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关键词： Implantable integrated circuit   area-efficient   low-power   compressive sensing   neural signals   learning-based digital signal processing   signal recovery   medical monitoring   adaptive compression. Far-end crosstalk   Decision-Feedback Equalizer   Inter-Symbol Interference   source-synchronous architecture   Continuous Time Linear Equalizer  

摘要： This multidisciplinary research project aims to investigate the optimized information extrac- tion from signals or data volumes and to develop tailored hardware implementations that trade-off the complexity of data acquisition with that of data processing, conceptually allowing radically new device designs. The mathematical results in classical Compressive Sampling (CS) support the paradigm of Analog-to-Information Conversion (AIC) as a replacement for conven- tional ADC technologies. The AICs simultaneously perform data acquisition and compression, seeking to directly sample signals for achieving specific tasks as opposed to acquiring a full signal only at the Nyquist rate to throw most of it away via compression. Our contention is that in order for CS to live up its name, both theory and practice must leverage concepts from learning. This work demonstrates our contention in hardware prototypes, with key trade-offs, for two different fields of application as edge and big-data computing. In the framework of edge-data computing, such as wearable and implantable ecosystems, the power budget is defined by the battery capacity, which generally limits the device performance and usability. This is more evident in very challenging field, such as medical monitoring, where high performance requirements are necessary for the device to process the information with high accuracy. Furthermore, in applications like implantable medical monitoring, the system performances have to merge the small area as well as the low-power requirements, in order to facilitate the implant bio-compatibility, avoiding the rejection from the human body. Based on our new mathematical foundations, we built different prototypes to get a neural signal acquisition chip that not only rigorously trades off its area, energy consumption, and the quality of its signal output, but also significantly outperforms the state-of-the-art in all aspects. In the framework of big-data and high-performance computation, s

关键词： 智能分拣   硬件设计   软件设计  

摘要： 本文提出了一种基于物联网技术的智能分拣系统,并对该系统实现智能分拣功能和数据采集方法的硬件和软件设计方案进行了详细的论述,以供各位读者参考。

关键词： 轮对跑合试验台   温度检测   单片机   故障诊断  

摘要： 设计了轮对跑合试验台温度检测系统.采用单片机控制技术,集温度信号采集、状态监测、超温报警、数据显示、信号分析和故障诊断为一体.轮对跑合试验台温度检测系统能够防止因轴承生产及组装质量问题而引发的轮对运行早期的热轴、燃轴等事故.

关键词： 演化硬件   遗传算法   Zynq   虚拟可重构电路  

摘要： 针对复杂度日益递增的电子系统,以人工设计电路的方式无法满足大规模复杂电路设计,提出基于Zynq的演化硬件电路设计方案。该设计通过在Zynq的PS部分利用自适应遗传算法对电路染色体进行快速演化迭代,在PL部分实现虚拟可重构电路加速染色体适应度评估,通过软硬件协同合作,快速收敛得到所需目标电路的配置信息。实验结果表明该设计方案能够高效快速得到满足设计要求的目标电路,对电子电路设计具有一定实用价值。

关键词： 信号检测   角钢材质   多通道   冲孔声音   剪切压力  

摘要： 针对角钢材质混料造成的错用型号现象,利用不同材质角钢具有不同的冲孔声音和剪切压力的特性,研发了多通道角钢材质检测系统。首先通过分析角钢加工过程中的检测原理,给出了角钢材质检测系统的设计框图;其次给出了硬件系统总体电路图,选择了硬件型号,合理布置了前面板接口和内部线路,且对声音-压力多通道角钢材质检测系统硬件进行了封装;最后,配合软件系统进行了现场安装测试。结果表明,该硬件系统能够完成多通道信号采集、处理、分析、识别等在线检测工作,对于提高角钢的材质检测效率具有参考价值。

关键词： 雷达模拟器   半实物仿真   硬件设计   软件设计  

摘要： 针对目前雷达模拟器功能较为单一、无法与上级模拟系统联网组训、模拟效果不够逼真等问题,提出了一种基于半实物仿真的雷达模拟器设计方案.基于雷达模拟器系统架构,对雷达模拟器硬件和软件进行了设计,构造出了软硬件结合、一体化的雷达模拟器.该雷达模拟器设计方案已应用于某型雷达模拟器.应用实例表明,该雷达模拟器达到了预期的效果,实现了训练模拟器动态部署、与上级系统平台远程异地联网组训、训练想定与训练数据实时传输等功能.

关键词： 热导检测器   微控制器   恒温控制   双闭环系统   灵敏度  

摘要： 针对传统气相色谱仪检测变压器油中溶解气体灵敏度较低的问题,提出了一种对热导检测器恒平均温度供电的方法。采用模拟和数字双闭环控制的方法,设计了对热导检测器进行高精度恒平均温度供电的控制系统,实现了对色谱分析仪热导检测器恒平均温度控制的功能。试验结果表明,模拟与数字双闭环反馈控制动态响应快,受风速扰动影响小,温度控制精度达到±0.1℃,可以满足热导检测器高精度检测的要求。

关键词： 嵌入式   FPGA系统   USB   通信接口   硬件  

摘要： 随着通信行业发展和通信技术不断进步,FPGA的嵌入式系统的应用也得到了不断的推动和进步。当然,FPGA嵌入式系统研究和发展中,应该关注其通信接口技术研究,在应用中应该根据嵌入式系统特点,进行针对性的通信接口研究。所以,本文将在现有的FPGA嵌入式系统发展中背景下,结合硬件设计,进行基于嵌入式FPGA系统USB通信接口硬件设计研究,为其发展和完善提供一定的参考。