自加热非晶锗热电阻MEMS流速传感器的制造与测试

为扩大流速传感器的测量范围并降低功耗,制造并测试了一种基于自加热非晶锗薄膜热电阻的MEMS流速传感器,它是由嵌入氮化硅薄膜的四个非晶锗热敏电阻和一对环境测温补偿电阻组成。四个非晶锗热电阻同时作为自加热热源和测温元件,相互连接以形成惠斯通电桥。给出了MEMS工艺流程,微加工制造了尺寸为8.9 mm×5.6 mm×0.4 mm的流速传感器芯片。搭建了低流速和高流速气流通道实验装置,对传感器的惠斯通电桥施加50μA的恒定电流(CCA),实现了0～50 m/s范围内的流速测量。结果表明,传感器在低流速(0～2 m/s)时的灵敏度约为81.6 mV/(m/s),在高流速(2～50 m/s)时的灵敏度约为51.9 mV/(m/s),最大功耗仅约为1.03 mW。     

一种快速低开销的移动节点分布式寻路RPL路由协议

针对低功耗有损网络(Low Power and Lossy Network, LLN)中移动节点作为中继节点时,存在无线链路易断开、数据传输成功率较低和控制开销较高的问题,提出了一种快速低开销的移动节点分布式寻路RPL路由协议(Mobile Node Distributed Pathfinding RPL Routing Protocol, MNA-RPL)。首先,提出一种邻居表自适应更新机制,依据邻居表快速切换父节点;其次,提出一种父子节点双断开机制,能够有效减少数据包丢失;最后,提出一种基于速度动态管理的Trickle计时器,根据自身速度动态调整DIO广播周期。理论分析和仿真结果表明,与主流路由协议相比,该协议在数据传输和控制开销等方面的性能均有一定的提升。     

检测技术类专业课程的思政教育实践探索

高校专业课程课堂教学是实践思政教育的主战场。针对检测技术类课程专业理论知识教学内容,探索了课程思政教育方法。通过设计思政教学切入点、凝练思政元素,形成课程知识与思政内容深度融合、互为促进的课程体系,实现了价值塑造与专业培养的目标融合。     

中短波红外图像传感器读出电路研究进展

随着红外技术和探测器性能的进步,中波和短波红外技术在恶劣天气中具有更优秀的成像性能,在民用、军事和航空航天等领域中得到了越来越广泛的应用。读出电路作为连接探测器阵列与后级图像处理电路的关键模块,其性能对中短波红外相机系统性能具有重要影响,决定了最终的成像质量。文章综述了中短波红外图像传感器读出电路的发展现状,分析了读出电路中噪声、动态范围、帧频等问题,重点探讨了针对以上问题的解决方案。最后对读出电路未来设计的改进方向进行了讨论。     

基于微纳电离的有毒有害气体传感器

为了有效避免有害气体浓度的超标对经济效益和人类生命财产安全造成损害,提出并设计了一种新型检测方法,通过对微纳电离型传感器进行一系列的测试,设计的传感器展现了较强的抗弯能力及较快的响应-恢复能力。在室温常压、相对湿度75%的实验条件下,对多个浓度的苯、甲苯气体展开了测试,并基于特征噪声强度识别气体的浓度,构建了对应的识别模型。实验结果表明：传感器对有害气体检测时的测量值和真实值的拟合度可达99%以上,体现了较高的检测精度,且在检测有害气体时传感器处于可逆电离平衡状态,重复性良好,不需要进行预热、安全无毒,因此适合于应用到苯类气体的检测中,能够满足效率、精度的要求,具备了一定的实用性。     

光纤电压传感器传感头优化与热致非互易误差抑制

针对普通光纤电压传感器在外界温度扰动下其输出精度和稳定性会受到严重影响这一问题,本文提出了一种采用保偏光子晶体光纤(polarization maintaining photonic crystal fiber,PM-PCF) 作为传感光纤的传感头优化方法抑制温度扰动下的系统非互易误差。在综合考虑PM-PCF与传统熊猫型保偏光纤(panda polarization maintaining fiber,panda-PMF) 尾纤间的熔接损耗、传输损耗以及现有工艺条件下的光纤制备等问题的基础上,优化得到了与panda-PMF间的熔接损耗低至0.14 dB的PM-PCF;并对比分析了这两种保偏光纤的双折射温度依赖特性,建立了全光纤类型的光学电压传感器(fiber optic voltage sensor,FOVS) 热致系统误差输出模型,分析验证了PM-PCF在解决反射式逆压电型FOVS热致非互易问题的可行性。结果表明,采用PM-PCF优化后的FOVS热致非互易误差约是传统保偏光纤电压传感器 的10-2量级。这在提高系统输出精度和稳定性等方面具有显著优势,同时这种低损耗的优化方法在光纤传感及通信等领域具有重要的应用价值。     

一种双梁互补式光纤光栅位移传感器及在地铁隧道变形监测中的应用

本文提出了一种双梁互补式光纤光栅(fiber Bragg grating, FBG)位移传感器,实现了正负双向位移的测量。采用双“悬臂梁+楔形滑块”的结构,当一个悬臂梁受位移作用处于变形状态时,另一个悬臂梁不变形并提供温度补偿功能。传感器处于零值测点状态时,两个悬臂梁处于零弯曲状态,且双梁互为温度补偿,消除了温度影响。通过性能测试实验证明,该传感器在±50的量程内,灵敏度为29.369 pm/mm,测量重复性好。制备出8个传感器应用于某市地铁的变形安全监测,从道床沉降、道床环缝、管片环缝3个方面开展了变形测量。在长期监测期间,传感器工作稳定,被测结构变形状态稳定,表明该传感器具有良好的测量性能,适用于长期的结构健康监测。     

One-Component Artificial Gustatory System Based on Hydrogen-Bond Organic Framework for Discrimination of Versatile Analytes

Hydrogen-bond organic frameworks (HOFs) with excellent structural and luminescent properties have emerged as a promising material for the construction of fluorescence sensors. However, designing a facile, universal and high throughput sensor with multiplex detection capacity still remains challenging. Herein, a one-component sensor array is constructed that mimics natural gustatory system for accurate and high-throughput discrimination and identification of versatile analytes. HOF as a single sensing element greatly simplifies the probe preparation in sensor array and detection procedure. Metal ions, proteins and bacteria as the model targets are rapid and accurately discriminated, presenting the universality of the system. Particularly, the system is successfully used for the classification of antibiotic mechanisms. The study expands the application scope of HOFs and provides a facile and universal system for sensing applications.     

A fixed clustering protocol based on random relay strategy for EHWSN

In Energy Harvesting Wireless Sensor Networks (EHWSN), the communication protocol will directly affect the final performance of the network, so it is necessary to study the communication protocol based on EHWSN. In this paper, for the low-cost fixed clustering problem, a fixed clustering protocol RRCEH is based on random relaying. Our proposed RRCEH abandons the inefficient inter-cluster communication method of the traditional fixed clustering protocol. To coordinate the data upload of the cluster head, RRCEH allocates different random relay vectors to each ring area of the network, and combines all the random relay vectors into a random relay matrix of RRCEH. In each communication round, the cluster head node randomly selects its relay target node to send data according to the probability distribution in the random relay vector in the area. For two different cluster head configuration scenarios, by optimizing the random relay matrix, RRCEH can effectively reduce the network's configuration requirements for cluster head energy harvesting capability, thus reducing the deployment cost of EHWSN.     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.