing the cyclic prefix of the OFDM symbols.The most representative algorithm was proposed by J.J.Van de Beek[7].However,good performanceachieves(a)(b)Fig.1.(a)Constellation variation due to the symbol timing error.The total subcarriers N=2048,cyclic prefix L=128,64-QAM mapping.No carrier frequency and sampling clock offset.The normalized symbol timing offset is 36(samples)Case1.(b)Constellation variation due to the symbol timing error. The total subcarriers N=2048,cyclic prefix L=128,64-QAM mapping. No carrier frequency and sampling clock offset.The normalized symbol timing offset is36(samples)Case2.only under the AWGN channel.When the channel condition becomes severely degraded,data in GIB is badly contaminated by ISI,there will be significantfluctuation for the starting point estimated for the OFDM symbol.And suchfluctuation will have the significant influence on the carrier frequency offset as well as the sampling clock offset estimation in frequency domain.To improve the performance of ML Estimator,a novel scheme utilizing both CP and pilots to do the coarse symbol timing synchronization was proposed by ndström [8].It has better performance compared to that of[7]under the multi-path fading channel.However,the nonnegligible fluctuation still exists because of the ISI contamination on the data within GIB and the limited number of pilots used for estimation.In order to mitigate thefluctuation,T.M.Schmidlintroduced a new method making use of the training symbols in time domain,in which a timing function was defined[9]. It has better performance compared to those proposed by J.J.Van de Beek and ndström.Unfortunately,it has a“flat region”in the estimation,which,to a great extent,increases the variance of the symbol timing estimator.Some new schemes has been proposed in the literatures [10]–[13]in recent years to overcome the defects of the al-gorithms mentioned above,with the target to decrease the fluctuation of the starting point of the estimated symbol as well as to make the estimation within the ISI-Free region.The convolution characteristic of the cyclic prefix are utilized in literature[10],while,PN sequences are adopted in[11]–[13], to take the advantage of the intrinsic,fairly good correlation property of PN:Kasami sequence is utilized in[11]with the excellent correlation properties;and in[12],[13],a novel timing recovery methods for TDS-OFDM(key techniques for the Terrestrial Digital Multimedia/Television Broadcasting System,namely DMB-T proposed by Tsinghua University [14])is developed.This scheme is based on the searching and tracking on the correlation peaks of the PN sequences,which is as the GIB for each OFDM symbol.Because of the excellent correlation properties of the so-called m-sequence,the perfor-mance of these algorithms[10]–[13]outperforms those from[7]–[9]under the multi-path fading channels.B.The Fine Symbol Timing Synchronization in Continuous ModeThefine symbol timing synchronization in frequency domain is often required to guarantee the estimation accuracy.A pre-amble structure including a synchronizationfield(S-filed)and a cell-searchingfield(C-field)is proposed in literature[15]with thefine symbol timing done by using the cell identification method.In[16],a specially designed pilot symbol structure is utilized to generate afine symbol timing -puter simulations and analysis verify their good estimation per-formances but low bandwidth efficiency.The residual symbol timing error may cause the phase rotation of the sub-carriers in frequency domain.In this Section,we propose three improved algorithms to do thefine symbol timing based on the algorithm introduced by[17].Computer simulations show that these pro-posed methods have better performance compared with the al-gorithm in[17]when under serious channel conditions.In the following,we referred the algorithm in[17]as Algorithm1,and named our proposed methods as Algorithm2,Algorithm3and Algorithm4respectively.Algorithm2:(1)(2)Where,denotes the number of scattered pilots(SP),isa complex variable forthe SP inthe OFDMsymbol,is the phase deviation of the two adjacent SP’s causedby the symbol timing offsetof OFDM symbol,is thedistance between the two adjacent SP’s.,,denotestheFig.2.Performance comparison among Algorithm1,2,3,and4for thesymbol timing estimation.The total subscribers N=2048,cyclic prefixL=128,SNR=5dB,Rayleigh fading channel[2],normalized carrierfrequency offset is0.135and048respectively.integer part of symbol timing offset,useful symbol duration pe-riod and the nominal sampling frequency respectively.This al-gorithm has the same limited estimation range as that in Algo-rithm1and its estimation accuracy is influenced by the carrierfrequency offset[17].Algorithm3::Algorithm1and2perform the estimation onthe adjacent SP’s within the same OFDM symbol.In algorithm3and4,we derive the offset for thefine symbol timing from thepilots in the two consecutive OFDM symbols(Fig.2).Thatis,(3)Where,denotes the complex conjugationof,Algorithm4::The same as that in Algorithm3,SP’s of con-secutive OFDM symbols can be utilized.But the only differentfrom algorithm3is the phase characteristic of known pilots isnow givenby:(4)Lots of computer simulations validate the following conclu-sions:a.The performances of algorithms2and4outperformthat of algorithms1and3under multi-path fading channels re-spectively.This is because the phase characteristic is utilized inalgorithms2and4,while,the power characteristic is utilizedin algorithms1and3.It is well known that,power character-istic is much more sensitive to the multi-path fading channelsthan phase characteristic.b.When the normalized decimal car-rier frequency offset is less than certain value(about0.15thatof sub-carrier spacing),the performance of algorithms3and4outperform that of algorithms1and2and the best estimationresults can be obtained with Algorithm4.c.When the normal-ized decimal carrier frequency offset is larger than certain value(about0.15that of sub-carrier spacing),the performances of al-gorithms1and2outperform that of algorithms3and4and thebest estimation results can be achieved by Algorithm2.The de-tailed analysis for the effects of the carrier frequency offset onthefine symbol timing synchronization can be found in[17].Fig.3.Frequency synchronization estimator.C.The Symbol Timing Synchronization Algorithms in Burst Packet Transmission ModeThe synchronization requirements vary with the applications, therefore,we should adopt the appropriate synchronization techniques in both continuous and burst packet transmission modes respectively.As being discussed in Section II-B,it is inappropriate to do the symbol timing synchronization with pi-lots in the burst packet mode due to the stringent requirements on synchronization time.In[18],a novel scheme to do the coarse symbol timing with training symbols is proposed and, the computer simulations based on IEEE802.11(a)standard [3]illustrate that more accurate coarse symbol timing synchro-nization can be achieved by the convolution method in time domain than that by the ordinary MLE method,no matter it is in the office environment[19]or under much severe channel conditions[2].This really comes from the fully utilization of the convolution property of CP.D.Symbol Timing Synchronization Control ModelOther than the accuracy of the estimation in the symbol timing synchronization process,the robust and efficient syn-chronization control structure to ensure the system stability is also requested.A new symbol timing synchronization control model has been proposed in[10].Similar to those control models in[17],[20],it also has two synchronization states:the acquisition state and the tracking state.The difference is that the threshold and counters are utilized to perform the control process with less computational complexity than those in[17].IV.C ARRIER F REQUENCY R ECOVERY T ECHNIQUES Carrier frequency offset(CFO)caused by the Doppler shift, local oscillators mismatch between the transmitter and the re-ceiver ends,may introduce ICI and destroy the orthogonality of OFDM sub-carriers,resulting in the losses of SNR.With the insertion of the GIB in OFDM symbols,symbol timing error within a certain range will not introduce ISI and ICI.OFDM system is more sensitive to the CFO and the sampling clock offset(SCO).Regarding to higher modulation modes such as 64-QAM,tiny CFO may introduce severe degradation on the system performance[21].Carrier frequencyoffset puts an extra phase factorofin the received signal,where is the sub-carrierspacing,is the CFO normalizedby and is usu-ally divided into an integerpart,(multiple of the sub-carrier spacing,causing a shift of the sub-carrier indices),and a dec-imalpart,(less than half of the sub-carrier spacing,causes a number of impairments,including attenuation and rotation of the sub-carriers and ICI).We can divide CFO into three parts:the integer part,the coarse decimal part and thefine decimal part.CFO can usually be compensated for through the following procedures shown in Fig.3.First,a coarse symbol starting point for the FFT demod-ulation is provided by the coarse symbol timing module and then,the estimation and correction of the coarse decimal fre-quency offset in time domain is performed to minimize the ICI impact on the estimation in frequency domain,with the integerpart estimated in frequency domain to get the correct sub-car-rier index.Finally,the residual frequencyoffset,i.e.thefine decimal frequency offset is estimated.A tracking loop structure (the Acquisition and the Tracking Mode Switching module)can be exploited to coordinate the coarse decimal part,the integer part and thefine decimal part of the frequency offset.Each of them makes unique contribution to the recovery of the carrier frequency offset[50].Many literatures have discussed how to make OFDM systems less sensitive to the carrier frequency offset,for instances,per-form the windowing on the transmitted signals or use self-can-cellation schemes[22],[23].However,long prefix adopted in systems with these approaches results in low bandwidth effi-ciency.Generally,we can divide the carrier frequency recovery algorithms into three categories:a.Methods are based on training symbols or pilots[9],[24]–[33],named Data Aided(DA)method.b.Methods use of the intrinsic structure of OFDM symbols,e.g.cyclic prefix[7],[34]–[40],which is called Non DataAided(NDA)method.c.Blind approaches [41]–[43],which relies on the signal statistics and often has very high computational com-plexity,some approaches may have extra requirements on the channel statistics.A.Integer Carrier Frequency OffsetThe integer as well as the coarse decimal CFO correction can make the sub-carriers spacing offset less than half of sub-car-rier spacing in the present of more than tens of sub-carriers.Most algorithms for the integer CFO estimation [9],[29],[31],[44]–[47]nowadays have two major defects:a.Limited esti-mation range on CFO;b.Stringent requirement on the symbol timing synchronization.The earliest algorithm in this category was proposed by P.H.Moose [47]with the estimation rangelimitedwithin,that is,only 1/2that of sub-carrier spacing.P.H.Moose tried to overcome this problem by increasing thesub-carrier spacing to avoid phase offsetexceeding .How-ever,the increase of sub-carrierspacing satisfying (5)may decrease the useful OFDM symbol durationtime ,resulting in tighter requirements on the symbol timing synchronization.Besides,the increase of the sub-carrier spacing will not enlarge the range of the integer part estimation to a very largeextent.(5)T.M.Schmidl et al.,later,proposed an improved algorithm [9]with better performance under multi-path fading channel,and its estimation range was one time wider than that by P.H.Moose [47].Unfortunately,a large pre fix is still needed,for ex-ample,in DVB-T [2]systems,pre fix (2k mode)must be used.On the other hand,its estimation range is still very limited and is sensitive to the symbol timing errors.Three improved estimation algorithms are proposed in litera-ture [48]to overcome these defects.All of them use the power and phase characteristic of the known pilots,which is insensitive to the symbol timing errors and have a wider estimation rangeof integer part of CFO (i.e.,as largeas,with the total number of useful sub-carriers in one OFDM symbol).B.Coarse Decimal Carrier Frequency OffsetAs mentioned earlier,CFO estimation should follow three procedures.If the decimal part of CFO,however,can be es-timated in frequency domain,why should we carry out the coarse CFO estimation in time domain first?There are two main reasons:a.To reduce ICI caused by CFO,which lays the foundation on a more accurate CFO estimation in frequency domain;b.To estimate and compensate for the CFO all in time do-main,reducing the synchronization time,and is suitable for the systems of burst packet transmission mode.The early-proposed typical algorithm on the coarse decimal CFO estimation was from J.J.Van de Beek et al.[7]with CP characteristic exploited.T.M.Schimdl et al.,later,proposed a new algorithm named SCA [9].However,either of them has a very stringent requirement on the symbol timing.An improved algorithm,not so sensitive to the symbol timing errors was pro-posed recently in literature [49],with only(is the length of the Guard Interval)correlation window length utilized for es-timation,avoiding the data portion contaminated by the incor-rect phase information from the symbol timing errors.Computer simulations show that when the decimal part of the CFO approaches to 0.5of the sub-carrier spacing,the estimated value may,due to the multi-path fading,the phase noises as well as the discontinuity of the arctangent function,jump to the inverse polarity,as pointed out in literature [47].For example,if the decimal frequency offset in data streams is 0.498of the sub-carrier spacing,the estimate result with the typical algorithms mentioned above may be -0.467of the sub-carrier spacing.The strategy to avoid the above problem in P.H.Moose algorithm is to reduce the length of the DFT and use larger carrier spacing,degrading the overall system performance.A second-order IIR filtering can be used to solve this problem [49].C.Fine Decimal Carrier Frequency OffsetAfter correction based on the coarse decimal CFO estima-tion,the residual decimal CFO in data streams may be reduced to only 1%,and then the fine decimal CFO estimation deals with the residual CFO.The typical algorithm was also proposed by P.H.Moose [47].However,it suffered a problem of poor band-width ef ficiency.In fact,pilots embedded in the OFDM symbols can be utilized to do the fine decimal CFO.D.Carrier Frequency Offset Control ModelIt is necessary to have a control module to coordinate the operations of the integer CFO,the coarse decimal CFO and the fine decimal CFO [48].As shown in Fig.3,this module consists of two modes:the acquisition mode and the trackingmode.After the estimation on the integerpartand fine dec-imalpartin frequency domain,the counter value COUN will increase or decrease depending on whether the valueofis larger than a constant A (set by the system per-formance requirement,forexample,).The value of COUN decides whether it is in the tracking or the acquisition mode.Performance and detailed analysis on this control model is presented in [50]showing excellent performance in estima-tion,tracking and correction of CFO.E.Carrier Frequency Offset in the Burst Packet Mode There is no stringent requirement on acquisition time in the continuous systems such as DAB,DVB-T [2]and DMB-T [14],averaging method or filtering over many OFDM symbols can be adopted to increase estimation accuracy,where it is appro-priate to adopt those methods based on CP or pilots.Some lit-eratures make use of the null sub-carriers for power detection to estimate the CFO [51].However,for systems in the burst packet mode,repetitive structure is often utilized with,no differ-ence either between these null sub-carriers or the idle time be-tween neighboring blocks.Those methods,therefore,are inap-propriate in the burst packet mode.Because of the short duration time of packets,it has more stringent requirement on synchro-nization acquisition time (i.e.,acquisition done within a single OFDM symbol).Besides the requirement on estimation accu-racy,fast convergence is also needed.The accuracy of the CFO estimation in time domain,nonfeed back synchronization model are equally important to these systems and,the synchronization should be established only in time domain [13],[48].(a)(b)Fig. 4.Constellation variation due to the sampling clock offset.Total sub-carriers N=2048,cyclic prefix L=128,64-QAM modulation, normalized sampling clock offset is1ppm,after200OFDM symbols.Other factors follow DVB-T standard[2].V.S AMPLING C LOCK S YNCHRONIZATIONThe sampling clock errors are mainly from the mismatch of the crystal oscillators between the transmitter and the re-ceiver.Other factors such as multi-path fading,noise distur-bance,symbol timing estimation errors may also contribute to the sampling clock offset(SCO).The sampling clock errors will negatively influence the symbol timing synchronization.For ex-ample,assume1ppm sampling clock offset in2K mode with a GIB of512samples in DVB-T[2],the FFT window will move one sample around every400symbols.The higher the sam-pling clock offset,the more the influence on the symbol timing synchronization.Fig.4shows signal constellation variation due to the sam-pling clock offset.It is obvious that the larger the SCO,the more severe the distortion.Detailed analysis on the effects of sampling clock offset on symbol timing is presented in[52].In order to analyze the effects of SCO on the system performance in a more explicit way,SCO is divided into two parts:the sam-pling clock phase offset and the sampling clock frequency offset [17],[20],[53],[54].Effects of the sampling clock phase offset is similar to that of the symbol timing offset,leading to the signal phase distortion;while the sampling clock frequency offset in-troduces ICI.By defining Inter-Sample-Interference,effects of the sampling clock offset on system performance could be ana-lyzed deeply[55].The synchronous sampling and the asynchronous sampling are two different kinds of methods for the sampling clock syn-chronizations[56]–[58].1)Timing algorithms are usually used in the synchronoussystems to control both phase and frequency of a V oltageControl Crystal Oscillator(VCXO)[53],[59]–[61].Compared to the asynchronous digital sampling systems,it has large timingfluctuation due to high-level phasenoises.The need of the analog circuits makes it inconve-nient for the system integration[62].2)An independent oscillator is often exploited for samplingin an all-digital system.Timing algorithms are used tocontrol NCO(Numerical Control Oscillator)and then usethe NCO output to control the interpolatorfilter.BER per-formance of the asynchronous system in[54],[62]showsthat the asynchronous systems are more sensitive to CFOthan the synchronous puter simulations in[63]demonstrate that unrealistic interpolator may causecyclic tracking errors in asynchronous systems,whichnever occurs in the synchronous systems.The estimated sampling clock offset and decimal part of symbol timing error may be considered as an adjusting variable when we do sampling clock synchronization.This sampling clock adjusting variable is derived in frequency domain and then fed back to time domain to adjust digital oscillator,guar-anteeing the stability of the loop control circuit.VI.J OINT E STIMATION A LGORITHMSSome algorithms can be utilized for the joint estimation of all the synchronization errors including the symbol timing,the carrier frequency and the sampling clock offsets.Algorithms mentioned in the former sections such as[7]–[9],[47],are the typical algorithms to do the joint estimation of symbol timing and decimal CFO.The decimal CFO estimation utilizing the de-tected phase of the received frequency-domain complex data in the pilot sub-carriers or training symbols,is to be performed after the estimation of symbol timing errors.However,just as we have analyzed in Section IV-B,they all have stringent require-ment on the symbol timing synchronization.Some new joint es-timation algorithms are proposed recently[64],[65],in[64], the proposed algorithm with a weighted least squares technique generates offset estimates with minimum RMS errors.Multiple received OFDM symbols as an observation interval are utilized in[65],both of which are less sensitive to the symbol timing errors.The joint estimation and tracking of symbol timing and sam-pling clock errors are presented in[17],[53].The main problem。

一种三明治型金属卟啉配合物的合成方法及其应用[发明专利]

一种三明治型金属卟啉配合物的合成方法及其应用[发明专利]

专利名称:一种三明治型金属卟啉配合物的合成方法及其应用专利类型:发明专利
发明人:沈珍,秦霞,徐海云,游效曾
申请号:CN200710022384.7
申请日:20070516
公开号:CN101307077A
公开日:
20081119
专利内容由知识产权出版社提供
摘要:本发明公开了一种在光电功能材料中作为可见光波段的吸光材料的稠合双环[2.2.2]辛二烯卟啉与稀土金属钆的三明治型夹心配合物的制备方法及其应用。

它们的Soret谱带和Q谱带相对于八乙基卟啉-钆配合物发生了明显的红移,表明卟啉环中π共轭体系的进一步延伸导致了HOMO-LUMO能隙降低。

这说明化合物1和2在光电功能材料中作为可见光波段的吸光材料将有较大的用途。

申请人:南京大学
地址:210093 江苏省南京市汉口路22号
国籍:CN
更多信息请下载全文后查看。

集成电路制造方法及结构[发明专利]

集成电路制造方法及结构[发明专利]

专利名称:集成电路制造方法及结构专利类型:发明专利
发明人:马丁·施莱姆斯
申请号:CN98118720.X
申请日:19980827
公开号:CN1214542A
公开日:
19990421
专利内容由知识产权出版社提供
摘要:一种形成电容器介质的方法,包括设置半导体;在半导体表面上形成非均匀层;使半导体和其上的非均匀层受热和加压,在半导体表面上产生凹坑;在整个半导体的凹坑表面上形成第二介质,提供为电容器的介质。

使半导体受热和加压,在半导体表面产生凹坑的步骤包括对其上带有介质层的半导体施加第一退火的步骤,使半导体表面上产生的凹坑变成圆形的步骤包括对半导体施加第二退火的步骤。

DRAM单元设有单晶硅本体。

申请人:西门子公司
地址:联邦德国慕尼黑
国籍:DE
代理机构:柳沈知识产权律师事务所
代理人:黄敏
更多信息请下载全文后查看。

给Ni-Mn基Heusler合金磁致应变和磁热效应研究进展

给Ni-Mn基Heusler合金磁致应变和磁热效应研究进展
Keywords
Heusler Alloys, Ni-Mn Based, Magnetic-Field-Induced Strain, Magnetocaloric Effect
Copyright © 2021 by author(s) and Hans Publishers Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). /licenses/by/4.0/
关键词
Heusler合金,Ni-Mn基,磁致应变,磁热效应
Research Progress on Magnetic-Field-Induced Strain and Magnetocaloric Effect of Ni-Mn Base Heusler Alloys
Ze Yan
Hubei Engineering Research Center of Weak Magnetic-Field Detection, College of Science, Three Gorges University, Yichang Hubei
Open Access
1. 引言
Heusler 合金是一种著名的金属间化合物,该类合金一般由三种金属元素(X, Y, Z)组成,其中,X,Y 元素为过渡金属元素,Z 为主族元素,合金组分一般有 XYZ (Heusler 合金)和 X2YZ (半 Heusler 合金)两种 [1] [2],其金的发现可以追溯到 1903 年,FritzHeusler 发现将 Cu, Mn, Al 这三种金属元素按照原子比 2:1:1 的比例熔炼之后合金具有铁磁性,有趣的是其组成元 素中并无铁磁性元素[2]。之后的研究显示,大部分 X2YZ 类型的 Heusler 合金会在特定的温度下发生马氏 体相变,由于马氏体相变是结构性相变,导致合金在相变温度点附近出现了很多新颖的物理性质,如磁 致应变,磁光效应,磁热效应以及磁电阻效应等[3]-[16]。

金纳米团簇 声动力

金纳米团簇 声动力

前言:前言:本文主要介绍的是关于《金纳米团簇声动力》的文章,文章是由本店铺通过查阅资料,经过精心整理撰写而成。

文章的内容不一定符合大家的期望需求,还请各位根据自己的需求进行下载。

本文档下载后可以根据自己的实际情况进行任意改写,从而已达到各位的需求。

愿本篇《金纳米团簇声动力》能真实确切的帮助各位。

本店铺将会继续努力、改进、创新,给大家提供更加优质符合大家需求的文档。

感谢支持!正文:就一般而言我们的金纳米团簇声动力具有以下内容:金纳米团簇与声动力学的交汇:探索新的可能性一、引言在材料科学和生物医学的交叉领域,金纳米团簇(Gold Nanoclusters, GNCs)因其独特的物理和化学性质,如超小尺寸、光稳定性好、低毒、高催化活性等,引起了广泛的关注。

而声动力学,作为研究声音对物体产生的力学效应的学科,其在医学、工程、能源等多个领域有着广泛的应用。

本文将探讨金纳米团簇与声动力学的交汇点,以及它们可能带来的新机遇和挑战。

二、金纳米团簇的特性与应用金纳米团簇,通常由几个至几百个金属原子组成,其尺寸在1至10纳米之间。

这种超小的尺寸使得金纳米团簇展现出独特的量子尺寸效应,包括强烈的光学性质和电学性质。

这些特性使得金纳米团簇在生物成像、药物输送、疾病诊断和治疗等领域具有巨大的应用潜力。

近年来,研究人员通过精确控制金纳米团簇的形貌、尺寸和表面化学性质,进一步提升了其性能和应用范围。

例如,通过表面配体层层组装策略,可以调控金纳米团簇的结构运动,从而实现其性能的最大化和可定制化。

此外,金纳米团簇还因其良好的生物相容性和稳定性,在生物医药领域展现出巨大的应用前景。

三、声动力学的概念与应用声动力学是研究声音对物体产生的力学效应的学科。

它主要关注声波的传播和相互作用产生的力学效应,如声压效应、声辐射效应和声激励效应等。

声动力学在医学、工程、能源等多个领域有着广泛的应用。

在医学领域,声波被广泛应用于超声检查和超声治疗。

等离激元异质结构纳米复合材料

等离激元异质结构纳米复合材料

等离激元异质结构纳米复合材料
等离激元异质结构纳米复合材料是一种利用表面等离激元共振现象来增强光能捕获和转换效率的先进材料。

以下是一些关于等离激元异质结构纳米复合材料的关键点:
1. 表面等离激元共振: 表面等离激元共振(SPR)是一种物理现象,当光波与金属纳米结构相互作用时,导体内的自由电子会集体振荡,产生强烈的局部电磁场增强效应。

这种现象可以用于提高各种光谱过程的效率,包括光催化、光伏效应和传感等。

2. 异质结构设计: 异质结构是指由不同材料组成的界面结构。

在纳米复合材料中,通过合理设计这些异质结,可以实现更优的光能捕获和转换性能。

3. 纳米复合材料的制备: 等离激元纳米粒子可以通过直接合成或物理力、生物分子介导的组装来制备。

这些纳米粒子具有在等离激元介导的能量转移、磁性等离激元以及超分子和纳米生物技术方面的巨大应用潜力。

4. 光催化活性: 表面等离激元增强的金属如金、银纳米结构可以显著提升光催化活性。

例如,Au/Ag2S异质纳米结构就是一个有效的光催化剂,能够利用等离激元效应提高化学反应的效率。

5. 全太阳光谱响应: 通过结构设计,可以实现对全太阳光谱的光学响应,并且表面等离激元结构的吸收散射截面远大于传统的染料或
半导体材料。

6. 电磁场增强: 局域的表面等离激元激发能够在纳米结构表面区域显着增强电磁场,这对于提高材料的光吸收能力至关重要。

等离激元异质结构纳米复合材料因其独特的光电性质而在能源转换、传感器、生物医学等领域展现出广阔的应用前景。

通过精确控制纳米结构的尺寸、形状和组成,可以优化这些材料的性能,以满足特定的技术需求。

超导体中的声子热耦合nature

超导体中的声子热耦合nature

一、超导体的基本原理超导体是指在一定条件下电阻突然消失而导电性达到最大值的物质。

超导体的基本原理是由于其电子成对运动和库仑排斥力的屏蔽,使得电子对的结合能足够大,超越了材料晶格的振动能量而形成了超导态。

二、声子热耦合声子是晶格振动的量子,它是介质中能量最低的激发态。

在超导体中,声子热耦合指的是电子与声子之间的相互作用。

声子热耦合对超导体的超导性质有着重要影响,它不仅可以影响超导临界温度,还可以影响超导态的对称性和电子的配对机制。

三、超导体中的声子热耦合机制超导体中声子热耦合的机制是一个复杂的问题,主要包括以下几个方面:1. 电子-声子相互作用:超导体中的电子通过与晶格的振动相互作用,引起了声子的产生和吸收。

这种相互作用使得电子在晶体中的传导变得更加复杂,大大影响了超导电性的性质。

2. 能带结构和电子输运:超导体的能带结构对声子热耦合有重要影响。

它通过晶格振动的分布和密度,调节了声子热耦合的强度。

3. 声子场的量子统计效应:声子是玻色子,其统计行为对超导体的声子热耦合也有着很大的影响。

声子场的量子统计效应会导致声子的配对和集体激发现象,从而影响超导电性的行为。

四、超导体中的声子热耦合与超导态的对称性声子热耦合不仅对超导体的临界温度有着重要影响,还可以影响超导态的对称性。

在高温超导体中,声子热耦合常常导致超导态的对称性发生变化,从而影响电子对的配对机制和超导性质。

五、超导体中的声子热耦合实验研究近年来,通过高分辨率的声子谱实验和声子能谱的测量,科学家们对超导体中声子热耦合进行了深入的研究。

这些研究不仅揭示了声子热耦合对超导性质的重要影响,还为超导体的理论模型提供了重要的实验验证。

六、超导体中的声子热耦合的应用前景超导体中的声子热耦合机制的深入研究,不仅有助于理解超导体的超导机制,还可以为新型超导体材料的设计和合成提供重要参考。

通过对声子热耦合的深入理解,科学家们可以设计出更高临界温度的超导体材料,为超导应用技术提供更广阔的发展空间。

Noise-induced coherent switch

Noise-induced coherent switch

Noise-induced coherent switch佚名【期刊名称】《中国科学》【年(卷),期】2008(000)006【摘要】Taking the famous genetic toggle switch as an example,we numerically investigated the effect of noise on bistability.We found that extrinsic noise resulting from stochastic fluctuations in synthesis and degradation rates and from the environmental fluctuation in gene regulatory processes can induce coherent switch,and that there is an optimal noise intensity such that the noise not only can induce this switch,but also can amplify a weak input signal.In addition,we found that the intrinsic noise introduced through the Poisson τ-leap algorithm cannot induce such a switch.【总页数】8页(P562-569)【正文语种】中文【中图分类】TB535【相关文献】1.Noise-induced bistable switching dynamics through a potential energy landscape2.Stability of Delayed Switched Systems With State-Dependent Switching3.Stability of Delayed Switched Systems With State-Dependent Switching4.Adaptability Analysis of Fluctuating Traffic for IP Switching andOptical Switching5.A New Switching Sequences of SVPWM for Six-Phase Induction Motor with Features of Reduced Switching Losses因版权原因,仅展示原文概要,查看原文内容请购买。

约瑟夫森

约瑟夫森
两块超导体通过一绝缘薄层(厚度为10埃左右)连接起来,绝缘层对电子来说是一势垒,一块超导体中的电子 可穿过势垒进入另一超导体中,这是特有的量子力学的隧道效应。当绝缘层太厚时,隧道效应不明显,太薄时, 两块超导体实际上连成一块,这两种情形都不会发生约瑟夫森效应。绝缘层不太厚也不太薄时称为弱连接超导体。 两块超导体夹一层薄绝缘材料的组合称S-I-S超导隧道结或约瑟夫森结。约瑟夫森效应主要表现为:
1996年,约瑟夫森还与加州大学女统计学家乌兹(Jessica Utts)教授合写了一篇文章《超自然:证据及其 对意识的含义》,部分发表于《泰晤士报高教增刊》上。乌兹就是那位与海曼一同为中央情报局(CIA)评估美 国星门计划的专家,她本人相信特异功能,而海曼不相信。他们俩人的评估结论也有分歧。约瑟夫森与乌兹合作, 重点在于解决实验数据的统计问题。因为经常有怀疑论者抨击超自然主义者误用统计,而乌兹是应用统计学专家, 她可以把超心理一类实验结果“整理”得很好,作出符合行家水准的统计分析。由此可见,现代灵学、超心理学 越来越精致化,从形式上看,与常规科学几乎无法区分。这也是外行、社会学家认为它们也是科学的一个理由。 当然,还是前面的话,科学与科学也有区别。将外部区别转化为内部区别,并没有真正解决争论。
约瑟夫森
英国物理学家
目录
01 人物生平
02 效应
约瑟夫森(1940~ )Josephson,Brian David英国物理学家。1940年1月4日生于英国威尔士的加迪夫。中 学毕业后在剑桥三一学院学数学和物理。还是大学生时就从事科学研究,1960年在剑桥大学获学士学位,1964 年获硕士博士学位。1962~1969年任剑桥大学三一学院初级研究员。1965~1966年在美国伊利诺伊大学任研究 助理教授。1967~1972年任剑桥大学研究部副主任。
  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

Eur.Phys.J.B(2014)87:198 DOI:10.1140/epjb/e2014-50437-1 Regular Article T HE E UROPEANP HYSICAL J OURNAL BNoise-induced synchronization transitions in neuronal network with delayed electrical or chemical couplingYanan Wu,Yubing Gong a,and Qi WangSchool of Physics and Optoelectronic Engineering,Ludong University,Yantai,Shandong264025,P.R.ChinaReceived30June2014Published online9September2014–c EDP Sciences,Societ`a Italiana di Fisica,Springer-Verlag2014Abstract.In this paper,we numerically study synaptic noise-induced synchronization transitions in scale-free network of thermo-sensitive neurons with delayed electrical or chemical coupling.It is found that theneurons exhibit synchronization transitions as synaptic noise strength is varied,and the synchronizationtransitions are enhanced when time delay is proper.For electrical coupling,noise can induce weak syn-chronization transitions,and the synchronization transitions decrease as network average degree increases;while,for chemical coupling,noise can induce strong synchronization transitions,and the synchronizationtransitions become strongest when network average degree is optimal.Different mechanisms of linear elec-trical and nonlinear chemical coupling could be the reason for these differences.These results show thatsynaptic noise can induce different synchronization transitions in the scale-free neuronal network with elec-trical or chemical coupling,and hence could play different roles in the information transmission of neuralsystems.1IntroductionSynchronization processes are ubiquitous in nature and play a very important role in many differentfields such as biology,ecology,climatology,sociology,technology,or even in arts.In the past decades,synchronization phe-nomenon has been intensively and extensively studied in nonlinear dynamic systems,particularly complex net-works[1,2].It is shown experimentally that synchroniza-tion can occur in many special areas of the brain,such as the olfactory system or the hippocampal region[3–5], and synchronous activity is considered to be related with several neurological diseases such as epilepsy and tremor in Parkinson’s disease[6–8].Due to its great importance, people have theoretically and experimentally studied syn-chronization phenomenon in various neuronal systems and many synchronization phenomena have been found, such as bursting synchronization in an array of modified Hodgkin-Huxley(HH)neurons[9]and a ring neuronal net-work[10]and a small-world neuronal network[11],chaos synchronization in gap-junction coupled neurons[12],sub-threshold stimulus-aided synchronization in a square lat-tice neuronal network[13],coupling-enhanced synchro-nization in small-world neuronal network[14],as well as many synchronization phenomena induced by time delay in coupled neurons and neuronal networks[15–20].In recent years,a novel phenomenon of synchronization transitions has been observed in neuronal networks,such as coupling strength induced synchronization transitions a e-mail:gongyubing09@ in bursting neuron network[21]and intra-and inter-neuronal coupling induced synchronization transitions in a neuronal network of subnetworks[22],as well as time delay induced synchronization transitions in two cou-pled fast spiking neurons[23],small-world neuronal net-works[24,25],scale-free neuronal networks[26,27]and inter-neuronal networks[28].These phenomena show that the synchronization of neuronal networks may be intermit-tently enhanced or reduced with the variation of coupling strength or time delay,which is crucial to the information transmission in neural systems.It is known that neurons are noisy units,and noise arises from the quasi-random release of neurotransmit-ters by the synapses and random synaptic input from other neurons(synaptic noise)and the random switching of ion channels(channel noise).Numerous studies have shown that neuronal noise can greatly enhance the tem-poral coherence by inducing stochastic resonance and co-herence resonance of neuronalfiring activity[29,30].It is also shown that neuronal noise can enhance the syn-chronization offiring behaviors in coupled neurons and neuronal networks,such as noise-induced synchronization in excitable media[31]and modified HH neurons[32] and scale-free Morris-Lecar neuron networks[33].How-ever,synchronization transitions induced by noise have not been observed in neuronal networks.In this paper,we numerically study the effect of synap-tic noise on the synchronization of scale-free thermo-sensitive neuron network with delayed electrical or chem-ical coupling.The goal of this work is to investigate if synaptic noise can induce synchronization transitions inPage 2of 6Eur.Phys.J.B (2014)87:198the neuronal network and how electrical or chemical cou-pling affects the phenomenon.We first study how noise induces synchronization transitions in case of electrical or chemical coupling,and then discuss the effects of coupling strength and network average degree.Meanwhile,the dif-ferences between the results for both coupling cases are discussed and the mechanisms for the results are briefly analyzed.Finally,conclusion is given.2Model and equationsThe modified HH neuron model [34]proposed by Braun et al.is used to describe the firing dynamics of thermo-sensitive neurons.The analysis of bifurcation of the model showed that the model can exhibit different temperature-dependent dynamics like regular spikes (T <7.31◦C),chaotic bursts (7.31◦C T 10◦C)and regular bursts (T >10◦C)[35].Here,we set T =8.2◦C such that each neuron initially exhibits chaotic bursting behavior.We use scale-free network generated via growth and preferential attachment as proposed by Barab´a si and Albert (BA)[36].The present network comprising N =100neurons starts with m 0connected nodes,and subse-quently every new node is attached to m ( m 0)different nodes already present in the network,whereby the prob-ability p that a new node will be connected to node i de-pends on its degree k i in accordance with p =k i / j k j .This growth and preferential attachment scheme yields a network with an average degree k = i k i /N and a power-law degree distribution with the slope of the line equaling −2.9on a double logarithmic graph.Throughout this paper,we use average degree k =4(m =m 0=2)unless stated otherwise.In the presence of delayed electrical coupling,the dynamics of the scale-free neuronal network can be written as:C dV i dt=−I i Na −I i K −I isd −I isa −I il+ jεij (V j (t −τ)−V i )+Dξi (t ),(1)where C is the membrane capacitance.I i Na =ρg Na a Na (V i −V Na ),I i K =ρg K a K (V i −V K ),I isd =ρg sd a sd (V i −V sd ),I isa =ρg sa a sa (V i −V sa ),I il =g l (V i −V l )are various membrane currents.da Na dt =φτNa (a Na ,∞−a Na ),da Kdt =φτK(a K ,∞−a K ),da sd dt =φτsd (a sd,∞−a sd ),da sa dt =φτsa(−ηI isd −ka sa ),ρ=1.3(T −T 0)/10,φ=3.0(T −T 0)/10,a Na ,∞=a K ,∞=11+exp [−0.25(V i +25)],a sd,∞=11+exp [−0.09(V i +40)].Table 1.Values of parameters used in the model.Membrane capacitance C M =1μF/cm 2Conductances (mS/cm 2)g Na =1.5g K =2.0g sd =0.25g sa =0.4g l =0.1Time constants (ms)τNa =0.05τK =2.0τsd =10τsa =20Reversal potentials (mV)V Na =V sd =50V K =V sa =−90V l =−60V syn =20Rise constant τrise =0.5ms Decay constant τdecay =8msT 0=25◦C,T =8.2◦C,η=0.012μA,γ=0.17.Ifchemical synaptic coupling is applied,the coupling term can be replaced by:jεij r j (t −τ)[V syn −V i ],thus we have:CdV idt =−I i Na −I i K −I isd −I isa −I il +jεij r j (t −τ)[V syn −V i ]+Dξi (t ),(2)where V syn is synaptic reverse potential,V i is the mem-brane potential of the i th neuron at time t ,V j (t −τ)is the membrane potential of the j th neuron at earlier time t −τand r j (t −τ)is the fraction of bond receptors of the j th neuron at earlier time t −τ,and τ(in unit of ms)is the information transmission delay between neurons i and j .The summation is taken over all neurons.The synaptic dynamics of the j th neuron is described by a receptor binding as [37]:dr j dt = 1τrise −1τdecay 11+exp (−V j −20)(1−r j )−1τdecay r j ,(3)where V j is the membrane potential of the presynaptic neuron,τrise is the rise time constant and τdecay the de-cay time constant of synapse.εij is coupling strength and is identical for any two neurons,and εij =εif neu-rons i and j are connected;ε=0otherwise.ξi (t )is Gaussian white noise with zero mean and autocorrelation ξi (t )ξj (t ) =δij δ(t −t ),D is noise strength.Other pa-rameter values are listed in Table 1.More interpretations of all parameters can be found in reference [34].The synchronization of the firing activity of the neurons on the network is characterized by standard deviation σdefined as [38]:σ=[ σ(t ) ]Eur.Phys.J.B(2014)87:198Page3of6Fig.1.σin dependence onτwhenε=0.04and D=0.02. Asτincreases,σpasses through a few peaks and valleys.This quantitatively characterizes the occurrence of synchronization transitions with time delay.withσ(t)=1NNi=1V i(t)2−1NNi=1V i(t)2N−1,(4)where · denotes the average over all neurons and[·]the average over different realizations of the scale-free net-works for each set of parameter values.Smallerσdenotes higher synchronization.Numerical integrations of equations(1)–(3)are car-ried out using explicit Euler method with time step of0.001ms.Parameter values for all neurons are as-sumed identical except for distinct initial values of the potential V i0and noise termsξi(t)for each neuron.3Results and discussionWe have previously observed synchronization transitions induced by time delay in scale-free thermosensitive neu-ron network with electrical or chemical coupling[21].This phenomenon is quantified by the dependence of standard deviationσagainst time delayτin Figure1.It is shown that time delay can induce multiple synchronization tran-sitions for chemical coupling,but only a single synchro-nization transition can be induced by time delay for elec-trical coupling.In the present work,wefix time delay and study the effect of synaptic noise on the synchronization of spiking behaviors for each coupling case.3.1Noise-induced synchronization transitionsfor electrical couplingWefixε=0.04andτ=40.In this case,thefiring behav-iors arrive at antiphase synchronization.In Figure2,the spatiotemporal patterns of the membrane potentials for different noise strengths D are displayed.It is seen that thefiring behaviors become synchronous at D=0.275,Fig.2.Spatiotemporal patterns of the membrane potentials for different noise strength D whenε=0.04andτ=40for electrical coupling.As D increases,thefiring behaviors perform transitions between in-phase synchronization and antiphasesynchronization.Fig.3.Contour plot ofσas functions ofτand D whenε=0.04 for electrical coupling.At aroundτ=40and10,σpasses through a few islands as D is increased,which characterizes the occurrence of synchronization transitions.0.65and antiphase synchronous for other D values,ex-hibiting synchronization transitions.In Figure3,the re-sults for other time delays are presented via contour plot ofσas functions ofτand D.It is seen that there are a few islands with the increase of D at aroundτ=40and τ=100,which quantitatively characterizes the occurrence of noise-induced synchronization transitions.The phe-nomenon that there areσislands with increasing D only at some time delays shows that noise-induced synchro-nization transitions are dependent on time delays.This reflects cooperative effect of synaptic noise and time de-lay on the synchronization offiring activity.In addition to ε=0.04,we have also obtained similar results for other coupling strengths.Page 4of 6Eur.Phys.J.B (2014)87:198Fig.4.σin dependence on D for different coupling strength εat τ=4for electrical coupling.For larger ε,there are more and higher σpeaks with the increase of D.Fig.5.Dependence of σon D for different average degree k at τ=40and ε=0.04for electrical coupling.σpeaks decrease and become lower as k increases.The effects of coupling strength εand network average degree k are displayed in Figures 4and 5,respectively.As εincreases,σpeaks increase and become higher.This means that increasing coupling strength can enhance synchronization transitions.As k increases,σpeaks rapidly decrease and become lower.This indicates that the synchronization transitions decrease as network average degree increases.3.2Noise-induced synchronization transitions for chemical couplingWe fix ε=0.04and τ=60.In Figure 6,the spatiotem-poral patterns of the membrane potentials for different noise strength D are displayed.As D is increased,the firing behaviors intermittently exhibit in-phase synchro-nization at D =0.038,0.078,0.09and antiphase syn-chronization at D =0.03,0.05,0.082,exhibiting synchro-nization transitions.The results for other time delays are also obtained.All results are displayed via the contour plot of σas functions of τand D in Figure 7.It is seen that σpasses through a few islands as D is increased,which quantitatively characterizes the occurrence of syn-chronization transitions induced by noise.Similarly,there are σislands with increasing D only at certain time de-lays.This again shows that noise-induced synchronization transitions are dependent on time delays,and thereisFig.6.Spatiotemporal patterns of the membrane potentials for different noise strength D when ε=0.04and τ=60for chemical coupling.As D increases,the firing behaviors perform transitions between in-phase synchronization and antiphasesynchronization.Fig.7.Contour plot of σas functions of τand D when ε=0.04for chemical coupling.At around τ=60,120,190,σpasses through a few islands as D is increased,which characterizes the occurrence of synchronization transitions.cooperation between synaptic noise and time delay in the synchronization of firing activity.Similar results for other coupling strengths are also obtained.The effects of εand k are plotted in Figures 8and 9,respectively.As εincreases,the number of σpeaks in-crease and the peaks grow higher.This indicates that the synchronization transitions enhance as coupling strength increases.As k increases,σpeaks increase and become highest at k =8.This shows that noise-induced synchro-nization transitions increase and become strongest when network average degree is optimal.By comparison,it is seen that the difference between the maximum and minimum of σin Figure 7is bigger than those in Figure 3,and there are more islands with increasing D in Figure 7than in Figure 3.This shows that,for same coupling strength,noise can induce more and stronger synchronization transitions for chemical coupling than for electrical coupling.Eur.Phys.J.B (2014)87:198Page 5of6Fig.8.Dependence of σon D for different coupling strength εat τ=60for chemical coupling.For larger ε,there are more and higher σpeaks with the increase of D.Fig.9.Dependence of σon D for different average degree k at τ=60and ε=0.04for chemical coupling.As k increases,σpeaks are maximal and highest when k is optimal at k =8.The phenomenon of synchronization transitions in-duced by noise can be briefly explained as follows.It is known that synchronization transitions are due to the change of the phases of neuronal firing behaviors,and time delay can induce synchronization transitions by introduc-ing phase slips in the firing behaviors.Presently,in the presence of delayed chemical synapses,through the chem-ical synapses,synaptic noise can randomly regulate time delay and can drive time delay switch between neighbor-ing delay lengths.Consequently,time delay may alter the phases of firing behaviors by introducing phase slips in the firing behaviors,and this gives rise to the synchroniza-tion transitions.This is why the firing behaviors exhibit synchronization transitions as noise intensity is varied.4ConclusionWe have numerically studied synchronization transitions induced by synaptic noise in delayed scale-free thermo-sensitive neuron network with electrical or chemical coupling.It is found that synaptic noise can inducedelay-dependent synchronization transitions in the neu-ronal network,but the synchronization transitions are de-pendent on coupling case.Noise-induced synchronization transitions are stronger for chemical coupling than for electrical coupling.As network average degree increases,the synchronization transitions for electrical coupling de-crease,but the synchronization transitions for electrical coupling increase and become strongest when the aver-age degree is optimal.Increasing coupling strength can enhance noise-induced synchronization transitions in both coupling cases.The difference between the results for both coupling cases may arise from different mechanisms of lin-ear electrical and nonlinear chemical coupling.For electri-cal coupling,the membrane potential directly transmits between two neurons through gap-junctions,and infor-mation processing and transmission is a simple process and delay effect is very weak;while,for chemical coupling,the membrane potential passes through complex biochem-ical processes,and information processing and transmis-sion take a longer time,which causes a strong delay ef-fect.Therefore,synaptic noise may have a bigger effect on the synchronization via regulating time delay and can induce stronger synchronization transitions for chemical coupling than for electrical coupling.In addition,in case of chemical coupling,there could be interactions between nonlinear network topology and the nonlinearity of bio-chemical reaction processes,which may lead to the result that synaptic noise-induced synchronization transitions become strongest when network average degree is optimal.These results show that synaptic noise can induce different synchronization transitions in scale-free neuronal network with electrical or chemical coupling,and hence could have different effects on the information transmission in neural systems.This work was supported by the Natural Science Foundation of Shandong Province of China (ZR2012AM013).References1. A.Arenas,A.D´ıaz-Guilera,J.Kurths,Y.Moreno,C.S.Zhou,Phys.Rep.469,93(2008)2.J.A.K.Suykens,G.V.Osipov,Chaos 18,037101(2008)3. C.M.Gray,W.Singer,A 86,1698(1989)4.M.Bazhenov,M.Stopfer,M.Rabinovich,R.Huerta,H.D.I.Abarbanel,Y.J.Sejnowski,urent,Neuron 30,553(2001)5.M.R.Mehta,A.K.Lee,M.A.Wilson,Nature 417,741(2001)6.R.Levy,W.D.Hutchison,A.M.Lozano,J.O.Dostrovsky,J.Neurosci.20,7766(2000)7. F.Mormann,T.Kreuz,R.G.Andrzejak,P.David,K.Lehnertz,C.E.Elger,Epilepsy Res.53,173(2003)8. C.Hammond,H.Bergman,P.Brown,Trends Neurosci.30,357(2007)9.S.Bahar,Fluct.Noise Lett.4,L87(2004)10.Q.Y.Wang,Q.S.Lu,G.R.Chen,Phys.A 374,869(2007)11.Y.H.Zheng,Q.S.Lu,Physica A 387,3719(2008)Page6of6Eur.Phys.J.B(2014)87:19812.M.Yoshioka,Phys.Rev.E71,065203R(2005)13.Q.Y.Wang,Q.S.Lu,G.R.Chen,Europhys.Lett.77,10004(2007)14.H.Hasegawa,Phys.Rev.E72,056139(2005)15.M.Dhamala,V.K.Jirsa,M.Z.Ding,Phys.Rev.Lett.92,074104(2004)16. E.Rossoni,Y.H.Chen,M.Z.Ding,J.F.Feng,Phys.Rev.E71,061904(2005)17.N.Buri´c,K.Todorovi´c,N.Vasovi´c,Phys.Rev.E78,036211(2008)18. B.Adhikari,A.Prasad,M.Dhamala,Chaos21,023116(2011)19.T.W.Ko,G.B.Ermentrout,Phys.Rev.E76,056206(2007)20. A.Roxin,N.Brunel, D.Hansel,Phys.Rev.Lett.94,238103(2005)21.Y.H.Hao,Y.B.Gong,L.Wang,X.G.Ma, C.L.Yang,Chaos Solitons Fractal44,260(2011)22.X.J.Sun,J.Z.Lei,M.Perc,J.Kurths,G.R.Chen,Chaos21,016110(2011)23.Q.Y.Wang,Q.S.Lu,G.R.Chen,Int.J.Bifurc.Chaos18,1189(2008)24.Q.Y.Wang,Z.S.Duan,M.Perc,G.R.Chen,Europhys.Lett.83,50008(2008)25.Q.Y.Wang,M.Perc,Z.S.Duan,G.R.Chen,PhysicaA389,3299(2010)26.Q.Y.Wang,M.Perc,Z.S.Duan,G.R.Chen,Phys.Rev.E80,026206(2009)27.Q.Y.Wang,G.R.Chen,M.Perc,PLoS ONE6,e15851(2011)28. D.Q.Guo,Q.Y.Wang,M.Perc,Phys.Rev.E85,061905(2012)29.L.Gammaitoni,P.H¨a nggi,P.Jung,F.Marchesoni,Rev.Mod.Phys.70,223(1998)30.P.H¨a nggi,ChemPhysChem.3,285(2002)31. A.Neiman,L.Schimansky-Geier,A.Cornell-Bell,F.Moss,Phys.Rev.Lett.83,4896(1999)32. C.S.Zhou,J.Kurths,Chaos13,401(2003)33.M.Perc,Biophys.Chem.141,175(2009)34.H.A.Braun,M.T.Huber,M.Dewald,K.Schafer,K.Voigt,Int.J.Bifurc.Chaos8,881(1998)35.U.Feudel,A.Neiman,X.Pei,W.Wojtenek,H.A.Braun,M.Huber,F.Moss,Chaos10,231(2000)36. A.-L.Barab´a si,R.Albert,Science286,509(1999)37. A.Destexhe,Z.F.Mainen,T.J.Sejnowski,NeuralComput.6,14(1994)38.Y.B.Gong,B.Xu,Q.Xu,C.L.Yang,T.Q.Ren,Z.H.Hou,H.W.Xin,Phys.Rev.E73,046137(2006)。

相关文档
最新文档