pvMsglq>greadg->gread=((q->gread+1)%TIMEWEIGHT_TASKQUEUE_SIZE)/*解锁return(pmSg);PushapmSgpacketontoapvMsgpacketqueue@parampisthepmsgtopushontothepacketqueue@paramgisthepacketqueue.W@return0ifsuccessful,-1ifqisfullntTWTMsgSend(TtWTMSg*p,TTWTMSGQUEUE*qintret/if(!TWT_QUEUEFULL(al)iThequeueisntfullsoweaddthenewframeatthecurrentwwritepositionandmovethewritepointer.g->pvMsgla->write]=pg->write=((q->qwrite+1)%TIMEWEIGHTTASKQUEUESIZE;ret=oThestackisfullsowearethrowingawaythisvalue.Keeptrackofthenumberoftimesthishappensg->overflow++ret=-1://*解锁return(ret)**米**米来米***来米*半米*米*半米*米求***半*米米求半**米求半**半求半和*米*//消息分发机制//*算法是//*正常返回0,出错返回-1水米米******水*米*水**米*半*水米米冰半**水水*水米米半米冰水*米水水*水*米水水externintRecToFileMsgProc(T_MSG_REC2FILE*ptMsg);intDispatchMsg(TTWTMSG*ptMsgitif(NULl=ptMsggotoErrRet/*dispatchmsg*/switch(ptMsg->enMsgType)caseTWTPINgPoNgBuffrecRecToFileMsgProcl(TMSG_REC2FE)(pmSg->pMsg);/*处理消息*/destroyMsg(pmSg;/*消毁消息breakdefault.printf("DispatchMsgMsgtypeError!n")break.return0ErrRetprintf("DispatchMsgFail!";return-1./*buffsize*/#defineP|NGPONG_BUFFBSIZE0X20000//10*1024*1024/*10M*/*pingpongbuff*///chargacPINGBUFF[PINGPONGBUFFBSIZE];/*PingBuff*///chargacPONGBUFF[PINGPONG_BUFFBSIZE]*PongBuff*/水米米*********米*水**米*半*水米米水**冰水*水米米半半水半米冰水*米水水*水*米米//*释放pingpongbuff/必然成功//*无返回木***木*水****本**水*水水*水****本水**水水****水水***本***米*水voidDestroyPingPongButt(TPINGPONGBUFFUSEDESptPingPongButt)nLoopif(NULL=ptPingpongbuffreturnfor(nLoop=0;nLooptIngBuffUse[nLoop].pcHeadAddr)free(ptPingPong->tPingBuffUse[nLoop].pcHeadAddr)free(ptPingpongBuff)/初始化pingpongbuff返回pignpongbuff的描述指针//*正常返回0,出错返回-1水水水水水水水水水木水木水水水水水木水木水水水水水水水水本水水水水水水水水水本水水水水水水水水水水水水水TPINGPONGBUFFUSEDESInitPingPongBuff(unsignedintnBuffSizeTPINGPONGBUFFUSEDES*ptBuffDes=NULLintnLoop/*获取buf描述*ifNULL==(ptBuffDes=malloc(sizeof(TPINGPONGBUFFUSEDES))))gotoErrRetmemset(ptBuffDes,0,sizeof(T_PINGPONGBUFFUSE_DES));/*分别初始化ping和pong*/for(nLoop=0;nLooptIngBuffUsenLooppcHeadAddr=mallocnBuffSize))gotoErrRet;ptBuffDes->tIngBuffUselnLoop]nBuffSizenBuffsizeptBuffDes->tPingBuffUse[nLoop].oFfsetptBuffDes->tPingBuffUsenLoop)eUseStatus-=BUFFWRITEABLE;ptBuffDes->eCurUseIDBUFFPINGreturnptBuffDesErrretprintf("lnitPingPongBuffFail!");DestroyIngPongBuff(ptBuffDes)turnnull平**米**米*米***来米米*米*米*半米*米米米来*半米平**米米求*来*半求半来*米求*和*米*/*Resetpingpongbuff//*正常返回0,出错返回-1米米米米水冰米*米米水**米米冰*米水米米米米水米水*水米米来米米x米来米米水冰来来宋来水米来米来冰#defineResetBuffUse(ptBuffuse)ptBuffUse->oFfset0ptBuffUse->eUseStatusBUFFWRITEABLEgenerateafilerecmsg*正常返回消息体的指针,异常返回NULLT_MSG_REC2lGKSenFRMSB(T_BUFF_USE_DES*ptBuffUse,REC_FILE_DESLIST*ptFileListRTMSGREC2FILEKE*ptRFMsg=NULL;if(NULL==(ptRFMsgmalloc(sizeof(T_MSGREC2FILE)returnnUllptRFMsg->ptBuffUseptBuffUseptRFMsg->ptFilelistptFilelist;returnptRFMsg-IMDN开发者社群-imdn.cn"> pvMsglq>greadg->gread=((q->gread+1)%TIMEWEIGHT_TASKQUEUE_SIZE)/*解锁return(pmSg);PushapmSgpacketontoapvMsgpacketqueue@parampisthepmsgtopushontothepacketqueue@paramgisthepacketqueue.W@return0ifsuccessful,-1ifqisfullntTWTMsgSend(TtWTMSg*p,TTWTMSGQUEUE*qintret/if(!TWT_QUEUEFULL(al)iThequeueisntfullsoweaddthenewframeatthecurrentwwritepositionandmovethewritepointer.g->pvMsgla->write]=pg->write=((q->qwrite+1)%TIMEWEIGHTTASKQUEUESIZE;ret=oThestackisfullsowearethrowingawaythisvalue.Keeptrackofthenumberoftimesthishappensg->overflow++ret=-1://*解锁return(ret)**米**米来米***来米*半米*米*半米*米求***半*米米求半**米求半**半求半和*米*//消息分发机制//*算法是//*正常返回0,出错返回-1水米米******水*米*水**米*半*水米米冰半**水水*水米米半米冰水*米水水*水*米水水externintRecToFileMsgProc(T_MSG_REC2FILE*ptMsg);intDispatchMsg(TTWTMSG*ptMsgitif(NULl=ptMsggotoErrRet/*dispatchmsg*/switch(ptMsg->enMsgType)caseTWTPINgPoNgBuffrecRecToFileMsgProcl(TMSG_REC2FE)(pmSg->pMsg);/*处理消息*/destroyMsg(pmSg;/*消毁消息breakdefault.printf("DispatchMsgMsgtypeError!n")break.return0ErrRetprintf("DispatchMsgFail!";return-1./*buffsize*/#defineP|NGPONG_BUFFBSIZE0X20000//10*1024*1024/*10M*/*pingpongbuff*///chargacPINGBUFF[PINGPONGBUFFBSIZE];/*PingBuff*///chargacPONGBUFF[PINGPONG_BUFFBSIZE]*PongBuff*/水米米*********米*水**米*半*水米米水**冰水*水米米半半水半米冰水*米水水*水*米米//*释放pingpongbuff/必然成功//*无返回木***木*水****本**水*水水*水****本水**水水****水水***本***米*水voidDestroyPingPongButt(TPINGPONGBUFFUSEDESptPingPongButt)nLoopif(NULL=ptPingpongbuffreturnfor(nLoop=0;nLooptIngBuffUse[nLoop].pcHeadAddr)free(ptPingPong->tPingBuffUse[nLoop].pcHeadAddr)free(ptPingpongBuff)/初始化pingpongbuff返回pignpongbuff的描述指针//*正常返回0,出错返回-1水水水水水水水水水木水木水水水水水木水木水水水水水水水水本水水水水水水水水水本水水水水水水水水水水水水水TPINGPONGBUFFUSEDESInitPingPongBuff(unsignedintnBuffSizeTPINGPONGBUFFUSEDES*ptBuffDes=NULLintnLoop/*获取buf描述*ifNULL==(ptBuffDes=malloc(sizeof(TPINGPONGBUFFUSEDES))))gotoErrRetmemset(ptBuffDes,0,sizeof(T_PINGPONGBUFFUSE_DES));/*分别初始化ping和pong*/for(nLoop=0;nLooptIngBuffUsenLooppcHeadAddr=mallocnBuffSize))gotoErrRet;ptBuffDes->tIngBuffUselnLoop]nBuffSizenBuffsizeptBuffDes->tPingBuffUse[nLoop].oFfsetptBuffDes->tPingBuffUsenLoop)eUseStatus-=BUFFWRITEABLE;ptBuffDes->eCurUseIDBUFFPINGreturnptBuffDesErrretprintf("lnitPingPongBuffFail!");DestroyIngPongBuff(ptBuffDes)turnnull平**米**米*米***来米米*米*米*半米*米米米来*半米平**米米求*来*半求半来*米求*和*米*/*Resetpingpongbuff//*正常返回0,出错返回-1米米米米水冰米*米米水**米米冰*米水米米米米水米水*水米米来米米x米来米米水冰来来宋来水米来米来冰#defineResetBuffUse(ptBuffuse)ptBuffUse->oFfset0ptBuffUse->eUseStatusBUFFWRITEABLEgenerateafilerecmsg*正常返回消息体的指针,异常返回NULLT_MSG_REC2lGKSenFRMSB(T_BUFF_USE_DES*ptBuffUse,REC_FILE_DESLIST*ptFileListRTMSGREC2FILEKE*ptRFMsg=NULL;if(NULL==(ptRFMsgmalloc(sizeof(T_MSGREC2FILE)returnnUllptRFMsg->ptBuffUseptBuffUseptRFMsg->ptFilelistptFilelist;returnptRFMsg 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首页 » Others » 内存乒乓缓存机制和消息分发机制的C代码实现

内存乒乓缓存机制和消息分发机制的C代码实现

于 2020-12-03 发布
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下载积分: 1 下载次数: 0

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用C代码实现乒乓内存缓冲机制,具体实用价值,帮助您提高内存响应速度与及时数据的处理。unsigned long writeunsigned long greadunsigned long overflowST TWTMSG QUEUE:/ Helper macros for accessing Msg queues. *#define tWt QUEUE EMPty(a)(((g->write==(q)->gread)? 1: 0)#define twt_ QUeUe full(a)(((((q)->qwrite +1% TIMEWEIGHT TASKQUEUE SIZED)==(q)->gread)?1: 0)米 generate a Msg entity*正常返回消息体的指针,异常返回NULLT TWTMSG* generateMsg(tT-TWTMSG* pmSg=nULL,if(NULL =-(ptMsg malloc(sizeof(T TWTMSG)))return NULL;memset(ptMsg, 0, sizeof(T TWTMSG)return pmSg;destroy a Msgvoid destroy Msg(t TWTMSG ptMsgif(NULL ptMsg->pfDestroyMsg)pt Msg->pfDestroy Msg(ptMsg->pvMsg)if (NULL != pt Msg)free(pmSgfree a Msg Queuevoid freeTWTMsg Que(T TWTMSG QUEUE* ptMsgQif(NULL =ptMsg Afree(ptMsg Q);Init a Msg QT TWTMSG QUEUE* initTWTMsg QueoT TWTMSG QUEUE pmSg Q= NULlif (NULL ==(ptMsgQ malloc(sizeof(T_ TWTMSG QUeue)goto ErrRetmemset(ptMsgQ, 0, sizeof(T TWTMSG QUEUE))return pmSg Q;Errretprintf( initTWTMsg Que Fail! ")freeTWTMsgQue(ptMsg Q)return nullPop a pvMsg packet from a msg packet queues param g is the packet queue from which to pop the pbuf@return pointer to pvMsg packet if available, NULl otherwiseT TWTMSG* TWTMsg Get(T_ TWTMSG QUEUE aT TWTMSG*//*加锁if(TWT_ QUEUE_ EMPTY(a))iReturn a NUll pointer if the queue is emptypmSg=NULL;else is The queue is not empty so return the next frame from itand adjust the read pointer accordinglypmSg=g->pvMsglq >greadg->gread =((q->gread +1)% TIMEWEIGHT_TASKQUEUE_ SIZE)/*解锁return(pmSg);Push a pmSg packet onto a pvMsg packet queue@param p is the pmsg to push onto the packet queue@param g is the packet queue.W @return 0 if successful, -1 if q is fullnt TWTMsg Send(T tWTMSg*p, T TWTMSG QUEUE *qint ret/if(!TWT_ QUEUE FULL(al)iThe queue isn t full so we add the new frame at the currentw write position and move the write pointer.g->pvMsgla->write]=pg- >write =((q->qwrite+1)% TIMEWEIGHT TASKQUEUE SIZE;ret =oThe stack is full so we are throwing away this value. Keep trackof the number of times this happensg->overflow++ret =-1://*解锁return(ret)**米**米来米***来米*半米*米*半米*米求***半*米米求半**米求半**半求半和*米*//消息分发机制//*算法是//*正常返回0,出错返回-1水米米******水*米*水**米*半*水米米冰半**水水*水米米半米冰水*米水水*水*米水水extern int RecToFile MsgProc(T_ MSG_ REC2 FILE* ptMsg);int DispatchMsg(T TWTMSG *ptMsgitif(NULl = ptMsg goto ErrRet/*dispatch msg*/switch(ptMsg->en Msg Type)case TWT PINgPoNgBuff recRecTo File Msg Procl(TMSG_REC2FE) (pmSg->pMsg);/*处理消息*/destroy Msg( pmSg;/*消毁消息breakdefault.printf("Dispatch Msg Msgtype Error! n")break.return 0ErrRetprintf("Dispatch Msg Fail! ";return-1./*buff size*/#defineP| NGPONG_ BUFF BSIZE0X20000//10*1024*1024/*10M*/*ping pong buff*///chargacPINGBUFF[PINGPONG BUFF BSIZE]; /* Ping Buff*///chargacPONGBUFF[PINGPONG_ BUFF BSIZE] *Pong Buff*/水米米*********米*水**米*半*水米米水**冰水*水米米半半水半米冰水*米水水*水*米米//*释放 ping pong buff/必然成功//*无返回木***木*水****本**水*水水*水****本水**水水****水水***本***米*水void Destroy Ping Pong Butt(T PINGPONGBUFF USE DES ptPing Pong Butt)nLoopif (NULL = pt Ping pong buffreturnfor (nLoop=0; nLooptIng BuffUse[nLoop]. pcHeadAddr)free(ptPing Pong ->t Ping BuffUse[nLoop]. pcHeadAddr)free(pt Ping pong Buff)/初始化 ping pong buff返回 pign pong buff的描述指针//*正常返回0,出错返回-1水水水水水水水水水木水木水水水水水木水木水水水水水水水水本水水水水水水水水水本水水水水水水水水水水水水水T PINGPONGBUFF USE DES InitPing Pong Buff(unsigned int n BuffSizeT PINGPONGBUFF USE DES* ptBuffDes=NULLintnLoop/*获取buf描述*if NULL==(ptBuffDes=malloc(sizeof(T PINGPONGBUFF USE DES))))goto ErrRetmemset(pt BuffDes, 0, sizeof(T_PINGPONGBUFF USE_ DES));/*分别初始化ping和pong*/for(nLoop=0; nLooptIng BuffUsenLoop pcHeadAddr =malloc n BuffSize))goto Err Ret;ptBuffDes->tIng BuffUselnLoop] nBuffSize nBuffsizeptBuffDes->tPing BuffUse[nLoop]. oFfsetptBuffDes->tPing BuffUsenLoop) eUseStatus-=BUFF WRITEABLE;pt BuffDes->eCurUseIDBUFF PINGreturn pt BuffDesErrretprintf("lnitPing Pong Buff Fail!");DestroyIng Pong Buff(pt BuffDes)turn null平**米**米*米***来米米*米*米*半米*米米米来*半米平**米米求*来*半求半来*米求*和*米*/*Reset ping pong buff//*正常返回0,出错返回-1米米米米水冰米*米米水**米米冰*米水米米米米水米水*水米米来米米x米来米米水冰来来宋来水米来米来冰#define ResetBuffUse(ptBuffuse)pt BuffUse->oFfset0pt BuffUse->eUseStatus BUFF WRITEABLEgenerate a file rec msg*正常返回消息体的指针,异常返回NULLT_MSG_REC2lGK SenFRMSB(T_BUFF_USE_DES *ptBuffUse, REC_FILE_DESLIST *ptFileListRT MSG REC2FILE KE* ptRFMsg= NULL;if(NULL ==(ptRFMsg malloc(sizeof(T_ MSG REC2 FILE)return nUllptRFMsg- >pt BuffUse pt BuffUseptRFMsg->pt Filelist ptFilelist;return ptRFMsg

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    一篇很好的关于模糊图像复原的论文,含详细需要的函数,讲解清楚易懂。闫永存,等匀速直线运动模糊图像复原的改进算法级的概率密度函数P(s)如下式所示:P(s)=P(rdr(6)8对于连续图像,当直方图均衡化(并归一化)后有P(s)即ds=p (r)dr =dT(r)3两边取积分得x=7(=P)h(8)式(8)就是所求的变换函数对于离散图像,假定数字图像中的总像索为N,灰度级总图3最优窗法区域分布图数为L,第k个灰度级的值为r,图像中具有灰度级r的像素Fig 3 Areal distribution of optimal window method数目为n,则该图像中灰度级n的像素出现的概率为取值范围为V-P+1,V1-11;在水平方向上,区域1、2、3的横P(r)="O≤r≤1;h=0,1,…(9)坐标取值范围1O,P1-2],区域4.8、9的横坐标取值范围为P3-1,V1-P3m,区域5、67的横坐标取值范围为[V1-Psn+对其进行均匀化处理的变换函数为:VL-11在图3中,每一个区域都有各白独立的边界,即各个S=7(r)=∑P(n)=2N子窗区域的尺寸不一定相同(10)最优窗计算公式为利用式(10)对图像做灰度变换,即可得到直方图均衡化后的图像。∑∑h(p,g)∑∑h(p.q)∑∑(p,q)D=O 0该方法可以将滤除高频噪声,提高有用信号的嗝度,增加∑h(p∑∑h(P)(12)对比度,同时缩小叠加噪声信号的动态范围,抑制振铃效应有效的结合起来,高文硕等证明了这一点。但不能完全去除振220)2A)铃效应,因此文中在滤波前用最优窗法对图像进行处理最优窗对模樹图像的边界进行加杈处理,以致像素值向3.4最优窗法外逐步过渡到零,其目的是待处理图像的边界结合处不会出在恢复图像过程中,由于图像边缘的像素没有足够的相现灰度的跳变,振铃效应因此得到抑制。邻像素可以利川,所以会导致恢复图像的边缘变差,并且整幅图像有明暗相间的条纹,即振铃效应。为了解决这个问题,早4实验结果及分析期学者常采用边界修正法,但效果不够令人满意。 Aghasi在1996年提出循环边界法,其缺点是图像尺寸变为原来的4文中通过实验验证了改进算法的可行性和有效性,以sadhna模糊图像的复原为例,图4(a)是原始图像,对其进行倍,运算量增加很多。基于循坼边界法的缺点, Bimetal提出了对二维模糊图像四进行恢复的最优窗。其具体实胞过程为:模糊加噪运算,模糊角度为53°,模糊长度为45,高斯噪声为0.0l图4(b)是降质后的模糊图像,采用本文算法估计岀的模恢复窗a,k将图像平面分成9个区域,每个区域编号如图3糊方向为51°,模糊长度为46,图4(c)是普通维纳滤波复原所示。标号为9的中央区域o:=1。图像,图4(d)是人工调整参数为真实值的复原结果。利用本区域1,8、7的纵坐标取值范围为[0,P-2],区城2,6、9文的改进算法得到的复原结果如图4()所示。实验结果如图的纵坐标取值范围为P-1,V4-P,区域3、4.5的纵坐标4所示。(a)原始图像(b)模糠图象(c)普通维纳波复原图象d)取冥际参数值的复原图像(e)太文算法的复原图(a)original image(t)blurred image (c)restored image of ordinary (d)res tored image fcr the (e)restored image ofiner filteringactua l )arameter valles algorithm in this pa per图4运动模糊图像及复原结杲Fig. 4 Motion blurred images and results of restoration147C1994-2012ChinaAcademicJournalElcctronicPublishingHousc.Allrightsrescrvcd.http://www.cnki.net电子设计工程》2012年第3期出实验结果可知,方向徵分法可以很人程度地提高模糊2005,10(5):590-595角度的估计准确性,利用自相关函效负尖峰值可以较准确地「31贺卫国,黎绍发,匀速直线运动模糊长度的精硝估计[鉴别出模糊长度,从而可以提高图像还原质量。最优窗法对振计算机应用,2005,25(6):1316-1320铃效应可以有较好的抑制作用,最后得到了复原效果较为理HIE Wei-guo, LI Shao-fa. Estimating the blurring length of想的图像。uniform linearmotion blurred images[J]. Computer Applications5结束语2005,25(6:1316-1320[4]吴振字.模糊图像复原方法研究[D]长沙:国防科学技术文中对运动模糊图像的退化模型、维纳滤波复原原理、点大学,2009扩散函数的求取过程选行了详细削述,提出了一种改进的模[5]高文硕.郊伟伟,杨磊运动模糊图像复原技术的改进算法糊图像复原算法,并对振铃效应进行处理,以 sadhna图像的中国传媒大学学报自然科学版,2010,7(1):72-76复原为例进行了实验验证结果表明,文中方法可以较准确地GAO Wen-shuo, ZHENG Wei-wei, YANG Lei. Improved估计出运动模糊参数,并且提高了运算速度,振铃效应得到有algorithm for restoration of the imagemotion blur IJ]. Journal效抑制。of Communication L niversity of China Science and Technology参考文献2010,17(1):72-76[1] Cannon M. Blind deconvolution of spatially invariant image [ 6] Aghdasi F, Ward R K. Reduction of boundary artifacts inblurs with phase [ J]. IEEE Trans on Acoustics, Speech andimage restoration[J]. IEEE Trans. Image. Proc. 1996. 5(4)signal Processing, 1976(24): 58-63611-6182]陈前荣,陆启生,成礼智,基于方向微分的迈动模糊方向鬥叶海.基于统计特征加权的模糊聚类方法及其应用鉴别!中国图象图形学报,2005,10(5):590-595现代电子技术,2009(1299-102chen Qiall-rong, LU Qi-sheng, CHENG Li-zhi. IdentificalionYE Hai-jun. Fuzzy clustering method and its applicationof motion blur direction from motion blurred image bybased on statistical characteristics weighting [J]. Moderndirection derivation method [J Journal of Image and GraphicsElectronics Technique, 2009(12): 99-102具ⅢP2优化和DC偏移消除的宽带丨/Q解调器可改善接收器性能加利福尼亚州米尔皮塔斯( MILPITAS,CA)推出超宽带宽直接转換lQ解调器LIC∶5S35,该件具卓越的线性性能(在1.95GIlκ时,ⅢP3-25.7dBm,IP2-60dBm)。LTC585能提供超过530MILz的基带输岀解调带宽,可满足新-代霓带LTE多模式接收器和毅宇预失真(DPD)接收器的带宽需。Q解调器在700WHx至3GHz的宽频率范围内二作,几乎覆盖了所有蜂窝基站频毁。这款器伫的独狩之处是两个内置的校准功能。其一是允许系统设计人员优化接收器IP2性能的高级也路以60dBm标称值提升至前所未有的80dBm或更高。另一个则是用于消除I和Q输出端上的DC偏移电压的片内电路。这两个功能电路均起到了増强接收嚣性能的作用。此外,LTC5585还可提供超卓的16 dBm pldB。为了进一步加强该器件在直接转换接收器应用中的使用,LTC5585捉供非常低的IQ幅度和相位失配ε幅度失配的典型值是0.05dB,而相位误差的典型值是0.7度,两个数值都是在1.95GHz频率上测得的。这两者的结合产生了一个43dB的接收器镜頰抑制能力。因为LTC5585能湜供非常宽的带宽,所以尢其适用于多模式LTE、 W-CDMA和TD- SCDMA基站DPD接收器以及于主收器的应門。尤其是对;DPD,这些最新一代基站正在将解调带宽推进到超过300MHz。LTC5585可以非常方便地配置应对这些带宽的挑战。除了无线基础设施应用,LrC5585还适用于军用接收器、宽蒂通信、点对点微波数据链路、镜频抑制接收器和长距离RFID阅读器。LTC5585内置了一个RF变压器以减少外部组件,再加上24引线4mm×4 mm QFN封装,因而可提供高度紧凑的解决方案。该器件规格在_40-105℃C的外壳工作温度范围。LTC585用单—5Ⅴ电源供电,吸取200mA的总电源电流。该器件提供数竽输λ以启用或停用该芯片。当俘用时,该IC吸取的典型溻电流为11μA。解调器的200ms快谜接通时间和800rs断开时间使该器件能在突发模弌接收器中使用。咨询编号:2012031009心·心;心·心,心·心普心心心·心·心心·心分·心心···心·心心心心·心·心心·心欢迎订阅2012年度《电子设计工程》(半月刊)国内邮发代号:52-142国际发行代号:M2996订价:15.00元/期360.00元/年148C1994-2012ChinaAcademicJournalElcctronicPublishingHousc.Allrightsrescrvcd.http://www.cnki.net
    2020-11-27下载
    积分:1
  • 凸优化在信号处理与通信中的应用Convex Optimization in Signal Processing and Communications
    凸优化理论在信号处理以及通信系统中的应用 比较经典的通信系统凸优化入门教程ContentsList of contributorspage IxPrefaceAutomatic code generation for real- time convex optimizationJacob Mattingley and stephen Boyd1.1 Introduction1.2 Solvers and specification languages61. 3 Examples121. 4 Algorithm considerations1.5 Code generation261.6 CVXMOD: a preliminary implementation281.7 Numerical examples291. 8 Summary, conclusions, and implicationsAcknowledgments35ReferencesGradient-based algorithms with applications to signal-recoveryproblemsAmir beck and marc teboulle2.1 Introduction422.2 The general optimization model432.3 Building gradient-based schemes462. 4 Convergence results for the proximal-gradient method2.5 A fast proximal-gradient method2.6 Algorithms for l1-based regularization problems672.7 TV-based restoration problems2. 8 The source-localization problem772.9 Bibliographic notes83References85ContentsGraphical models of autoregressive processes89Jitkomut Songsiri, Joachim Dahl, and Lieven Vandenberghe3.1 Introduction893.2 Autoregressive processes923.3 Autoregressive graphical models983. 4 Numerical examples1043.5 Conclusion113Acknowledgments114References114SDP relaxation of homogeneous quadratic optimization: approximationbounds and applicationsZhi-Quan Luo and Tsung-Hui Chang4.1 Introduction1174.2 Nonconvex QCQPs and sDP relaxation1184.3 SDP relaxation for separable homogeneous QCQPs1234.4 SDP relaxation for maximization homogeneous QCQPs1374.5 SDP relaxation for fractional QCQPs1434.6 More applications of SDP relaxation1564.7 Summary and discussion161Acknowledgments162References162Probabilistic analysis of semidefinite relaxation detectors for multiple-input,multiple-output systems166Anthony Man-Cho So and Yinyu Ye5.1 Introduction1665.2 Problem formulation1695.3 Analysis of the SDr detector for the MPsK constellations1725.4 Extension to the Qam constellations1795.5 Concluding remarks182Acknowledgments182References189Semidefinite programming matrix decomposition, and radar code design192Yongwei Huang, Antonio De Maio, and Shuzhong Zhang6.1 Introduction and notation1926.2 Matrix rank-1 decomposition1946.3 Semidefinite programming2006.4 Quadratically constrained quadratic programming andts sdp relaxation201Contents6.5 Polynomially solvable QCQP problems2036.6 The radar code-design problem2086.7 Performance measures for code design2116.8 Optimal code design2146.9 Performance analysis2186.10 Conclusions223References226Convex analysis for non-negative blind source separation withapplication in imaging22Wing-Kin Ma, Tsung-Han Chan, Chong-Yung Chi, and Yue Wang7.1 Introduction2297.2 Problem statement2317.3 Review of some concepts in convex analysis2367.4 Non-negative, blind source-Separation criterion via CAMNS2387.5 Systematic linear-programming method for CAMNS2457.6 Alternating volume-maximization heuristics for CAMNS2487.7 Numerical results2527.8 Summary and discussion257Acknowledgments263References263Optimization techniques in modern sampling theory266Tomer Michaeli and yonina c. eldar8.1 Introduction2668.2 Notation and mathematical preliminaries2688.3 Sampling and reconstruction setup2708.4 Optimization methods2788.5 Subspace priors2808.6 Smoothness priors2908.7 Comparison of the various scenarios3008.8 Sampling with noise3028. 9 Conclusions310Acknowledgments311References311Robust broadband adaptive beamforming using convex optimizationMichael Rubsamen, Amr El-Keyi, Alex B Gershman, and Thia Kirubarajan9.1 Introduction3159.2 Background3179.3 Robust broadband beamformers3219.4 Simulations330Contents9.5 Conclusions337Acknowledgments337References337Cooperative distributed multi-agent optimization340Angelia Nedic and asuman ozdaglar10.1 Introduction and motivation34010.2 Distributed-optimization methods using dual decomposition34310.3 Distributed-optimization methods using consensus algorithms35810.4 Extensions37210.5 Future work37810.6 Conclusions38010.7 Problems381References384Competitive optimization of cognitive radio MIMO systems via game theory387Gesualso Scutari, Daniel P Palomar, and Sergio Barbarossa11.1 Introduction and motivation38711.2 Strategic non-cooperative games: basic solution concepts and algorithms 39311.3 Opportunistic communications over unlicensed bands411.4 Opportunistic communications under individual-interferenceconstraints4151.5 Opportunistic communications under global-interference constraints43111.6 Conclusions438Ackgment439References43912Nash equilibria: the variational approach443Francisco Facchinei and Jong-Shi Pang12.1 Introduction44312.2 The Nash-equilibrium problem4412. 3 EXI45512.4 Uniqueness theory46612.5 Sensitivity analysis47212.6 Iterative algorithms47812.7 A communication game483Acknowledgments490References491Afterword494Index49ContributorsSergio BarbarossaYonina c, eldarUniversity of rome-La SapienzaTechnion-Israel Institute of TechnologyHaifaIsraelAmir beckTechnion-Israel instituteAmr El-Keyiof TechnologyAlexandra universityHaifEgyptIsraelFrancisco facchiniStephen boydUniversity of rome La sapienzaStanford UniversityRomeCaliforniaItalyUSAAlex b, gershmanTsung-Han ChanDarmstadt University of TechnologyNational Tsing Hua UniversityDarmstadtHsinchuGermanyTaiwanYongwei HuangTsung-Hui ChangHong Kong university of scienceNational Tsing Hua Universityand TechnologyHsinchuHong KongTaiwanThia KirubarajanChong-Yung chiMcMaster UniversityNational Tsing Hua UniversityHamilton ontarioHsinchuCanadaTaiwanZhi-Quan LuoJoachim dahlUniversity of minnesotaanybody Technology A/sMinneapolisDenmarkUSAList of contributorsWing-Kin MaMichael rebsamenChinese University of Hong KongDarmstadt UniversityHong KonTechnologyDarmstadtAntonio de maioGermanyUniversita degli studi di napoliFederico iiGesualdo scutariNaplesHong Kong University of Sciencealyand TechnologyHong KongJacob MattingleyAnthony Man-Cho SoStanford UniversityChinese University of Hong KongCaliforniaHong KongUSAJitkomut songsinTomer michaeliUniversity of californiaTechnion-Israel instituteLoS Angeles. CaliforniaogyUSAHaifaMarc teboulleTel-Aviv UniversityAngelia NedicTel-AvUniversity of Illinois atIsraelUrbana-ChampaignInoSLieven VandenbergheUSAUniversity of CaliforniaLos Angeles, CaliforniaUSAAsuman OzdaglarMassachusetts Institute of TechnologyYue WangBoston massachusettsVirginia Polytechnic InstituteUSAand State UniversityArlingtonDaniel p palomarUSAHong Kong University ofScience and TechnologyYinyu YeHong KongStanford UniversityCaliforniaong-Shi PangUSAUniversity of illinoisat Urbana-ChampaignShuzhong zhangIllinoisChinese university of Hong KongUSAHong KongPrefaceThe past two decades have witnessed the onset of a surge of research in optimization.This includes theoretical aspects, as well as algorithmic developments such as generalizations of interior-point methods to a rich class of convex-optimization problemsThe development of general-purpose software tools together with insight generated bythe underlying theory have substantially enlarged the set of engineering-design problemsthat can be reliably solved in an efficient manner. The engineering community has greatlybenefited from these recent advances to the point where convex optimization has nowemerged as a major signal-processing technique on the other hand, innovative applica-tions of convex optimization in signal processing combined with the need for robust andefficient methods that can operate in real time have motivated the optimization commu-nity to develop additional needed results and methods. The combined efforts in both theoptimization and signal-processing communities have led to technical breakthroughs ina wide variety of topics due to the use of convex optimization This includes solutions tonumerous problems previously considered intractable; recognizing and solving convex-optimization problems that arise in applications of interest; utilizing the theory of convexoptimization to characterize and gain insight into the optimal-solution structure and toderive performance bounds; formulating convex relaxations of difficult problems; anddeveloping general purpose or application-driven specific algorithms, including thosethat enable large-scale optimization by exploiting the problem structureThis book aims at providing the reader with a series of tutorials on a wide varietyof convex-optimization applications in signal processing and communications, writtenby worldwide leading experts, and contributing to the diffusion of these new developments within the signal-processing community. The goal is to introduce convexoptimization to a broad signal-processing community, provide insights into how convexoptimization can be used in a variety of different contexts, and showcase some notablesuccesses. The topics included are automatic code generation for real-time solvers, graphical models for autoregressive processes, gradient-based algorithms for signal-recoveryapplications, semidefinite programming(SDP)relaxation with worst-case approximationperformance, radar waveform design via SDP, blind non-negative source separation forimage processing, modern sampling theory, robust broadband beamforming techniquesdistributed multiagent optimization for networked systems, cognitive radio systems viagame theory, and the variational-inequality approach for Nash-equilibrium solutionsPrefaceThere are excellent textbooks that introduce nonlinear and convex optimization, providing the reader with all the basics on convex analysis, reformulation of optimizationproblems, algorithms, and a number of insightful engineering applications. This book istargeted at advanced graduate students, or advanced researchers that are already familiarwith the basics of convex optimization. It can be used as a textbook for an advanced graduate course emphasizing applications, or as a complement to an introductory textbookthat provides up-to-date applications in engineering. It can also be used for self-study tobecome acquainted with the state of-the-art in a wide variety of engineering topicsThis book contains 12 diverse chapters written by recognized leading experts worldwide, covering a large variety of topics. Due to the diverse nature of the book chaptersit is not possible to organize the book into thematic areas and each chapter should betreated independently of the others. a brief account of each chapter is given nextIn Chapter 1, Mattingley and Boyd elaborate on the concept of convex optimizationin real-time embedded systems and automatic code generation. As opposed to genericsolvers that work for general classes of problems, in real-time embedded optimization thesame optimization problem is solved many times, with different data, often with a hardreal-time deadline. Within this setup the authors propose an automatic code-generationsystem that can then be compiled to yield an extremely efficient custom solver for theproblem familyIn Chapter 2, Beck and Teboulle provide a unified view of gradient-based algorithmsfor possibly nonconvex and non-differentiable problems, with applications to signalrecovery. They start by rederiving the gradient method from several different perspectives and suggest a modification that overcomes the slow convergence of the algorithmThey then apply the developed framework to different image-processing problems suchas e1-based regularization, TV-based denoising, and Tv-based deblurring, as well ascommunication applications like source localizationIn Chapter 3, Songsiri, Dahl, and Vandenberghe consider graphical models for autore-gressive processes. They take a parametric approach for maximum-likelihood andmaximum-entropy estimation of autoregressive models with conditional independenceconstraints, which translates into a sparsity pattern on the inverse of the spectral-densitymatrix. These constraints turn out to be nonconvex. To treat them the authors proposea relaxation which in some cases is an exact reformulation of the original problem. Theproposed methodology allows the selection of graphical models by fitting autoregressiveprocesses to different topologies and is illustrated in different applicationsThe following three chapters deal with optimization problems closely related to SDPand relaxation techniquesIn Chapter 4, Luo and Chang consider the SDP relaxation for several classes ofquadratic-optimization problems such as separable quadratically constrained quadraticprograms(QCQPs)and fractional QCQPs, with applications in communications and signal processing. They identify cases for which the relaxation is tight as well as classes ofquadratic-optimization problems whose relaxation provides a guaranteed, finite worstcase approximation performance. Numerical simulations are carried out to assess theefficacy of the SDP-relaxation approach
    2020-12-10下载
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  • OFDM for Underwater Acoustic Communications
    适合研究水声通信的同学,里面介绍的很详细,希望能对大家有帮助
    2020-12-04下载
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  • LTspice_MOS Tool.rar
    VDmostool软件是一款LTspice中MOS建模软件。它可以从MOS数据手册创建板级mosfet模型,该模型只能在LTspice中使用。 这是因为它利用了称为VDMOS的新mosfet模型,并且仅在LTspice中可用。 该设备替代了子电路模型,子电路模型通常不起作用,即使可以工作,也会因模拟运行太慢而无法完全使用。 LTspice中的VDmos模型不是子电路,而是使用模型语句的新的内置设备模型。 进行了一些改进,从而使模拟运行更快。
    2021-05-06下载
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