Data Fault Detection in Wireless Sensor Networks Using Machine Learning Techniques

Wireless Personal Communications - Wireless Sensor Network (WSN) is a wireless network that consists of spatially distributed autonomous devices with sophisticated subsystem called sensors to...     

Deep Learning Based Semi-Blind Tracking for Aging Wireless Communication Channels

The time-varying nature of the wireless propagation channel under high user mobilities, termed as channel aging, is a major performance impediment in many communication systems. In this paper, we discuss deep learning models for semi-blind channel estimation in a single input single output wireless communication system under channel aging. In our proposed scheme, we first use pilot based training to obtain initial channel estimates. Following this, we treat the detected symbols as pilots and perform further channel estimation using an Encoder-Decoder LSTM network for constant and sliding window schemes. To show the effectiveness of our method, we show the training capabilities of our models and the BER vs SNR graphs for multiple simulations. We discuss integrating these Encoder-Decoder LSTM models with deep learning enabled symbol detection techniques like the DetNet to further improve spectral efficiency. The Encoder-Decoder LSTM network gives us a low BER, with the moving window scheme outperforming the constant window scheme.

     

Live Detection of Face Using Machine Learning with Multi-feature Method

Wireless Personal Communications - Facial expression detection (FED) and extraction show the most important role in face recognition. This research proposed a new algorithm for automatic live FED...     

Machine Learning for Wireless Communication Channel Modeling: An Overview

Wireless Personal Communications - In real time communication system, packets of lower prioritized flows suffer a longer queuing delay than the packets having higher priority. As a result, they...     

基于迁移学习的机场场面目标检测与跟踪技术研究

本文分析了深度学习技术的基本原理,讨论了其应用与目标检测与跟踪领域的基本方式.利用实地采集的广汉机场场面视频数据,采用深度迁移学习的策略,研究了面向机场场面目标的检测与跟踪技术.     

IoT Enabled RFID Authentication and Secure Object Tracking System for Smart Logistics

Wireless Personal Communications - Object tracking is a fundamental problem in Supply Chain Management (SCM). Recent innovations eliminate the difficulties in traditional approach such as manual...     

Cooperative Relaying and Jamming Design for Secure LPWAN Wireless Communication

Physical layer security methods based on joint relay and jammer selection（JRJS） have been widely investigated in the study of secure wireless communications. Different from current works on JRJS schemes, which assumed that the global channel state information（CSI） of the eavesdroppers（Eves）was known beforehand, then the optimal relaying and jamming relays were determined. More importantly,the time complexity of selecting optimal jamming relay is O（N²）, where N is the maximum number of relays/Eves. In this paper, for the scenario where the source wants to exchange the message with the destination, via relaying scheme due to longer communication distance and limited transmission power, in the presence of multiple Eves, with the assumption of Eves' perfect CSI and average CSI, we propose two kinds of JRJS methods. In particular, the time complexity of finding the optimal jammer can be reduced to O（N）. Furthermore, we present a novel JRJS scheme for no CSI of Eves by minimizing the difference between expected signal and interfering signal at the destination. Finally, simulations show that the designed methods are more effective than JRJS and other existing strategies in terms of security performance.     

基于自适应差分的多目标检测和跟踪

运动目标检测与跟踪在诸多领域里都有广泛的应用,是当前计算机视觉的一个热门领域。利用自适应阈值差分的方法对运动目标进行检测,并使用轨迹关联的方法对多目标进行跟踪。实验结果表明,该种算法很好地实现了视频图像中多目标检测和跟踪,更符合实际应用的需求。     

目标检测与跟踪技术专利综述

目标检测与跟踪技术在军事、航天以及国民经济的各个领域发挥着重要的作用.对目标检测与跟踪的专利技术进行了综述,重点关注近年的热点技术,将近年来目标检测与跟踪的专利技术大致分为以下几类:块匹配、基于特征的方法、图像间相减、基于变换域的方法、基于梯度的方法和统计方法,并介绍了每类方法的优势与问题,希望对审查与申请提供一定的借鉴.     

基于对称差分算法的背景更新和运动检测

本文针对时间差分将导致差分图像中背景显露过多影响了背景模型建立的缺点,利用对称差分能够快速检测中间帧运动目标的特点,提出一种使用间隔多帧对称差分来更新背景模型,从而建立可靠的背景模板,再结合背景消减进行运动检测的方法。实验表明该算法能快速地建立背景模型,同时有效地检测出背景中的运动物体,具有一定的实际应用价值。     

智能视频监控系统目标检测和跟踪技术分析

简述视频监控技术发展历史,提出了视频监控系统的智能化是未来监控技术发展的必然趋势.针对智能图像分析中的目标检测与跟踪技术,对目前采用的各种算法进行分类,并对各种方法的技术特点和优缺点进行了分析和比较.最后,根据公安的实战应用需求,总结了该技术的应用范围及前景.     

基于空间和通道注意力机制的目标跟踪方法

目标跟踪是计算机视觉中重要的研究领域之一,大多跟踪算法不能有效学习适合于跟踪场景的特征限制了跟踪算法性能的提升.该文提出了一种基于空间和通道注意力机制的目标跟踪算法(CNNSCAM).该方法包括离线训练的表观模型和自适应更新的分类器层.在离线训练时,引入空间和通道注意力机制模块对原始特征进行重新标定,分别获得空间和通道权重,通过将权重归一化后加权到对应的原始特征上,以此挑选关键特征.在线跟踪时,首先训练全连接层和分类器层的网络参数,以及边界框回归.其次根据设定的阈值采集样本,每次迭代都选择分类器得分最高的负样本来微调网络层参数.在OTB2015数据集上的实验结果表明:相比其他主流的跟踪算法,该文所提算法获得了更好的跟踪精度,重叠成功率和误差成功率分别为67.6％,91.2％.     

Performance Analysis of Machine Learning Classification Algorithms in Static Object Detection for Video Surveillance Applications

Video surveillance system plays a pivotal role in automatic detection of abandoned luggage/bag in public places which causes threats to the public. As, the terrorist attacks are increasing world-wide, the detection and prevention of such attack is necessary to safeguard the people in public places. In this, a novel framework for the detection and classification of static object is proposed. In the proposed work first the static objects are identified and then it is classified to check the detected object is bag or anything else. In this study, the performance of machine learning techniques like Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbour, and Random Forest methods are analyzed. The performance is tested in standard (PETS 2006, PETS 2007 and AVSS i-LIDS) and custom datasets. The SVM and ANN produce best results in terms of classification and accuracy. Applications of various machine learning algorithms could clearly assist for identification and prevention of terrorist attacks in public places.

     

Object Detection, Tracking and Recognition for Multiple Smart Cameras

Video cameras are among the most commonly used sensors in a large number of applications, ranging from surveillance to smart rooms for videoconferencing. There is a need to develop algorithms for tasks such as detection, tracking, and recognition of objects, specifically using distributed networks of cameras. The projective nature of imaging sensors provides ample challenges for data association across cameras. We first discuss the nature of these challenges in the context of visual sensor networks. Then, we show how real-world constraints can be favorably exploited in order to tackle these challenges. Examples of real-world constraints are a) the presence of a world plane, b) the presence of a three-dimiensional scene model, c) consistency of motion across cameras, and d) color and texture properties. In this regard, the main focus of this paper is towards highlighting the efficient use of the geometric constraints induced by the imaging devices to derive distributed algorithms for target detection, tracking, and recognition. Our discussions are supported by several examples drawn from real applications. Lastly, we also describe several potential research problems that remain to be addressed.      

红外动目标识别跟踪系统硬件设计与实现

本文论述红外动目标识别跟踪系统的具体实现及相关理论分析,重点在于该系统的硬件模块实现及分析,并针对系统的可靠性进行了讨论.     

A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity

This paper addresses the issue of tracking a single visual object through crowded scenarios, where a target object may be intersected or partially occluded by other objects for a long duration, experience severe deformation and pose changes, and different motion speed in cluttered background. A robust visual object tracking scheme is proposed that exploits the dynamics of object shape and appearance similarity. The method uses a particle filter where a multi-mode anisotropic mean shift is embedded to improve the initial particles. Comparing with the conventional particle filter and mean shift-based tracking (Shan et al. 2004), our method offers the following novelties: We employ a fully tunable rectangular bounding box described by five parameters (2D central location, width, height, and orientation) and full functionaries in the joint tracking scheme; We derive the equations for the multi-mode version of the anisotropic mean shift where the rectangular bounding box is partitioned into concentric areas, allowing better tracking objects with multiple modes. The bounding box parameters are then computed by using eigen-decomposition of mean shift estimates and weighted averaging. This enables a more efficient re-distributions of initial particles towards locations associated with large weights, hence an efficient particle filter tracking using a very small number of particles (N = 15 is used). Experiments have been conducted on video containing a range of complex scenarios, where tracking results are further evaluated by using two objective criteria and compared with two existing tracking methods. Our results have shown that the propose method is robust in terms of tracking drift, tightness and accuracy of tracked bounding boxes, especially in scenarios where the target object contains long-term partial occlusions, intersections, severe deformation, pose changes, or cluttered background with similar color distributions.     

A Method for Anomaly Detection of User Behaviors Based on Machine Learning

1Introduction Intrusiondetectiontechniquescanbecategorizedinto misusedetectionandanomalydetection.Misusedetec tionsystemsmodelattacksasspecificpatterns,anduse thepatternsofknownattackstoidentifyamatchedac tivityasanattackinstance.Anomalydetectionsystems u…     

视频序列中的运动目标检测与跟踪

提出一种视频序列中的运动目标检测跟踪算法。该方法采用直方图统计与多帧平均混合作为动态背景更新法,经过噪音消除、形态学处理、阴影处理后,用区域标记法提取目标。利用目标特征参数建立目标数组,通过当前帧目标数组和前一帧目标数组距离匹配实现运动目标的快速跟踪。该方法与传统方法相比具有更好的学习能力,从而有效地提高了运动目标检测的正确率和快速性。实验结果表明该方法具有良好的鲁棒性和自适应性。