基于压缩感知的多特征加权目标跟踪算法

针对被跟踪目标运动、纹理或环境变化时, 采用基于压缩感知目标跟踪算法目标易漂移、丢失的问题, 提出了改进的压缩感知目标跟踪算法。通过压缩感知算法提取灰度和纹理特征, 计算特征对样本分类结果并更新特征的权值, 使用加权过的特征寻找目标在下一帧的位置。对不同视频的测试结果表明, 提出的算法在目标运动、纹理或环境变化的情况下跟踪准确, 在目标大小80×120像素时平均帧速为25 fps。与传统的压缩感知跟踪算法和其他跟踪算法相比, 所提出的算法在目标运动、纹理或环境变化时能快速准确地获取跟踪目标, 并具有更强的鲁棒性。     

基于压缩感知的实时心电信号压缩算法

远程无线心电实时监护系统的心电采集终端基于嵌入式设备开发,系统资源和网络带宽都收到限制,这要求系统设计具有低功耗和数据实时压缩的功能,因此引入压缩感知算法对心电信号进行处理。压缩感知算法具有编码计算简单快速,解码计算相对复杂的特点。对于心电信号,需要对压缩感知算法在实时心电压缩系统中的可行性进行研究。     

基于压缩感知的实时手势检测和跟踪算法

基于计算机视觉的实时手势检测与跟踪算法是人机交互领域的一项关键技术,传统的手势检测与跟踪算法将检测和跟踪分成两个独立的模块进行,检测与跟踪结果受手势姿态变化、目标遮挡、运动模糊以及外界环境干扰等因素的影响。提出了一种基于压缩感知的实时手势检测和跟踪算法,将基于检测得到的手势信息与基于压缩感知跟踪算法得到的目标信息进行有效融合,从而实现有效的手势检测与跟踪,与传统算法相比,该算法能实现手势跟踪自动初始化和跟踪错误后自我恢复功能。实验结果表明,提出的算法能对手势运动进行快速、连续、准确的识别,满足人机交互的要求。     

压缩感知目标跟踪

视频分析通常在分类或检测等高级任务之前解码并重构视频序列。但是,有时希望只进行视频分析而不暴露敏感信息,例如人员身份。提出了一个能够跟踪目标而不需要重构视频序列的编码方案。根据压缩感知理论,用每帧的少量伪随机投影编码一个视频序列。解码器利用背景消除图像的稀疏性重构前景目标。以粒子滤波器估计的目标位置作为先验知识,可以改进前景目标位置的重构。该编码方案同时具有隐私保护和安全加密功能。     

压缩感知形状检测

压缩感知以随机投影的形式利用较少的非传统采样,重构稀疏的或可压缩的信号。Hough变换通常用于检测图像中的直线和其他参数化形状。提出利用Hough变换域的稀疏性,用CS寻找图像中的参数化形状的方法。进行了用基于CS的方法检测噪声图像中的直线和圆的实验。     

Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing

This work explores the novel use of Bayesian compressive sensing (BCS) in radio tomographic imaging (RTI), which aims at addressing the performance degradation of shadow fade imaging due to multipath interferences, through the sophisticated efforts on enhancing BCS with the capability of heterogeneous-noise-variance learning. Our contribution is twofold. Firstly, we incorporate a hierarchical model of heterogeneous noise variances into sparse Bayesian learning, which can contribute to the enhancement of BCS in terms of noise-variance awareness. Then, under our enhanced BCS (namely heterogeneous BCS) framework, we develop two learning algorithms for the RTI reconstruction. Theoretical analysis will show the potential advantages of using our heterogeneous BCS in mitigating the effect of multipath interferences, as well as in improving the RTI performance with our learning algorithms. Finally, the experimental results in the context of device-free localization and tracking are reported to demonstrate the effectiveness of the proposed approach.     

Efficient heterogeneous execution on large multicore and accelerator platforms: Case study using a block tridiagonal solver

The algorithmic and implementation principles are explored in gainfully exploiting GPU accelerators in conjunction with multicore processors on high-end systems with large numbers of compute nodes, and evaluated in an implementation of a scalable block tridiagonal solver. The accelerator of each compute node is exploited in combination with multicore processors of that node in performing block-level linear algebra operations in the overall, distributed solver algorithm. Optimizations incorporated include: (1) an efficient memory mapping and synchronization interface to minimize data movement, (2) multi-process sharing of the accelerator within a node to obtain balanced load with multicore processors, and (3) an automatic memory management system to efficiently utilize accelerator memory when sub-matrices spill over the limits of device memory. Results are reported from our novel implementation that uses MAGMA and CUBLAS accelerator software systems simultaneously with ACML (2013)  [2] for multithreaded execution on processors. Overall, using 940 nVidia Tesla X2090 accelerators and 15,040 cores, the best heterogeneous execution delivers a 10.9-fold reduction in run time relative to an already efficient parallel multicore-only baseline implementation that is highly optimized with intra-node and inter-node concurrency and computation–communication overlap. Detailed quantitative results are presented to explain all critical runtime components contributing to hybrid performance.     

Comprehensive tempo-spatial data collection in crowd sensing using a heterogeneous sensing vehicle selection method

The wide distribution of mobile vehicles installed with various sensing devices and wireless communication interfaces has made vehicular mobile crowd sensing possible in practice. However, owing to the heterogeneity of vehicles in terms of sensing interfaces and mobilities, collecting comprehensive tempo-spatial sensing data with only one sensing vehicle is impossible. Moreover, the sensing data collected may expire in the future; as a result, sensing vehicles may have to continuously collect sensing data to ensure the relevance of such data. Although including more sensing vehicles can improve the quality of collected sensing data, this step also requires additional cost. Thus, how to continuously collect comprehensive tempo-spatial sensing data with a limited number of heterogeneous sensing vehicles is a critical issue in vehicular mobile crowd sensing systems. In this work, a heterogeneous sensing vehicle selection (HVS) method for the collection of comprehensive tempo-spatial sensing data is proposed. On the basis of the spatial distribution and sensing interfaces of sensing vehicles and the tempo-spatial coverage of collected sensing data, a utility function is designed in HVS to estimate the sensing capacity of sensing vehicles. Then, according to the utilities of sensing vehicles and the restriction on the number of recruited sensing vehicles, sensing vehicle selection is modeled as a knapsack problem. Finally, a greedy optimal sensing vehicle selection algorithm is designed. Real trace-driven simulations show that the HVS algorithm can collect sensing data with a higher coverage ratio in a more uniform and continuous manner than existing mobile crowd sensing methods.     

Energy-efficient multithreading for a hierarchical heterogeneous multicore through locality-cognizant thread generation

Energy costs have become increasingly problematic for high performance processors, but the rising number of cores on-chip offers promising opportunities for energy reduction. Further, emerging architectures such as heterogeneous multicores present new opportunities for improved energy efficiency. While previous work has presented novel memory architectures, multithreading techniques, and data mapping strategies for reducing energy, consideration to thread generation mechanisms that take into account data locality for this purpose has been limited. This study presents methodologies for the joint partitioning of data and threads to parallelize sequential codes across an innovative heterogeneous multicore processor called the Passive/Active Multicore (PAM) for reducing energy consumption from on-chip data transport and cache access components while also improving execution time. Experimental results show that the design with automatic thread partitioning offered reductions in energy-delay product (EDP) of up to 48%.     

Robust static resource allocation of DAGs in a heterogeneous multicore system

In this study, we consider an environment composed of a heterogeneous cluster of multicore-based machines used to analyze satellite data. The workload involves large data sets and is subject to a deadline constraint. Multiple applications, each represented by a directed acyclic graph (DAG), are allocated to a dedicated heterogeneous distributed computing system. Each vertex in the DAG represents a task that needs to be executed and task execution times vary substantially across machines. The goal of this research is to assign the tasks in applications to a heterogeneous multicore-based parallel system in such a way that all applications complete before a common deadline, and their completion times are robust against uncertainties in execution times. We define a measure that quantifies robustness in this environment. We design, compare, and evaluate five static resource allocation heuristics that attempt to maximize robustness. We consider six different scenarios with different ratios of computation versus communication, and loose and tight deadlines.     

异构多核计算系统的Codelet任务调度策略

Codelet数据流计算模型在处理大规模并行计算任务时效果显著,但该模型目前缺少在异构多核环境中的任务调度策略。因此,提出了一种在异构多核环境下基于蚁群算法的Codelet任务调度策略。该调度策略将启发式算法与蚁群算法相融合,在发挥各自优势的同时克服了启发式算法不能得出最优解的缺陷以及蚁群算法初始信息匮乏的问题。实验结果表明,智能蚁群任务调度策略相比Codelet运行时系统中原生的动态调度和静态调度策略具有更高的执行效率。     

Real-time scheduling algorithm for safety-critical systems on faulty multicore environments

An algorithm (called FTM) for scheduling of real-time sporadic tasks on a multicore platform is proposed. Each task has a deadline by which it must complete its non-erroneous execution. The FTM algorithm executes backups in order to recover from errors caused by non-permanent and permanent hardware faults. The worst-case schedulability analysis of FTM algorithm is presented considering an application-level error model, which is independent of the stochastic behavior of the underlying hardware-level fault model. Then, the stochastic behavior of hardware-level fault model is plugged in to the analysis to derive the probability of meeting all the deadlines. Such probabilistic guarantee is the level of assurance (i.e., reliability) regarding the correct functional and timing behaviors of the system. One of the salient features of FTM algorithm is that it executes some backups in active redundancy to exploit the parallel multicore architecture while other backups passively to avoid unnecessary execution of too many active backups. This paper also proposes a scheme to determine for each task the number of backups that should run in active redundancy in order to increase the probability of meeting all the deadlines. The effectiveness of the proposed approach is demonstrated using an example application.     

Volcano monitoring using remote sensing data

Abstract

The paper brings out the theoretical basis and utility of near-infrared band data sets obtained from Earth resources satellites, in the estimation of very high temperatures witnessed during volcanic eruptions. The Landsat Thematic Mapper (TM) and Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning System (LISS-II) data sets for the period 4 April 1991-4 August 1991 were used for studying the volcanic eruption at Barren Island (India). The effect of the sub-pixel size vent in the estimation of pixel-integrated temperature has been discussed. The volcanic vent temperature on 6 May 1991 was found to be 1084K. The availability of mid-IR bands (1-55-1-75 μm and 2.08-2.35μm spectral region) in Landsat-TM enabled bringing out the vent region in the false colour composite (FCC) generated using these and the near-IR (0.76-0.90μm) band. The very high or saturated values in mid-IR bands brought a good contrast between vent and its surroundings     

Real-time heart activity monitoring with optical illusion using a smartphone

Multimedia Tools and Applications - In this study, we obtained a pulsatile photoplethysmogram (PPG) signal from a fingertip using the built-in camera of an iPhone 6 s and displayed a...     

Encryption of image data using compressive sensing and chaotic system

Multimedia Tools and Applications - An image cryptosystem using chaotic compressive sensing is designed to achieve simultaneous compression - encryption. Compressive sensing requires a measurement...     

Real-time EEG-based emotion monitoring using stable features

In human–computer interaction (HCI), electroencephalogram (EEG) signals can be added as an additional input to computer. An integration of real-time EEG-based human emotion recognition algorithms in human–computer interfaces can make the users experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the applications. Currently, the most accurate EEG-based emotion recognition algorithms are subject-dependent, and a training session is needed for the user each time right before running the application. In this paper, we propose a novel real-time subject-dependent algorithm with the most stable features that gives a better accuracy than other available algorithms when it is crucial to have only one training session for the user and no re-training is allowed subsequently. The proposed algorithm is tested on an affective EEG database that contains five subjects. For each subject, four emotions (pleasant, happy, frightened and angry) are induced, and the affective EEG is recorded for two sessions per day in eight consecutive days. Testing results show that the novel algorithm can be used in real-time emotion recognition applications without re-training with the adequate accuracy. The proposed algorithm is integrated with real-time applications “Emotional Avatar” and “Twin Girls” to monitor the users emotions in real time.     

Streamline integration using MPI-hybrid parallelism on a large multicore architecture

Streamline computation in a very large vector field data set represents a significant challenge due to the nonlocal and data-dependent nature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programming and execution as applied to streamline integration on a large, multicore platform. With multicore processors now prevalent in clusters and supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice. We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize over seeds and parallelize over blocks, and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing between cores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication and I/O bandwidth than a traditional, nonhybrid distributed implementation.     

基于异构多核可编程系统的大点FFT卷积设计与实现

如今FFT卷积广泛应用于数字信号处理,并且过去几年证实了异构多核可编程系统(HMPS)的发展。另外,HMPS已经成为DSP领域的主流趋势。因此,研究基于HMPS大点FFT卷积的高效地实现显得非常重要。基于重叠相加FFT卷积方法,设计一款针对输入数据流的高效流水重叠相加滤波器。介绍了基于HMPS的大点FFT卷积实现,获得了高精度的滤波效果。此外,采用流水技术的滤波器设计,提高系统处理速度、数据吞吐率和任务并行度。基于Xilinx XC7V2000T FPGA开发板上的实验表明,参与运算的采样点越大,系统的任务并行度、处理速度和数据吞吐率就会越高。当采样点达到1M时,系统的平均任务平行度达到了5.33,消耗了2.745×10~6个系统时钟周期数,并且绝对误差精度达到10~(-4)。     

基于压缩传感的手写字符识别方法

基于新出现的压缩传感理论,提出了一种鲁棒的手写字符识别方法,能很好地对含有噪声的字符进行识别.该方法通过对测试字符进行稀疏表示,采用l1范数最小化算法求得最稀疏的系数解,所获得的系数具有明显的类别信息,从而易于对测试字符进行分类.实验结果表明,该方法具有很好的噪声鲁棒性.