Efficient radar target classification using adaptive jointtime-frequency processing

This paper presents a new target recognition scheme via adaptive Gaussian representation, which uses adaptive joint time-frequency processing techniques. The feature extraction stage of the proposed scheme utilizes the geometrical moments of the adaptivity spectrogram. For this purpose, we have derived exact and closed form expressions of geometrical moments of the adaptive spectrogram in the time, frequency, and joint time-frequency domains. Features obtained by this method can provide substantial savings of computational resources, preserving as much essential information for classifying targets as possible. Next, a principal component analysis is used to further reduce the dimension of feature space, and the resulting feature vectors are passed to the classifier stage based on the multilayer perceptron neural network. To demonstrate the performance of the proposed scheme, various thin-wire targets are identified. The results show that the proposed technique has a significant potential for use in target recognition     

Efficient encoding of facsimile using an adaptive technique

Two binary encoding schemes which lead to bandwidth and power saving are proposed. The binary source is encoded into digrams, which then undergo certain restrictions prior to transmission. Resultant ambiguity at the decoder is avoided by predictive feedback at the encoder. The specific examples proposed belong to a wide class of such adaptive encoding schemes.     

Efficient fine granularity scalability using adaptive leaky factor

Fine Granularity Scalability (FGS) video coding has been adopted by the MPEG-4 standard for video streaming applications. In this paper, we propose a novel FGS coding scheme, which applied adaptive leaky factors for the enhancement layer prediction to further improve the coding efficiency of FGS. A flexible method that can dynamically determine the leaky factors according to the network conditions is also presented. With the proposed method, a better trade-off between the coding efficiency and drifting reduction can be achieved, and the coding performance is further improved compared with using a fixed leaky factor. Experimental results show that the proposed method can further improve the coding efficiency over a wide range of bitrate and packet loss ratio, and still keep the original characteristics, such as fine granularity, and bandwidth adaptation.     

Scalable adaptive hierarchical clustering

We propose a new application-level clustering algorithm capable of building an overlay spanning tree among participants of large multicast sessions, without any specific help from the network routers. The algorithm and associated protocol are shown to exhibit scalable properties     

OpenWebServer: an adaptive Web server using software patterns

The explosive growth of the Web requires servers to be extensible and configurable. This article describes our adaptive Web server, OpenWebServer, which uses the Reflection architectural pattern. The server supports the dynamic adoption of functionality, such as introducing additional protocols, modifying execution policies, and tuning system performance. This is achieved by specifying and coordinating metaobjects that represent various aspects within the Web server. We present a Java version of OpenWebServer, and describe its design using Reflection and other design patterns: Singleton, Bridge, Mediator, Observer, and Decorator. These patterns provide a better-factored design and allow the Web server to evolve continually beyond static and monolithic servers     

Efficient web content delivery using proxy caching techniques

Web caching technology has been widely used to improve the performance of the Web infrastructure and reduce user-perceived network latencies. Proxy caching is a major Web caching technique that attempts to serve user Web requests from one or a network of proxies located between the end user and Web servers hosting the original copies of the requested objects. This paper surveys the main technical aspects of proxy caching and discusses recent developments in proxy caching research including caching the "uncacheable" and multimedia streaming objects, and various adaptive and integrated caching approaches.     

Efficient multiple moving object detection and tracking using combined background subtraction and clustering

Object detection and tracking is a fundamental, challenging task in computer vision because of the difficulties in tracking. Continuous deformation of objects during movement and background clutter leads to poor tracking. In this paper, a method of multiple moving object detection and tracking by combining background subtraction and K-means clustering is proposed. The proposed method can handle objects occlusion, shadows and camera jitter. Background subtraction filters irrelevant information, and K-means clustering is employed to select the moving object from the remaining information, and it is capable of handling merging and splitting of moving objects using spatial information. Experimental results show that the proposed method is robust when compared to other techniques.     

Scalable Web server clustering technologies

The exponential growth of the Internet, coupled with the increasing popularity of dynamically generated content on the World Wide Web, has created the need for more and faster Web servers capable of serving the over 100 million Internet users. Server clustering has emerged as a promising technique to build scalable Web servers. We examine the seminal work, early products, and a sample of contemporary commercial offerings in the field of transparent Web server clustering. We broadly classify transparent server clustering into three categories     

Efficient adaptive linear predictor

Linear predictors with the capability of exactly updating their internal parameters in correspondence to each time step have been recently introduced. It is shown in the present letter that their computational cost can be noticeably reduced by suitable simplifications without impairing their performance.     

An adaptive clustering algorithm for image segmentation

The problem of segmenting images of objects with smooth surfaces is considered. The algorithm that is presented is a generalization of the K-means clustering algorithm to include spatial constraints and to account for local intensity variations in the image. Spatial constraints are included by the use of a Gibbs random field model. Local intensity variations are accounted for in an iterative procedure involving averaging over a sliding window whose size decreases as the algorithm progresses. Results with an 8-neighbor Gibbs random field model applied to pictures of industrial objects, buildings, aerial photographs, optical characters, and faces show that the algorithm performs better than the K-means algorithm and its nonadaptive extensions that incorporate spatial constraints by the use of Gibbs random fields. A hierarchical implementation is also presented that results in better performance and faster speed of execution. The segmented images are caricatures of the originals which preserve the most significant features, while removing unimportant details. They can be used in image recognition and as crude representations of the image     

Efficient computation of time-varying and adaptive filters

Two techniques for efficient computation of filters that support time-varying coefficients are developed. These methods are forms of distributed arithmetic that encode the data, rather than the filter coefficients. The first approach efficiently computes scalar-vector products, with which a digital filter is easily implemented in a transpose-form structure. This method, based on digital coding, supports time-varying coefficients with no additional overhead. Alternatively, distributed-arithmetic schemes that encode the data stream in sliding blocks support efficient direct-form filter computation with time-varying coefficients. A combination of both of these techniques greatly reduces the computation required to implement LMS adaptive filters     

基于遗传算法的Web文档聚类算法

考虑到传统Web文档聚类算法聚类效果差、速度慢等问题,针对Web文档聚类算法进行深入研究,使用目标优化策略将Web文档聚类认为是最佳划归文档集合的范畴,并通过引入优化算法进行聚类划分。针对使用SVD表示的Web文档向量存在高维稀疏性等问题,使用LDA对Web文档簇的潜在语义子空间进行重构,从而降低Web文档向量空间的维数,最后在低维空间使用遗传算法进行寻优。常规的GA算法通常存在算法早熟以及局部寻优能力弱等问题。故提出一种改进型GA算法,通过引入自适应对偶种群、自适应终止规则以及新的生成子代规则来保证种群在迭代过程中的多样性以解决算法早熟问题,并且要提高算法的搜索效率以提高算法对局部寻优的能力。最后通过实验验证提出的基于改进型GA算法的Web文档聚类算法的聚类有效性。     

Efficient radar signature prediction using a frequency-aspectinterpolation technique based on adaptive feature extraction

A radar cross section (RCS) interpolation technique in both frequency and aspect is proposed for the efficient prediction of radar signatures from computational electromagnetics data. Our approach is based on a multiple-arrival model for the induced current on the target. The model parameters are determined by an adaptive feature extraction (AFE) algorithm, which uses an iterative search-and-extract procedure to find the individual model features. Random frequency and aspect sampling is used to circumvent the ambiguity in selecting the features. Numerical examples are presented to test the interpolation algorithm. It is found that sufficient accuracy in the predicted radar features can be achieved even when the original computed data is sampled at 5:1 below the Nyquist criterion in either frequency or aspect. The algorithm is also applied to efficiently predict the radar images of the benchmark VFY218 airplane at UHF band with good results     

Efficient method for lossless image compression using suboptimal,adaptive multiplicative autoregressive models

An efficient, lossless, predictive image compression scheme is introduced, based on suboptimal, adaptive multiplicative autoregressive models. The results of a comparative experimental study indicate that the proposed scheme achieves better compression than other predictive/interpolative lossless image coding techniques considered     

Efficient adaptive algorithms for ARX identification

Two efficient adaptive algorithmic families are developed for multichannel combiners characterized by unequal memory lengths for each input channel. The prewindowed, the covariance, and the sliding window case are addressed. The difference between the proposed methods lies in the way the Kalman gain vector is order-updated in each case. The first algorithm operates on both inputs simultaneously and thus utilizes block multichannel structured order recursions. The resulting scheme is called the diagonal-update algorithm. The second approach updates the Kalman gain in a two-step procedure by first reducing the size of one input and then the other input. The resulting method is called stairwise-update algorithm. Both algorithms are applicable to adaptive ARX (autoregressive exogenous) system identification, to adaptive control, and to the design of decision-feedback equalizers. Simulations are included     

低能量自适应分群分层协议的改进

为了避免能量较少节点当选为群首而过早死亡,对Leach协议群首的选取进行改进。采用结合节点剩余能量重新设置阈值的方法,选取剩余能量较多的节点作为群首,解决了能量较少节点当选为群首和群首负担载过重的问题。仿真结果表明,采用改进后的算法可以有效减少网络能量的消耗,延长网络生存时间。     

Efficient simulation of blood flow past complex endovascular devices using an adaptive embedding technique

The simulation of blood flow past endovascular devices such as coils and stents is a challenging problem due to the complex geometry of the devices. Traditional unstructured grid computational fluid dynamics relies on the generation of finite element grids that conform to the boundary of the computational domain. However, the generation of such grids for patient-specific modeling of cerebral aneurysm treatment with coils or stents is extremely difficult and time consuming. This paper describes the application of an adaptive grid embedding technique previously developed for complex fluid structure interaction problems to the simulation of endovascular devices. A hybrid approach is used: the vessel walls are treated with body conforming grids and the endovascular devices with an adaptive mesh embedding technique. This methodology fits naturally in the framework of image-based computational fluid dynamics and opens the door for exploration of different therapeutic options and personalization of endovascular procedures.     

Efficient RFI suppression in SAR using LMS adaptive filterintegrated with range/Doppler algorithm

Radio frequency interference (RFI) suppression in SAR images often requires a great amount of computation. The authors describe how significant computational savings can be achieved by integrating the RFI suppression stage, implemented with a least-mean-squared (LMS) adaptive filter, with the range compression stage of the range/Doppler SAR processing algorithm     

Efficient clustering algorithms for self-organizing wireless sensor networks

Self-organization of wireless sensor networks, which involves network decomposition into connected clusters, is a challenging task because of the limited bandwidth and energy resources available in these networks. In this paper, we make contributions towards improving the efficiency of self-organization in wireless sensor networks. We first present a novel approach for message-efficient clustering, in which nodes allocate local “growth budgets” to neighbors. We introduce two algorithms that make use of this approach. We analyze the message complexity of these algorithms and provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the earlier, commonly used, Expanding Ring approach. Next, we present a new randomized methodology for designing the timers of cluster initiators. This methodology provides a probabilistic guarantee that initiators will not interfere with each other. We derive an upper bound on the expected time for network decomposition that is logarithmic in the number of nodes in the network. We also present a variant that optimistically allows more concurrency among initiators and significantly reduces the network decomposition time. However, it produces slightly more clusters than the first method. Extensive simulations over different topologies confirm the analytical results and demonstrate that our proposed methodology scales to large networks.     

Efficient robust adaptive beamforming for cyclostationary signals

This paper deals with the problem of robust adaptive array beamforming for cyclostationary signals. By exploiting the signal cyclostationarity, the LS-SCORE algorithm presented in a paper by Agee et al. (1990) has been shown to be effective in performing adaptive beamforming without requiring the direction vector of the desired signal. However, this algorithm suffers from severe performance degradation even if there is a small mismatch in the cycle frequency of the desired signal. In this paper, we first evaluate the performance of the LS-SCORE algorithm in the presence of cycle frequency error (CFE). An analytical formula is derived to show the behavior of the performance degradation due to CFE. An efficient method is then developed based on the fact that the array output power approaches its maximum as the CFE is reduced. We formulate the problem as an optimization problem for reducing the CFE iteratively to achieve robust adaptive beamforming against the CFE. Simulation examples for confirming the theoretical analysis and showing the effectiveness of the proposed method are provided