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.     

BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.     

Proximity-Induced Fully Ferromagnetic Order with Eightfold Magnetic Anisotropy in Heavy Transition Metal Oxide CaRuO3

Ferromagnetic materials with a strong spin-orbit coupling (SOC) have attracted much attention in recent years because of their exotic properties and potential applications in energy-efficient spintronics. However, such materials are scarce in nature. Here, a proximity-induced paramagnetic to ferromagnetic transition for the heavy transition metal oxide CaRuO₃ in (001)-(LaMnO₃/CaRuO₃) superlattices is reported. Anomalous Hall effect is observed in the temperature range up to 180 K. Maximal anomalous Hall conductivity and anomalous Hall angle are as large as ∼15 Ω⁻¹ cm⁻¹ and ∼0.93%, respectively, by one to two orders of magnitude larger than those of the typical 3d ferromagnetic oxides such as La_0.67Sr_0.33MnO₃. Density functional theory calculations indicate the existence of avoid band crossings in the electronic band structure of the ferromagnetic CRO layer, which enhances Berry curvature thus strong anomalous Hall effects. Further evidences from polarized neutron reflectometry show that the CaRuO₃ layers are in a fully ferromagnetic state (∼0.8 μ_B/Ru), in sharp contrast to the proximity-induced canted antiferromagnetic state in 5d oxides SrIrO₃ and CaIrO₃ (∼0.1 μ_B/Ir). More than that, the magnetic anisotropy of the (001)-(LaMnO₃/CaRuO₃) superlattices is eightfold symmetric, showing potential applications in the technology of multistate data storage.     

Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.