基于模糊控制的ATM网络VBR视频传输平滑策略

VBR视频传输的突发性是影响ATM网络服务质量的关键因素,文中通过模糊控制方法对视频传输流量的阈值进行监控,实现了接入的平滑并可动态调整传输速度。文中以传输流量及这发级作为评价指标进行分析,结果表明VBR视频传输自适应平滑策略的实效性。     

Classification of Battlefield Ground Vehicles Using Acoustic Features and Fuzzy Logic Rule-Based Classifiers

In this paper, we demonstrate, through the multicategory classification of battlefield ground vehicles using acoustic features, how it is straightforward to directly exploit the information inherent in a problem to determine the number of rules, and subsequently the architecture, of fuzzy logic rule-based classifiers (FLRBC). We propose three FLRBC architectures, one non-hierarchical and two hierarchical (HFLRBC), conduct experiments to evaluate the performances of these architectures, and compare them to a Bayesian classifier. Our experimental results show that: 1) for each classifier the performance in the adaptive mode that uses simple majority voting is much better than in the non-adaptive mode; 2) all FLRBCs perform substantially better than the Bayesian classifier; 3) interval type-2 (T2) FLRBCs perform better than their competing type-1 (T1) FLRBCs, although sometimes not by much; 4) the interval T2 nonhierarchical and HFLRBC-series architectures perform the best; and 5) all FLRBCs achieve higher than the acceptable 80% classification accuracy     

A Unified Traffic Model for MPEG-4 and H.264 Video Traces

This paper presents a frame-level hybrid framework for modeling MPEG-4 and H.264 multi-layer variable bit rate (VBR) video traffic. To accurately capture long-range-dependent and short-range-dependent properties of VBR sequences, we use wavelets to model the distribution of I-frame sizes and a simple time-domain model for P/B frame sizes. However, unlike previous studies, we analyze and successfully model both inter-GOP (group of pictures) and intra-GOP correlation in VBR video and build an enhancement-layer model using cross-layer correlation. Simulation results demonstrate that our model effectively preserves the temporal burstiness and captures important statistical features (e.g., the autocorrelation function and the frame-size distribution) of original traffic. We also show that our model possesses lower complexity and has better performance than the previous methods in both single- and multi-layer sequences.     

Type-2 fuzzy logic-based classifier fusion for support vector machines

As a machine-learning tool, support vector machines (SVMs) have been gaining popularity due to their promising performance. However, the generalization abilities of SVMs often rely on whether the selected kernel functions are suitable for real classification data. To lessen the sensitivity of different kernels in SVMs classification and improve SVMs generalization ability, this paper proposes a fuzzy fusion model to combine multiple SVMs classifiers. To better handle uncertainties existing in real classification data and in the membership functions (MFs) in the traditional type-1 fuzzy logic system (FLS), we apply interval type-2 fuzzy sets to construct a type-2 SVMs fusion FLS. This type-2 fusion architecture takes considerations of the classification results from individual SVMs classifiers and generates the combined classification decision as the output. Besides the distances of data examples to SVMs hyperplanes, the type-2 fuzzy SVMs fusion system also considers the accuracy information of individual SVMs. Our experiments show that the type-2 based SVM fusion classifiers outperform individual SVM classifiers in most cases. The experiments also show that the type-2 fuzzy logic-based SVMs fusion model is better than the type-1 based SVM fusion model in general.     

一种基于模糊神经网络的VBR MPEG视频流量预测模型

针对VBR MPEG视频流的复杂特性，充分利用人工智能方法的优势，提出了一种基于模糊神经网络的视频流量预测模型，利用模糊逻辑模型达到减少预测误差的目的，采用神经网络满足网络通信的实时性要求．实验结果表明，该模型比传统AR模型显著提高了预测的准确度和可靠性．     

VBR MPEG视频源流量的智能集成预测模型

由于VBR视频流量的预测能力是直接关系缓冲区设计、动态带宽分配及拥塞控制等提高网络服务质量的关键因素,因此,以多媒体主要应用形式VBRMPEG视频源为研究对象,提出了一种基于模糊神经网络的视频流量智能预测模型。通过设计模糊预测器减少输出比特流的预测误差,采用神经网络减少多步预测的计算量。仿真试验表明,与标准AR模型预测结果相比,该模型显著提高了预测的准确度和可靠性,且易于推广使用。     

An adaptive control for video transmission over bluetooth

This paper is concerned with transmission of a moving picture expert group (MPEG) video stream over a Bluetooth channel, using an adaptive neuro-fuzzy technique. MPEG variable bit rate (VBR) video sources over a network generally experience long delay and unacceptable data loss, due to high variations in bit rate. Furthermore, transmission rate could be unpredictable in a Bluetooth network due to interferences by other wireless devices or general Bluetooth channel noises. Subsequently, it is almost impossible to transmit VBR data sources over Bluetooth without excessive delay or data loss. In this work, an adaptive scheme is introduced so that the controller may adjust itself to the current state of the system under control. This paper utilizes a traffic-shaping buffer to prevent excessive back-to-back transmissions of MPEG VBR data sources. A novel adaptive neuro-fuzzy scheme regulates the output rate of the buffer to ensure that the video stream from the host conforms to the traffic conditions of the Bluetooth channel during the transmission period. The computer simulation results show that the use of the neuro-fuzzy controller reduces excessive delay and data loss at the host-controller-interface, as compared with a conventional VBR video transmission and a rule-based fuzzy controller (RBF1) in Bluetooth.     

基于NS-2的视频流仿真方法研究

MPEG标准将视频图像序列编码为I帧、P帧和B帧,具有压缩比高、节省网络带宽的重要特性。随着视频会议等网络多媒体应用在因特网上的部署,视频流的流量特性的仿真研究具有越来越重要的作用。文中对MPEG视频流的流量特性及其仿真方法进行了研究。在分析MPEG编码技术原理的基础上,用C／C＋＋语言扩展了NS-2仿真器内核,添加了MPEG视频流量产生器模块,对NS-2中的流量产生器模块及视频仿真接口进行了扩展。实验结果表明,所提出的NS-2视频流仿真扩展方法是可行的和有效的。     

模式识别的统计模糊方法和模糊统计方法

本文在论述模式识别的统计方法和模糊方法的共同性、差异以及各自适用范围的基础上,研究了模式识别的统计模糊方法和模糊统计方法.统计模糊方法是在模糊分类器中充分利用模式分量统计信息的隶属函数,使分类性能优于普通的模糊分类器.模糊统计方法是在以统计方法为基础的分类器中,用模式分量的模糊隶属函数代替模式分量作为分类器输入.从对本文中几个数据集所作的分类试验结果看,这种方法只需要不大的训练样本集便可使分类性能接近于Bayes分类器的最佳水平.     

可变比特率视频编码及其业务量特征分析

本文就可变比特率视频编码的概念及其在ＡＴＭ网络中传输的优势作了详细阐述对ＶＢＲ视频业务特征进行了分析,特别就目前广泛应用的ＭＰＥＧ视频业务做了大量的统计分析,为该业务建模奠定了基础。     

Properties of information sets and information processing with an application to face recognition

This paper presents the properties of information sets that help derive local features from a face when partitioned into windows and devises the information rules from the generalized fuzzy rules for information processing that helps match the unknown test face with the known for authenticating a user. information set is constituted from the information values that result from representing the uncertainty in a type-1 fuzzy set by Hanman–Anirban entropy function. The information values are shown to be the products of information sources (gray levels) in a window and their membership function values. The Hanman filter (HF) is devised to modify the information values using a cosine function whereas the Hanman transform (HT) is devised to evaluate the information source values based on the information obtained on them. Three classifiers, namely the inner product classifier, normed error classifier, and Hanman classifier are formulated. The two feature types based on HF and HT are tested on the AT&T (ORL) database, which contains pose variations in the face images and two other face databases: Indian face Database (IIT Kanpur) and UMIST (Sheffield) using new as well as known classifiers like Euclidean distance- based, Bayesian, and support vector machine classifiers.     

How good are fuzzy If-Then classifiers?

This paper gives some known theoretical results about fuzzy rule-based classifiers and offers a few new ones. The ability of Takagi-Sugeno-Kang (TSK) fuzzy classifiers to match exactly and to approximate classification boundaries is discussed. The lemma by Klawonn and Klement about the exact match of a classification boundary in R (2) is extended from monotonous to arbitrary functions. Equivalence between fuzzy rule-based and nonfuzzy classifiers (1-nn and Parzen) is outlined. We specify the conditions under which a class of fuzzy TSK classifiers turn into lookup tables. It is shown that if the rule base consists of all possible rules (all combinations of linguistic labels on the input features), the fuzzy TSK model is a lookup classifier with hyperbox cells, regardless of the type (shape) of the membership functions used. The question "why fuzzy?" is addressed in the light of these results.     

A fuzzy control scheme for video transmission in Bluetooth wireless

This paper is concerned with transmission of Moving Picture Expert Group (MPEG) video over a Bluetooth wireless network using a fuzzy approach. MPEG Variable Bit Rate (VBR) video sources suffer from long delay and excessive loss due to the sudden bursts in bit rate. Constant Bit Rate (CBR) encoding scheme may work well for a network with a guaranteed bandwidth. However, a Bluetooth channel is subject to wireless interference and can never guarantee a constant bandwidth. Subsequently, it is impossible to transmit a CBR video over Bluetooth wireless without data loss or image quality degradation. To resolve this problem, a fuzzy control system is introduced at the Host Controller Interface (HCI). The system consists of a traffic-shaping buffer whose role is to prevent excessive back-to-back cells being generated during the peak transmissions of MPEG video sources. The output bit rate of the traffic-shaping buffer is controlled by a fuzzy controller to ensure that the video stream from the host conforms to the traffic condition of the Bluetooth channel. Another fuzzy controller regulates the average arrival bit rate to the traffic-shaper to guarantee that the buffer is neither full nor empty. Computer simulation results demonstrate that the use of the fuzzy controllers reduces excessive data loss at the HCI as compared with an open loop VBR/CBR video transmission in Bluetooth.     

MPEG VBR视频流—统计特性及其模型

该文分析了已有的MPEG VBR视频流模型及其缺点,并在此基础上对VBR视频流的统计特性进行了研究,实验结果表明,通过对整个视频流进行场景划分（聚类）,聚类间用Markov调制链建模,而每一聚类中独立的场景则可以采用TES模型基于GOP（Group Of Picture)而非帧进行建模,则该方法既能避免状态空间过大,又能避免帧间周期性的自相关,因而能够更好地拟合VBR视频流序列的一阶和二阶统计特性,同时,对独立场景的GOP分布函数可以采用Gamma函数进行拟合,自相关函数则可以采用双指数函数更好地拟合。     

Skin segmentation using color pixel classification: analysis and comparison

This work presents a study of three important issues of the color pixel classification approach to skin segmentation: color representation, color quantization, and classification algorithm. Our analysis of several representative color spaces using the Bayesian classifier with the histogram technique shows that skin segmentation based on color pixel classification is largely unaffected by the choice of the color space. However, segmentation performance degrades when only chrominance channels are used in classification. Furthermore, we find that color quantization can be as low as 64 bins per channel, although higher histogram sizes give better segmentation performance. The Bayesian classifier with the histogram technique and the multilayer perceptron classifier are found to perform better compared to other tested classifiers, including three piecewise linear classifiers, three unimodal Gaussian classifiers, and a Gaussian mixture classifier.     

Bandwidth Allocation for the Transmission of Scalable MPEG Video Traffic with Deterministic Guarantees

This paper studies bandwidth allocation schemes for the transmission of VBR video traffic with deterministic guarantees, i.e. no packets lost and no missed deadlines. Specifically, we propose and investigate a policy namedquasi-constant policy . A VBR encoder attempts to keep the quality of video output constant by varying the transmission rate (constant quality); on the other hand, with a quasi-constant policy the video quality transmission is reduced when congestion occurs. We assume that this reduction is obtained by exploiting source scalability. By characterizing the traffic of a source with a constraint function, we define the relationship between the amount of bandwidth assigned to a VBR source and the maximum delay the application may experience. By applying this relationship to long MPEG 2 traces (more than one hour) we show that the quasi-constant policy is a very promising direction for providing, in an efficient way, a deterministic QoS to VBR video traffic. Specifically, the results presented indicate that with the quasi-constant quality approach we can achieve a utilization greater than 50% with a deadline of one second with only eight reduced-quality frames out of 1000. On the other hand, previous works ([1, 2]) indicate that with a constant quality transmission, a 50% network utilization can be achieved only by tolerating a seven-second deadline.     

On the time scales in video traffic characterization for queueing behavior

To guarantee quality of service (QoS) in future integrated service networks, traffic sources must be characterized to capture the traffic characteristics relevant to network performance. Recent studies reveal that multimedia traffic shows burstiness over multiple time scales and long range dependence (LRD). While researchers agree on the importance of traffic correlation, there is no agreement on how much correlation should be incorporated into a traffic model for performance estimation and dimensioning of networks.In this article, we present an approach for defining a relevant time scale for the characterization of VBR video traffic in the sense of queueing delay. We first consider the Reich formula and characterize traffic by the Piecewise Linear Arrival Envelope Function (PLAEF). We then define the cutoff interval above which the correlation does not affect the queue buildup. The cutoff interval is the upper bound of the time scale which is required for the estimation of queue size and thus the characterization of VBR video traffic. We also give a procedure to approximate the empirical PLAEF with a concave function; this significantly simplifies the calculation in the estimation of the cutoff interval and delay bound with little estimation loss.We quantify the relationship between the time scale in the correlation of video traffic and the queue buildup using a set of experiments with traces of MPEG/JPEG-compressed video. We show that the critical interval, i.e. the range for the correlation relevant to the queueing delay, depends on the traffic load: as the traffic load increases, the range of the time scale required for estimation for queueing delay also increases. These results offer further insights into the implication of LRD in VBR video traffic.     

Genetic tuning of fuzzy inference within fuzzy classifier systems

Abstract: In generating a suitable fuzzy classifier system, significant effort is often placed on the determination and the fine tuning of the fuzzy sets. However, in such systems little thought is given to the way in which membership functions are combined within the fuzzy rules. Often traditional fuzzy inference strategies are used which consequently provide no control over how strongly or weakly the inference is applied within these rules. Furthermore such strategies will allow no interaction between grades of membership. A number of theoretical fuzzy inference operators have been proposed for both regression and classification problems but they have not been investigated in the context of real-world applications. In this paper we propose a novel genetic algorithm framework for optimizing the strength of fuzzy inference operators concurrently with the tuning of membership functions for a given fuzzy classifier system. Each fuzzy system is generated using two well-established decision tree algorithms: C4.5 and CHAID. This will enable both classification and regression problems to be addressed within the framework. Each solution generated by the genetic algorithm will produce a set of fuzzy membership functions and also determine how strongly the inference will be applied within each fuzzy rule. We investigate several theoretical proven fuzzy inference techniques (T-norms) in the context of both classification and regression problems. The methodology proposed is applied to a number of real-world data sets in order to determine the effects of the simultaneous tuning of membership functions and inference parameters on the accuracy and robustness of fuzzy classifiers.     

Fuzzy-based rate control for real-time MPEG video

We propose a fuzzy logic-based control scheme for real-time motion picture expert group (MPEG) video to avoid long delay or excessive loss at the user-network interface (UNI) in an asynchronous transfer mode (ATM) network. The system consists of a shaper whose role is to smooth the MPEG output traffic to reduce the burstiness of the video stream. The input and output rates of the shaper buffer are controlled by two fuzzy logic-based controllers. To avoid a long delay at the shaper, the first controller aims to tune the output rate of the shaper in the video frame time scale based on the number of available transmission credits at the UNI and the occupancy of the shaper's buffer. Based on the average occupancy of the shaper's buffer and its variance, the second controller tunes the input rate to the shaper over a much larger time scale by applying a closed-loop MPEG encoding scheme. With this approach, the traffic enters the network at an almost constant bit rate (with a very small variation) allowing simple network management functions such as admission control and bandwidth allocation, while guaranteeing a relatively constant video quality since the encoding rate is changed only in critical periods when the shaper buffer “threatens” to overflow. The performance of the proposed scheme is evaluated through numerical tests on real video sequences     

Distinguishability of interval type-2 fuzzy sets data by analyzing upper and lower membership functions

In this paper, we deal with the problem of classification of interval type-2 fuzzy sets through evaluating their distinguishability. To this end, we exploit a general matching algorithm to compute their similarity measure. The algorithm is based on the aggregation of two core similarity measures applied independently on the upper and lower membership functions of the given pair of interval type-2 fuzzy sets that are to be compared. Based on the proposed matching procedure, we develop an experimental methodology for evaluating the distinguishability of collections of interval type-2 fuzzy sets. Experimental results on evaluating the proposed methodology are carried out in the context of classification by considering interval type-2 fuzzy sets as patterns of suitable classification problem instances. We show that considering only the upper and lower membership functions of interval type-2 fuzzy sets is sufficient to (i) accurately discriminate between them and (ii) judge and quantify their distinguishability.