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关键词： 动液面   LABVIEW虚拟仪器   硬件设计   软件设计  

摘要： 传统的动液面测量方法由人工操作,工作人员需驱车至井场,采用手动测量液面装置开展测试工作,然后将实测数据导入电脑,人工解析液面深度。测试成本较高,测试效率低下,存在一定安全风险,遇到极端恶劣天气时无法赶赴现场测试,实时性较差。为了解决国内油田动液面测量系统存在问题,采用LABVIEW虚拟仪器技术,设计动液面连续在线监测控制系统,主要包括硬件设计与软件设计两个方面,实现了动液面在线连续监测功能,测量间隔时间可任意设置,测试精度高,完全满足现场测试需求。解决了低产井自动间开控制、油井测压等技术难题。

关键词： 双回路   爆炸试验设备   效率   时间成本   经济成本  

摘要： 系统的介绍了双回路爆炸试验设备的硬件试验罐、配气系统等,各系统的功能、设计思路与构成以及计量性能的要求和通用技术的要求,此方案经过验证得到良好的效果,为业内同仁设计开发类似试验设备提供参考,具备很大的价值。

关键词： 惯导信号   多通道   数据采集   FPGA   定时器  

摘要： 为了实现惯导系统对多种信号的测量要求,设计了一种基于FPGA的多模式硬件平台。设计了1路内部定时器,48路脉冲计数器,锁存器及64x32bit的FIFO,外加8路AD采集通道,共实现4通道惯导产品的速率及加速度及测温等多类型信号的测量。与传统单一信号测量设备相比,该设计具有较高的可靠性和稳定性,有很强的扩展性,提高了测量效率,降低了成本。

关键词： 架构设计   硬件设计   HMI   传感器  

摘要： 随着人们的生活水平不断提高,大众对呼吸健康也越来越看重,而目前市场上的新风机就能让人们呼吸道更加健康清洁的自然空气。现如今,市场上的新风机品种繁多,而其实际上的使用效果到底如何消费者则是知之甚少。鉴于此,本文介绍了一种新风机性能测试实验室的整体架构设计及最终实现,并且通过HMI人机界面直观显示出各项测量参数以及性能测试结果,让新风机的实际运行效果更加直观,同时也分析了对测试结果产生误差的因素。

关键词： 用电信息采集系统   通信智能适配器   硬件设计   软件编程  

摘要： 影响用电信息采集系统采集成功率的因素很多,但电能表RS485通信异常造成采集失败的现象突出。为解决RS485总线中多个节点容易引发总线竞争、数据冲突和故障电表长时间独占总线的问题,设计了RS485通信智能适配器。介绍RS485通信智能适配器的硬件电路以及模型编程实现总线令牌机制、忙检查机制的方法。RS485通信智能适配器能够解决电表通信冲突、自动隔离异常工作的电能表,提高用电信息采集系统的可靠性。

关键词： 智能屏幕   硬件设计   软件设计   调试  

摘要： 高校学生食堂智能屏幕应用广泛。以AT89S51单片机为核心,另外使用数码管、16×64点阵、独立按键、温度传感器以及CMOS等器件,设计一种超低价格用于显示餐厅温度、菜品的智能电子屏幕。采用Protues软件对电路设计的硬件部分进行仿真,使用Keil 5编程并仿真调试,在满足要求的基础上,购买元件并进行实物制作,最后对实物进行调试,结果符合控制要求。

关键词： 火电厂   输煤电气控制系统   硬件设计   软件设计  

摘要： 火电厂输煤电气控制系统是保证火电厂平稳运行的关键。现围绕火电厂输煤电气控制系统,详细分析了该系统的设计流程,包括硬件设计和软件设计,旨在为日后设计工作的顺利开展奠定坚实基础。

关键词： 数据采集模块   MSP430AFE221   SD_24A  

摘要： 以16位的微控制器MSP430AFE221为控制核心;设计单电源供电的信号放大电路和低通滤波电路,对压力传感器输出信号进行处理;通过MSP430AFE221内部24位ADC模块(SD_24A)进行模数转换;设计电源电路,应用于低功耗情况.设计与CC1101无线数据传输模块连接的接口电路.

关键词： DNN compression   ECG authentication   fixed-coefficient filtering   neural network optimization   structured sparsity  

摘要： Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern authentication systems. These methods are now popular and have found their way into many portable electronics such as smartphones, tablets, and laptops. Furthermore, the use of biometrics enables secure access to private medical data, now collected in wearable devices such as smartwatches. In this work, we present an accurate low-power device authentication system that employs electrocardiogram (ECG) signals as the biometric modality. The proposed ECG processor consists of front-end signal processing of ECG signals and back-end neural networks (NNs) for accurate authentication. The NNs are trained using a cost function that minimizes intra-individual distance over time and maximizes inter-individual distance. Efficient low-power hardware was implemented by using fixed coefficients for ECG signal pre-processing and by using joint optimization of low-precision and structured sparsity for the NNs. We implemented two instances of ECG authentication hardware with 4X and 8X structurally-compressed NNs in 65 nm LP CMOS, which consume low power of 62.37 mu W and 75.41 mu W for real-time ECG authentication with a low equal error rate of 1.36% and 1.21%, respectively, for a large 741-subject in-house ECG database. The hardware was evaluated at 10 kHz clock frequency and 1.2 V voltage supply.

关键词： AI   algorithm   biomedical application   proce-ssor  

摘要： This paper reviews the state of the arts and trends of the AI-Based biomedical processing algorithms and hardware. The algorithms and hardware for different biomedical applications such as ECG, EEG and hearing aid have been reviewed and discussed. For algorithm design, various widely used biomedical signal classification algorithms have been discussed including support vector machine (SVM), back propagation neural network (BPNN), convolutional neural networks (CNN), probabilistic neural networks (PNN), recurrent neural networks (RNN), Short-term Memory Network (LSTM), fuzzy neural network and etc. The pros and cons of the classification algorithms have been analyzed and compared in the context of application scenarios. The research trends of AI-Based biomedical processing algorithms and applications are also discussed. For hardware design, various AI-Based biomedical processors have been reviewed and discussed, including ECG classification processor, EEG classification processor, EMG classification processor and hearing aid processor. Various techniques on architecture and circuit level have been analyzed and compared. The research trends of the AI-Based biomedical processor have also been discussed.