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。     

基于BAS-BP-Bagging模型的光纤陀螺温度补偿

为提高光纤陀螺的输出精度,以天牛须搜索算法(BAS)优化后的BP神经网络模型为基学习器,采用Bagging并行集成学习算法建立了BAS-BP-Bagging温度补偿模型,并对某型号光纤陀螺进行了温度补偿实验。实验结果表明,在-40～+60℃温度变化环境下,该方法补偿后的光纤陀螺温度漂移相较于补偿前减小了近80%,相较于多项式补偿算法减小了55%,相较于BP神经网络补偿算法减小了30%左右。同时该模型在对新鲜样本的补偿过程中表现出了较为优越的泛化性能。     

嵌埋材料对分布式布拉格反射镜腔共振模式的影响

低维材料嵌入微腔已经广泛应用于纳米激光器和探测器等。为了实现增益材料和光学微腔之间的有效耦合,需要深入研究嵌埋材料对腔共振模式的影响。本文主要讨论了嵌埋材料的厚度、位置、腔层厚度以及分布式布拉格反射镜的对数对腔供着模式的影响。结果表明,腔共振模式随嵌埋材料位置的不同呈现周期性变化并且在λ/2光程周期内存在最大峰位移。最大峰位移随腔层厚度增加而减小,但与嵌埋材料的厚度成正比。分布式布拉格反射镜的对数不影响腔共振模式。这些结果为光学器件的设计和实验现象的分析提供了指导,并且可以应用于不同波长分布式布拉格反射腔结构。     

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。     

多高斯关联扭曲光束在GRIN光纤中的轨道角动量演化

轨道角动量(orbital angular momentum, OAM)的引入丰富了光通信领域内的调制维度,为寻找更高效快捷的通信方式提供了新思路,相较于传统携带涡旋相位的涡旋光束,部分相干扭曲光束携带的OAM性质更为复杂。本文针对椭圆多高斯关联扭曲光束在梯度折射率(gradient-index, GRIN)光纤中的OAM传输,基于GRIN光纤的ABCD光学传输矩阵,着重分析了多高斯关联扭曲光束在GRIN光纤传输的OAM的传输演化特性。研究结果表明,光束的光强、OAM通量密度以及归一化OAM通量密度在光纤传输过程中均呈现周期性变化,通过对关联结构以及扭曲因子的调控不仅可以改变光束OAM通量密度的分布,还可以调控单光子携带OAM的大小。本文的研究有助于丰富扭曲部分相干光束在GRIN光纤中传输的理论结果,在光纤通信中具有潜在的应用前景。     

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.     

基于宇称-时间对称光子晶体结构的磁场传感器研究

提出了一种基于宇称-时间(parity-time,PT) 对称结构的磁场检测微腔模型,利用水 基Fe₃O₄磁流体介质层的磁光效应和PT对称结构的光放大效应,当磁场强度发生变化时 ,引 起结构禁带中缺陷模位置的改变。因此,可以通过检测缺陷模位置实现磁场强度的检测。本 文利用传递矩阵法(transfer matrix method,TMM)从理论上分析了结构的透射光谱,在此 基础上对结构周期、介质层厚度、PT对称单元中宏观洛伦兹振荡强度等参数进行了数值优化 。优化后的仿真结果表明,该传感器能够实现有效的磁场磁强检测,磁场强度在0—500Oe 的 范围内,传感器的灵敏度可以达到97.14 nm/RIU,优值和检测极限分 别可以达到10⁴和10－7 RIU。同时, 传感器的分辨率数值最小可以达到0.05 Oe和最 大可以达到2Oe。本文所设计的传感器结构可用于高综合性能磁场强度检测设备的设计。     

5G时代,有线接入网基础资源布局思路探讨

双千兆时代,网络规模空前巨大,有线接入网更是因覆盖范围广,建设规模大,投入人力多,投资占比高等因素,成为研究重点。文章根据有线接入网架构演进,从“点(各类业务及分纤点)、线(各级管线)、面(业务汇聚区)”三个层面对基站、家客、集客等业务接入、分纤点规划及业务汇聚机房等基础资源规划进行研究。