一种快速、鲁棒的压缩视频光流场估计算法

该文针对视频运动特性的快速分析需求,提出了一种压缩域中的光流场估计算法。首先利用DCT变换后的两个AC系数估计出图像的空间偏导数,在此基础上再利用预测残差以及运动矢量对图像的时间偏导数进行估计。另外对编码过程中未进行前向运动预测的宏块特性进行了详细的分析,给出了这些宏块相对于前向参考帧的运动信息估计方法,并对时域不连续的图像块给出了一种修正的偏导数估计方法,以此解决遮挡、切换等现象。然后通过最小二乘法并结合图像的偏导数进行光流场估计。实验表明该方法在准确度上可以达到或超过像素域中的L-K估计方法,比起现有的压缩域估计方法也有一定提高,而在计算时间上相比像素域估计有了大幅度降低。     

一种MPEG4标准的快速及高效视频编码器

提出了一个基于MPEG4的快速高效存储的DCT视频编码器，用他把一个H．261视频数据流转换为一个低比特率、低空间分辨率的MPEG4流，并进行无线视频处理。和现有编码器相比，他不仅能够大大地节省实际需要存储空间，计算复杂度也降低了许多，实验结果表明设计的编码器得到的视频质量与象素领域方法得到的视频质量相当。     

基于CUDA的DCT快速变换实现方法

为了加快DCT快速变换的处理速度,提出了一种基于CUDA在图形处理器(GPU)上实现DCT快速变换的方法,其中主要利用DCT变换中各分块之间的独立性适合GPU并行处理架构的特点,把传统串行DCT快速变换算法映射到CUDA并行编程模型,并从线程分配,内存使用,硬件资源划分等方面进行优化,来充分利用GPU的巨大运算能力,实验表明,该方法能有效地提高D阻快速变换的速度.     

一种模糊函数时频差联合估计快速算法

直接相关时延估计方法仅适用于接收站和辐射源都相对静止的情况,若接收信号存在多普勒频差则会严重影响时差(TDOA)估计精度,为解决相关时延估计方法的局限性,采用基于模糊函数的时频差联合估计方法,并针对模糊函数计算量巨大的问题,提出一种变时频分辨率峰值搜索方法。利用GPU并行计算架构实现来实现算法,以提高算法效率,加快算法收敛速度。仿真试验结果表明了算法的有效性。     

Efficient Stereoscopic Video Coding Using Joint Disparity-Motion Estimation

The goal of this paper is to determine dense disparity and motion fields jointly and efficiently, given a sequence of stereoscopic images. At each time instant, the motion field of left sequences is estimated by using the current disparity field, the next disparity field, and the motion field of right sequences. The improved concepts for the proposed joint estimation are based on an edge-preserving regularization, an ambiguous vector region detection, and a median filtering for block-based vector generation. The reported approach is verified by processing a set of stereo sequences. Results are given with real stereoscopic data.     

Fast Algorithm and Efficient Architecture for Integer and Fractional Motion Estimation

Motion estimation in H.264/AVC, is done in two parts – integer motion estimation, and fractional motion estimation. Hardware reuse for both parts is inefficient due to the differences between them. In this paper we address the hardware reuse problem by proposing a, fast motion estimation algorithm as well as a pipelined FPGA-based, field programmable system-on-chip (FPSoC), for integer and fractional motion estimation. Our results show that the rate-distortion loss of our algorithm is insignificant when compared to full search in H.264/AVC. Its average Y-PSNR loss is 0.065 dB, its average percentage bit rate increase is 5 %, and its power consumption is 76 mW. Our FPSoC is hardware-efficient, even out-performing some state-of-the-art ASIC implementations. It can support up to high definition 1280?×?720p video at 24Hz. Thus, our proposed algorithm and architecture is suitable for delivery of high quality video on low power devices and low bit rate applications which typically use H.264/AVC baseline profile@levels 1–3.1.     

Fast and Efficient Method for Fire Detection Using Image Processing

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision‐based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand‐alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state‐of‐the‐art fire detection method.     

A Fast Statistical Soft Error Rate Estimation Method for Nano-scale Combinational Circuits

Nano-scale digital integrated circuits are getting increasingly vulnerable to soft errors due to aggressive technology scaling. On the other hand, the impacts of process variations on characteristics of the circuits in nano era make statistical approaches as an unavoidable option for soft error rate estimation procedure. In this paper, we present a novel statistical Soft Error Rate estimation framework. The vulnerability of the circuits to soft errors is analyzed using a newly defined concept called Statistical Vulnerability Window (SVW). SVW is an inference of the necessary conditions for a Single Event Transient (SET) to cause observable errors in the given circuit. The SER is calculated using a probabilistic formulation based on the parameters of SVWs. Experimental results show that the proposed method provides considerable speedup (about 5 orders of magnitude) with less than 5 % accuracy loss when compared to Monte-Carlo SPICE simulations. In addition, the proposed framework, keeps its efficiency when considering a full spectrum charge collections (more than 36X speedups compared to the most recently published similar work).     

Fast and Robust DOA Estimation Method Using Searching Space Pre-estimation

An improved MUSIC localization algorithm is proposed in this paper in order to locate a sound source in reverberant and directional noise scenario, with comparatively low computational complexity. Based on Time delay of arrival （TDOA） estimation, a candidate searching space is first predetermined and then Beamspace MUSIC combined with Spatial smoothing （BMUSIC-SS） algorithm is applied in this space to find the Direction of arrival （DOA） of the desired source. This two-step method not only reduces the number of calculations by avoiding the exhaustive search, but also improves the localization performance in coherent noise environment due to the fact that the spaces including directional noises are partially removed. Besides, based on the localization system we developed, the DOA parameters are separated to reduce a two-dimension search into a couple of one dimensional ones that further cuts down the computational load. The computer simulations and practical experiments demonstrate the robustness and efficiency of the proposed method.     

Efficient Frequency Estimation Using the Pulse-Pair Method at Various Lags

The pulse-pair (PP) method is an efficient time-domain-based technique of frequency estimation, which requires a very low number of multiplications, thus ideally suited for field-programmable gate array-type hardware implementations. The optimum frequency estimation using the PP method needs a lag value equal to two-thirds of the signal length, and requires phase unwrapping that usually makes the technique computationally unattractive. In this letter, we propose efficient techniques for estimating frequency using the optimum PP method and multiple correlation lags.     

Hardware Efficient Double Diamond Search Block Matching Algorithm for Fast Video Motion Estimation

Application of video in multimedia communication has become feasible due to efficient block matching algorithm (BMA) based motion estimation (ME) and motion compensation (MC) methods, that facilitate high data compression. To sustain visual quality of video, large amount of computation is involved in ME which can be reduced by fast search BMA and making fast search faster by various means like predicting initial search center (ISC) and early search termination. But more challenging work is to design an architecture which performs computation hungry search process in fewer clock cycles which will actually make fast search rapid for real time encoding. Implementations are available for matching multiple macroblocks in single clock cycle, but bottleneck is accessing macroblocks from memory while following sequential irregular search patterns of most of fast search algorithms. This paper proposes a novel, Hardware Efficient Double Diamond Search (HEDDS) algorithm which reaches far in search window more rapidly to identify best match and minimizes number of iterations of search pattern and hence diminish required clock cycles to read macroblocks from memory. From implementation perspective, HEDDS is up to 7.5 % to 33 % faster than existing BMAs and also offers reasonably good quality of encoding. With variable block size, HEDDS demonstrate average BD-PSNR improvement of 0.381, 0.088, 0.87 and 0.233 dB at BD-bitrate drop of 12.994 %, 2.499 %, 25.599 %, 6.887 % in comparison of HS, HMDS, LDPS and UMHS correspondingly. Proposed HEDDS architecture can process 259 HD frames per second in average case for fixed block size which is promising figure for real time encoding on devices having inadequate computational resources.     

利用奇异值分解快速计算单站RCS的算法

在特征基函数方法中,应用多层奇异值分解技术,由空间各个角度入射的一系列平面波谱生成的初次特征基函数构造出与入射角度、极化方式无关的正交特征基函数组,通过该组基函数的不同线性组合,求出平面波以任意角度照射下金属目标的表面电流分布,进而实现金属目标单站RCS曲线的快速计算。文中结合物理光学法和二叉树动态存储技术,进一步降低存储需求和初次基函数的计算时间。数值算例表明本文方法的正确性和高效性。     

Scalable, Memory Efficient, High-Speed IP Lookup Algorithms

One of the central issues in router performance is IP address lookup based on longest prefix matching. IP address lookup algorithms can be evaluated on a number of metrics—lookup time, update time, memory usage, and to a less important extent, the time to construct the data structure used to support lookups and updates. Many of the existing methods are geared toward optimizing a specific metric, and do not scale well with the ever expanding routing tables and the forthcoming IPv6 where the IP addresses are 128 bits long. In contrast, our effort is directed at simultaneously optimizing multiple metrics and provide solutions that scale to IPv6, with its longer addresses and much larger routing tables. In this paper, we present two IP address lookup schemes—Elevator-Stairs algorithm and logW-Elevators algorithm. For a routing table with$N$prefixes, The Elevator-Stairs algorithm uses optimal$cal O(N)$memory, and achieves better lookup and update times than other methods with similar memory requirements. The logW-Elevators algorithm gives$cal O(log W)$lookup time, where$W$is the length of an IP address, while improving upon update time and memory usage. Experimental results using the MAE-West router with 29 487 prefixes show that the Elevator-Stairs algorithm gives an average throughput of 15.7 Million lookups per second (Mlps) using 459KB of memory, and the logW-Elevators algorithm gives an average throughput of 21.41Mlps with a memory usage of 1259KB.     

Estimation of Respiratory Rate From Photoplethysmogram Data Using Time–Frequency Spectral Estimation

We present a new method that uses the pulse oximeter signal to estimate the respiratory rate. The method uses a recently developed time-frequency spectral estimation method, variable-frequency complex demodulation (VFCDM), to identify frequency modulation (FM) of the photoplethysmogram waveform. This FM has a measurable periodicity, which provides an estimate of the respiration period. We compared the performance of VFCDM to the continuous wavelet transform (CWT) and autoregressive (AR) model approaches. The CWT method also utilizes the respiratory sinus arrhythmia effect as represented by either FM or AM to estimate respiratory rates. Both CWT and AR model methods have been previously shown to provide reasonably good estimates of breathing rates that are in the normal range (12-26 breaths/min). However, to our knowledge, breathing rates higher than 26 breaths/min and the real-time performance of these algorithms are yet to be tested. Our analysis based on 15 healthy subjects reveals that the VFCDM method provides the best results in terms of accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.3 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12-36 breaths/min.     

单通道SAR无模糊估计快速运动目标速度

该文提出一种利用单通道相位信息无模糊估计地面运动目标径向速度方法。在距离频域做两视处理,并在多普勒域经过两视干涉处理,使得运动目标的干涉相位满足相位连续性假设。利用最小二乘线性拟合估计干涉相位关于多普勒频率的斜率,径向速度可由该斜率计算得到。该文方法具有不受相位缠绕影响的优点。相比于单通道幅度方法,该方法具有更高的估计精度和实时性。该方法在多普勒域能够同时估计多个目标径向速度。通过实测数据处理验证了该文方法的有效性。     

一种基于高效FrFT的LFM信号检测与参数估计快速算法

针对传统方法对线性调频(LFM)信号检测与参数估计运算量大的问题,该文提出一种基于高效FrFT的快速算法。首先,分析了高效FrFT原理,指出高效FrFT存在旋转角度的选取、易受初始频率影响以及抗噪性能差等问题。针对以上问题,该文利用修正的功率谱平滑滤波方法对高效FrFT进行改进。理论分析表明,该文提出的改进算法仅用3次旋转角度即可实现较低信噪比下LFM信号的检测和参数估计。与传统的FrFT相比,在保证参数估计精度不变的情况下,运算复杂度大大降低,更符合工程上实时处理的要求。仿真结果验证了该算法的有效性。     

利用频谱排序和筛选的突变噪声快速估计

针对最小值控制递归平均(Minima Controlled Recursive Averaging,MCRA)算法不能快速跟踪突变噪声的问题,提出了一种基于频谱排序和筛选的突变噪声快速估计方法。该方法在MCRA算法的基础上对带噪语音的功率谱进行排序,筛选出不含语音信号的频点来估计噪声的平均功率谱;当检测到噪声突变时,对当前的平滑参数和状态变量进行校正。仿真结果表明,该方法可以将突变噪声的跟踪时间缩短90%以上;用于语音降噪处理时,音质可以提升约0.4分。该方法具有一定的工程应用价值。     

在脉冲噪声环境中用于快衰信道估计的改进型算法

该文分析了在存在噪声干扰的情况下,进行估计快衰信道的方法。在无线通信系统中,快衰信道可以采用AR(Auto-Regressive)模型进行预测,而LS (Least Square)算法和自适应Kalman滤波器可以分别对AR模型的参数和信道的冲激响应进行估计,但是这两种算法对噪声干扰非常敏感。该文提出改进型的RLM算法和Kalman 滤波器,并在存在噪声的情况下,使用它们并行对AR参数和信道的冲激响应进行联合估计。仿真结果显示：相比于传统的算法,改进后的算法在联合估计信道时,提高了抵抗大脉冲干扰的能力,加快了待估的参数的收敛速度。     

Efficient Inter Prediction Mode Decision Method for Fast Motion Estimation in High Efficiency Video Coding

High Efficiency Video Coding (HEVC) is the most recent video coding standard to achieve a higher coding performance than the previous H.264/AVC. In order to accomplish this improved coding performance, HEVC adopted several advanced coding tools; however, these cause heavy computational complexity. Similar to previous video coding standards, motion estimation (ME) of HEVC requires the most computational complexity; this is because ME is conducted for three inter prediction modes — namely, uniprediction in list 0, uniprediction in list 1, and biprediction. In this paper, we propose an efficient inter prediction mode (EIPM) decision method to reduce the complexity of ME. The proposed EIPM method computes the priority of all inter prediction modes and performs ME only on a selected inter prediction mode. Experimental results show that the proposed method reduces computational complexity arising from ME by up to 51.76% and achieves near similar coding performance compared to HEVC test model version 10.1.     

Dynamic Origin–Destination Flow Estimation Using Cellular Communication System

This paper aims to develop a new approach for the estimation of dynamic origin–destination (O–D) flow using cell phones as traffic probes. The state-space model, which depends on the autoregressive dynamics of O–D flow and the time series for link volume counts, was adopted. Unlike a direct approach that uses sample O–D flows extracted from the cell-based location data as additional observations, an indirect approach is proposed wherein the assignment map in the model is derived from the passing time at observation locations and the path choice proportion. A probe phone's passing time at a certain point in a cell was approximated with its entry and exit times at cell boundaries. The average path choice proportion was also estimated using cell-based trajectories of probe phones. The simulation experiments confirmed that the approach was successfully applicable to the real-world freeway network. The results suggest that the O–D flows estimated from the present approach are promising in that the mean absolute error ratio was smaller than the case wherein only historical O–D flows are concerned. A sensitivity analysis revealed that the present approach met the requirements for an urban area encompassing a huge number of cell phone users in a microcell system.