Minimum payment collaborative sensing network using mobile phones

Mobile phones with embedded sensors have been applied in various collaborative sensing applications. To encourage mobile phone users to perform collaborative sensing, the data demanders usually pay mobile phone users for required data. In this paper, we study the Minimum Payment of Attaining the Required Data with mobile phones (MPARD) problem in collaborative sensing network: given sensing regions \(R = \{R_1, R_2, \ldots , R_m\}\) , the set of requisite data \(D_i\) for each sensing region \(R_i\) and a set of mobile phones \(M\) , the \(MPARD\) problem studies how to select mobile phones to obtain all the required data such that the data demanders’ total payment to mobile phone users is minimized. In reality, some systems need the fresh sensing data from mobile phones at each pre-determined time slot, and others don’t require the real-time data and the sensing data from previous time slots is also deemed useful. Based on the above two different requirements of data timeliness, we first define two subproblems derived from \(MPARD\) problem: \(MPARD_t\) and \(MPARD_p\) . After that, for each subproblem, we propose an approximation algorithm for the situation where the trajectories of mobile phones are determinate and a heuristic for the situation where trajectories are unknown. Simulation results demonstrate that our algorithms are efficient.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

Adaptive encoding of zoomable video streams based on user access pattern

Zoomable video allows users to selectively zoom and pan into regions of interest within the video for viewing at higher resolutions. Such interaction requires dynamic cropping of RoIs on the source video. We have previously explored two different ways of encoding and transmitting video to support dynamic RoI cropping: (i) Monolithic streaming uses a standard video encoder to encode the video. When an RoI is requested, the bits belonging to the RoI along with other bits required to decode the RoIs (due to encoding dependencies) are transmitted. (ii) Tile streaming divides regions in the standard video into rectangular tiles that are encoded independently. The tiles that intersect with a requested RoI are transmitted. In this paper, we consider how the bandwidth needed to transmit the RoIs can be reduced by carefully encoding the source video for each of the two encoding schemes. The goal is to support bandwidth efficient compressed domain RoI cropping in the context of virtual zoom and pan by tuning encoder parameters. Our key idea is to exploit user access patterns to the RoIs, and encode different regions of the video with different encoding parameters based on the popularity of the region. We show that our encoding method can reduce the expected bandwidth by up to 43% in the test video sequence which we have used.     

RFID系统防碰撞算法的研究及其改进

射频识别技术中,防碰撞问题是提高系统读取效率的关键问题。文章在传统防碰撞算法的基础上,提出一种优化的基于动态帧时隙ALOHA的防碰撞（Improved dynamic framed slotted ALOHA,IDFSA）算法。实验结果表明：IDFSA算法大幅度地提高了系统的时隙利用率和读取效率。     

3D holoscopic image coding scheme using HEVC with Gaussian process regression

3D holoscopic image, also known as integral imaging, light field imaging and plenoptic imaging, can provide a natural and fatigue-free 3D visualization. However, a large amount of data is required to represent the 3D holoscopic content. Therefore, efficient coding schemes for such particular type of image are needed. In this paper, we propose a Gaussian process regression based prediction scheme to compress the 3D holoscopic image. In the proposed scheme, the coding block and its prediction supports are modeled as a Gaussian process (GP) and Gaussian process regression (GPR) is used to obtain a better prediction of the coding block. Limited searching windows in horizontal and vertical directions are used to obtain the prediction supports, and a filtration method is designed to judge the reliability of the obtained prediction supports. Moreover, in order to alleviate the high complexity caused by GPR, a sparsification method is also put forward. Experimental results demonstrate the advantage of the proposed scheme for 3D holoscopic image coding in terms of different quality metrics as well as the visual quality of the views rendered from decompressed 3D holoscopic content, compared to the HEVC intra-prediction method and several other prediction methods in this field.     

Gaussian-Hermite moment-based depth estimation from single still image for stereo vision

Depth information of objects plays a significant role in image-based rendering. Traditional depth estimation techniques use different visual cues including the disparity, motion, geometry, and defocus of objects. This paper presents a novel approach of focus cue-based depth estimation for still images using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. The GHMs are chosen due to their superior reconstruction ability and invariance properties to intensity and geometric distortions of objects as compared to other moments. Since depths of local neighboring pixels are significantly correlated, the Laplacian matting is employed to obtain final depth map from the moment-based focus map. Experiments are conducted on images of indoor and outdoor scenes having objects with varying natures of resolution, edge, occlusion, and blur contents. Experimental results reveal that the depth estimated from GHMs can provide anaglyph images with stereo quality better than that provided by existing methods using traditional visual cues.     

基于智能网格降水和水文滑坡耦合模型的全国洪水和滑坡灾害滚动预报

为解决洪水和滑坡灾害在大尺度下实时预报中面临驱动数据、土水参数难以获取以及计算负荷的问题,以水文-滑坡耦合模型(CRESLIDE)为基础,引入无缝隙精细化智能网格降水数据作为驱动因子,利用土壤厚度模型及土壤类型数据获取分布式土水参数,结合地表覆盖信息及累积降水量动态识别滑坡敏感区域,采用并行技术加速计算过程,构建了全国尺度的CRESLIDE模型,对2022—2023年降雨诱发洪水和滑坡灾害进行实时预报,并基于汛期6—8月的长序列和个例灾害对模型进行检验。结果表明：CRESLIDE模型在并行计算时的加速比最高可达5.53;洪水灾害整体上集中于华北和西南一带,滑坡灾害多发生在我国南方地区,7—8月北方滑坡开始增加;在长序列检验中,模型预报精度评价指标ROC曲线下面积均超过或接近0.7,其中洪水灾害预报效果优于滑坡灾害;在个例检验中,模型预测的灾害发生位置和时间与实测结果较为一致。     

数据库形式化安全策略模型建模及分析方法

目前数据库形式化安全策略模型存在抽象层次较高、缺乏对数据库状态与约束的充分描述等问题,难以辅助用户发现商用数据库设计中的微小缺陷。提出了一种基于PVS语言的数据库形式化安全策略模型建模和分析方法,该方法较以往模型能够更加贴近实际数据库,应用范围更广,安全属性描述更加完整,描述的模型具有灵活的可扩展性,并且保证了建模与验证的效率。最后,将该方法应用于数据库管理系统BeyonDB的安全策略建模分析中,帮助发现了系统若干设计缺陷,证明了方法的有效性。