A correlation‐based and spectrum‐aware admission control mechanism for multimedia streaming in cognitive radio sensor networks

Bandwidth management and traffic control are critical issues to guarantee the quality of service in cognitive radio networks. This paper exploits a network load refinement approach to achieve the efficient resource utilization and provide the required quality of service. A connection admission control approach is introduced in cognitive radio multimedia sensor networks to provide the data transmission reliability and decrease jitter and packet end‐to‐end delay. In this approach, the admission of multimedia flows is controlled based on multimedia sensors' correlation information and traffic characteristics. We propose a problem, connection admission control optimization problem, to optimize the connection admission control operation. Furthermore, using a proposed weighting scheme according to the correlation of flows issued by multimedia sensors enables us to convert the connection admission control optimization problem to a binary integer‐programming problem. This problem is a kind of a Knapsack problem that is solved by a branch and bound method. Simulation results verify the proposed admission control method's effectiveness and demonstrate the benefits of admission control and traffic management in cognitive radio multimedia sensor networks. Copyright © 2015 John Wiley & Sons, Ltd.     

基于模糊规划的无线传感器网络节点选择方法

针对无线传感器网络区域重构问题,提出了一种新的基于模糊规划算法的传感器选择方法。算法利用反距离加权插值法对插值点的数据进行预测,并且使用容斥原理计算每个节点的正常工作概率。以传感器正常工作概率,误差精度为约束条件,以传感器数量最少化为目标函数,求解0-1整数规划。进一步,考虑误差阈值和工作概率模糊的情况,将节点选择问题公式化为模糊规划求解。利用传感器温度数据对0-1整数规划和模糊规划算法进行分析评估,结果证明模糊规划算法在相同约束情况下,相较于0-1整数规划约能减少35%的传感器节点数量。     

Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis

Automated cell segmentation and tracking are critical for quantitative analysis of cell cycle behavior using time-lapse fluorescence microscopy. However, the complex, dynamic cell cycle behavior poses new challenges to the existing image segmentation and tracking methods. This paper presents a fully automated tracking method for quantitative cell cycle analysis. In the proposed tracking method, we introduce a neighboring graph to characterize the spatial distribution of neighboring nuclei, and a novel dissimilarity measure is designed based on the spatial distribution, nuclei morphological appearance, migration, and intensity information. Then, we employ the integer programming and division matching strategy, together with the novel dissimilarity measure, to track cell nuclei. We applied this new tracking method for the tracking of HeLa cancer cells over several cell cycles, and the validation results showed that the high accuracy for segmentation and tracking at 99.5% and 90.0%, respectively. The tracking method has been implemented in the cell–cycle analysis software package, DCELLIQ, which is freely available.      

Crowd Segmentation Using both Appearance and Stereo Information

Crowd segmentation is an important issue in video surveillance. With the decrease in their cost, stereo cameras can be used to help develop new algorithms to achieve better accuracy in crowd segmentation. This paper aims to develop a method to explore the depth cues for crowd segmentation in video surveillance. The contributions of this paper are twofold. First, a novel crowd segmentation method closely coupling appearance and stereo information has been developed. Instead of performing disparity calculation as a preprocessing step, stereo information is obtained concurrently with appearance-based crowd segmentation. Second, an object-level disparity algorithm is proposed for object segmentation in surveillance scenarios. Only one disparity value for each hypothetical object greatly reduces the computational complexity and simplifies the segmentation method. Experimental results and quantitative evaluations based on two surveillance scenarios are presented in this paper. The results consistently show the effectiveness of the algorithm in exploring depth cues for crowd segmentation.     

Data fusion in ubiquitous networked robot systems for urban services

There is a clear trend in the use of robots to accomplish services that can help humans. In this paper, robots acting in urban environments are considered for the task of person guiding. Nowadays, it is common to have ubiquitous sensors integrated within the buildings, such as camera networks, and wireless communications like 3G or WiFi. Such infrastructure can be directly used by robotic platforms. The paper shows how combining the information from the robots and the sensors allows tracking failures to be overcome, by being more robust under occlusion, clutter, and lighting changes. The paper describes the algorithms for tracking with a set of fixed surveillance cameras and the algorithms for position tracking using the signal strength received by a wireless sensor network (WSN). Moreover, an algorithm to obtain estimations on the positions of people from cameras on board robots is described. The estimate from all these sources are then combined using a decentralized data fusion algorithm to provide an increase in performance. This scheme is scalable and can handle communication latencies and failures. We present results of the system operating in real time on a large outdoor environment, including 22 nonoverlapping cameras, WSN, and several robots.     

Joint sink deployment and association for multi‐sink wireless camera networks

In this paper, we investigate the problem of joint sink deployment and association (JSDA) in multi‐sink wireless camera networks (WCNs). Each camera in a WCN requires a different streaming rate of delivering its stream back to an access point (sink) because of various surveillance requirements, for example, event detection or target tracking. In a WCN where multiple channels are supported, the sinks must be placed in suitable locations (sink deployment) to collect streams from cameras over nonoverlapping channels, and in addition, each camera must associate with the appropriate sink (sink association) so that its demand rate can be optimally satisfied. To achieve this goal, we first formulate the JSDA problem as an optimization model using mixed‐integer linear programming and prove its NP‐completeness. Two approaches, branch‐and‐bound and our heuristics, iterative sink deployment and association (ISDA), are then developed to solve JSDA. We evaluate the performance via simulations with the traces collected by real measurements and show that ISDA can effectively satisfy cameras' demands with a reasonable computational cost. Copyright © 2014 John Wiley & Sons, Ltd.     

Directional Sensor Placement with Optimal Sensing Range,Field of View and Orientation

This paper introduces a new wireless sensor network planning problem referred to as the Optimal Sensor Configuration (OSC) problem. In this problem, the goal is to find an optimal subset of locations where directional sensors and base stations are installed in order to minimize the total network cost while satisfying the requirements of coverage and connectivity. This goal is achieved by appropriately choosing the base station type and configuring each sensor to be installed in the sensor field. The optimal configuration of each sensor is determined by three parameters which are sensing range, field of view and orientation. The paper also gives an integer linear programming formulation of the OSC problem. The viability and effectiveness of the proposed formulation are illustrated through numerical results.     

Automatic objects segmentation with RGB-D cameras

Automatic object segmentation is a fundamentally difficult problem due to issues such as shadow, lighting, and semantic gaps. Edges play a critical role in object segmentation; however, it is almost impossible for the computer to know which edges correspond to object boundaries and which are caused by internal texture discontinuities. Active 3-D cameras, which provide streams of depth and RGB frames, are poised to become inexpensive and widespread. The depth discontinuities provide useful information for identifying object boundaries, which makes automatic object segmentation possible. However, the depth frames are extremely noisy. Also, the depth and RGB information often lose synchronization when the object is moving fast, due to different response time of the RGB and depth sensors. We show how to use the combined depth and RGB information to mitigate these problems and produce an accurate silhouette of the object. On a large dataset (24 objects with 1500 images), we provide both qualitative and quantitative evidences that our proposed techniques are effective.     

A genetic algorithm based exact approach for lifetime maximization of directional sensor networks

This paper addresses the problem of maximizing lifetime of directional wireless sensor networks, i.e., where sensors can monitor targets in an angular sector only and not all the targets around them. These sectors usually do not overlap, and each sensor can monitor at most one sector at a time. An exact method is proposed using a column generation scheme where a two level strategy, consisting of a genetic algorithm and an integer linear programming approach, is used to solve the auxiliary problem. The role of integer linear programming (ILP) approach is limited to either escaping from local optima or proving the optimality of the current solution. Computational results clearly show the advantage of the proposed approach over a column generation approach based on solving the auxiliary problem through ILP approach alone as the proposed approach is several times faster.     

电磁式白板定位方法的研究

在电磁白板的设计中,电子白板的定位技术是一个难点.定位算法的好坏直接影响到电子白板的性能.针对在生产实际中由于定位算法的不精确而产生的跳变、不连续等问题,提出了一种切实可行的电磁白板的定位方法,在原有定位的基础上进行了更为精确的细分,并通过编程进行了实现.实验表明此算法具有很好的效果,并且可以应用到生产实际中.     

基于分块相位梯度算法的多舰船尾迹自动识别

提出一种基于分块相位梯度算法(PGA)的多舰船尾迹合成孔径雷达(SAR)图像自动识别方法。首先在多结构形态学滤波和自适应阈值(OTSU)分割的基础上,利用分块PGA算法消除运动舰船的位置偏移和像元模糊,还原舰船实际信息;利用改进的局部Hough变换对分块图像进行检测和识别,根据识别出的尾迹坐标和角度反演运动舰船位置和航向信息。检测结果表明,该算法可准确检测SAR图像中舰船的实际位置和航向,识别准确度高,抗干扰性强。     

Filtering approaches to accelerated consensus in diffusion sensor networks

The main objective in distributed sensor networks is to reach agreement or consensus on values acquired by the sensors. A common methodology to approach this problem is using the iterative and weighted linear combination of those values to which each sensor has access. Different methods to compute appropriate weights have been extensively studied, but the resulting iterative algorithm still requires many iterations to provide a fairly good estimate of the consensus value. In this paper, different accelerating consensus approaches based on adaptive and non‐adaptive filtering techniques are studied and applied on the problem of acoustic source localization using the adaptive projected subgradient method. A comparative simulation study shows that the non‐adaptive polynomial filters based on Newton's interpolating polynomials and semi‐definite programming can provide more accelerated consensus and better estimation accuracy than adaptive filters evaluated using constrained affine projection algorithm or stochastic gradient algorithm provided that the network topology is known beforehand. Copyright © 2013 John Wiley & Sons, Ltd.     

基于PCI总线的电视图像处理仿真系统设计

为便于科研人员在电视图像处理系统设计过程中对图像处理的新算法进行评估和测试,降低评估测试板硬件电路的设计复杂性,在此提出了解决方案,并实现了基于PCI总线的电视图像实时仿真系统的设计。该系统首先通过PCI插卡实现对电视图像进行采集、预处理和视频A／D转换,然后选用具有高速特性的PCI总线将数字化后的数字图像信息写入计算机系统内存,最后在计算机终端上,使用高级语言编程,完成图像处理和控制接口软件开发,实现计算机软件对PCI硬件设备的访问,数字图像的实时处理、分割、匹配等算法仿真。     

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.     

System reliability design with deteriorative components

In constrained optimum system reliability problems, the reliability of each component is usually assumed to be fixed, and the optimal number of redundancies at each stage is determined. However, in real world the component reliability decreases as component deteriorates; i.e. the component reliability is dependent on its age. This paper presents a system reliability optimization problem with deteriorative components. We formulate this problem as a parametric nonlinear integer programming problem where the objective function has a time parameter t. A solution method is proposed for solving it. We believe that this model can provide very useful information for decision makers and reliability designers.     

异构传感器网络多目标多重覆盖策略

在传感器网络环境监测应用中,常存在多种监测对象。此类应用中,每个异构网络节点搭配不同类型的传感器,要求网络部署可多重覆盖监测区以监测各个子对象。针对节点随机分布的传感器网络,该文提出一种平均子网寿命模型以评价网络中某子对象的监测寿命。在给定成本预算与各子对象的基本覆盖率需求下,采用一种基于整数向量规划的多目标多重覆盖算法权衡成本、网络覆盖性能以及网络中不同子对象的监测寿命。该算法分两部分,首先确定监测不同子对象的传感器数量,然后基于平均子网寿命模型,确定不同类型的异构节点数量。针对向量规划问题,文中给出两种不同次优解法。在仿真实验部分,将不同次优解法进行了对比,并分析了算法计算复杂度。仿真示例验证了该文的覆盖算法在多对象监测应用中的有效性。     

(论坛)基于信息融合技术的空气质量检测系统

随着社会的发展和人民环保意识的不断提高,人们越来越重视周围的生存环境质量状况。为了从多方面反映区域内空气的状态,设计了一种基于信息融合技术的空气质量检测系统,该系统采用模块化设计,通过集成和扩展多种传感器对区域内空气进行局部检测,获得彼此独立的数据,再运用信息融合技术对各个节点的数据进行融合,最终实现对区域内空气质量状况的实时检测,反映空气污染程度。通过构建模型的方式对该系统进行验证,初步证明了该系统的有效性和可靠性。     

Enhancing Output Power of Cylindrical Triboelectric Nanogenerators by Segmentation Design and Multilayer Integration

The triboelectric nanogenerator (TENG) is a newly invented technology that is effective for harvesting ambient mechanical energy for portable electronics, self‐powered sensor networks, etc. Here, by introducing segmentation and multilayer integration into the cylindrical TENG, the generator's output is enhanced significantly. With a four‐layer and thirty‐segment configuration, the TENG produces a short‐circuit current of 86 μA (13.5 μA m^?2) and power of 4.3 mW (676 mW m^?2) at a rotating speed of 600 rpm, which are respectively over 70 and 15 times higher than those of the one‐layer and one‐segment structure. This makes the TENG a sufficient power supply for conventional electronics, such as light bulbs and temperature sensors. Furthermore, it is demonstrated that the segmentation design is a perfect self‐power management technique to automatically lower the TENG's output voltage and increase its output current without scarifying the output power. The fractal geometry is an effective way to maximize the TENG's contact surface area and thereby the output performance.     

改进的全卷积神经网络的脑肿瘤图像分割

针对现有机器学习算法分割脑肿瘤图像精度不高的问题,提出一种基于改进的全卷积神经网络的脑肿瘤图像分割算法。算法首先将FLAIR、T2和T1C三种模态的MR脑肿瘤图像进行灰度归一化,随后利用灰度图像融合技术得到肿瘤信息更加全面的预处理图像;然后采用融合三次脑肿瘤特征信息的改进全卷积神经网络对预处理图像进行粗分割,并且在每个卷积层后加入批量正则化层以加快网络训练的收敛速度,提高训练模型精度;最后融合全连接条件随机场细化粗分割结果中的脑肿瘤边界。实验结果表明,相较于传统的卷积神经网络脑肿瘤图像分割算法,本算法在分割精度和稳定性上有了较大提升,平均Dice可达91.29%,实时性较好,利用训练模型平均1s内可完成单张脑肿瘤图像的分割。      

Minimal shape and intensity cost path segmentation

A new generic model-based segmentation algorithm is presented, which can be trained from examples akin to the active shape model (ASM) approach in order to acquire knowledge about the shape to be segmented and about the gray-level appearance of the object in the image. Whereas ASM alternates between shape and intensity information during search, the proposed approach optimizes for shape and intensity characteristics simultaneously. Local gray-level appearance information at the landmark points extracted from feature images is used to automatically detect a number of plausible candidate locations for each landmark. The shape information is described by multiple landmark-specific statistical models that capture local dependencies between adjacent landmarks on the shape. The shape and intensity models are combined in a single cost function that is optimized noniteratively using dynamic programming, without the need for initialization. The algorithm was validated for segmentation of anatomical structures in chest and hand radiographs. In each experiment, the presented method had a significant higher performance when compared to the ASM schemes. As the method is highly effective, optimally suited for pathological cases and easy to implement, it is highly useful for many medical image segmentation tasks.