Medium access control layer management for saving energy in wireless sensor networks routing algorithms

Wireless sensor networks (WSNs) consist of small nodes that are capable of sensing, computing, and communication. One of the greatest challenges in WSNs is the limitation of energy resources in nodes. This limitation applies to all of the protocols and algorithms that are used in these networks. Routing protocols in these networks should be designed considering this limitation. Many papers have been published examining low energy consumption networks. One of the techniques that has been used in this context is cross-layering. In this technique, to reduce the energy consumption, layers are not independent but they are related to each other and exchange information with each other. In this paper, a cross-layer design is presented to reduce the energy consumption in WSNs. In this design, the communication between the network layer and medium access layer has been established to help the control of efforts to access the line to reduce the number of failed attempts. In order to evaluate our proposed design, we used the NS2 software for simulation. Then, we compared our method with a cross-layer design based on an Ad-hoc On-demand Distance Vector routing algorithm. Simulation results show that our proposed idea reduces energy consumption and it also improves the packet delivery ratio and decreases the end-to-end delay in WSNs.     

基于Kinect传感器的移动机器人环境检测及行为学习

研究了一种基于深度图像和强化学习算法的移动机器人导航行为学习方法。该方法利用机器人装配的Kinect传感器检测工作环境信息,然后对获取的深度图像数据和视频图像进行处理、融合和识别,并由此构建机器人任务学习的状态空间,最终利用强化学习方法实现移动机器人的导航任务的自学习。该方法的有效性通过实验得到验证。实验表明,该方法能够使机器入具有较强的环境感知能力,并能够通过自学习的方式掌握行为能力。     

石墨烯/银纳米颗粒复合材料的制备及其对双氧水的电化学检测

以氧化石墨烯(GO)和硝酸银为原材料,聚乙烯吡咯烷酮(PVP)为还原剂和稳定剂,通过水热法制备出还原氧化石墨烯/银纳米颗粒(rGO/AgNPs)复合材料。采用透射电子显微镜(TEM)、X射线衍射(XRD)及紫外-可见分光光度计(UV-Vis)对rGO/AgNPs复合材料的形貌、组成和结构进行表征。同时,将rGO/AgNPs复合材料修饰到玻碳电极表面制备出过氧化氢(H_2O_2)电化学传感器,通过循环伏安法(CV)和计时安培响应法(i-t)对传感器进行电化学性能测试。实验结果表明:制备的rGO/AgNPs传感器具有较好的电化学性能,其对H_2O_2检测的灵敏度为340.6μA·(mmol/L)~(-1)·cm~(-2),响应时间为3s,最低检测极限为7.5μmol/L(S/N=3),线性检测范围为20~4950μmol/L(线性相关系数为R=0.9973)。     

旋转状态下微波叶尖间隙传感器校准影响因素分析

主要针对应用于发动机叶尖间隙测量的微波叶尖间隙传感器的校准需求,对测量原理、传感器的校准原理等进行了详细阐述,并分析了转速和回缩值等对校准结果的影响。为微波叶尖间隙传感器的进一步优化设计及应用提供了数据依据。     

基于混沌人工蜂群算法的无线传感器网络覆盖优化

针对无线传感器网络随机播撒的节点严重冗余并且导致网络寿命短、覆盖效率不高等缺陷,提出了一种混沌人工蜂群算法的无线传感器网络覆盖优化算法;将节点的利用率和覆盖率作为优化目标函数,建立与之对应的数学模型,之后用混沌人工蜂群算法改善人工蜂群算法陷入局部最优、收敛慢等问题,提高算法收敛速度和精度,对节点覆盖模型进行求解,得出网络最优覆盖方案;通过实验仿真,提出的算法提高了无线传感器网络的覆盖率,覆盖率可达93.48%以上,减少了网络节点冗余,提高了网络寿命,降低了网络成本。     

基于扩展卡尔曼滤波的卫星组合姿态确定物理仿真技术研究

针对未来卫星应用的高精度、高可靠性定姿技术需求,设计了卫星单轴组合姿态确定物理仿真系统;采用太阳敏感器和光纤陀螺组合定姿模式,提出了一种扩展卡尔曼滤波组合姿态确定算法,用C语言进行了实验软件设计,系统采用DSP为处理器,还设计了多敏感器数据采集扩展接口;物理仿真实验结果:姿态确定精度优于0.5°;陀螺常值漂移估计的平均值为3.625758e-4°/s,证明了组合姿态确定算法的有效性和该项技术对工程应用的可行性。     

面向物联网食品货架期监测智能包装研究进展

目的 对智能传感器技术在智能包装果蔬等食品监测中的应用进行介绍和分类总结,为果蔬的货架期智能监测提供依据和帮助,并推动传感器技术在智能包装果蔬领域中的实际应用。方法 分别介绍湿敏型、气敏型及微生物检测这几个方面的智能传感器,以及结合NFC/RFID等智能技术在果蔬包装货架期监测中的应用。相较于传统的包装方式,此类智能包装结合当下发展势态良好的互联网,给人们的生活带来了更多的便捷。结论 智能传感器标签在果蔬等食品智能监测中发挥着重要的作用,可通过连接智能手机等终端,快速监测食品新鲜度情况。近年来,包装材料研发呈现多样化,例如多元复合纳米材料、金属有机骨架材料等,因此未来可以通过优化包装传感材料和器件结构,更好地发挥智能包装标签在果蔬智能包装中的优势。     

Feature Fusion-Based Deep Learning Network to Recognize Table Tennis Actions

A system for classifying four basic table tennis strokes using wearable devices and deep learning networks is proposed in this study. The wearable device consisted of a six-axis sensor, Raspberry Pi 3, and a power bank. Multiple kernel sizes were used in convolutional neural network (CNN) to evaluate their performance for extracting features. Moreover, a multiscale CNN with two kernel sizes was used to perform feature fusion at different scales in a concatenated manner. The CNN achieved recognition of the four table tennis strokes. Experimental data were obtained from 20 research participants who wore sensors on the back of their hands while performing the four table tennis strokes in a laboratory environment. The data were collected to verify the performance of the proposed models for wearable devices. Finally, the sensor and multi-scale CNN designed in this study achieved accuracy and F1 scores of 99.58% and 99.16%, respectively, for the four strokes. The accuracy for five-fold cross validation was 99.87%. This result also shows that the multi-scale convolutional neural network has better robustness after five-fold cross validation.     

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the vision of the Wireless Body Sensor Network (WBSN) to improve the health monitoring systems and has an enormous impact on the healthcare system for recognizing the levels of risk/severity factors (premature diagnosis, treatment, and supervision of chronic disease i.e., cancer) via wearable/electronic health sensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy plays a very significant role in the detection of gastric cancer. Convolutional Neural Network (CNN) has been widely used to diagnose gastric cancer based on various feature extraction models, consequently, limiting the identification and categorization performance in terms of cancerous stages and grades associated with each type of gastric cancer. This paper proposed an optimized AI-based approach to diagnose and assess the risk factor of gastric cancer based on its type, stage, and grade in the endoscopic images for smart healthcare applications. The proposed method is categorized into five phases such as image pre-processing, Four-Dimensional (4D) image conversion, image segmentation, K-Nearest Neighbour (K-NN) classification, and multi-grading and staging of image intensities. Moreover, the performance of the proposed method has experimented on two different datasets consisting of color and black and white endoscopic images. The simulation results verified that the proposed approach is capable of perceiving gastric cancer with 88.09% sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.     

Experimental deformation analysis of an adhesively bonded multi-material joint for marine applications

The strength and deformation of full-scale adhesively bonded multi-material joints is studied in this paper. Four joints with a thick layer of methyl methacrylate adhesive (MMA) have been manufactured in shipyard conditions. In two specimens, cracks have been introduced at steel–adhesive and composite–adhesive interfaces. One cracked and one un-cracked specimen were subjected to quasi-static tensile testing; the two remaining specimens were stepwise loaded/unloaded with increasing load until failure. The strain in the adhesive layers was measured with digital image correlation (DIC). This showed a predominant shear deformation and dissimilar shear strain patterns for different bond lines. Fibre Bragg (FBG) sensors were used to monitor strains at steel and composite constituents and to detect the onset and evolution of damage in the un-cracked specimen. Strains measured by FBG sensors correspond well with DIC results at nearby regions. All specimens failed by delamination of the composite panel near the composite–adhesive interface.