图像压缩技术在网络教室中的应用

网络教室需要在教师端和学生端之间传输大量图像,传输效率直接影响了系统的交互性,需要对传输的静态图像进行压缩。对网络传输中前后两幅连续的静态图像分析,通过算法提取出两幅图像的差异数据,在图像变化不大时比采用单一的压缩技术效果好,达到较高的压缩比。     

基于改进注意力迁移的实时目标检测方法

目前深度神经网络模型需要部署在资源受限的环境中,故需要设计高效紧凑的网络结构。针对设计紧凑的神经网络提出一种基于改进注意力迁移的模型压缩方法(KE),主要使用一个宽残差教师网络(WRN)指导一个紧凑的学生网络(KENet),将空间和通道的注意力迁移到学生网络来提升性能,并将该方法应用于实时目标检测。在CIFAR上的图像分类实验验证了经过改进注意力迁移的知识蒸馏方法能够提升紧凑模型的性能,在VOC上的目标检测实验验证了模型KEDet具有很好的精度(72.7 mAP)和速度(86 fps)。实验结果充分说明基于改进注意力迁移的目标检测模型具有很好的准确性和实时性。     

基于多层前馈神经网络的图像压缩的仿真研究

神经网络研究的再度兴起及其在图像编码中的应用，开辟了图像压缩的新途径。该文论述了多层前馈网络用于图像压缩的网络模型、原理、算法及关键技术，并通过大量的仿真实验说明了在BP网络图像压缩中，算法、激活函数和压缩率等参数的选择是至关重要的，它们与收敛时间以及重建图像的压缩性能息息相关。通过对实验结果的详细分析得知，BP网络图像压缩必须综合考虑压缩率、失真率和训练时间等因素，在学习规则、激活函数、隐层神经元数和压缩性能之间进行权衡，以满足实际应用。     

基于局域网的实时语音组播系统的设计与实现

多媒体和网络技术的迅速发展为基于网络的视、音频通信提供了可能，在很多的网络语音应用都需要将某一发送端发出的声音迅速地传给多个接收端。实时语音组播系统为这些应用需求提供了一个不错的解决方案。实时语音组播系统可以分成发送端、接收端和网络传输3个子系统。该系统是利用Microsoft公司的DirectSound开发包来实现PC上的语音采集和播放，调用音频压缩管理器ACM来实现音频信号的压缩，并利用实时传输协议RTP在网络中组播和接收语音数据，其中重点讨论了系统开发过程中所用到的以上关键技术及其实现。     

一种基于压缩感知的双粒度连接准入控制策略

为提高数据传输效率,通常对数据进行压缩处理,传统准入控制机制没有考虑数据压缩情况,以连接阻塞（中断）率度量性能。提出一种基于压缩感知的双粒度多门限准入控制策略,通过压缩感知,从连接级和原始信息级两种粒度进行准入控制,采用门限控制机制。同时提出原始信息损失率和原始信息传输效率两种新的准入控制性能标准。理论分析与仿真表明,原始信息损失和原始信息传输效率能较好度量准入控制的性能,压缩感知的准入控制机制具有明显的性能增益。     

基于ARM+DSP的网络视频服务器的设计

网络视频服务器可对音视频数据进行编码处理并完成网络传输,实现远程监控。为了达到对高尺寸的图像进行全屏的视频压缩或无失真的压缩,并且实现图像数据在网络的高效传输,可采用一种ARM+DSP的网络视频服务器,采用TI公司高性能的DSP芯片TMS320C6415-500进行图像数据处理,用Intel公司的Strong ARM SA1110芯片掌控系统管理与网络传输。用ARM+DSP设计的网络视频服务器,采用H.264视音频压缩编码算法,图像分辨率为352×288象素,可同时访问4个网络摄像机,并输出4路视频信号,内置Webserver实现网上浏览。用ARM+DSP设计网络视频服务器是一种先进且高效的方案。     

一种面向WSN节点的数据压缩简化算法

为了在存储空间和能量受限的无线传感器网络(WSN)节点上压缩数据来节省无线发送和外部存储所耗能量,分析并强调了面向WSN节点的数据压缩的特点与研究意义.针对资源受限节点在存储和传送数据中对压缩率和精度及能耗等性能的需要,基于傅立叶分析原理,通过简化基频测量和按允许误差调整傅立叶级数最高阶次,提出了一种可在节点常用的超低功微处理器上实现数据压缩的简化应用方法.实验表明了该简化压缩算法的有效性.     

基于动态数据压缩的能量采集无线传感网络数据收集优化

针对能量采集无线传感网络（WSN）中的数据收集优化问题,考虑传感器节点能量采集的时空变化特性,提出一种基于节点动态采样速率和数据压缩的策略,以实现网络中采样数据总量的最大化。首先,提出一种根据节点的邻居信息决定其最优压缩策略的本地压缩算法,基于节点在数据汇聚树中的拓扑位置考虑其数据接收和转发能耗,逐渐增加其采样速率直到其总能耗到达采集能耗阈值。接着构造网络性能的全局优化问题并提出一种启发式的算法,通过迭代求解线性规划问题计算最优的采样速率和压缩策略。实验结果表明,与现有的自适应传感和压缩率选择方案相比,所提出的两种数据收集优化算法能够维持更加稳定的传感器节点电量水平并实现更高的网络性能。     

A new method of speech transmission over space time block coded co-operative MIMO–OFDM networks using time and space diversity

Reliable and good quality of service for speech transmission over wireless network has been a major challenge for the communication engineers and researchers. In this paper a new technique of speech compression and transmission using different Daubechies wavelets in a space time block coded co-corporative MIMO–OFDM networks using time and space diversity has been proposed. The main focus has been laid on design and development of wavelet based compression of multimedia signals for cooperative MIMO–OFDM system. We tried to find out various major issues regarding the wavelet compression of a speech signal. These issues include choice of a wavelet, decomposition level and thresholding criteria suitable for speech compression and transmission in co-operative MIMO–OFDM systems. A wavelet based speech compression technique using hard and soft thresholding algorithm has been proposed. The work shows that wavelet compression with QPSK modulation is a promising compression technique in a cooperative MIMO–OFDM system which makes use of the elegant theory of wavelets. The performance has been evaluated using mean square error, peak signal to noise ratio, compression ratio, bit error rate, and retained signal energy. It has been found that the transmitted speech signal is retrieved well under noisy conditions at higher order Daubechies wavelets. From the results it is clear that proposed technique aims at a radio access technology that can provide service performance comparable to that of current fixed Line accesses. To evaluate the performance of the proposed method, various performance parameters have been compared with previously implemented techniques and it has been found that the proposed work shows better performance as compared to the already existing techniques.     

一个适用于DTN网络的拥塞避免与解除方案

DTN(delay-tolerant network,延迟容忍网络)的网络特点及其采用的托管传输机制易造成网络受限资源(如缓存、带宽等)的耗尽,形成网络拥塞,导致网络性能的下降。传统TCP拥塞控制机制不适用于DTN网络。提出了一个全新的适用于DTN网络的拥塞避免与拥塞解除方案。拥塞避免根据在足够小的时间段内DTN链路的传输延迟和传输能力的确定性,建立DTN网络有向多径图,对数据发送速率、接收速率、带宽使用等链路负载分割与约束控制,尽可能地提高网络资源的利用率。拥塞解除在节点存储资源划分的基础上,通过节点内存储资源转换与节点间报文转移相结合的方法,解除DTN网络的拥塞状况。仿真结果显示,与其他DTN拥塞控制机制相比,所提方案具有良好的报文交付率、网络开销等网络性能。     

Mirage+: A kernel implementation of distributed shared memory on a network of personal computers

We describe the evolution of a distributed shared memory (DSM) system, Mirage, and the difficulties encountered when moving the system from a Unix-based* kernel on the VAX to a Unix-based kernel on personal computers. Mirage provides a network transparent form of shared memory for a loosely coupled environment. The system hides network boundaries for processes that are accessing shared memory and is upward compatible with the Unix System V Interface Definition. This paper addresses the architectural dependencies in the design of the system and evaluates performance of the implementation. The new version, MIRAGE +, performs well compared to Mirage even though eight times the amount of data is sent on each page fault because of the larger page size used in the implementation. We show that performance of systems with a large page size to network packet size can be dramatically improved on conventional hardware by applying three well-known techniques: packet blasting, compression, and running at interrupt level. The measured time for a page fault in MIRAGE + has been reduced 37 per cent by sending a page using packet blasting instead of using a handshake for each portion of the page. When compression was added to MIRAGE +, the time to fault a page across the network was further improved by 47 per cent when the page was compressed into one network packet. Our measured performance compares favorably with the amount of time it takes to fault a page from disk. Lastly, running at interrupt level may improve performance 16 per cent when faulting pages without compression.     

基于改进科学计算浮点数压缩算法的工业远程监控数据无损压缩方法

为解决大量工业远程监控数据在通用分组无线服务(GPRS)网络上的传输延迟问题,提出了基于改进科学计算浮点数压缩(FPC)算法的工业远程监控数据无损压缩方法.首先,根据工业监控数据中浮点数部分的特点对原FPC算法中的预测器结构进行改进,并将该改进算法作为浮点数部分的压缩算法; 然后,与区间编码相结合作为整个数据域的压缩方法.改进前后的浮点数部分压缩实验结果表明改进的FPC算法提高了预测器的预测精度,且在保持较高压缩效率的同时提高了压缩率.与通用无损压缩算法相比,所提算法提高了12%以上的平均压缩率,减少了38.5%以上的平均压缩时间,使得传输时间降低了23.7%以上,在传输数据量大且传输速率不高的情况下大大提高了监控的实时性.     

知识蒸馏方法研究与应用综述

随着深度学习方法的不断发展,其存储代价和计算代价也不断增长,在资源受限的平台上,这种情况给其应用带来了挑战。为了应对这种挑战,研究者提出了一系列神经网络压缩方法,其中知识蒸馏是一种简单而有效的方法,成为研究热点之一。知识蒸馏的特点在于它采用了“教师—学生”架构,使用一个大型网络指导小型网络进行训练,以提升小型网络在应用场景下的性能,从而间接达到网络压缩的目的。同时,知识蒸馏具有不改变网络结构的特性,从而具有较好的可扩展性。本文首先介绍知识蒸馏的由来以及发展,随后根据方法优化的目标将知识蒸馏的改进方法分为两大类,即面向网络性能的知识蒸馏和面向网络压缩的知识蒸馏,并对经典方法和最新方法进行系统的分析和总结,最后列举知识蒸馏方法的几种典型应用场景,以便加深对各类知识蒸馏方法原理及其应用的理解。知识蒸馏方法发展至今虽然已经取得较好的效果,但是各类知识蒸馏方法仍然有不足之处,本文也对不同知识蒸馏方法的缺陷进行了总结,并根据网络性能和网络压缩两个方面的分析,给出对知识蒸馏研究的总结和展望。     

片上多处理器中的Cache压缩和接口压缩

提出一种简单的基于频繁值和频繁模式的压缩方法,给出结合Cache压缩技术和接口压缩技术的片上多处理器结构。全系统的模拟结果表明Cache压缩技术和接口压缩技术能提高片上多处理器中Cache的有效容量和pin的有效带宽,从而提高系统的性能。实验表明只采用Cache压缩技术平均能提高10%的性能,只采用接口压缩技术平均能提高5.5%的性能,同时采用Cache压缩技术和接口压缩技术平均能提高12%的性能。     

Understanding and reducing Web delays

Slow performance costs e-commerce Web sites as much as $4.35 billion annually in lost revenue. Perceived latency-the amount of time between when a user issues a request and receives a response-is a critical issue. Research into improving performance falls into two categories: work on servers and work on networks and protocols. On the server side, previous work has focused on techniques for improving server performance. Such studies show how Web servers behave under a range of loads. These studies often suggest enhancements to application implementations and the operating systems those servers run. On the network side, research has focused on improving network infrastructure performance for Internet applications. Studies focusing on network dynamics have resulted in several enhancements to HTTP, including data compression, persistent connections, and pipelining. These improvements are all part of HTTP 1.1. However, little work has been done on common latency sources that cause the overall delays that frustrate end users. The future of performance improvements lies in developing additional techniques to help implement efficient, scalable, and stable improvements that enhance the end-user experience     

Variable-length image compression based on controllable learning network

While neural-network-based lossy image compression methods have shown impressive performance, most of them output a fixed-length coding using a trained-specific network. However, it is essential to support the variable-length compression or meet a target rate with a high-coding performance in practice. This paper steps forward the neural-network-based image compression method, making it possible for a single network model to generate variable compression rates. Our network model combines an auto-encoder (AE) and a generative adversarial network (GAN) for generative compression. We introduce a noise interference mechanism to train the feature representation produced by the encoder, making the feature nodes training controllable and distributed from top to bottom according to their importance in feature expression. Based on this importance distribution, the latent nodes are quantized into bits and the variable-length compression can be achieved by discarding bits of those less-important feature nodes to meet the compression target. We propose several noise interference methods, and the experiments confirm the feasibility of method Random-add and Dropout in controllable learning. Further experiments illustrate that our compression method can not only achieve variable-length compression but also can recover high-quality compressed images at extremely low bit rates, outperforming that with a fixed rate.

     

数据传输模式对用户态通信的性能影响分析

用户态通信允许应用程序从应用层直接访问网络接口，主机与网络接口之间的数据传输模式对协议性能有重要影响．有效的数据传输模式可以减少数据拷贝次数，降低数据传输开销，尽可能将网络硬件的高性能反映到用户层．本文详细分析了通信系统开销来源，讨论了Myrinet网络环境下不同数据传输模式的实现与特点，测试并分析了不同数据传输模式对用户态通信性能的影响，并给出了相应的适用环境．     

Efficient Speaker Recognition Using Approximated Cross Entropy (ACE)

Techniques for efficient speaker recognition are presented. These techniques are based on approximating Gaussian mixture modeling (GMM) likelihood scoring using approximated cross entropy (ACE). Gaussian mixture modeling is used for representing both training and test sessions and is shown to perform speaker recognition and retrieval extremely efficiently without any notable degradation in accuracy compared to classic GMM-based recognition. In addition, a GMM compression algorithm is presented. This algorithm decreases considerably the storage needed for speaker retrieval.     

APCFS: Autonomous and Parallel Compressed File System

APCFS (Autonomous and Parallel Compressed File System) is a file system that supports fast autonomous compression at high compression rates. It is designed as a virtual layer inserted over existing file system, compressing and decompressing data by intercepting kernel calls. The system achieves enhanced compression ratio by combining two compression techniques. Speed is attained by performing the two compression techniques in parallel. Experimental results indicate good performance.