Supporting multi-user real-time VBR video service using dynamic bandwidth management based on prediction

Real-time variable bit rate (VBR) video is expected to take a significant portion of multimedia applications. However, plentiful challenges to VBR video service provision have been raised for its characteristic of high traffic abruptness. To support multi-user real-time VBR video transmission with high bandwidth utilization and satisfied quality of service (QoS), this article proposes a practical dynamic bandwidth management scheme. This scheme forecasts future media rate of VBR video by employing time-domain adaptive linear predictor and using media delivery index (MDI) as both QoS measurement and complementary management reference. In addition, to support multi-user application, an adjustment priorities classified strategy is also put forward. Finally, a test-bed based on this management scheme is established. The experimental results demonstrate that the scheme proposed in this article is efficient with bandwidth utilization increased by 20%-60% compared to a fixed service rate and QoS guaranteed.     

Efficient traffic prediction scheme for real-time VBR MPEG videotransmission over high-speed networks

The efficient transportation of real-time variable bit rate (VBR) video traffic in high-speed networks is currently an active area of research. The capability to predict VBR video traffic can significantly improve the effectiveness of numerous management tasks, including dynamic bandwidth allocation and congestion control. This paper proposes an adaptive traffic prediction method for VBR MPEG videos, a major multimedia application. Rapid traffic variations due to scene changes are analyzed, then a prediction scheme using the identification of scene changes related to I and P frames is presented. For predicting multiplexed MPEG traffic, a prediction interval is derived that represents a highly correlated traffic sequence. In addition, to reduce the prediction error, a less fluctuating signal instead of the original multiplexed traffic is used as the input for the predictor. Simulation results show that the proposed method is able to predict the original traffic more accurately than the conventional LMS method     

Lossless image coding via adaptive linear prediction andclassification

In past years, there have been several improvements in lossless image compression. All the recently proposed state-of-the-art lossless image compressors can be roughly divided into two categories: single and double-pass compressors. Linear prediction is rarely used in the first category, while TMW, a state-of-the-art double-pass image compressor, relies on linear prediction for its performance. We propose a single-pass adaptive algorithm that uses context classification and multiple linear predictors, locally optimized on a pixel-by-pixel basis. Locality is also exploited in the entropy coding of the prediction error. The results we obtained on a test set of several standard images are encouraging. On the average, our ALPC obtains a compression ratio comparable to CALIC while improving on some images     

深度果蝇神经网络及其实时能见度预测

能见度预测属于时间序列预测领域的问题,如何实现对时序数据的处理以及捕获数据中的时间长期依赖关系,是当前研究人员的主要关注焦点.针对能见度预测模型构建难的问题,依据果蝇视觉系统在图像感知、学习和信息反馈过程中的生物学原理,提出一种基于视频监控图像的深度果蝇神经网络.首先,通过设计前馈果蝇视觉神经网络,提取图像的能见度特征...     

Screening of vibroarthrographic signals via adaptive segmentationand linear prediction modeling

This paper proposes a noninvasive method to diagnose chondromalacia patella at its early stages by recording knee vibration signals (also known as vibroarthrographic or VAG signals) over the mid-patella during normal movement. An adaptive segmentation method was developed to segment the nonstationary VAG signals. The least squares modeling method was used to reduce the number of data samples to a few model parameters. Model parameters along with a few clinical parameters and a signal variability parameter were then used as discriminant features for screening VAG signals by applying logistic and discriminant algorithms. The system was trained using ten normal and eight abnormal signals. It correctly screened a separate test set of ten normal and eight abnormal signals except for one normal signal. The proposed method should find use as an alternative technique for diagnosis of knee joint pathology or as a test before arthroscopy or major knee surgery     

Constraint-based model for network service provisioning

As the network services deployed over the Internet become more complex, their configurations are more difficult to manage. The constraints that exist among the network service configuration parameters are an important source of complexity in the configuration, validation and fault management processes. Existing solutions do not provide adequate high-level models to capture and integrate various types of configuration constraints. It is therefore necessary to adopt a structured approach in order to solve this problem. Thus, in this paper, we propose a structured and integrated constraint-based model for network service configuration, validation and fault management.     

Universal linear prediction by model order weighting

A common problem that arises in adaptive filtering, autoregressive modeling, or linear prediction is the selection of an appropriate order for the underlying linear parametric model. We address this problem for linear prediction, but instead of fixing a specific model order, we develop a sequential prediction algorithm whose sequentially accumulated average squared prediction error for any bounded individual sequence is as good as the performance attainable by the best sequential linear predictor of order less than some M. This predictor is found by transforming linear prediction into a problem analogous to the sequential probability assignment problem from universal coding theory. The resulting universal predictor uses essentially a performance-weighted average of all predictors for model orders less than M. Efficient lattice filters are used to generate the predictions of all the models recursively, resulting in a complexity of the universal algorithm that is no larger than that of the largest model order. Examples of prediction performance are provided for autoregressive and speech data as well as an example of adaptive data equalization     

A new programming model for dependable adaptive real-time applications

The CORTEX project envisions a new class of applications comprising (possibly many) smart components, called sentient objects, that can sense their environment and interact with it and each other by accepting and producing events. These interactions might occur in ways that demand predictable and sometimes guaranteed quality of service (QoS). However, achieving predictability is difficult given the objects' changing environments dictated by unstable and mobile object populations, unpredictable network load, varying connectivity, and failed system components. We devised an innovative programming model that aims to reconcile uncertainty with an application's required predictability. The model is based on fulfilling two predicates - coverage stability and no-contamination - by programs or protocols subject to varying conditions of system timeliness.     

Nonlinear adaptive prediction of speech with a pipelined recurrentneural network

New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined recurrent neural network (PRNN) are presented. A computationally efficient gradient descent (GD) learning algorithm, together with a novel extended recursive least squares (ERLS) learning algorithm, are proposed. Simulation studies based on three speech signals that have been made public and are available on the World Wide Web (WWW) are used to test the nonlinear predictor. The gradient descent algorithm is shown to yield poor performance in terms of prediction error gain, whereas consistently improved results are achieved with the ERLS algorithm. The merit of the nonlinear predictor structure is confirmed by yielding approximately 2 dB higher prediction gain than a linear structure predictor that employs the conventional recursive least squares (RLS) algorithm     

移动网自适应业务的CAC算法研究

宽带移动网络中的连续媒体流业务能够根据无线链路质量和小区负载情况自适应调整其源速率，因而使网络容量具有一定的弹性。自适应业务的CAC算法既要能保护用户的QoS降级期小于一定上限，从而保护用户的Qos；又要尽量提高接入率以充分利用无线资源。本文提出了一种基于滑动窗口的CAC算法，仿真结果表明该算法获得的接入率明显高于固定门限算法，并且能够有效的保护自适应业务的QoS。     

Resource allocation using adaptive linear prediction in WDM/TDM EPONs

This letter discusses dynamic-wavelength and bandwidth-allocation (DWBA) algorithm for differentiated services in hybrid WDM/TDM EPONs (Ethernet passive optical networks). We integrate the traffic estimation for variable-bit-rate (VBR) video traffic with the resource allocation to satisfy the increasing demand of video streaming services. A new DWBA algorithm using an adaptive linear prediction model is proposed to estimate the bandwidth required by VBR video traffic arriving during the next waiting period, so that the DWBA algorithm can assign wavelength and bandwidth more effectively. Simulation results show that the proposed DWBA-ALP algorithm can significantly improve the delay jitter performance for both CBR and VBR services.     

采用传输时延约束进行MPEG-2VBR视频业务平滑

由于ＭＰＥＧ－２ＶＢＲ视频码率变化剧烈,不利于信道带宽分配,因此,针对非交互式应用中视频传输延时可以大于帧周期这一情况,提出了一种基于给定传输时延下的ＶＢＲ业务平滑算法,算法主要是根据ＭＰＥＧ－２视频Ｉ,Ｐ,Ｂ帧码字数的差异,调整各帧的传输时间,在确保帧传输延进的前提下,减小帧之间的信道接入码率变化次数,从而便于进行接入控制。     

A real-time prediction model of electrode extension for GMAW

This paper presents the development of an electrode extension model for the gas metal arc welding process based on the process voltage. The full dynamic model for the electrode extension is derived by combining a dynamic resistivity model with the voltage model. The electrode extension model was found to be represented mathematically by a nonlinear, time-varying, second-order ordinary differential equation. This model can be used in through-the-arc sensing and arc length control systems. To experimentally verify the model, the process dynamics were excited by a continuous sinusoidal variation of arc current. Using a constant current power source with the electrode positive, sinusoidal perturbations of variable amplitude were superimposed on the current to allow direct measurement of changes in electrode extension, arc length, and total voltage. A high-speed video system was used to capture the experimental electrode extension dynamics. The model was verified by comparing the frequency response of the model to the frequency response of the real process. Agreement between the simulations and the experimental results was found to be very good. The accuracy of this model was found to be approximately /spl plusmn/0.6 mm, which is considered to be suitable for process control applications.     

Microcell prediction model based on support vector machine algorithm

A new microcell prediction model for mobile radio environment is presented in this paper. The popular support vector machine algorithm is used as an optimizing tool to build a model. In order to validate the model quality, extensive electric field strength measurements were carried out in the city of Belgrade, for two different test transmitter locations. The analysis of the model has shown that proposed model is fast, accurate (on the order of the local mean measurements uncertainty), reliable, and suitable for computer implementation.     

自适应线性神经网络LMM算法的谐波辨识技术研究

介绍了组合适应线性神经网络最小平均值评估法(Adaline-LMM)对脉冲控制信号的拟合分析方法,用于对电力控制系统中的信号评估。通过对系统信号中的各个谐波分量的幅值和相位进行谐波辨识,并对Adaline的权重向量进行更新,同时对目标函数进行技术估计。其中,自适应神经网络中的权重向量由LMM算法进行迭代更新,通过最小平均值估计算法的引入,减小由于脉冲噪声引起的暂时波动的影响。通过对给定脉冲信号进行拟合,可以发现所提方法具有较高的计算精度。     

WCDMA网络分组数据业务对网络模型影响的分析

由于分组网络中,长期以关注数据流量为主,而忽视了信令问题对网络的影响,一定程度上影响了网络建设的准确性和针对性。各种低速数据应用的快速发展给网络带来了较大的影响,按照仅考虑流量需求的建设方式已不适合现阶段的业务发展。从分析网络现状入手,通过分析对比2G和3G在网络流程方面的差异,以及终端因素在业务流程中的影响程度,找出3G网络信令冲击大的原因及信令冲击的产生场景,最后通过修正网络模型,增加网络模型中新的模型参数,以指导后期网络规划建设。     

Reliability prediction models to support conceptual design

During the early stages of conceptual design, the ability to predict reliability is very limited. Without a prototype to test in a lab environment or without field data, component failure rates and system reliability performance are usually unknown. A popular method for early reliability prediction is to develop a computer model for the system. However, most of these models are extremely specific to an individual system or industry. This paper presents three general procedures (using both simulation and analytic solution techniques) for predicting system reliability and average mission cost. The procedures consider both known and unknown failure rates and component-level and subsystem-level analyzes. The estimates are based on the number of series subsystems and redundant (active or stand-by) components for each subsystem. The result is a set of approaches that engineers can use to predict system reliability early in the system-design process. Software was developed (and is discussed in this paper) that facilitates the application of the simulation-based techniques. For the specific type of system and mission addressed in this paper, the analytic approach is superior to the simulation-based prediction models. However, all three approaches are presented for two reasons: (1) to convey the development process involved with building these prediction tools; and (2) the simulation-based approaches are of greater value as the research is extended to consider more complex systems and scenarios     

Using linear programming to Decode Binary linear codes

A new method is given for performing approximate maximum-likelihood (ML) decoding of an arbitrary binary linear code based on observations received from any discrete memoryless symmetric channel. The decoding algorithm is based on a linear programming (LP) relaxation that is defined by a factor graph or parity-check representation of the code. The resulting "LP decoder" generalizes our previous work on turbo-like codes. A precise combinatorial characterization of when the LP decoder succeeds is provided, based on pseudocodewords associated with the factor graph. Our definition of a pseudocodeword unifies other such notions known for iterative algorithms, including "stopping sets," "irreducible closed walks," "trellis cycles," "deviation sets," and "graph covers." The fractional distance d/sub frac/ of a code is introduced, which is a lower bound on the classical distance. It is shown that the efficient LP decoder will correct up to /spl lceil/d/sub frac//2/spl rceil/-1 errors and that there are codes with d/sub frac/=/spl Omega/(n/sup 1-/spl epsi//). An efficient algorithm to compute the fractional distance is presented. Experimental evidence shows a similar performance on low-density parity-check (LDPC) codes between LP decoding and the min-sum and sum-product algorithms. Methods for tightening the LP relaxation to improve performance are also provided.     

Middleware to support sensor network applications

Current trends in computing include increases in both distribution and wireless connectivity, leading to highly dynamic, complex environments on top of which applications must be built. The task of designing and ensuring the correctness of applications in these environments is similarly becoming more complex. The unified goal of much of the research in distributed wireless systems is to provide higher-level abstractions of complex low-level concepts to application programmers, easing the design and implementation of applications. A new and growing class of applications for wireless sensor networks require similar complexity encapsulation. However, sensor networks have some unique characteristics, including dynamic availability of data sources and application quality of service requirements, that are not common to other types of applications. These unique features, combined with the inherent distribution of sensors, and limited energy and bandwidth resources, dictate the need for network functionality and the individual sensors to be controlled to best serve the application requirements. In this article, we describe different types of sensor network applications and discuss existing techniques for managing these types of networks. We also overview a variety of related middleware and argue that no existing approach provides all the management tools required by sensor network applications. To meet this need, we have developed a new middleware called MiLAN. MiLAN allows applications to specify a policy for managing the network and sensors, but the actual implementation of this policy is effected within MiLAN. We describe MiLAN and show its effectiveness through the design of a sensor-based personal health monitor.