A time-sharing multi-channel pulse amplitude analyzer

A conventional multi-channel pulse amplitude analyzer acquires single energy spectrum,but provides no information on its tendency with time.To address the limitation,we propose a scheme of time-sharing multichannel pulse amplitude analyzer (TSMCA).A dual-port random access memory is divided into two storage spaces,one for current energy spectrum data acquisition and another for previous energy spectrum data storage.The two tasks can be performed simultaneously,and the time-related variation tendency of energy spectrum can be obtained.A prototype system of TSMCA is designed.It performs nicely,with maximum channel number of 4096 in capacity of 232/Ch,minimal time-sharing slice of 25 ms,the differential nonlinearity of ＜1.5％,and the integral nonlinearity of ＜0.3％.     

多通道混沌调制模拟-信息转换

为了降低混沌调制中采样通道的采样速率,该文提出一种多通道混沌调制模拟-信息转换结构。该结构通过采样参数调制混沌系统的多个状态输出作为压缩测量,可实现在总采样速率不变的情况下降低每个采样通道的采样速率。与混沌调制相比,多通道混沌调制增加了低速采样单元的个数,但显著提高了高稀疏度信号的重构性能。基于混沌脉冲同步理论,该文给出了多通道混沌调制结构的信号可重构条件与被采样状态变量选择方法。以Lorenz系统为例,仿真验证了多通道混沌调制结构的有效性。     

数字化多道脉冲幅度分析技术研究

多道脉冲幅度分析技术正在朝数字化方向发展,基于数字信号处理技术的数字化多道具有脉冲处理能力强、速度快、稳定性高和灵活性强等特点。文章分析了数字核脉冲处理关键技术,利用MATLAB完成了梯形成形、数字基线估计、数字极零零极补偿、极零点识别等核心算法的模拟。给出了数字化多道初步总体设计方案,探讨了工程没计中的关键技术问题,为研制国产数字化核谱仪打下了基础。     

一种基于负载均衡的动态多信道自组网MAC协议

自组网的信道是多跳共享的,使用传统的单信道接入协议会带来隐终端和暴露终端等问题,而目前提出的多信道协议没考虑到负载均衡问题会导致系统性能下降.在本文中提出一种基于负载均衡的动态多信道接入协议,该协议将可用频带分为一个控制信道和多个数据信道,源站与目的站在控制信道上交换控制分组信息来决定在最佳的数据信道上传输数据分组,信道选择是基于信道接收功率最大的原则.仿真结果表明,它比单信道协议有更好的性能.     

基于改进SSD算法的城市轨道交通多通道闸机控制研究

城市轨道交通多通道闸机独立工作，对多个闸机精准控制难度较大，所以研究基于改进SSD算法的城市轨道交通多通道闸机控制方法。根据SSD算法应用流程生成先验框，再联合损失函数对基础SSD算法进行改进，利用改进后的SSD算法融合处理闸机数据。结合闸机数据融合处理结果确定闸机设备阵列布置，根据通行目标识别结果和传感器设备确定闸机通道内的通行情况，根据通行情况设计多通道闸机控制误动控制的处理子程序，完成城市轨道交通多通道闸机控制。实验结果表明，改进SSD算法作用下，闸机闭合状态始终与行人的当前行进方向保持一致，实时通过率较高，能够解决因闸机错误闭合而导致城市轨道交通混乱的问题。     

TaBE: Decoupling spatial and spectral processing with Taylor’s unfolding method in the beamspace domain for multi-channel speech enhancement

In recent years, significant advancements have been made in neural beamforming, leveraging spectral and spatial cues to enhance their performance in multi-channel speech enhancement. When the frame-wise processing mechanism is required, there exists a trade-off between performance and algorithmic delay for existing all-neural beamformers. However, from the perspective of multi-source information fusion, the network is often encapsulated into a black box to entangle and fuse the spatial and spectral features into a non-linear feature space, which hinders our understanding of how they work collaboratively for target speech extraction. In this regard, this paper proposes to decouple the spatial and spectral domain processing inspired by Taylor’s approximation theory. Specifically, we reformulate the time-variant beamforming defined in the spatial domain into the adaptive weighting and mixing of different beam components in the beamspace domain. This reformulation enables us to model the recovery of target speech as a weighted sum operation in the beamspace domain, where each beam component is associated with an introduced unknown term for residual interference cancellation. By virtue of Taylor’s series expansion, the recovery process can be decomposed into the superimposition of the 0th-order non-derivative and high-order derivative terms, where the former acts as spatial filtering in the spatial domain, and the latter serves as a residual interference canceller in the spectral domain. We conduct extensive experiments on the spatialized LibriSpeech and L3DAS Challenge datasets. Experimental results show that, compared with existing advanced approaches, the proposed method not only achieves competitive performance in terms of multiple objective metrics but also provides feasible guidance in multi-channel speech enhancement pipeline design.