COMPILE-TIME ANALYSIS AND OPTIMIZATION OF EXPLICITLY PARALLEL PROGRAMS*

Data flow analysis has been used by compilers in diverse contexts, from optimization to register allocation. Traditional analysis of sequential programs has centered on scalar variables. More recently, several researchers have investigated analysis of array sections for optimizations on modern architectures. This information has been used to distribute data, optimize data movement and vectorize or parallelize programs. As multiprocessors become more common-place. we believe there will be considerable interest in explicitly parallel programming languages. In this paper, we extend traditional analysis to array section analysis for parallel languages which include additional control and synchronization structures.

We show how to compute array section data flow information, i.e., Communication Sets, for a class of parallel programs. To illustrate its use, we show how this information can be applied in compile-time program partitioning. Information about array accesses can also be used to improve estimates of program execution time used to direct runtime thread scheduling.     

基于柔性阵列传感器的脉象检测系统的设计

当前的脉象仪大部分采集手段单一,取脉压力的调节性差,无法模拟医生的布指方式,而且只能实现脉搏图的描记及分析,无法给出脉象的三维信息。针对这一问题研制了基于柔性阵列传感器的脉象检测系统,用硅胶制作了专门的脉搏换能器;设计了压力控制系统,实现压力自动调节,完成了对最佳取脉压力下脉象的采集;搭建了信号处理电路,实现了对脉象信号的提取和预处理;以USB-7660为核心构建了A/D采集系统,实现了对多路脉象信号的显示、存储。实验表明,该系统不仅可以有效采集到受试者的七路二维脉图,而且能清晰地观测到受试者的脉道宽度,并重建出相应脉搏波的三维传导地形图。     

涡流阵列传感器半解析模型的直接FFT构建方法

针对分布式激励源涡流阵列传感器半解析模型的构建问题,提出了传感器半解析模型的直接FFT构建方法。该方法首先通过虚拟采样和FFT得到传感器导线界面处线电流密度的傅里叶变换系数,然后根据层状介质空间中场量傅里叶变换系数传递关系,经反FFT得到场量的空间分布。建模过程和数值计算结果表明此方法不仅简化了半解析模型的建立过程,而且直接将激励源导线宽度考虑在建模过程之中,提高了建模精度;建立了该传感器的应用物理模型,数值仿真结果表明该传感器对其周围介质空间的电磁属性较为敏感,可望将该类型的传感器运用于金属结构的在线监测之中。     

用于麦克风阵列的阵元筛选方法研究

在使用TDOA算法进行被动声定向时,为精确获取麦克风阵列阵元之间接收声源信号的时间差,进而计算目标的方位角,麦克风阵元之间必须保持较高的相位一致性。而从论文与专利检索来看,目前还没有有效方法筛选出符合要求的麦克风阵元。针对此问题,本文基于麦克风阵列阵元相位一致性的指标,设计出麦克风阵元筛选系统,通过实测数据分析表明:一致性较高的麦克风更适合组建麦克风阵列。     

多分量调频信号的阵列处理时频交叉项的抑制方法

在应用时频-MUSIC方法对多分量调频信号进行到达角估计的问题中,针对时频分布中交叉项的存在对估计性能的影响,提出了一种基于阵列信号处理抑制时频分布交叉项的新方法。该方法利用多信源在各阵元上的延迟对时频分布交叉项振荡相位的影响,联合运用多通道信号处理和图像处理的方法,采用二维乘法滤波,减小高频振荡交叉项在时频图像中的占据区域,从而达到抑制交叉项的目的。仿真实验显示该方法具有良好的效果。     

基于虚拟可重构平台的时序电路演化设计

目前,电路进化设计是演化硬件研究的主要方向之一。而时序电路由于存在反馈环不便于进行电路描述和软件仿真。文中对时序电路的演化设计方法进行了改进,提出了专门针对时序电路演化的虚拟可重构平台,建立起电路编码与HDL代码的映射关系。应用TEXTIO和MATLAB来辅助仿真测试过程,使测试向量数量巨大、难以处理的问题得到很好地解决。最后调用ModelSim完成了FSM的演化实验。实验结果验证了基于此平台演化时序电路的可行性和有效性。     

紧扣内存变量关系巧解矩阵类问题

C语言学习过程中,不少初学者不能建立思维的模型,无法将形象可行的矩阵运算、旋转事件抽象化成算法语言。讲解了指导学生掌握矩阵操作算法的推导方法。     

可重构电解加工机床研究

电解加工机床通常是根据用户具体需求单机生产，机床研制周期长、设计重复、成本高。为此，提出采用开放式控制系统、模块化思想开发可重构电解机床，并对机床的机械部分、控制系统及通信系统进行了研究。     

A Novel Artificially Humanized Anti-Cripto-1 Antibody Suppressing Cancer Cell Growth

Cripto-1 is a member of the EGF-CFC/FRL1/Cryptic family and is involved in embryonic development and carcinogenesis. We designed a novel anti-Cripto-1 artificial antibody and assessed the recognition to the antigen and the potential to suppress the growth of cancer stem cells. First, single chain antibody clones were isolated by bio-panning with the affinity to recombinant Cripto-1 protein from our original phage-display library. Then, the variable regions of heavy chain VH and light chain VL in each clone were fused to constant regions of heavy chain CH and light chain CL regions respectively. These fused genes were expressed in ExpiCHO-S cells to produce artificial humanized antibodies against Cripto-1. After evaluation of the expression levels, one clone was selected and the anti-Cripto-1 antibody was produced and purified. The purified antibody showed affinity to recombinant Cripto-1 at 1.1 pmol and immunoreactivity to cancer tissues and cell lines. The antibody was available to detect the immunoreactivity in tissue microarrays of malignant tumors as well as in Cripto-1 overexpressing cells. Simultaneously, the antibody exhibited the potential to suppress the growth of human colon cancer derived GEO cells overexpressing Cripto-1 with IC₅₀ at approximately 110 nM. The artificially humanized antibody is proposed to be a good candidate to target cancer cells overexpressing Cripto-1.