Physiological signal acquisition system based on wireless sensor networks

Based on wireless sensor networks, a physiological signal acquisition system is proposed. The system is used in classroom education in order to understand the physiological changes in the students. In the system,the biological electrical signal related to student attention and emotion states can be measured by electrocardiography signals. The bioelectrical signal is digitalized at a 200 Hz sampling rate and is transmitted by the ZigBee protocol. Simultaneously, the Bluetooth technology is also embedded in the nodes so as to meet the high sampling rate and the high-bandwidth transmission. The system can implement the monitoring tasks for 30 students, and the experimental results of using the system in the classroom are proposed. Finally, the applications of wireless sensor networks used in education is also discussed.

Direction finding of coexisted independent and coherent signals using electromagnetic vector sensor

The existing direction of arrival （DOA） estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the co- herent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.

A Survey on Underwater Acoustic Sensor Networks: Perspectives on Protocol Design for Signaling, MAC and Routing

Underwater acoustic sensor networks (UASNs) are often used for environmental and industrial sensing in undersea/ocean space, therefore, these networks are also named underwater wireless sensor networks (UWSNs). Underwater sensor networks are different from other sensor networks due to the acoustic channel used in their physical layer, thus we should discuss about the specific features of these underwater networks such as acoustic channel modeling and protocol design for different layers of open system interconnection (OSI) model. Each node of these networks as a sensor needs to exchange data with other nodes;however, complexity of the acoustic channel makes some challenges in practice, especially when we are designing the network protocols. Therefore based on the mentioned cases, we are going to review general issues of the design of an UASN in this paper. In this regard, we firstly describe the network architecture for a typical 3D UASN, then we review the characteristics of the acoustic channel and the corresponding challenges of it and finally, we discuss about the different layers e.g. MAC protocols, routing protocols, and signal processing for the application layer of UASNs.

Low-Complexity DOA Estimation of Noncircular Signals for Coprime Sensor Arrays

This paper presents a low?complexity method for the direction?of?arrival(DOA)estimation of noncircular signals for coprime sensor arrays.The noncircular property is exploited to improve the performance of DOA estimation.To reduce the computational complexity,the rotational invariance propagator method(RIPM)is included in the algorithm.First,the extended array output is reconstructed by combining the array output and its conjugated counterpart.Then,the RIPM is utilized to obtain two sets of DOA estimates for two subarrays.Finally,the true DOAs are estimated by combining the consistent results of the two subarrays.This illustrates the potential gain that both noncircularity and coprime arrays provide when considered together.The proposed algorithm has a lower computational complexity and a better DOA estimation performance than the standard estimation of signal parameters by the rotational invariance technique and Capon algorithm.Numerical simulation results illustrate the effectiveness and superiority of the proposed algorithm.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

SENSOR FAILURE DETECTION AND SIGNAL RECOVERY BASED ON BP NEURAL NETWORK

A study is given on the application of BP neural network (BPNN) in sensorfailure detection in control systems, and on the networ architecture desgn, the redun-dancy,the quickness and the insensitivity to sensor noise of the BPNN based sensor detec-tion methed. Besules, an exploration is made into tbe factors accounting for the quality ofsignal recovery for failed sensor using BPNN. The results reveal clearly that BPNN can besuccessfully used in sensor failure detection and data recovery.

A Sensor Failure Detection Method Based on Artificial Neural Network and Signal Processing

This paper proposes a sensor failure detection method based on artificial neural network and signal processing,in comparison with other methods,which does not need any redundancy information among sensor outputs and divides the output of a sensor into'Signal dominant component'and'Noise dominant component'because the pattern of sensor failure often appears in the'Noise dominant component'.With an ARMA model built for'Noise dominant component'using artificial neural network,such sensor failures as bias failure,hard failure,drift failure,spike failure and cyclic failure may be detected through residual analysis,and the type of sensor failure can be indicated by an appropriate indicator.The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.

Design of ispPAC-based Humidity Sensor Signal Processing Circuits

The widely used sensitive elements of humidity sensors can be divided into 3 types,i.e.,resistor,capacitor,and electrolyte.Humidity sensors consisting of these sensitive elements have corresponding signal processing circuit unique to each type of sensitive elements.This paper presents an ispPAC (in-system programmable Programmable Analog Circuit) -based humidity sensor signal processing circuit designed with software method and implemented with in-system programmable simulators.Practical operation shows that humidity sensor signal processing circuits of this kind,exhibit stable and reliable performance.

Study on key technologies of GNSS-based train state perception for traincentric railway signaling

The application of Global Navigation Satellite Systems(GNSSs)in the intelligent railway systems is rapidly developing all over the world.With the GNSs-based train positioning and moving state perception,the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities.Considering the safety critical features of the railway signaling applications,the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements.In this paper,the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated,including the multi-sensor data fusion,Virtual Balise(VB)capturing and messaging,train integrity monitoring and system performance evaluation.According to the practical characteristics of the novel train control system under the moving block mode,the details of the key technologies are introduced.Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System(BDS)and related solutions.It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.

Structural Design of High-precision Positioning System in Weak Signal Environment Based on UWB and IMU Fusion

Aiming at the problem that indoor positioning technology based on wireless ultra-wideband pulse technology is susceptible to non-line-of-sight effects and multipath effects in confined spaces and weak signal environments,a high-precision positioning system based on UWB and IMU in a confined environment is designed.The STM32 chip is used as the main control,and the data information of IMU and UWB is fused by the fusion filtering algorithm.Finally,the real-time information of the positioning is transmitted to the host computer and the cloud.The experimental results show that the positioning accuracy and positioning stability of the system have been improved in the non-line-of-sight case of closed environment.The system has high positioning accuracy in a closed environment,and the components used are consumer-grade,which has strong practicability.

基于多传感数据融合的变速运行齿轮异常振动故障诊断

变速运行齿轮异常振动故障诊断性能过差会增加汽车维护成本,缩短齿轮使用寿命。为了及时识别齿轮故障,保证汽车变速器总成具有良好的振动特性,提出基于多传感数据融合的变速运行齿轮异常振动故障诊断方法。通过分析多传感器数据融合技术,掌握变速运行齿轮异常振动故障诊断的理论框架,并以此为基础,参考传感器融合模块、特征级并行多神经网络局部诊断模块和终端分类模块,结合变分模态分解、多通道加权融合和单隐层前馈神经网络训练算法,从信号采集、信号特征提取和信号特征分类3个步骤实现变速运行齿轮异常振动故障诊断。实验结果表明:在齿轮发生轻度磨损时,磨损振动信号的幅值在20~40 mV之间,磨损振动信号的频率在0~4000 Hz区间;中度磨损时,信号的幅值在30~55 mV之间,信号频率在3000~7000 Hz区间;重度磨损时,信号幅值在50~70 mV之间,信号频率在6000~12000 Hz区间,且各阶段诊断结果均与故障程度的实际转折点吻合。由此可知在各样本数量均相同的情况下,提出的故障诊断方法预测值与真实值均相同,故障程度和故障类型的诊断性能均较好。

无线传感节点中频域传输信号解调与控制方法

为提高无线传感网络节点中的频域传输信号抗噪能力,优化前端镜像与噪声信号处理能力,以无线传感节点中的频域传输信号为研究对象,提出一种新的信号控制与解调方法。将无线传感信号输入设计的滤波器中,通过电容器、电阻、运算放大器相互协作完成信号滤波。引入前馈补偿通路组建两级节点信号解调线路,当输出传感信号功率超过标准,便可根据超出值,以前馈补偿的方式调节增益带宽积实现解调。在传感节点中外接一个校正电路,以校正电容调节码的方式,控制传感信号频域传输特性满足正常运行标准。仿真结果表明:在1 V的电源电压下,此滤波器中心频率为2 MHz,邻近信号抑制为55 dB,镜像信号抑制为33 dB,带内纹波为0.1 dB,通带电压增益为18 dB,工作电流为382μA。验证了所提方法具有较高的频域传输信号深度处理能力,且有助于实现无线传感网络高质量通信。

基于热释电红外传感器的弱磁信号智能采集方法研究

对弱磁信号采集受到环境干扰和采集传感设备部署节点不合理等因素的影响,导致采集的信号具有大量干扰噪声,为了提高弱磁信号采集的分辨能力,结合阵列信号处理技术,提出基于热释电红外传感器的弱磁信号智能采集方法。构建弱磁信号智能采集的热释电红外传感器的节点分布阵列模型,结合相位偏移修正和噪声干扰抑制的方法,构建弱磁信号采集的滤波模型,基于信号波形的相干阵列检测方法实现对弱磁信号的空域特征增强,结合多径信号相干匹配技术和相应的多个DOA估计技术,通过热释电红外传感器的阵列节点输出信号的增强和滤波处理实现信号智能采集。实验结果表明,采用该方法进行弱磁信号智能采集的输出信号幅度和相位增益性较好,输出信噪比为45.9 dB,弱磁信号的误码率为0.55 bit,说明本方法具有实用性。

基于雷达传感器的非接触式睡眠呼吸检测系统设计

为解决当前接触式睡眠呼吸监测系统操作复杂、影响睡眠质量的问题,设计了一种非接触式睡眠呼吸检测系统。硬件系统以STM32G431VBT6单片机为核心,设计了信号调理与采集电路、WiFi通信电路,采用K-LC5雷达传感器采集用户睡眠过程中的呼吸信号。软件系统采用数字滤波算法对雷达传感器采集的胸腔位移信号进行处理,从中提取呼吸信号,并基于能量法进行呼吸暂停判定。测试结果表明,所设计系统能有效检测出睡眠状态下的呼吸状态,呼吸率的测试准确率达到了94.44%以上。

外部随机激励干扰下掘进机振动信号多传感采集方法

掘进机运行过程中,在外部随机激励源的作用下,造成多传感器模式下的传感敏感性波动,结合振动信号不稳定,会导致采集的振动信号大幅度失真。为保证掘进机的稳定运行,提出多传感器模式下的随机激励振动信号采集方法。将掘进机看作多自由度系统,建立设备动力学模型;结合掘进机动力学特征,利用虚拟激励法构建符合外部源的随机激励函数;在建立的随机激励下,选择传感器类型和参数并安装多个传感器,设定采样频率;根据正弦稳态校准方法需求,设计校准传感器采集系统,提高传感器敏感性;在随机激励函数约束下预测传感器输出延时,计算每次测量的同步误差和误差补偿值,通过补偿确保多传感器实现振动信号的同步动态采集。实验结果表明:在多传感模式下,这种方法降低了外部的随机迟滞干扰,在随机激励作用下采集到的掘进机多传感振动信号波形与实际波形相符。

基于混合神经网络的多维视觉传感信号模式分类

传感器采集的数字信号分类精度差,导致关键信息的丢失。为了提高传感数据的可靠性和有效性,提出基于混合神经网络的多维视觉传感信号模式分类方法。结合卷积神经网络(CNN)、循环神经网络(RNN)构建混合神经网络,以更有效地表示多维视觉数据中的特征;其中,卷积神经网络负责对多维的空间信号进行去噪处理并提取特征;循环神经网络负责对时域和频域信号进行特征提取;混合神经网络通过联合训练CNN和RNN各自的参数,以调整其权重,并且结合两者从不同层级提取的特征来实现多维视觉传感信号模式的分类。仿真结果表明,使用所提方法进行分类时,信号光滑度保持在0.9以上,传感信号分类结果与实际结果拟合度较高,有效实现多维视觉传感信号模式分类。

基于LabVIEW-VISA的传感器检测虚拟实验平台设计

为提升应用本科机电专业学生智能传感应用能力,设计传感器检测虚拟实验平台。提出基于LabVIEWVISA的传感器检测虚拟实验平台及实验方法,在LabVIEW-VISA应用设计基础上搭建了传感器检测虚拟实验平台整体架构;设计了传感器实验箱、MyDAQ便携式测量、万用表、应变式传感器等主要硬件;进行了数据采集模块、数据处理、输出模块及信号处理电路等软件设计,利用信号处理电路实现数据在平台中的双通道传输,实现了预期目标。结果表明,设计的传感器检测虚拟实验平台实验验证灵敏度高、线性误差小,有利于提高人才实践动手能力培养。

强信号全覆盖下反射式强度型传感器信号调制设计

反射式强度型传感器在强信号全覆盖环境下常常受到干扰和影响,导致传感器信号的质量下降甚至失真,给后续的信号处理和数据分析带来了困难。对此,提出强信号全覆盖下反射式强度型传感器信号调制方法。首先,判断强信号全覆盖环境下传感器信号的稀疏性,并根据环境的干扰因素建立信号的观测矩阵,通过优化算法计算出稀疏向量的估计值,使用稀疏向量的估计值对观测信号进行补偿,得到补偿后的信号;然后,应用正交锁相放大技术,将补偿后的信号与严格正交的两路参考信号相乘,提高信噪比,获得增强后的信号;最后,利用连续相位多支路调制方法,对增强后的传感器信号展开比特信息分离、二维信号映射与载波相位调制操作,完成信号调制。实验结果表明,所提方法可扩展性强、稳定性高且抗干扰能力强。

扩展卡尔曼滤波的改进蛇定位算法在WSN中的应用

针对接收信号强度指示(Received Signal Strength Index,RSSI)定位易受到环境因素的影响,提出了一种基于RSSI扩展卡尔曼滤波的改进蛇定位算法(RSSI Extended Kalman Filter-based Improved Snake Optimization Localization Algorithm,RSSI-EISL)。该算法利用扩展卡尔曼滤波(Extended Kalman Filter,EKF)模型对RSSI信号值进行平滑处理,使其能够抑制噪声和异常值对估计结果的影响,从而提高测距的准确性和鲁棒性。通过引入Levy飞行和非线性收敛因子的改进蛇优化算法(Improved Snake Optimization Algorithm,ISO),提升了蛇优化算法(Snake Optimization Algorithm,SO)的寻优能力,使之能够更加准确地计算出待测节点的坐标。根据仿真结果显示,相较于基于RSSI最小二乘定位算法(RSSI Ordinary Least Squares Localization Algorithm,ROL)、基于RSSI EKF的灰狼定位算法(RSSI Extended Kalman Filter-based Grey Wolf Optimization Algorithm,REGL)和基于RSSI EKF的蛇定位算法(RSSI EKF-based Snake Optimization Localization Algorithm,RESL),RSSI-EISL的定位精度分别提高了26.4%、8.75%和5.6%,算法的收敛速度和全局搜索能力也有所提升。