A new fast friendly window-based congestion control for real-time streaming media transmission

1 Introduction In the current Internet, not all applications use TCP and they do not follow the same concept of fairly sharing the available bandwidth. The rapid growing of real-time streaming media applications will bring much UDP traffic without integrating TCP compatible congestion control mechanism into Internet. It threats the quality of service (QoS) of real-time applications and the stability of the current Internet. For this reason, it is desirable to define appropriate rate rule…     

A multiple logistic function for pilot trainee selection

Using the case of a pilot trainee selection problem as a vehicle for demonstration, this paper develops a multiple logistic function (MLF) for screening candidates unfit for the job on the basis of their test scores.

Using animated computer graphics on a personal computer, 15 single plus 9 dual tasks involving various perceptual/psychomotor functions and information processing are administered to 105 subjects, and 62 performance measures are collected. By statistically evaluating these measures, 7 most important measures relevant to flight performance are selected to be used as a new pilot trainee selection battery, applicable in the field without much logistic difficulty.

Treating the measures from 7 different single tasks as “risk” variables, an MLF involving a linear discriminant function is developed. Retrospective classification demonstrates that the MLF is capable of predicting 80% of the observations correctly regarding their group origin.     

A fast, compact approximation of the exponential function

Neural network simulations often spend a large proportion of their time computing exponential functions. Since the exponentiation routines of typical math libraries are rather slow, their replacement with a fast approximation can greatly reduce the overall computation time. This article describes how exponentiation can be approximated by manipulating the components of a standard (IEEE-754) floating-point representation. This models the exponential function as well as a lookup table with linear interpolation, but is significantly faster and more compact.     

具有P2P功能的嵌入式流媒体播放器的设计

基于IP流媒体技术的嵌入式流媒体终端因其使用简单、便于携带而迅速发展,但传统的嵌入式流媒体终端在成本、性能和算法易升级性等方面存在诸多问题;以S3C6410微处理器为核心,综合考虑硬件的多媒体处理能力和软件的可升级性,提出一种层次化、模块化的设计方案;同时在终端引入P2P播放模式,使得近距离终端设备之间以P2P方式共享视频流,减轻服务器负载。详细讨论了流媒体终端缓存空间管理机制、流媒体数据下载速度调整策略和播放流程设计。通过实验验证了该方案的可行性。     

A fast hybrid image cryptosystem based on random generator and modified logistic map

Multimedia Tools and Applications - This paper demonstrates an image encryption technique using a hybrid method. This method consists of two stages. The image is XORed with the Pseudo-Random Number...     

InstantLeap: an architecture for fast neighbor discovery in large-scale P2P VoD streaming

In large-scale peer-to-peer (P2P) video-on-demand (VoD) streaming applications, a fundamental challenge is to quickly locate new supplying peers whenever a VCR command is issued, in order to achieve smooth viewing experiences. For many existing commercial systems which use tracker servers for neighbor discovery, the increasing scale of P2P VoD systems has overloaded the dedicated servers to the point where they cannot accurately identify the suppliers with the desired content and bandwidth. To avoid overloading the servers and achieve instant neighbor discovery over the self-organizing P2P overlay, we design a novel method of organizing peers watching a video. The method features a light-weight indexing architecture to support efficient streaming and fast neighbor discovery at the same time. InstantLeap separates the neighbors at each peer into a streaming neighbor list and a shortcut neighbor list, for streaming and neighbor discovery respectively, which are maintained loosely but effectively based on random neighbor list exchanges. Our analysis shows that InstantLeap achieves an O(1) neighbor discovery efficiency upon any playback “leap” across the media stream in streaming overlays of any size, and low messaging costs for overlay maintenance upon peer join, departure, and VCR operations. We also verify our design with large-scale simulation studies of dynamic P2P VoD systems based on real-world settings.     

A fast approximation to the sine function

A function approximating the sine function is given which was designed for high speed when coded into a computer routine. The domain is divided into a small number of intervals and in each of these a straight line approximation is used. The slopes of the straight line segments have denominators which are powers of 2, and so an implementation needs no floating point multiplication or division operations. With [0, 1/2£] divided into only four intervals, an absolute error of about 0.013 is achieved by a routine taking just over a third of the time used by a more conventional one. Such accuracy is adequate for certain graphics applications, especially moving displays.     

Performance analysis of the CM-2, a massively parallel SIMD computer

The performance evaluation process for a massively parallel distributed-memory SIMD computer is described generally. The performance in basic computation, grid communication, and computation with grid communication is analysed. A practical performance evaluation and analysis study is done for the Connection Machine 2, and conclusions about its performance are drawn.     

A P2P streaming architecture supporting scalable media

Peer-to-peer (P2P) streaming solutions are changing the way real-time multimedia transmission is distributed over the Internet. The advances in video coding, like Scalable Video Coding, also turns possible high-quality/definition video distribution and consumption. The recent push in using the standard HTTP protocol for streaming videos in the World Wide Web, is also making converged entertainment services come closer to global deployment across TV, Tablet and mobile devices. The combination of Scalable Video, P2P networks and Web technologies for transmitting high-quality live and time-shifted media contents, is a potential area of innovation and a very actual research topic. This paper describes the architecture of a quality-centric P2P distribution network supporting adaptive and scalable streamable media, that decouples the transport mechanisms from the media content type and structure (video, audio, timed-data, timed-text), irrespective of their encoding schemes. The set of P2P streaming protocols designed for the network enables streaming of live and on-demand media, with very low signaling cost. Prototypes of the P2P network components were implemented and integrated in the streaming platform of EU FP7 SARACEN Project. A suite of tests for evaluation of the performance of the solution demonstrates that it keeps a fairly stable quality level with reduced amplitude and frequency of variations, raising the overall quality perceived by the end-user.     

K元2—立方体网络SIMD计算机图像模板匹配并行算法

模板匹配是进行虑波、边缘检测、目标识别和图像匹配的一种基本和有效的方法 .对于 N× N的图像和M× N ( M相似文献   

A fast learning method for streaming and randomly ordered multi-class data chunks by using one-pass-throw-away class-wise learning concept

Presently, the amount of data occurring in several business and academic areas such as ATM transactions, web searches, and sensor data are tremendously and continuously increased. Classifying as well as recognizing patterns among these data in a limited memory space complexity are very challenging. Various incremental learning methods have proposed to achieve highly accurate results but both already learned data and new incoming data must be retained throughout the learning process, causing high space and time complexities. In this paper, a new neural learning method based on radial-shaped function and discard-after-learn concept in the data streaming environment was proposed to reduce the space and time complexities. The experimental results showed that the proposed method used 1 to 95 times fewer neurons and 1.2 to 2,700 times faster than the results produced by MLP, RBF, SVM, VEBF, ILVQ, ASC, and other incremental learning methods. It is also robust to the incoming order of data chunks.     

P2P流媒体网络的关键节点识别算法

P2P流媒体网络中普遍存在一些关键节点,关键节点对网络的安全和通信性能起着重要作用,识别网络中的关键节点尤为重要,而传统方法对于大规模网络的关键节点识别时间开销很大,无法保证实时性。提出P2P流媒体网络中的关键节点识别算法,结合混合模式的网络结构特点,采用分区域的计算模型解决网络规模过大造成的巨大时间开销问题,根据节点的贡献度和传播能力差异定量化描述节点的重要性程度。仿真结果表明,所提算法可以快速获得节点重要性排序,有效识别P2P流媒体网络中的关键节点。     

一种新型P2P流媒体协议及其流量模型研究

针对FlashP2P技术,对其RTMFP协议进行了深入分析,提出了一种基于RTMFP包检测的FlashP2P流量识别算法,并采用该算法对国内主流视频网站的FlashP2P流进行了有效的识别。在此基础上,对FlashP2P流量特征进行分析并证明其具有自相似性。最后,提出了一种基于ARIMA模型的经验模式分解预测自相似网络流量的方法,而且进行了仿真验证。结果表明,该模型不仅降低了算法的复杂度,并且对短期预测精度较高。     

A fast bilateral filtering algorithm based on rising cosine function

Neural Computing and Applications - In order to speed up the bilateral filtering algorithm, a fast bilateral filtering algorithm based on raised cosine with compressibility factor (BRCF) is...     

On a fast, compact approximation of the exponential function

Recently Schraudolph (1999) described an ingenious, fast, and compact approximation of the exponential function through manipulation of the components of a standard (IEEE-754 (IEEE, 1985)) floating-point representation. This brief note communicates a recoding of this procedure that overcomes some of the limitations of the original macro at little or no additional computational expense.     

A Speculative Parallel DFA Membership Test for Multicore,SIMD and Cloud Computing Environments

We present techniques to parallelize membership tests for Deterministic Finite Automata (DFAs). Our method searches arbitrary regular expressions by matching multiple bytes in parallel using speculation. We partition the input string into chunks, match chunks in parallel, and combine the matching results. Our parallel matching algorithm exploits structural DFA properties to minimize the speculative overhead. Unlike previous approaches, our speculation is failure-free, i.e., (1) sequential semantics are maintained, and (2) speed-downs are avoided altogether. On architectures with a SIMD gather-operation for indexed memory loads, our matching operation is fully vectorized. The proposed load-balancing scheme uses an off-line profiling step to determine the matching capacity of each participating processor. Based on matching capacities, DFA matches are load-balanced on inhomogeneous parallel architectures such as cloud computing environments. We evaluated our speculative DFA membership test for a representative set of benchmarks from the Perl-compatible Regular Expression (PCRE) library and the PROSITE protein database. Evaluation was conducted on a 4 CPU (40 cores) shared-memory node of the Intel Academic Program Manycore Testing Lab (Intel MTL), on the Intel AVX2 SDE simulator for 8-way fully vectorized SIMD execution, and on a 20-node (288 cores) cluster on the Amazon EC2 computing cloud. Obtained speedups are on the order of $\mathcal O \left( 1+\frac{|P|-1}{|Q|\cdot \gamma }\right) $ , where $|P|$ denotes the number of processors or SIMD units, $|Q|$ denotes the number of DFA states, and $0<\gamma \le 1$ represents a statically computed DFA property. For all observed cases, we found that $0.02<\gamma <0.47$ . Actual speedups range from 2.3 $\times $ to 38.8 $\times $ for up to 512 DFA states for PCRE, and between 1.3 $\times $ and 19.9 $\times $ for up to 1,288 DFA states for PROSITE on a 40-core MTL node. Speedups on the EC2 computing cloud range from 5.0 $\times $ to 65.8 $\times $ for PCRE, and from 5.0 $\times $ to 138.5 $\times $ for PROSITE. Speedups of our C-based DFA matcher over the Perl-based ScanProsite scan tool range from 559.3 $\times $ to 15079.7 $\times $ on a 40-core MTL node. We show the scalability of our approach for input-sizes of up to 10 GB.     

A fast calculation strategy of density function in ISAF reconstruction algorithm

The ISAF reconstruction algorithm is a new method for reconstructing icosahedral molecules from their projections. This algorithm works in spherical coordinate system and can achieve higher resolution than the traditional Fourier-Bessel algorithm in cylindrical coordinate system; however this method needs huge computations, which limits its application in reality. The main bottleneck lies in the calculation of density function as it occupies 90% running time of the whole algorithm. A fast calculation strategy of density function is proposed to solve this problem. This strategy is composed of three components： the fast calculation method of density function of mesh point in spherical coordinate system, the transformation method of density function of mesh point from spherical coordinate system to Cartesian coordinate system and the fast two-phase mapping method. The time complexity of calculating density function is decreased from O[（LM）8] to O[（LM）7] in our strategy. The experimental results on Psv-F simulated data indicate that the speed of calculating density function is increased almost two orders of magnitude and the speedup of the whole algorithm could reach 30 times. In addition, the speedup could go up with the increase in the number of images and the requirement of accuracy.     

A new and fast cryptographic hash function based on RC4

This article proposes a new cryptographic hash function, called RC4-BHF, which is designed to be both fast and secure. This is a new attempt to design a cryptographic hash function based on the RC4 algorithm. Since vulnerabilities have been discovered in many of the existing hash functions, it is beneficial to construct a hash function which has different internal structure, and RC4-BHF is such a new hash function. Moreover, RC4-BHF is suitable for ultra-low power devices, such as sensor node, which are normally equipped with 8-bit processors, and most other hash functions cannot be implemented efficiently or are not applicable. RC4-BHF can run much faster compared to the existing well-known hash functions and is exceptionally fast on 8-bit processors.     

A fast learning algorithm of neural network with tunable activation function

This paper presents a modified structure of a neural network with tunable activation function and provides a new learning algorithm for the neural network training. Simulation results of XOR problem, Feigenbaum function, and Henon map show that the new algorithm has better performance than BP (back propagation) algorithm in terms of shorter convergence time and higher convergence accuracy. Further modifications of the structure of the neural network with the faster learning algorithm demonstrate simpler structure with even faster convergence speed and better convergence accuracy.