http://www.hindawi.comThe latest articles from Hindawi Publishing Corporation© 2012, Hindawi Publishing Corporation. All rights reserved.http://www.hindawi.com/journals/asp/2011/645964/In multilabel classification, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. The most commonly used approach for multilabel classification is where a binary classifier is learned independently for each possible class. However, multilabeled data generally exhibit relationships between labels, and this approach fails to take such relationships into account. In this paper, we describe an original method for multilabel classification problems derived from a Bayesian version of the k-nearest neighbor (k-NN) rule. The method developed here is an improvement on an existing method for multilabel classification, namely multilabel k-NN, which takes into account the dependencies between labels. Experiments on simulated and benchmark datasets show the usefulness and the efficiency of the proposed approach as compared to other existing methods.Zoulficar Younes, Fahed Abdallah, Thierry Denoeux, and Hichem SnoussiCopyright © 2011 Zoulficar Younes et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/267641/The performance of MIMO systems with intentional timing offset between the transmitters has recently been the focus of study of different researchers. In these schemes, a nonzero (but known) symbol timing offset is introduced between the signals transmitted from the different transmitters to improve the performance of MIMO systems. This leads to a reduction in Interantenna Interference (IAI), and it is shown that an advanced receiver can utilize this information to extract significant performance gains. In this paper, we show that this transmission scheme may be used in conjunction with different kinds of receivers including ZF, MMSE, and sequence detection-based receivers. We also consider the design of novel pulse shapes that reduce the IAI at the expense of slightly higher intersymbol interference (ISI) and show that additional gains may be achieved.Aniruddha Das (Nandan) and Bhaskar D. RaoCopyright © 2011 Aniruddha Das (Nandan) and Bhaskar D. Rao. All rights reserved.http://www.hindawi.com/journals/asp/2011/817947/In many situations, there exist plenty of spatial and temporal redundancies in original signals. Based on this observation, a novel Turbo Bayesian Compressed Sensing (TBCS) algorithm is proposed to provide an efficient approach to transfer and incorporate this redundant information for joint sparse signal reconstruction. As a case study, the TBCS algorithm is applied in Ultra-Wideband (UWB) systems. A space-time TBCS structure is developed for exploiting and incorporating the spatial and temporal a priori information for space-time signal reconstruction. Simulation results demonstrate that the proposed TBCS algorithm achieves much better performance with only a few measurements in the presence of noise, compared with the traditional Bayesian Compressed Sensing (BCS) and multitask BCS algorithms.Depeng Yang, Husheng Li, and Gregory D. PetersonCopyright © 2011 Depeng Yang et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/495394/Signal extrapolation is an important task in digital signal processing for extending known signals into unknown areas. The Selective Extrapolation is a very effective algorithm to achieve this. Thereby, the extrapolation is obtained by generating a model of the signal to be extrapolated as weighted superposition of basis functions. Unfortunately, this algorithm is computationally very expensive and, up to now, efficient implementations exist only for basis function sets that emanate from discrete transforms. Within the scope of this contribution, a novel efficient solution for Selective Extrapolation is presented for utilization with arbitrary basis functions. The proposed algorithm mathematically behaves identically to the original Selective Extrapolation but is several decades faster. Furthermore, it is able to outperform existent fast transform domain algorithms which are limited to basis function sets that belong to the corresponding transform. With that, the novel algorithm allows for an efficient use of arbitrary basis functions, even if they are only numerically defined.Jürgen Seiler and André KaupCopyright © 2011 Jürgen Seiler and André Kaup. All rights reserved.http://www.hindawi.com/journals/asp/2011/806516/The problem of automatic detection of image areas appropriate for accurate estimation of additive noise standard deviation (STD) irrespectively to processed image properties is considered in this paper. For accurate estimation of either image texture or noise STD, we distinguish two complementary informative maps: noise- (NI-) and texture- (TI-) informative ones. The NI map is determined and iteratively upgraded based on the Fisher information on noise STD calculated in scanning window (SW) fashion. Fractional Brownian motion (fBm) model for image texture is used to derive the required Fisher information. To extract final noise STD from NI map, fBm- and DCT-based estimators are implemented. The performance of these two estimators is comparatively assessed on large image database for different noise levels. It is also compared with performance of two competitive state-of-the-art estimators recently published. Utilizing NI map along with DCT-based noise STD estimator has proved to be significantly more efficient.M. Uss, B. Vozel, V. Lukin, S. Abramov, I. Baryshev, and K. ChehdiCopyright © 2011 M. Uss et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/294010/Multimodal speech processing has been a subject of investigation to increase robustness of unimodal speech processing systems. Hard fusion of acoustic and visual speech is generally used for improving the accuracy of such systems. In this paper, we discuss the significance of two soft belief functions developed for multimodal speech processing. These soft belief functions are formulated on the basis of a confusion matrix of probability mass functions obtained jointly from both acoustic and visual speech features. The first soft belief function (BHT-SB) is formulated for binary hypothesis testing like problems in speech processing. This approach is extended to multiple hypothesis testing (MHT) like problems to formulate the second belief function (MHT-SB). The two soft belief functions, namely, BHT-SB and MHT-SB are applied to the speaker diarization and audio-visual speech recognition tasks, respectively. Experiments on speaker diarization are conducted on meeting speech data collected in a lab environment and also on the AMI meeting database. Audiovisual speech recognition experiments are conducted on the GRID audiovisual corpus. Experimental results are obtained for both multimodal speech processing tasks using the BHT-SB and the MHT-SB functions. The results indicate reasonable improvements when compared to unimodal (acoustic speech or visual speech alone) speech processing.D. Kumar, P. Vimal, and Rajesh M. HegdeCopyright © 2011 D. Kumar et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/176026/One of the fundamental requirements for visual surveillance with Visual Sensor Networks (VSN) is the correct association of camera's observations with the tracks of objects under tracking. In this paper, we model the data association in VSN as an inference problem on dynamic Bayesian networks (DBN) and investigate the key problems for efficient data association in case of missing detection. Firstly, to deal with the problem of missing detection, we introduce a set of random variables, namely routine variables, into the DBN model to describe the uncertainty in the path taken by the moving objects and propose the high-order spatio-temporal model based inference algorithm. Secondly, for the problem of computational intractability of exact inference, we derive two approximate inference algorithms by factorizing the belief state based on the marginal and conditional independence assumptions. Thirdly, we incorporate the inference algorithm into EM framework to make the algorithm suitable for the case when object appearance parameters are unknown. Simulation and experimental results demonstrate the effect of the proposed methods.Jiuqing Wan and Qingyun LiuCopyright © 2011 Jiuqing Wan and Qingyun Liu. All rights reserved.http://www.hindawi.com/journals/asp/2011/703752/The wavelet-based approach that combines a reversible round-off nonrecursive discrete periodized wavelet transform (RRO-NRDPWT) and the set partitioning in hierarchical trees (SPIHT) scheme is an efficient ECG data compression. However, this RRO-NRDPWT-based system suffers from the high complexity of the SPIHT scheme during realization. In this paper, a modified run-length coding (MRLC) algorithm is proposed towards the realization of a RRO-NRDPWT-based ECG data compression system. The MRLC with its regularity and low computational complexity is suitable for hardware implementation, but at a cost of compression performance. This sacrifice is compensated by an efficient quantization scheme. By using the MIT-BIH arrhythmia database, the experimental results show that the proposed scheme can compete with the SPIHT scheme for a compression ratio (CR) greater than 8. Hardware simulations are taken using both the Verilog logic simulator with Cadence design platform, and a Xilinx FPGA EP2C35F672C6.Hsieh-Wei Lee, King-Chu Hung, Tsung-Ching Wu, and Cheng-Tung KuCopyright © 2011 Hsieh-Wei Lee et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/831724/This study presents a hybrid technique for simultaneously completing images by using geometry and texture components of input data. The approaches using inpainting methods based on partial differential equations (PDEs) to fill in large image regions usually fail if these regions contain textures. On the other hand, texture synthesis algorithms sometimes fail due to complex structures and textures in the image. However, this study, suggesting a hybrid method using both techniques, produces satisfactory results in completing the missing parts of images. In the proposed method, the given image is decomposed into two components. The geometry component, obtained by using the regularization PDE based on a trace operator, was inpainted by a tensor-driven PDE algorithm that takes curvatures of line integral curves into account, and the texture component, obtained by subtracting the given image from the geometry component, was reconstructed by the modified exemplar-based inpainting algorithm. Both of these methods work on color information. The main contribution of this paper is that it uses decomposition and montage stages together which provides superior results compared with the existing methods. Experimental results show that the proposed method efficiently fills in target regions, which is promising.Bekir DizdaroğluCopyright © 2011 Bekir Dizdaroğlu. All rights reserved.http://www.hindawi.com/journals/asp/2011/425203/ISAR imaging of ships at sea with significant motion results in the Doppler frequency shift for the received signal is time-varying, which will deteriorate the ISAR image quality for the Range-Doppler (RD) algorithm. In this paper, the received signal is modeled as a multicomponent cubic phase signal (CPS), and a new method for estimating the parameters of CPS based on the integrated high-order matched phase transform (IHMPT) is proposed. This algorithm is simpler and more computational efficient than some of other parameters estimation algorithms proposed previously. Then, combined with the Range-Instantaneous-Doppler (RID) technique, the high quality instantaneous ISAR images can be obtained. The results of simulated and measured data are provided to demonstrate the effectiveness of the new method proposed.Yong Wang and Yi-Cheng JiangCopyright © 2011 Yong Wang and Yi-Cheng Jiang. All rights reserved.http://www.hindawi.com/journals/asp/2011/412647/Fingerprint smear detection has become a challenging issue due to the erratic texture of the smear tissue and its similarity to normal finger area. This paper presents a novel fingerprint image smear detection approach integrating symmetric wavelet transform (SWT), gray level co-occurrence matrix, and DCT. A feature extraction algorithm is first proposed by utilizing SWT to decompose each fingerprint and characterizing local texture features of defective finger tissue with the SWT coefficients in subbands 4~19. Concurrence matrix-based texture features are incorporated into the feature vector to further improve the texture classification sensitivity. The concatenated feature vector is then fed into a pretrained genetic neural network classifier, which identifies smears by labeling fingerprint subblocks into different categories. Finally, DCT decomposition is used to detect abnormalities in fingerprint images containing small smear areas and abrupt breakages. Experimental results indicate that the hybrid method can effectively identify various types of fingerprint smears.Xiukun YangCopyright © 2011 Xiukun Yang. All rights reserved.http://www.hindawi.com/journals/asp/2011/649675/Pavement crack types provide important information for making pavement maintenance strategies. This paper proposes an automatic pavement crack classification approach, exploiting the spatial distribution features (i.e., direction feature and density feature) of the cracks under a neural network model. In this approach, a direction coding (D-Coding) algorithm is presented to encode the crack subsections and extract the direction features, and a Delaunay Triangulation technique is employed to analyze the crack region structure and extract the density features. As regarding skeletonized crack sections rather than crack pixels, the spatial distribution features hold considerable feature significance for each type of cracks. Empirical study indicates a classification precision of over 98% of the proposed approach.Qingquan Li, Qin Zou, and Xianglong LiuCopyright © 2011 Qingquan Li et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/528734/Parameterized quadratic programming (Lasso) is a powerful tool for the recovery of sparse signals based on underdetermined observations contaminated by noise. In this paper, we study the problem of simultaneous sparsity pattern recovery and approximation recovery based on the Lasso. An extended Lasso method is proposed with the following main contributions: (1) we analyze the recovery accuracy of Lasso under the condition of guaranteeing the recovery of nonzero entries positions. Specifically, an upper bound of the tuning parameter h of Lasso is derived. If h exceeds this bound, the recovery error will increase with h; (2) an extended Lasso algorithm is developed by choosing the tuning parameter according to the bound and at the same time deriving a threshold to recover zero entries from the output of the Lasso. The simulation results validate that our method produces higher probability of sparsity pattern recovery and better approximation recovery compared to two state-of-the-art Lasso methods.Jun Zhang, Yuanqing Li, Zhuliang Yu, and Zhenghui GuCopyright © 2011 Jun Zhang et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/651960/Interpolators have found widespread applications in communication systems such as multimedia. In this paper, the interpolated timing recovery employing raised cosine pulse for digital magnetic recording channel is investigated. This study indicates that the raised cosine interpolator with rolloff factor β between 0.4 and 0.6 is shown to have less aliasing effect and achieve better MSE performance than other interpolators such as the sinc, polynomial, and MMSE interpolators with similar computational complexity. The superiority of the raised cosine interpolator over other interpolators is also demonstrated on the ME2PRIV recording channel through computer simulations. The main advantage of the raised cosine interpolator is that it is potentially simpler and can be fully digitally implemented.Hui-Feng Tsai and Zang-Hao JiangCopyright © 2011 Hui-Feng Tsai and Zang-Hao Jiang. All rights reserved.http://www.hindawi.com/journals/asp/2011/852934/An adaptive single image superresolution (SR) method using a support vector data description (SVDD) is presented. The proposed method represents the prior on high-resolution (HR) images by hyperspheres of the SVDD obtained from training examples and reconstructs HR images from low-resolution (LR) observations based on the following schemes. First, in order to perform accurate reconstruction of HR images containing various kinds of objects, training HR examples are previously clustered based on the distance from a center of a hypersphere obtained for each cluster. Furthermore, missing high-frequency components of the target image are estimated in order that the reconstructed HR image minimizes the above distances. In this approach, the minimized distance obtained for each cluster is utilized as a criterion to select the optimal hypersphere for estimating the high-frequency components. This approach provides a solution to the problem of conventional methods not being able to perform adaptive estimation of the high-frequency components. In addition, local patches in the target low-resolution (LR) image are utilized as the training HR examples from the characteristic of self-similarities between different resolution levels in general images, and our method can perform the SR without utilizing any other HR images.Takahiro Ogawa and Miki HaseyamaCopyright © 2011 Takahiro Ogawa and Miki Haseyama. All rights reserved.http://www.hindawi.com/journals/asp/2011/875132/The problem of component estimation from a multicomponent signal in additive white Gaussian noise is considered. A parametric ML approach, where all components are represented as a multiplication of a polynomial amplitude and polynomial phase term, is used. The formulated optimization problem is solved via nonlinear iterative techniques and the amplitude and phase parameters for all components are reconstructed. The initial amplitude and the phase parameters are obtained via time-frequency techniques. An alternative method, which iterates amplitude and phase parameters separately, is proposed. The proposed method reduces the computational complexity and convergence time significantly. Furthermore, by using the proposed method together with Expectation Maximization (EM) approach, better reconstruction error level is obtained at low SNR. Though the proposed method reduces the computations significantly, it does not guarantee global optimum. As is known, these types of non-linear optimization algorithms converge to local minimum and do not guarantee global optimum. The global optimum is initialization dependent.Z. Deprem, K. Leblebicioglu, O. Arıkan, and A. E. ÇetinCopyright © 2011 Z. Deprem et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/461563/Recently, an enhanced spatiotemporal chaotic image/video cryptosystem was proposed by Rhouma and Belghith. This paper demonstrates that the enhanced cryptosystem is not secure against the following three different classical types of attacks: chosen plaintext, chosen ciphertext, and known plaintext. In the three attacks, only a pair of (plaintext/ciphertext) was needed to totally break the cryptosystem.Eun-Jun Yoon, Jeong-Woo Hong, Sang-Yoon Yoon, Dong-In Park, and Myung-Jin ChoiCopyright © 2011 Eun-Jun Yoon et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/982936/This paper presents a study of automatic detection and recognition of tonal bird sounds in noisy environments. The detection of spectro-temporal regions containing bird tonal vocalisations is based on exploiting the spectral shape to identify sinusoidal components in the short-time spectrum. The detection method provides tonal-based feature representation that is employed for automatic bird recognition. The recognition system uses Gaussian mixture models to model 165 different bird syllables, produced by 95 bird species. Standard models, as well as models compensating for the effect of the noise, are employed. Experiments are performed on bird sound recordings corrupted by White noise and real-world environmental noise. The proposed detection method shows high detection accuracy of bird tonal components. The employed tonal-based features show significant recognition accuracy improvements over the Mel-frequency cepstral coefficients, in both standard and noise-compensated models, and strong robustness to mismatch between the training and testing conditions.Peter Jančovič and Münevver KöküerCopyright © 2011 Peter Jančovič and Münevver Köküer. All rights reserved.http://www.hindawi.com/journals/asp/2011/913189/Symbol error rate (SER) of quadrature subbranch hybrid selection/maximal-ratio combining (HS/MRC) scheme for 1-D modulations in Rayleigh fading under employment of the generalized receiver (GR), which is constructed based on the generalized approach to signal processing (GASP) in noise, is investigated. N diversity input branches are split into 2N in-phase and quadrature subbranches. M-ary pulse amplitude modulation, including coherent binary phase-shift keying (BPSK), with quadrature subbranch HS/MRC is investigated. GR SER performance for quadrature HS/MRC and HS/MRC schemes is investigated and compared with the conventional HS/MRC receiver. Comparison shows that the GR with quadrature subbranch HS/MRC and HS/MRC schemes outperforms the traditional HS/MRC receiver. Procedure of partial cancellation factor (PCF) selection for the first stage of hard-decision partial parallel interference cancellation (PPIC) using GR employed by direct-sequence code-division multiple access (DS-CDMA) systems under multipath fading channel in the case of periodic code scenario is proposed. Optimal PCF range is derived based on Price's theorem. Simulation confirms that the bit error rate (BER) performance is very close to potentially achieved one and surpasses the BER performance of real PCF for DS-CDMA systems discussed in the literature.Vyacheslav TuzlukovCopyright © 2011 Vyacheslav Tuzlukov. All rights reserved.http://www.hindawi.com/journals/asp/2011/216783/As a solution to copyright protection of the digital media, digital watermarking techniques have been developed for embedding specific information to identify the owner in the host data imperceptibly. Nowadays, most watermarking methods mainly focused on digital media such as images, video, audio, and text, and very few watermarking methods have been presented for 3D models relatively. In the paper, a new robust watermarking scheme is presented which is based on biorthogonal nonuniform B-spline wavelets (BNBW) in the frequency domain for the purpose of copyright protection in the area of CAD, CAM, CAE, and CG. The watermark is embedded by modulating the wavelet coefficient vectors with the watermark in the frequency domain. The relative experiments prove that this approach not only can withstand common attacks on 3D models such as polygon mesh simplifications, addition of random noise, model cropping, translation, rotation, scaling, as well as a combination of such attacks but also can detect and locate tampered vertices.Liping Chen, Xiangzeng Kong, Bin Weng, Zhiqiang Yao, and Rijing PanCopyright © 2011 Liping Chen et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/234090/This paper deals with convolution setting at boundary regions for 1D convolution computed during recursive Gaussian and Gabor filtering as well as staged Gabor filtering computed more efficiently as modulation, recursive Gaussian, and demodulation. These are established fast approximations to their filters. Until recently, all the three applications of recursive filters suffered from distortions near boundary as a result of inappropriate boundary treatment. The extension of input data with constant border value is presumed. We review a recently suggested setting for recursive Gaussian and Gabor filtering. Then, a new convolution setting for the more efficient staged Gabor filtering is presented. We also offer a formula to compute the scale coefficient, using which a zero-mean Gabor filter can be obtained from either recursive or staged Gabor filter.Vladimír UlmanCopyright © 2011 Vladimír Ulman. All rights reserved.http://www.hindawi.com/journals/asp/2011/490289/This paper studies the problem of direction finding and polarization estimation of coherent sources using a uniform linear electromagnetic vector-sensor (EmVS) array. A novel preprocessing algorithm based on EmVS subarray averaging (EVSA) is firstly proposed to decorrelate sources' coherency. Then, the proposed EVSA algorithm is combined with the propagator method (PM) to estimate the EmVS steering vector, and thus estimate the direction-of-arrival (DOA) and the polarization parameters by a vector cross-product operation. Compared with the existing estimate methods, the proposed EVSA-PM enables decorrelation of more coherent signals, joint estimation of the DOA and polarization of coherent sources with a lower computational complexity, and requires no limitation of the intervector sensor spacing within a half-wavelength to guarantee unique and unambiguous angle estimates. Also, the EVSA-PM can estimate these parameters by parameter-space searching techniques. Monte-Carlo simulations are presented to verify the efficacy of the proposed algorithm.Zhaoting Liu, Jing He, and Zhong LiuCopyright © 2011 Zhaoting Liu et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/748430/Motivated by the fact that in computer vision data samples are matrices, in this paper, we propose a matrix-variate probabilistic model for canonical correlation analysis (CCA). Unlike probabilistic CCA which converts the image samples into the vectors, our method uses the original image matrices for data representation. We show that the maximum likelihood parameter estimation of the model leads to the two-dimensional canonical correlation directions. This model helps for better understanding of two-dimensional Canonical Correlation Analysis (2DCCA), and for further extending the method into more complex probabilistic model. In addition, we show that two-dimensional Linear Discriminant Analysis (2DLDA) can be obtained as a special case of 2DCCA.Mehran Safayani and Mohammad Taghi Manzuri ShalmaniCopyright © 2011 Mehran Safayani and Mohammad Taghi Manzuri Shalmani. All rights reserved.http://www.hindawi.com/journals/asp/2011/621390/TDOA- (time difference of arrival-) based algorithms are common methods for speech source localization. The generalized cross correlation (GCC) method is the most important approach for estimating TDOA between microphone pairs. The performance of this method significantly degrades in the presence of noise and reverberation. This paper addresses the problem of 3D localization in joint noisy and reverberant conditions and a single-speaker scenario. We first propose a modification to make the GCC-PHAse transform (GCC-PHAT) method robust against environment noise. Then, we use an iterative technique that employs location estimation to improve TDOAs accuracy. Extensive experiments on both simulated and real (practical) data (in a single-source scenario) show the capability of the proposed methods to significantly improve TDOA accuracy and, consequently, source location estimates.Hamid Reza Abutalebi and Hossein MomenzadehCopyright © 2011 Hamid Reza Abutalebi and Hossein Momenzadeh. All rights reserved.http://www.hindawi.com/journals/asp/2011/290950/This study utilized a feed-forward neural network model along with computer vision techniques to discriminate sweet red pepper products prepared by different methods such as freezing and pureeing. The differences among the fresh, frozen and pureed samples are investigated by studying their bio-crystallogram images. The dissimilarity in visually analyzed bio-crystallogram images are defined as the distribution of crystals on the circular glass underlay and the thin or the thick structure of crystal needles. However, the visual description and definition of bio-crystallogram images has major disadvantages. A methodology called process neural network (ProcNN) has been studied to overcome these shortcomings.Sevcan Unluturk, Mehmet S. Unluturk, Fikret Pazir, and Alper KuscuCopyright © 2011 Sevcan Unluturk et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/656494/Applications related to distributed microphone systems are typically initiated with sound source detection. This paper introduces a novel method for the automatic detection of sound sources in images created with steered response power (SRP) algorithms. The method exploits the near-symmetric coherent power noise distribution to estimate constant false-alarm rate (CFAR) thresholds. Analyses show that low-frequency source components degrade CFAR threshold performance due to increased nonsymmetry in the coherent power distribution. This degradation, however, can be offset by partial whitening or increasing differential path distances between the microphone pairs and the spatial locations of interest. Experimental recordings are used to assess CFAR performance subject to variations in source frequency content and partial whitening. Results for linear, perimeter, and planar microphone geometries demonstrate that experimental false-alarm probabilities for CFAR thresholds ranging from 10-1 and 10-6 are limited to within one order of magnitude when proper filtering, partial whitening, and noise model parameters are applied.Kevin D. Donohue, Sayed M. SaghaianNejadEsfahani, and Jingjing YuCopyright © 2011 Kevin D. Donohue et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/257095/In the design of capsule endoscope, the trade-offs between battery-life and video-quality is imperative. Typically, the resolution of capsule gastrointestinal (GI) image is limited for the power consumption and bandwidth of RF transmitter. Many fast compression algorithms for reducing computation load; however, they may result in a distortion of the original image, which is not suitable for the use of medical care. This paper presents a novel image compression for capsule gastrointestinal endoscopy, called GICam-II, motivated by the reddish feature of GI image. The reddish feature makes the luminance or sharpness of GI image sensitive to the red component as well as the green component. We focus on a series of mathematical statistics to systematically analyze the color sensitivity in GI images from the RGB color space domain to the two-dimensional discrete-cosine-transform spatial frequency domain. To reduce the compressed image size, GICam-II downsamples the blue component without essential loss of image detail and also subsamples the green component from the Bayer-patterned image. From experimental results, the GICam-II can significantly save the power consumption by 38.5% when compared with previous one and 98.95% when compared with JPEG compression, while the average peak signal-to-noise ratio of luminance (PSNRY) is 40.73 dB.Meng-Chun Lin and Lan-Rong DungCopyright © 2011 Meng-Chun Lin and Lan-Rong Dung. All rights reserved.http://www.hindawi.com/journals/asp/2011/540375/Temporal shape variations intuitively appear to provide a good cue for human activity modeling. In this paper, we lay out a novel framework for human action recognition based on fuzzy log-polar histograms and temporal self-similarities. At first, a set of reliable keypoints are extracted from a video clip (i.e., action snippet). The local descriptors characterizing the temporal shape variations of action are then obtained by using the temporal self-similarities defined on the fuzzy log-polar histograms. Finally, the SVM classifier is trained on these features to realize the action recognition model. The proposed method is validated on two popular and publicly available action datasets. The results obtained are quite encouraging and show that an accuracy comparable or superior to that of the state-of-the-art is achievable. Furthermore, the method runs in real time and thus can offer timing guarantees to real-time applications.Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, and Usama SayedCopyright © 2011 Samy Sadek et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/373205/A novel algorithm, called the signed regressor least mean fourth (SRLMF) adaptive algorithm, that reduces the computational cost and complexity while maintaining good performance is presented. Expressions are derived for the steady-state excess-mean-square error (EMSE) of the SRLMF algorithm in a stationary environment. A sufficient condition for the convergence in the mean of the SRLMF algorithm is derived. Also, expressions are obtained for the tracking EMSE of the SRLMF algorithm in a nonstationary environment, and consequently an optimum value of the step-size is obtained. Moreover, the weighted variance relation has been extended in order to derive expressions for the mean-square error (MSE) and the mean-square deviation (MSD) of the proposed algorithm during the transient phase. Computer simulations are carried out to corroborate the theoretical findings. It is shown that there is a good match between the theoretical and simulated results. It is also shown that the SRLMF algorithm has no performance degradation when compared with the least mean fourth (LMF) algorithm. The results in this study emphasize the usefulness of this algorithm in applications requiring reduced implementation costs for which the LMF algorithm is too complex.Mohammed Mujahid Ulla Faiz, Azzedine Zerguine, and Abdelmalek ZidouriCopyright © 2011 Mohammed Mujahid Ulla Faiz et al. All rights reserved.http://www.hindawi.com/journals/asp/2011/485738/Acoustic event detection (AED) aims at determining the identity of sounds and their temporal position in audio signals. When applied to spontaneously generated acoustic events, AED based only on audio information shows a large amount of errors, which are mostly due to temporal overlaps. Actually, temporal overlaps accounted for more than 70% of errors in the real-world interactive seminar recordings used in CLEAR 2007 evaluations. In this paper, we improve the recognition rate of acoustic events using information from both audio and video modalities. First, the acoustic data are processed to obtain both a set of spectrotemporal features and the 3D localization coordinates of the sound source. Second, a number of features are extracted from video recordings by means of object detection, motion analysis, and multicamera person tracking to represent the visual counterpart of several acoustic events. A feature-level fusion strategy is used, and a parallel structure of binary HMM-based detectors is employed in our work. The experimental results show that information from both the microphone array and video cameras is useful to improve the detection rate of isolated as well as spontaneously generated acoustic events.Taras Butko, Cristian Canton-Ferrer, Carlos Segura, Xavier Giró, Climent Nadeu, Javier Hernando, and Josep R. CasasCopyright © 2011 Taras Butko et al. All rights reserved.