http://www.hindawi.comThe latest articles from Hindawi Publishing Corporation© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/491503We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function of the received symbols for discrete inputs. GPR can be presented as a nonlinear MMSE estimator and thus capable of achieving optimal performance from MMSE viewpoint. Also, the design of digital communication receivers can be viewed as a detection problem, for which GPC is specially suited as it assigns posterior probabilities to each transmitted symbol. We explore the suitability of GPs as nonlinear digital communication receivers. GPs are Bayesian machine learning tools that formulates a likelihood function for its hyperparameters, which can then be set optimally. GPs outperform state-of-the-art nonlinear machine learning approaches that prespecify their hyperparameters or rely on cross validation. We illustrate the advantages of GPs as digital communication receivers for linear and nonlinear channel models for short training sequences and compare them to state-of-the-art nonlinear machine learning tools, such as support vector machines.Fernando Pérez-Cruz and Juan José Murillo-Fuentes© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/439203An adaptive delta-sigma modulator of 1st order with one-bit quantization is presented. Adaptation is instantaneous and based on an exponential law. The feedback signal is a multibit discrete-level signal generated by a digital-to-analog converter (DAC). Compared to a nonadaptive delta-sigma modulator of 1st order, the input dynamic range is significantly enhanced. The gain in dynamic range is 6 dB per bit defining the feedback amplitude. The influence of nonideal DAC performance is discussed. It is demonstrated that an implementation of the system is realistic with standard CMOS technology. To relax the requirements to the one-bit quantizer, the quantizer input signal is amplified adaptively (Q-Switching).Clemens M. Zierhofer© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/726854System aspects of an anti-intruder multistatic radar based on impulse radio ultrawideband (UWB) technology are addressed. The investigated system is composed of one transmitting node and at least three receiving nodes, positioned in the surveillance area with the aim of detecting and locating a human intruder (target) that moves inside the area. Such systems, referred to also as UWB radar sensor networks, must satisfy severe power constraints worldwide imposed by, for example, the Federal Communications Commission (FCC) and by the European Commission (EC) power spectral density masks. A single transmitter-receiver pair (bistatic radar) is considered at first. Given the available transmitted power and the capability of the receiving node to resolve the UWB pulses in the time domain, the surveillance area regions where the target is detectable, and those where it is not, are obtained. Moreover, the range estimation error for the transmitter-receiver pair is discussed. By employing this analysis, a multistatic system is then considered, composed of one transmitter and three or four cooperating receivers. For this multistatic system, the impact of the nodes location on area coverage, necessary transmitted power and localization uncertainty is studied, assuming a circular surveillance area. It is highlighted how area coverage and transmitted power, on one side, and localization uncertainty, on the other side, require opposite criteria of nodes placement. Consequently, the need for a system compromising between these factors is shown. Finally, a simple and effective criterion for placing the transmitter and the receivers is drawn.Enrico Paolini, Andrea Giorgetti, Marco Chiani, Riccardo Minutolo, and Mauro Montanari© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/560749Wireless communication of video, with Bluetooth as an example, represents a compromise between channel conditions, display and decode deadlines, and energy constraints. This paper proposes fuzzy logic control (FLC) of automatic repeat request (ARQ) as a way of reconciling these factors, with a 40% saving in power in the worst channel conditions from economizing on transmissions when channel errors occur. Whatever the channel conditions are, FLC is shown to outperform the default Bluetooth scheme and an alternative Bluetooth-adaptive ARQ scheme in terms of reduced packet loss and delay, as well as improved video quality.Rouzbeh Razavi, Martin Fleury, and Mohammed Ghanbari© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/834973Support vector machines (SVMs) have gained great attention and have been used extensively and successfully in the field of sounds (events) recognition. However, the extension of SVMs to real-world signal processing applications is still an ongoing research topic. Our work consists of illustrating the potential of SVMs on recognizing impulsive audio signals belonging to a complex real-world dataset. We propose to apply optimized one-class support vector machines (1-SVMs) to tackle both sound detection and classification tasks in the sound recognition process. First, we propose an efficient and accurate approach for detecting events in a continuous audio stream. The proposed unsupervised sound detection method which does not require any pretrained models is based on the use of the exponential family model and 1-SVMs to approximate the generalized likelihood ratio. Then, we apply novel discriminative algorithms based on 1-SVMs with new dissimilarity measure in order to address a supervised sound-classification task. We compare the novel sound detection and classification methods with other popular approaches. The remarkable sound recognition results achieved in our experiments illustrate the potential of these methods and indicate that 1-SVMs are well suited for event-recognition tasks.Asma Rabaoui, Hachem Kadri, Zied Lachiri, and Noureddine Ellouze© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/480835Previous studies dedicated to source localization are based on the spectral matrix algebraic properties. In particular, two noneigenvector methods, namely, propagator and Ermolaev and Gershman (EG) algorithms, exhibit a low computational load. Both methods are based on spectral matrix structure. The first method is based on the spectral matrix partitioning. The second one obtains directly an approximation of noise subspace using an adjustable power parameter of the spectral matrix and choosing a threshold value. It has been shown that these algorithms are efficient in nonnoisy or high signal to noise ratio (SNR) environments. However, both algorithms will be improved. Firstly, propagator is not robust to noise. Secondly, EG algorithm that requires the knowledge of a threshold value between largest and smallest eigenvalues, which are not available as eigendecomposition, is not performed. In this paper, we aim firstly at demonstrating the usefulness of QR and LU factorizations of the spectral matrix for these methods and secondly we propose a new way to reduce the computational load of a high resolution algorithm by estimating only the needed eigenvectors. For this, we adapt fixed-point algorithm to compute only the leading eigenvectors. We evaluate the performance of the proposed methods by a comparative study.S. Bourennane, C. Fossati, and J. Marot© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/728409The use of electrocardiograms (ECGs) to diagnose and analyse atrial fibrillation (AF) has received much attention recently. When studying AF, it is important to isolate the atrial activity (AA) component of the ECG plot. We present a new autoregressive (AR) model for semiblind source extraction of the AA signal. Previous researchers showed that one could extract a signal with the smallest normalized mean square prediction error (MSPE) as the first output from linear mixtures by minimizing the MSPE. However the extracted signal will be not always the desired one even if the AR model parameters of one source signal are known. We introduce a new cost function, which caters for the specific AR model parameters, to extract the desired source. Through theoretical analysis and simulation we demonstrate that this algorithm can extract any desired signal from mixtures provided that its AR parameters are first obtained. We use this approach to extract the AA signal from 12-lead surface ECG signals for hearts undergoing AF. In our methodology we roughly estimated the AR parameters from the fibrillatory wave segment in the V1 lead, and then used this algorithm to extract the AA signal. We validate our approach using real-world ECG data.Gang Wang, Ni-ni Rao, Simon J. Shepherd, and Clive B. Beggs© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/253706Wireless local area networks (WLANs) such as IEEE 802.11a/g utilise numerous transmission modes, each providing different throughputs and reliability levels. Most link adaptation algorithms proposed in the literature (i) maximise the error-free data throughput, (ii) do not take into account the content of the data stream, and (iii) rely strongly on the use of ARQ. Low-latency applications, such as real-time video transmission, do not permit large numbers of retransmission. In this paper, a novel link adaptation scheme is presented that improves the quality of service (QoS) for video transmission. Rather than maximising the error-free throughput, our scheme minimises the video distortion of the received sequence. With the use of simple and local rate distortion measures and end-to-end distortion models at the video encoder, the proposed scheme estimates the received video distortion at the current transmission rate, as well as on the adjacent lower and higher rates. This allows the system to select the link-speed which offers the lowest distortion and to adapt to the channel conditions. Simulation results are presented using the MPEG-4/AVC H.264 video compression standard over IEEE 802.11g. The results show that the proposed system closely follows the optimum theoretic solution.Pierre Ferré, James Chung-How, David Bull, and Andrew Nix© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/231367A novel approach for onset detection of musical notes from audio signals is presented. In contrast to most commonly used conventional approaches, the proposed method features new detection functions constructed from the linear temporal bases that are obtained from the decomposition of musical spectra using nonnegative matrix factorization (NMF). Three forms of detection function, namely, first-order difference function, psychoacoustically motivated relative difference function, and constant-balanced relative difference function, are considered. As the approach works directly on input data, no prior knowledge or statistical information is therefore required. Practical issues, including the choice of the factorization rank and detection robustness to instruments, are also examined experimentally. Due to the scalability issue with the generated nonnegative matrix, the proposed method is only applied to relatively short, single instrument (or voice) recordings. Numerical examples are provided to show the good performance of the proposed method, including comparisons between the three detection functions.Wenwu Wang, Yuhui Luo, Jonathon A. Chambers, and Saeid Sanei© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/570896The rapid increase in digital signal processing (DSP) applications has generated a strong demand for electrical engineers with DSP backgrounds; however, the gap between industry needs and university curricula still exists. To answer this challenge, a sequence of innovative DSP courses that emphasize hands-on experiments and practical applications were developed for continuing education in electrical and computer engineering. These courses are taught in the evening for night-school students having at least three years of work experience. These courses enable students to experiment with sophisticated DSP applications to augment the theoretical, conceptual, and analytical materials provided in traditional DSP courses. The inclusion of both software and hardware developments allows students to undertake a wide range of DSP projects for real-world applications. Assessment data concludes that the digital signal processor fundamentals course can increase learning interest and overcome the prerequisite problem of DSP laboratory experiments. This paper also briefly introduces representative examples of some challenging DSP applications.Hsien-Tsai Wu and Sen M. Kuo© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/763264Mobile communication systems beyond the third generation will see the interconnection of heterogeneous radio access networks (UMTS, WiMax, wireless local area networks, etc.) in order to always provide the best quality of service (QoS) to users with multimode terminals. This scenario poses a number of critical issues, which have to be faced in order to get the best from the integrated access network. In this paper, we will investigate the issue of parallel transmission over multiple radio access technologies (RATs), focusing the attention on the QoS perceived by final users. We will show that the achievement of a real benefit from parallel transmission over multiple RATs is conditioned to the fulfilment of some requirements related to the kind of RATs, the multiradio resource management (MRRM) strategy, and the transport-level protocol behaviour. All these aspects will be carefully considered in our investigation, which will be carried out partly adopting an analytical approach and partly by means of simulations. In this paper, in particular, we will propose a simple but effective MRRM algorithm, whose performance will be investigated in IEEE802.11a-UMTS and IEEE802.11a-IEEE802.16e heterogeneous networks (adopted as case studies).Alessandro Bazzi, Gianni Pasolini, and Oreste Andrisano© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/785243Quantification of nonlinear interactions between two nonstationary signals presents a computational challenge in different research fields, especially for assessments of physiological systems. Traditional approaches that are based on theories of stationary signals cannot resolve nonstationarity-related issues and, thus, cannot reliably assess nonlinear interactions in physiological systems. In this review we discuss a new technique called multimodal pressure flow (MMPF) method that utilizes Hilbert-Huang transformation to quantify interaction between nonstationary cerebral blood flow velocity (BFV) and blood pressure (BP) for the assessment of dynamic cerebral autoregulation (CA). CA is an important mechanism responsible for controlling cerebral blood flow in responses to fluctuations in systemic BP within a few heart-beats. The MMPF analysis decomposes BP and BFV signals into multiple empirical modes adaptively so that the fluctuations caused by a specific physiologic process can be represented in a corresponding empirical mode. Using this technique, we showed that dynamic CA can be characterized by specific phase delays between the decomposed BP and BFV oscillations, and that the phase shifts are significantly reduced in hypertensive, diabetics and stroke subjects with impaired CA. Additionally, the new technique can reliably assess CA using both induced BP/BFV oscillations during clinical tests and spontaneous BP/BFV fluctuations during resting conditions.Men-Tzung Lo, Kun Hu, Yanhui Liu, C.-K. Peng, and Vera Novak© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/612397We detect key information of high-dimensional microarray profiles based on wavelet analysis and genetic algorithm. Firstly, wavelet transform is employed to extract approximation coefficients at 2nd level, which remove noise and reduce dimensionality. Genetic algorithm (GA) is performed to select the optimized features. Experiments are performed on four datasets, and experimental results prove that approximation coefficients are efficient way to characterize the microarray data. Furthermore, in order to detect the key genes in the classification of cancer tissue, we reconstruct the approximation part of gene profiles based on orthogonal approximation coefficients. The significant genes are selected based on reconstructed approximation information using genetic algorithm. Experiments prove that good performance of classification is achieved based on the selected key genes.Yihui Liu© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/469109This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP), which is based on Daugman’s method for iris recognition and the local XOR pattern (LXP) operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC) method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP) using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.Baochang Zhang, Zongli Wang, and Bineng Zhong© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/451273Users of cochlear implants (auditory aids, which stimulate the auditory nerve electrically at the inner ear) often suffer from poor speech understanding in noise. We evaluate a small (intermicrophone distance 7 mm) and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. The system is evaluated in simulated and real, anechoic and reverberant environments. Results from simulations show improvements of 3.4 to 9.3 dB in signal to noise ratio for rooms with realistic reverberation and more than 18 dB under anechoic conditions. Speech understanding in noise is measured in 6 adult cochlear implant users in a reverberant room, showing average improvements of 7.9–9.6 dB, when compared to a single omnidirectional microphone or 1.3–5.6 dB, when compared to a simple directional two-microphone device. Subjective evaluation in a cafeteria at lunchtime shows a preference of the cochlear implant users for the evaluated device in terms of speech understanding and sound quality.Martin Kompis, Matthias Bertram, Pascal Senn, Joachim Müller, Marco Pelizzone, and Rudolf Häusler© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/451389The goal of interest point detectors is to find, in an unsupervised way, keypoints easy to extract and at the same time robust to image transformations. We present a novel set of saliency features based on image singularities that takes into account the region content in terms of intensity and local structure. The region complexity is estimated by means of the entropy of the gray-level information; shape information is obtained by measuring the entropy of significant orientations. The regions are located in their representative scale and categorized by their complexity level. Thus, the regions are highly discriminable and less sensitive to confusion and false alarm than the traditional approaches. We compare the novel complex salient regions with the state-of-the-art keypoint detectors. The presented interest points show robustness to a wide set of image transformations and high repeatability as well as allow matching from different camera points of view. Besides, we show the temporal robustness of the novel salient regions in real video sequences, being potentially useful for matching, image retrieval, and object categorization problems.Sergio Escalera, Oriol Pujol, and Petia Radeva© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/438051This paper presents a real-time target detection architecture for hyperspectral image processing. The architecture is based on a reduced complexity algorithm for high-throughput applications.We propose an efficient pipelined processing element architecture and a scalable multiple-processing element architecture by exploiting data partitioning. We present a processing unit modeling based on the data reduction algorithm in hyperspectral image processing and propose computing structure, that is, to optimize memory usage and eliminates memory bottleneck. We investigate the interconnection topology for the multipleprocessing element architecture to improve the speed. The proposed architecture is designed and implemented in FPGA to illustrate the relationship between hardware complexity and execution throughput of hyperspectral image processing for target detection.Kyoung-Su Park, Shung Han Cho, Sangjin Hong, and We-Duke Cho© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/216453We propose two algorithms that decompose the joint likelihood of observing multidimensional neural input data into marginal likelihoods. The first algorithm, boosted mixtures of hidden Markov chains (BMs-HMM), applies techniques from boosting to create implicit hierarchic dependencies between these marginal subspaces. The second algorithm, linked mixtures of hidden Markov chains (LMs-HMM), uses a graphical modeling framework to explicitly create the hierarchic dependencies between these marginal subspaces. Our results show that these algorithms are very simple to train and computationally efficient, while also reducing the input dimensionality for brain-machine interfaces (BMIs).Shalom Darmanjian and Jose C. Principe© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/736460This paper aims to develop a generalized framework to systematically trade off computational complexity with output distortion in linear transforms such as the DCT, in an optimal manner. The problem is important in real-time systems where the computational resources available are time-dependent. Our approach is generic and applies to any linear transform and we use the DCT as a specific example. There are three key ideas: (a) a joint transform pruning and Haar basis projection-based approximation technique. The idea is to save computations by factoring the DCT transform into signal-independent and signal-dependent parts. The signal-dependent calculation is done in real-time and combined with the stored signal-independent part, saving calculations. (b) We propose the idea of the complexity-distortion framework and present an algorithm to efficiently estimate the complexity distortion function and search for optimal transform approximation using several approximation candidate sets. We also propose a measure to select the optimal approximation candidate set, and (c) an adaptive approximation framework in which the operating points on the C-D curve are embedded in the metadata. We also present a framework to perform adaptive approximation in real time for changing computational resources by using the embedded metadata. Our results validate our theoretical approach by showing that we can reduce transform computational complexity significantly while minimizing distortion.Yinpeng Chen and Hari Sundaram© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/647502Users of cochlear implant systems, that is, of auditory aids which stimulate the auditory nerve at the cochlea electrically, often complain about poor speech understanding in noisy environments. Despite the proven advantages of multimicrophone directional noise reduction systems for conventional hearing aids, only one major manufacturer has so far implemented such a system in a product, presumably because of the added power consumption and size. We present a physically small (intermicrophone distance 7 mm) and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. Supporting algorithms, which allow the adjustment of the opening angle and the maximum noise suppression, are proposed and evaluated. A portable real-time device for test in real acoustic environments is presented.Martin Kompis, Matthias Bertram, Jacques François, and Marco Pelizzone© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/873204In this study, two new approaches for speech signal noise reduction based on the empirical mode decomposition (EMD) recently introduced by Huang et al. (1998) are proposed. Based on the EMD, both reduction schemes are fully data-driven approaches. Noisy signal is decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs), using a temporal decomposition called sifting process. Two strategies for noise reduction are proposed: filtering and thresholding. The basic principle of these two methods is the signal reconstruction with IMFs previously filtered, using the minimum mean-squared error (MMSE) filter introduced by I. Y. Soon et al. (1998), or thresholded using a shrinkage function. The performance of these methods is analyzed and compared with those of the MMSE filter and wavelet shrinkage. The study is limited to signals corrupted by additive white Gaussian noise. The obtained results show that the proposed denoising schemes perform better than the MMSE filter and wavelet approach.Kais Khaldi, Abdel-Ouahab Boudraa, Abdelkhalek Bouchikhi, and Monia Turki-Hadj Alouane© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/674974In many ensemble classification paradigms, the function which combines local/base classifier decisions is learned in a supervised fashion. Such methods require common labeled training examples across the classifier ensemble. However, in some scenarios, where an ensemble solution is necessitated, common labeled data may not exist: (i) legacy/proprietary classifiers, and (ii) spatially distributed and/or multiple modality sensors. In such cases, it is standard to apply fixed (untrained) decision aggregation such as voting, averaging, or naive Bayes rules. In recent work, an alternative transductive learning strategy was proposed. There, decisions on test samples were chosen aiming to satisfy constraints measured by each local classifier. This approach was shown to reliably correct for class prior mismatch and to robustly account for classifier dependencies. Significant gains in accuracy over fixed aggregation rules were demonstrated. There are two main limitations of that work. First, feasibility of the constraints was not guaranteed. Second, heuristic learning was applied. Here, we overcome these problems via a transductive extension of maximum entropy/improved iterative scaling for aggregation in distributed classification. This method is shown to achieve improved decision accuracy over the earlier transductive approach and fixed rules on a number of UC Irvine datasets.David J. Miller, Yanxin Zhang, and George Kesidis© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/843232Binocular vision systems have been widely used for detecting obstacles in advanced driver assistant systems (ADASs). These systems normally utilise disparity information extracted from left and right image pairs, but ignore the optic flows able to be extracted from the two image sequences. In fact, integration of these two methods may generate some distinct benefits. This paper proposes two algorithms for integrating stereovision and motion analysis for improving object detection and tracking. The basic idea is to fully make use of information extracted from stereo image sequence pairs captured from a stereovision rig. The first algorithm is to impose the optic flows as extra constraints for stereo matching. The second algorithm is to use a Kalman filter as a mixer to combine the distance measurement and the motion displacement measurement for object tracking. The experimental results demonstrate that the proposed methods are effective for improving the quality of stereo matching and three-dimensional object tracking.Yingping Huang and Ken Young© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/165792This paper presents a novel video enhancement system based on an adaptive spatio-temporal connective (ASTC) noise filter and an adaptive piecewise mapping function (APMF). For ill-exposed videos or those with much noise, we first introduce a novel local image statistic to identify impulse noise pixels, and then incorporate it into the classical bilateral filter to form ASTC, aiming to reduce the mixture of the most two common types of noises—Gaussian and impulse noises in spatial and temporal directions. After noise removal, we enhance the video contrast with APMF based on the statistical information of frame segmentation results. The experiment results demonstrate that, for diverse low-quality videos corrupted by mixed noise, underexposure, overexposure, or any mixture of the above, the proposed system can automatically produce satisfactory results.Chao Wang, Li-Feng Sun, Bo Yang, Yi-Ming liu, and Shi-Qiang Yang© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/328041Delay-and-sum (DS) beamforming is a simple processing method that can estimate the direction-of-arrival from multiple signal sources. The major advantage of DS beamforming is that it can handle wideband as well as narrowband signals. However, DS beamforming exhibits high computational complexity. The multiangle resolution fast beamformer was proposed as a computationally efficient approximation of DS beamforming, reducing the computational order of complexity from O(n3) to O(n2log n). In this paper, we introduce the pruned multiangle resolution fast beamformer to further reduce the computational complexity. The new algorithm includes an energy detector at intermediate stages of the fast beamformer to prune sectors that do not exhibit increasing energy consistent with coherent integration. Simulations are provided to assess the performance of the pruned fast beamformer. One use for the estimates from the pruned fast beamformer is to initialize high-resolution direction-of-arrival (DOA) estimators such as coherent signal subspace methods.Yeo-Sun Yoon, Lance M. Kaplan, Seung-Mok Oh, and James H. McClellan© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/548741This paper proposes a curve fitting technique for fast and accurate estimation of the perceived quality of streaming media contents, delivered within a wireless network. The model accounts for the effects of various network parameters such as congestion, radio link power, and video transmission bit rate. The evaluation of the perceived quality of service (PQoS) is based on the well-known VQM objective metric, a powerful technique which is highly correlated to the more expensive and time consuming subjective metrics. Currently, PQoS is used only for offline analysis after delivery of the entire video content. Thanks to the proposed simple model, we can estimate in real time the video PQoS and we can rapidly adapt the content transmission through scalable video coding and bit rates in order to offer the best perceived quality to the end users. The designed model has been validated through many different measurements in realistic wireless environments using an ad hoc WiFi test bed.Pasquale Pace and Emanuele Viterbo© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/849040We proposed an electroencephalographic (EEG) signal analysis approach to investigate the driver's cognitive response to traffic-light experiments in a virtual-reality-(VR-) based simulated driving environment. EEG signals are digitally sampled and then transformed by three different feature extraction methods including nonparametric weighted feature extraction (NWFE), principal component analysis (PCA), and linear discriminant analysis (LDA), which were also used to reduce the feature dimension and project the measured EEG signals to a feature space spanned by their eigenvectors. After that, the mapped data could be classified with fewer features and their classification results were compared by utilizing two different classifiers including k nearest neighbor classification (KNNC) and naive bayes classifier (NBC). Experimental data were collected from 6 subjects and the results show that NWFE+NBC gives the best classification accuracy ranging from 71%∼77%, which is over 10%∼24% higher than LDA+KNN1. It also demonstrates the feasibility of detecting and analyzing single-trial EEG signals that represent operators' cognitive states and responses to task events.Chin-Teng Lin, Ken-Li Lin, Li-Wei Ko, Sheng-Fu Liang, Bor-Chen Kuo, and I-Fang Chung© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/731835An RF-based indoor localization design targeted for wireless sensor networks (WSNs) is presented. The energy-efficiency of mobile location nodes is maximized by a localization medium access control (LocMAC) protocol. For location estimation, a location resolver algorithm is introduced. It enables localization with very scarce energy and processing resources, and the utilization of simple and low-cost radio transceiver HardWare (HW) without received signal strength indicator (RSSI) support. For achieving high energy-efficiency and minimizing resource usage, LocMAC is tightly cross-layer designed with the location resolver algorithm. The presented solution is fully calibration-free and can cope with coarse grained and unreliable ranging measurements. We analyze LocMAC power consumption and show that it outperforms current state-of-the-art WSN medium access control (MAC) protocols in location node energy-efficiency. The feasibility of the proposed localization scheme is validated by experimental measurements using real resource constrained WSN node prototypes. The prototype network reaches accuracies ranging from 1 m to 7 m.With one anchor node per a typical office room, the current room of the localized node is determined with 89.7% precision.Ville A. Kaseva, Mikko Kohvakka, Mauri Kuorilehto, Marko Hännikäinen, and Timo D. Hämäläinen© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/691924Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.Javier A. Montoya-Zegarra, João Paulo Papa, Neucimar J. Leite, Ricardo da Silva Torres, and Alexandre X. Falcão© 2008, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2008/816507Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing is presented and analyzed, including comparative evaluations of its performance from the points of view of estimation and detection capabilities, and of robustness with respect to non-Gaussian additive noise.José Luis Rojo-Álvarez, Manel Martínez-Ramón, Jordi Muñoz-Marí, Gustavo Camps-Valls, Carlos M. Cruz, and Aníbal R. Figueiras-Vidal© 2008, Hindawi Publishing Corporation. All rights reserved.