WSEAS CONFERENCES. WSEAS, Unifying the Science

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 Volume 4, 2008
Print ISSN: 1790-5052
E-ISSN: 2224-3488








Issue 1, Volume 4, January 2008

Title of the Paper:  One Approach to the Analysis Influence of Change Background Statistical Parameters on the Capability of Tracking Objects using “Mean Shift” Procedure


Authors: Dimitrije Bujakovic, Milenko Andric

Abstract: A quantitative analysis of change background statistics on the capability of tracking objects using “mean shift” procedure is present in this paper. Change of background statistics assumed changing of mean of brightness and changing noise variance in the scene. Quantitative analysis implies detection error and number of iteration needed for position determination using “mean shift” procedure.

Keywords: Object detection, object tracking, background statistical parameters, “mean shift” procedure, quantitative analysis, detection error, number of iterations,

Title of the Paper:  Modelling Geomagnetic Activity Data


Authors: Ernst D. Schmitter

Abstract: Strong geomagnetic activity is a hazard to electronics and electric power facilities. Assessment of the actual geomagnetic activity level from local magnetometer monitoring therefore is of importance for risk assessment but also in earth sciences and exploration. Wavelet based signal processing methods are applied to extract meaningful information from magnetic field time series in a noisy environment. Using a proper feature vector a local geomagnetic activity index can be derived under not ideal circumstances using computer intelligence methods. Locally linear radial basis function nets and self organizing maps are discussed in this context as data based process models.

Keywords: geomagnetism, signal processing, wavelets, neuro fuzzy modelling, self organizing map

Title of the Paper:  Adaptive Approach on Trimulus Color Image Enhancement and Information Theory Based Quantitative Measuring


Authors: Zhengmao Ye, Habib Mohamadian, Yongmao Ye

Abstract: Image enhancement and image clustering are two practical implementation approaches for pattern recognition with a variety of engineering applications. In most cases, the actual outcomes of some advanced image processing approaches will directly affect the decision making, such as in target detection and medical diagnosis. Among these approaches, image adaptive contrast stretching is a typical enhancement approach under conditions of improper illumination and unpleasant disturbances, which adapts to the intensity distribution of an image. K-means clustering is a typical segmentation approach to minimize the medium dispersing impact, which produces the distinctive clusters or layers for representing different components of the information being detected. In trimulus color systems, each of three color components takes an independent role along with image processing procedures. To evaluate actual effects of image enhancement and image segmentation, quantitative measures should be taken into account rather than qualitative evaluations exclusively. In this article, quantitative measures for trimulus color systems are proposed instead of the existing gray level ones. Considering the gray level image measures, the corresponding true color RGB component energy, discrete entropy, relative entropy and mutual information are proposed to measure the effectiveness of color image enhancement and segmentation techniques.

Keywords: Image Enhancement, Image Segmentation, Trimulus Color, Energy, Discrete Entropy, Relative Entropy, Mutual Information, Contrast Stretching, K-means Clustering

Title of the Paper:  Comparison of Methods to Estimate Individual Tree Attributes Using Color Aerial Photographs and LiDAR Data


Authors: Anjin Chang, Jung Ok Kim, Kiyun Ryu, Yong Il Kim

Abstract: The main objective of this study was to compare methods to estimate the number of trees and individual tree height using LiDAR data and aerial photography. A Korean pine tree study area for these techniques was selected the methods of watershed segmentation, region-growing segmentation, and morphological filtering were compared to estimate their accuracy. The algorithm was initiated by developing a normalized digital surface model (NDSM). A tree region was then extracted using classification and elimination errors of the NDSM and the photograph. The NDSM of the tree region was prefiltered and information about individual trees was extracted by segmentation and morphological methods. By using local maximum filtering, the tree height was obtained. Field observations were compared with the predicted values for accuracy assessment. The accuracy test showed the watershed segmentation algorithm to be the best estimator for tree modeling. Regression models for the study area explained 80% of the tree numbers and 89% of the heights.

Keywords: Aerial photography, LiDAR, Segmentation, Tree modeling

Issue 2, Volume 4, February 2008

Title of the Paper:  Analysis of Neuromuscular Disorders Using Statistical and Entropy Metrics on Surface EMG


Authors: Rok Istenic, Prodromos A. Kaplanis, Constantinos S. Pattichis, Damjan Zazula

Abstract: This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, myopathic or neuropathic, regarding to the acquired EMG signals. 39 subjects in total participated in the experiment, 19 normal, 11 myopathic and 9 neuropathic. Surface EMG was recorded using 4-channel surface electrodes on the biceps brachii muscle at isometric voluntary contractions. The recording time was only 5 seconds long to avoid muscle fatigue, and contractions at five force levels were performed, i.e. 10, 30, 50, 70 and 100 % of maximal voluntary contraction. The feature extraction routine deployed the wavelet transform and calculation of the Shannon entropy across all the scales in order to obtain a feature set for each subject. Subjects were classified regarding the extracted features using three machine learning techniques, i.e. decision trees, support vector machines and ensembles of support vector machines. Four 2-class classifications and a 3-class classification were performed. The scored classification rates were the following: 64±11% for normal/abnormal, 74±7% for normal/myopathic, 79±8% for normal/neuropathic, 49±20% for myopathic/neuropathic, and 63±8% for normal/myopathic/neuropathic.

Keywords: surface electromyography, neuromuscular disorders, neuropathy, myopathy, isometric voluntary contraction, entropy, wavelet transform

Title of the Paper:  Vision-Based Distance and Area Measurement System


Authors: Cheng-Chuan Chen, Ming-Chih Lu, Chin-Tun Chuang, Cheng-Pei Tsai

Abstract: The objective of this paper is to enable CCD camera for area measuring while recording images simultaneously. Based on an established relationship between pixel number and distance in this paper, we can derive the horizontal and vertical length of a targeted object, and subsequently calculate the area covered by the object. Because of the advantages demonstrated, the proposed system can be used for large-area measurements. For example, we can use this system to measure the size of the gap in the embankments during flooding, or the actual area affected by the landslides. Other applications include the surveying of ecosystems by inspecting how widely spread is a certain type of life form. For places which are difficult or impossible to reach, this system can be particularly useful in performing area measurements. Experiments conducted in this paper have indicated that different shooting distances and angles do not affect the measuring results.

Keywords: CCD camera, area measurement system, laser beams, pixels.

Title of the Paper:  Decomposition of Multi-Exponential and Related Signals – Functional Filtering Approach


Authors: Vairis Shtrauss

Abstract: Decomposition of multi-exponential and related signals is generalized as an inverse filtering problem on a logarithmic time or frequency scale, and discrete-time filters operating with equally spaced data on a logarithmic scale (geometrically spaced on linear scale) are proposed for its implementation. Ideal prototypes, algorithms and types of filters are found for various time- and frequency-domain mono-components. It is disclosed that the ill-posedness in the decomposition originates as high sampling-rate dependent noise amplification coefficients arising from the large areas under the increasing frequency responses. A novel regularization method is developed based on the noise transformation regulation by filter bandwidth control, which is implemented by adaptation of the appropriate sampling rate. Algorithm design of decomposition filters is suggested joining together signal acquisition, regularization and discrete-time filter implementation. As an example, decomposition of a frequency-domain multi-component signal is considered by a designed filter.

Keywords: Decomposition, Multi-Component Signals, Distribution of Time Constants, Functional Filters, Logarithmic Sampling, Ill-posedness, Regularization

Issue 3, Volume 4, March 2008

Title of the Paper:  Comparative study of several Fir Median Hybrid Filters for blink noise removal in Electrooculograms


Authors: Marcelino Martinez, Emilio Soria, Rafael Magdalena, Antonio Jose Serrano, Jose David Martín, Joan Vila

Abstract: The presence of a kind of impulsive noise due to eye blinks is typical during the acquisition of electrooculograms. This paper describes a comparative study of several algorithms used to remove the blink noise in the electroculogram preserving the sharp edges in the signal produced by the so-called saccadic eye movements. Median filters (MF) and several types of Fir Median Hybrid Filters (FMH) have been analyzed. Two types of real electrooculogram register with saccadic movements in controlled position were used to test the performance of the pre-processing filters (sampling rate 20Hz). The filtered signals were later processed with a saccadic eye movement detector algorithm in order to detect changes in the sensitivity and positive predictive value. The results show that neither FMH filters nor WFMH filters produce better results than median filters, in this particular study. The highest averaged values of sensitivity and positive predictive value are obtained by using a median filter of length I=6 samples (S=96.22 %, V++=95.42%) and the variant SWFMH of the same length (S=96.27 %, V++=91.91%). Although the differences in detection rates are not meaningful between these filters, median filters obtain slightly higher rates of saccades detection than SWFMH, but a reduction in computational burden is obtained by using FHM variants.

Keywords: electrooculogram, median filter, fir median hybrid filter, blink, saccadic, eye movement, eye tracking

Title of the Paper:  The Detection of Gear Noise Computed by Integrating the Fourier and Wavelet Methods


Authors: Niola Vincenzo, Quaremba Giuseppe, Forcelli Aniello

Abstract: This paper presents a new gearbox noise detection algorithm based on analyzing specific points of vibration signals using the Wavelet Transform. The proposed algorithm is compared with a previouslydeveloped algorithm associated with the Fourier decomposition using Hanning windowing. Simulation carried on real data demonstrate that the WT algorithm achieves a comparable accuracy while having a lower computational cost. This makes the WT algorithm an appropriate candidate for fast processing of noise gear box.

Keywords: Signal processing, gear noise, Wavelet Transform, multiresolution analysis.

Title of the Paper:  Object-Oriented Analysis Applied to High Resolution Satellite Data


Authors: Vincenzo Barrile, Giuliana Bilotta

Abstract: The aim of this contribute is to examine an application of Object Oriented Image Analysis on very high resolution data, on Ikonos images - multispectral and panchromatic – of Bagnara Calabra, in the province of Reggio Calabria. Our objectives are to show as an automatic analysis as we implemented in a unitary package for segmentation and classification Neuro Fuzzy – with a minimal manual participation - can get a good classification also in presence of high and very high resolution data of small cities, where higher is an error possibility.

Keywords: Object-Oriented Image Analysis - Morphological Based Segmentation – Fuzzy Classification.

Title of the Paper:  Word and Triphone Based Approaches in Continuous Speech Recognition for Tamil Language


Authors: R. Thangarajan, A. M. Natarajan, M. Selvam

Abstract: Building a continuous speech recognizer for the Indian language like Tamil is challenging due to the unique inherent features of the language like long and short vowels, lack of aspirated stops, aspirated consonants and many instances of allophones. Stress and accent vary in spoken language from region to region. But in read Tamil speech, stress and accents are ignored. There are three approaches to continuous speech recognition (CSR) based on the sub-word unit viz. word, phoneme and syllable. Like other Indian languages, Tamil is syllabic in nature. Pronunciation of words and sentences is strictly governed by set of rules. Many attempts have been made to build continuous speech recognizers for Tamil for small and restricted tasks. However medium and large vocabulary CSR for Tamil is relatively new and not explored. In this paper, the authors have attempted to build a Hidden Markov Model (HMM) based word and triphone acoustic models. The objective of this research is to build a small vocabulary word based and a medium vocabulary triphone based continuous speech recognizers for Tamil language. In this experimentation, a word based Context Independent (CI) acoustic model for 371 unique words and a triphone based Context Dependent (CD) acoustic model for 1700 unique words have been built. In addition to the acoustic models a pronunciation dictionary with 44 base phones and trigram based statistical language model have also been built as integral components of the linguist. These recognizers give very good word accuracy for trained and test sentences read by trained and new speakers.

Keywords: Acoustic Model, Context Dependent, Context Independent, Continuous Speech Recognition, Hidden Markov Model, Tamil language, Triphone.

Title of the Paper:  Genetic Algorithms based Adaptive Search Area Control for Real Time Multiple Face Detection using Neural Networks


Authors: Stephen Karungaru, Minoru Fukumi, Takuya Akashi, Norio Akamatsu

Abstract: Fast and automatic face detection from visual scenes is a vital preprocessing step in many face applications like recognition, authentication, analysis, etc. While detection of a single face can be accomplished with good accuracy, multiple faces detection in real time is more challenging not only because of different face sizes and orientations, but also due to limits of the processing power available. In this paper, we propose a real time multiple face detection method using multiple neural networks and an adaptive search area control method base on genetic algorithms. Although, neural networks and genetic algorithms may not be suitable for real time application because of their long processing times, we show that high detection accuracies and fast speeds can be achieved using small sized effective neural networks and a genetic algorithm with a small population size that requires few generations to converge. The proposed method subdivides the face into several small regions, each connected to an individual neural network. The subdivision guarantees small size networks and presents the ability to learn different face regions features using region-specialized input coding methods. The genetic algorithm is used during the real time search to extract possible face samples from face candidates. The fitness of the face samples is calculated using the neural networks. In the successive frames, the search area is adaptively controlled based on the information inherited from the proceeding frames. To prove the effectiveness of our approach we performed real time simulation using an inexpensive USB camera.

Keywords: Adaptive search area control, Genetic Algorithms, Neural networks, Real-time processing, feature extraction.

Title of the Paper:  The Fractal Dimension Correlated to the Bone Mineral Density


Authors: Khaled Harrar, Latifa Hamami

Abstract: Osteoporosis is a condition of decreased bone mass. This leads to fragile bones which are at an increased risk for fractures, more often, it affects postmenopausal women. In this paper we propose a study of osteoporosis with the fractal dimension. After an introduction to the theory and fractal dimension, we use the box counting method for the segmentation of radiographic images, the study of the influence of range size boxes on the fractal dimension will be investigated, and the correlation between a reference dimension and bone mineral density. Other imaging techniques will be given in order to see the results of the application of the method on these types of images.

Keywords: Box counting method, Osteoporosis, Fractal dimension, Radiographic images, Side length, Threshold.

Title of the Paper:  Rotating Projection Algorithm for Computer Tomography of Discrete Structures


Authors: A. Grebennikov, J. G. Vazquez Luna, T. Valencia Perez, M. Najera Enriquez

Abstract: Traditional computer tomography requires scanning the object to obtain a lot of projections. Then the image reconstruction is realized on the base of some mathematical model that corresponds to the concrete physical field producing this tomography. For example, in the X-rays tomography the inversion of the Radon transform is used. It seems necessary for difficult structures and can be realized in sufficiently fast manner. We consider in this paper the situation, when the investigating object has the “Discrete Structure”, so its reconstruction consists only in localization of some point-wise elements with different characteristic inside of the homogeneous (or quasi homogeneous) substance in the considered region. We propose for this case the Rotating Projection algorithm with a little number of scanning angles. This algorithm do not requires application of some inverse transforms. It simplifies the image reconstruction. Proposed approach is faster in its computer realization, gives possibility to reduce the time of the radiation treatment. The good properties of the developed algorithm are demonstrated on simulated numerical examples.

Keywords: Image reconstruction, rotating projection, computer tomography

Issue 4, Volume 4, April 2008

Title of the Paper:  Ships Classification Basing On Acoustic Signatures


Authors: Andrzej Zak

Abstract: The paper presents the technique of artificial neural networks used as classifier of hydroacoustic signatures generated by moving ship. The main task of proposed solution is to classify the objects which made the underwater noises. Firstly, the measurements were carried out dynamically by running ship past stationary hydrophones, mounted on tripods 1 m above the sea bottom. Secondly to identify the source of noise the level of vibration were measured on board by accelerometers, which were installed on important components of machinery. On the base of this measurement there was determined the sound pressure level, noise spectra and spectograms, transmission of acoustic energy via the hull into water. More over it was checked by using coherence function that components of underwater noise has its origin in vibrations of ship’s mechanisms. Basing on this research it was possible to create the hydroacoustic signature or so called “acoustic portrait” of moving ship. Next during the complex ships’ measurements on Polish Navy Test and Evaluation Acoustic Range hydroacoustic noises generated by moving ship were acquired. Basing on these results the classifier of acoustic signatures using artificial neural network was worked out. From the technique of artificial neural networks the Kohonen networks which belongs to group of self organizing networks where chosen to solve the research problem of classification. The choice was caused by some advantages of mentioned kind of neural networks like: they are ideal for finding relationships amongst complex sets of data, they have possibility to self expand the set of answers for new input vectors. To check the correctness of classifier work the research in which the number of right classification for presented and not presented before hydroacoustic signatures were made. Some results of research were presented on this paper. Described method actually is extended and its application is provided as assistant subsystem for hydrolocations systems of Polish Naval ships.

Keywords: Self-Organizing Map, Kohonen’s neural networks, Hydroacousitc signatures, Classification.

Title of the Paper:  Data Fusion and Topology Control in Wireless Sensor Networks


Authors: Vrinda Gupta, Rajoo Pandey

Abstract: The design of large-scale sensor networks interconnecting various sensor nodes has spurred a great deal of interest due to its wide variety of applications. Data fusion is a critical step in designing a wireless sensor network as it handles data acquired by sensory devices. Wireless sensor networks allow distributed sensing and signal processing while collaborating during energy efficient operations. Wireless sensor networks are battery powered; therefore prolonging the network lifetime through an energy aware node organization is highly desirable. The main goal of a topology control scheme in wireless sensor networks is to reduce power consumption in order to extend network lifetime. Our aim is to provide a better understanding of the current research issues in this field. The paper provides a more detailed look at some existing data fusion and topology management algorithms. The most important design issues of data fusion and topology control are also highlighted.

Keywords: Wireless sensor networks, data aggregation, data fusion, topology control, protocols.

Title of the Paper:  Single Channel Audio Source Separation


Authors: Bin. Gao, W. L. Woo, S. S. Dlay

Abstract: Blind source separation is an advanced statistical tool that has found widespread use in many signal processing applications. However, the crux topic based on one channel audio source separation has not fully developed to enable its way to laboratory implementation. The main idea approach to single channel blind source separation is based on exploiting the inherent time structure of sources known as basis filters in time domain that encode the sources in a statistically efficient manner. This paper proposes a technique for separating single channel recording of audio mixture using a hybrid of maximum likelihood and maximum a posteriori estimators. In addition, the algorithm proposes a new approach that accounts for the time structure of the speech signals by encoding them into a set of basis filters that are characteristically the most significant.

Keywords: Single Channel Separation, Blind Source Separation, Characteristic Filters, ML, MAP

Title of the Paper:  Association-Based Image Retrieval


Authors: Arun Kulkarni, Harikrisha Gunturu, Srikanth Datla

Abstract: With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content-based image retrieval (CBIR) systems have been developed in the last decade. A typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the database with similar features. Recent advances in CBIR systems include relevance feedback based interactive systems. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, we propose a new approach for image storage and retrieval called association-based image retrieval (ABIR). We try to mimic human memory. The human brain stores and retrieves images by association. We use a generalized bi-directional associative memory (GBAM) to store associations between feature vectors. The results of our simulation are presented in the paper

Keywords: Content-Based Image Retrieval, Association-Based Image Retrieval, Bi-directional Associative Memories.

Title of the Paper:  Blind Tamper Detection in Audio using Chirp based Robust Watermarking


Authors: O. Farooq, S. Datta, J. Blackledge

Abstract: In this paper, we propose the use of ‘chirp coding’ for embedding a watermark in audio data without generating any perceptual degradation of audio quality. A binary sequence (the watermark) is derived using energy based features from the audio signal and chirp coding used to embed the watermark in audio data. The chirp coding technique is such that the same watermark can be derived from the original audio signal as well as recovered from the watermarked signal. This not only enables the ‘blind’ recovery of the watermark, but also provides a solution for deriving two independent extraction processes for the watermark from which it is possible to ensure the authenticity of audio data and any mismatch indicating that the data may have been tampered with. To evaluate the robustness of the proposed scheme, different attacks such as compression, filtering, sampling rate alteration, for example, have been simulated. The results obtained reflect the high robustness of the watermark method used and is effectiveness in detecting any data tampering that may have occurred. For perceptual transparency of the watermark, Perceptual Assessment of Audio Quality (PEAQ ITU-R BS.1387) on Speech Quality Assessment Material (SQAM) has been undertaken and an average of -0.5085 Objective Difference Grade achieved.

Keywords: Chirp Coding, Robust Audio Watermarking, Self-Authentication, Tamper Detection, Wavelet Transform.

Title of the Paper:  Polyphonic Music Separation based on the Simplified Energy Splitter


Authors: Kristof Aczel, Istvan Vajk

Abstract: In the past years many approaches have been developed that target the separation of polyphonic music material into independent source signals. Due to lack of information on the original signals it is currently practically impossible to extract the original waveforms from their mixture. Thus all of the approaches target the reconstruction of signals that are at least in some way close to the original. For that purpose common features of harmonic sounds are usually exploited. This paper proposes a system that uses frequency-domain instrument models as prior knowledge for reinserting information needed for the separation. The system provides over 18dB Signal to Distortion Ratio for two simultaneous notes, which slowly degrades as the level of polyphony increases. This makes the approach highly applicable both as a standalone separation tool and the ground of other signal manipulation methods.

Keywords: sound separation, instrument print, polyphonic music, energy split

Title of the Paper:  Syllable-Based Automatic Arabic Specch Recognition in Noisy-Telephone Channel


Authors: Mohamed Mostafa Azmi, Hesham Tolba, Sherif Mahdy, Mervat Fashal

Abstract: The performance of well-trained speech recognizers using high quality full bandwidth speech data is usually degraded when used in real world environments. In particular, telephone speech recognition is extremely difficult due to the limited bandwidth of transmission channels. In this paper, we concentrate on the telephone recognition of Egyptian Arabic speech using syllables. Arabic spoken digits were described by showing their constructing phonemes, triphones, syllables and words. Speaker-independent hidden markov models (HMMs)-based speech recognition system was designed using Hidden markov model toolkit (HTK). The database used for both training and testing consists from forty-four Egyptian speakers. In clean environment, experiments show that the recognition rate using syllables outperformed the rate obtained using monophones, triphones and words by 2.68%, 1.19% and 1.79% respectively. Also in noisy telephone channel, syllables outperformed the rate obtained using monophones, triphones and words by 2.09%, 1.5% and 0.9% respectively. Comparative experiments have indicated that the use of syllables as acoustic units leads to an improvement in the recognition performance of HMM-based ASR systems in noisy environments. A syllable unit spans a longer time frame, typically three phones, thereby offering a more parsimonious framework for modeling pronunciation variation in spontaneous speech. Moreover, syllable-based recognition has relatively smaller number of used units and runs faster than word-based recognition.

Keywords: Speech recognition, syllables, Arabic language, HMMs, Noisy-channel.

Title of the Paper:  Design and Implementation of Digital FIR Equiripple Notch Filter on ECG Signal for Removal of Power line Interference


Authors: Mahesh S. Chavan, Ra. Agarwala, M. D. Uplane

Abstract: Filtering of power line interference is very meaningful in the measurement of biomedical events recording, particularly in the case of recording signals as weak as the ECG. The available filters for power line interference either need a reference channel or regard the frequency as fixed 50/60Hz. Methods of noise reduction have decisive influence on performance of all electro-cardio-graphic (ECG) signal processing systems. This work deals with problems of power line interference reduction. In the literature of the last twenty years several solutions of removal of power line interference on electrocardiogram (ECG) signals can be found. Some analogue and digital approaches to this problem are presented and its properties, advantages and disadvantages are shown. Present paper deals with design and development of digital FIR equiripple filter. The basic ECG has the frequency range from .5Hz to 100Hz. Artifacts plays the vital role in the processing of the ECG signal. It becomes difficult for the Specialist to diagnose the diseases if the artifacts are present in the ECG signal. In the present work notch filter is designed and applied to the ECG signal containing power line noise. Complete design is performed with FDA tool in the Matlab. The equiripple notch filter designed is having higher order due to which increase in the computational complexity observed. For accessing real time ECG the related instrumentation has been developed in the laboratory. The result shows the ECG signal before filtering and after filtering with their frequency spectrums which clearly indicates the reduction of the power line interference in the ECG signal.

Keywords: Electrocardiogram, Simulation, Equiripple Filter, Real Time Filtering. Noise reduction.

Title of the Paper:  Novel Statistical Approach to Blind Recovery of Earth Signal and Source Wavelet using Independent Component Analysis


Authors: Aws Al-Qaisi, W. L. Woo, S. S. Dlay

Abstract: This paper provides a new statistical approach to blind recovery of both earth signal and source wavelet given only the seismic traces using independent component analysis (ICA) by explicitly exploiting the sparsity of both the reflectivity sequence and the mixing matrix. Our proposed blind seismic deconvolution algorithm consists of three steps. Firstly, a transformation method that maps the seismic trace convolution model into multiple inputs multiple output (MIMO) instantaneous ICA model using zero padding matrices has been proposed. As a result the nonzero elements of the sparse mixing matrix contain the source wavelet. Secondly, whitening the observed seismic trace by incorporating the zero padding matrixes is conducted as a pre-processing step to exploit the sparsity of the mixing matrix. Finally, a novel logistic function that matches the sparsity of reflectivity sequence distribution has been proposed and fitted into the information maximization algorithm to obtain the demixing matrix. Experimental simulations have been accomplished to verify the proposed algorithm performance over conventional ICA algorithms such as Fast ICA and JADE algorithm. The mean square error (MSE) of estimated wavelet and estimated reflectivity sequence shows the improvement of proposed algorithm

Keywords: blind deconvolution, seismic signal processing, sparse ICA, information maximization algorithm fast ICA algorithm, JADE algorithm, zero padding matrixes

Title of the Paper:  Analysis of Heart Rate Variation Filtering Using LMS Based Adaptive Systems


Authors: S. Seyedtabaii

Abstract: Heart Rate Variability (HRV) is widely used as an index of human autonomic nervous activity. HRV is composed of two major components: high frequency respiratory sinus arrhythmia (RSA) and low frequency sympathetic components. The ratio of LF/HF is viewed as an index of human autonomic balance, so the low frequency sympathetic and the high frequency parasympathetic components of an ECG R-R interval must be adequately separated. Adaptive filters can isolate the low frequency, enabling the attainment of more accurate heart rate variability measures. For the raised case, this paper suggests an efficient (short size) case based model and illustrates its performance in adaptive filtering of heart rate signal. This method renders analogous results to what a higher order conventional FIR model adaptive filter may yield. The strength of the proposed model comes out of its ability in tracking the phase difference variation between the reference and the main signal of an adaptive filtering system. This capability, then is shown, that leads to the increase in the convergence rate of the LMS algorithm in HRV adaptive filtering. Simulation results supporting the proposed concept are presented.

Keywords: Adaptive filter, All pass filter, FIR model, First order equalizer, HRV filtering, Rate of convergence, Least Mean Squares.

Issue 5, Volume 4, May 2008

Title of the Paper:  A Low Complexity Approach to Turbo Equalizer


Authors: Aruna Tripathy, Sant Saran Pathak, Saswat Chakrabarti

Abstract: The turbo equalizers (TEQ) proposed in literature utilize equalizers based on trellis, soft Wiener filters. The resulting complexity of these equalizers is exponential and cubic in terms of the sampled channel impulse response (CIR). The interference cancellation based decision feedback filter based equalizers requires adaptation of two filters simultaneously. In this paper, a low complexity equalizer is proposed that neither uses a trellis nor a Wiener filter. The proposed equalizer utilizes a soft interference cancellation (SIC) technique that uses the log likelihood ratio (LLR) available at the matched filter (MF) using all the coded bits in a given block of data. The MF output is justified as Gaussian distributed and the LLRs are computed accordingly. This is fed as the apriori to the decoder after suitable deinterleaving. The soft estimates for the bits are used to form an estimate of the interference with the help of perfect channel tap knowledge at the decoder output. This estimate of interference is subtracted from the MF output giving the SIC framework. We call it a soft decision feedback equalizer (SDFE).The SDFE bypasses the filters completely resulting in a linear complexity in CIR. Simulation results over four different channels show that the receiver performance improves with iterations and a gap of 1-3 dB is observed from the coded AWGN bound depending on the channel type. Two different TEQs based on namely soft output Viterbi algorithm (SOVA) and the Wiener filter respectively are compared with the SDFE.

Keywords: SIC, Wiener Filter, LLR, SDFE, SOVA

Title of the Paper:  Semi-Hierarchical Based Motion Estimation Algorithm for the Dirac Video Encoder


Authors: M. Tun, K. K. Loo, J. Cosmas

Abstract: Having fast and efficient motion estimation is crucial in today’s advance video compression technique since it determines the compression efficiency and the complexity of a video encoder. In this paper, a method which we call semi-hierarchical motion estimation is proposed for the Dirac video encoder. By considering the fully hierarchical motion estimation only for a certain type of inter frame encoding, complexity of the motion estimation can be greatly reduced while maintaining the desirable accuracy. The experimental results show that the proposed algorithm gives two to three times reduction in terms of the number of SAD calculation compared with existing motion estimation algorithm of Dirac for the same motion estimation accuracy, compression efficiency and PSNR performance. Moreover, depending upon the complexity of the test sequence, the proposed algorithm has the ability to increase or decrease the search range in order to maintain the accuracy of the motion estimation to a certain level.

Keywords: Semi-Hierarchical, Motion Estimation, Dirac Wavelet Video Codec

Title of the Paper:  FastICA Algorithm for the Separation of Mixed Images


Authors: Arti Khaparde, M. Madha Vilatha, M. B. L. Manasa, P. Anil Babu, S. Pradeep Kumar

Abstract: Independent component analysis is a generative model for observed multivariate data, which are assumed to be mixtures of some unknown latent variables. It is a statistical and computational technique for revealing hidden factors that underlies set of random variable measurements of signals. A common problem faced in the disciplines such as statistics, data analysis, signal processing and neural network is finding a suitable representation of multivariate data. The objective of ICA is to represent a set of multidimensional measurement vectors in a basis where the components are statistically independent. In the present paper we deal with a set of images that are mixed randomly. We apply the principle of uncorrelatedness and minimum entropy to find ICA. The original images are then retrieved using fixed point algorithm known as FastICA algorithm and compared with the original images with the help of estimated error. The outputs from the intermediate steps of algorithm such as PCA, Whitening matrix, Convergence of algorithm and dewhitening matrix are also discussed

Keywords: PCA, ICA, Statistical independence, Non-gaussianity, Maximum Likelihood, Feature Extraction.

Title of the Paper:  A Study on Ultrasonic Signals Processing Generated From Automobile Engine Block Using Statistical Analysis


Authors: M. Z. Nuawi, S. Abdullah, A. R. Ismail, R. Zulkifi, M. K. Zakaria, M. F. H. Hussin

Abstract: The development of statistical analysis has played an important part in studying large data that captured from engine block as apart of engine monitoring and diagnose . Within this paper the application of statistical analysis was introduced by utilizing Kurtosis, I-kaz coefficient, and Crest Factor and Skewness parameter. There is potential that these statistical parameters could serve as pattern recognition to identify engine type and characteristic. The study was performed in two stages. The first stage is an experimental process that uses two three-cylinder automobile 845 cc and 850 cc engines and two four-cylinder automobile 1468 cc and 1784 cc engines which run under idle condition. In the second stage, the plots of signal’s statistical parameter based on the engine type were done accordingly. As a result, the plot of the statistical parameter against I-kaz coefficient shows an excellent classification pattern. The pattern was useful in determining engine type for signal confirmation and engine fault detection

Keywords: Statistical Analysis, Signal Processing, Ultrasonic signal

Title of the Paper:  A Novel Postfiltering Technique Using Adaptive Spectral Decomposition for Quality Enhancement of Coded Speech


Authors: Hassan Farsi

Abstract: An adaptive time-domain postfiltering technique based on the synthesis LP filter factorisation is proposed. Information is gathered about the relation between the LP filter poles and formants for this factorisation. This technique shapes the main formant differently from the other formants. Pole locations representing the main formant are modified and optimum shaping constants for each formant are searched to make more narrower main formant bandwidth while maintaining other formant information and more attenuation in valley regions.

Keywords: Speech spectrum, postfilter, formant frequency, synthesis filter.

Title of the Paper:  Real Time Generation of the Quinquenary Pulse Compression Sequence using FPGA


Authors: N. Balaji, K. Subba Rao, M. Srinivasa Rao

Abstract: Quinquenary codes have been widely used in radar and communication areas, but the design of Quinquenary codes with good merit factor is a nonlinear multivariable optimization problem, which is usually difficult to tackle. To get the solution of above problem many global optimization algorithms like genetic algorithm, simulated annealing, and tunneling algorithm were reported in the literature. All these optimization algorithms have serious drawbacks of non guaranteed convergence, slow convergence rate and require large number of evaluations of the objective function. To overcome these drawbacks, recently we proposed an efficient VLSI architecture for identification of the Quinquenary Pulse compression sequences. Integrating this architecture with the currently proposing architecture provides an efficient real time Hardware solution for identification and generation of the Quinquenary Pulse compression sequences. This paper describes the real time generation of the Quinquenary Pulse compression sequences using Field Programmable Gate Array (FPGA). In this paper an effort is made for the generation of the Pulse compression sequences using an efficient VLSI architecture. The Proposed VLSI architecture is implemented on the FPGA as it provides the flexibility of reconfigurability and reprogrammability.

Keywords: Pulse compression, Quinquenary sequence, VLSI architecture, FPGA, Merit Factor, Behavioral Simulation.

Title of the Paper:  Computationally Efficient Algorithm for Fuzzy Rule-Based Enhancement on JPEG Compressed Color Images


Authors: Camelia Popa, Mihaela Gordan, Aurel Vlaicu, Bogdan Orza, Gabriel Oltean

Abstract: In the past few years the resolution of images increased and the requirement for large storage space and fast process, directly in the compressed domain, becomes essential. Fuzzy rule-based contrast enhancement, is a well-known rather simple approach with good visual results. As any fuzzy algorithm, it is by default nonlinear, thus not straightforward applicable on the JPEG bitstream data – zig-zag ordered quantized DCT (Discrete Cosine Transform) coefficients. Because of their nonlinear nature the fuzzy techniques don’t have yet a well-defined strategy for their implementation in the compressed domain. In this paper, we propose an implementation strategy suitable for single input – single output Takagi-Sugeno fuzzy systems with trapezoidal shaped input membership function, directly in the JPEG compressed domain. The fuzzy sets parameters are adaptively chosen by analyzing the histogram of the image data in the compressed domain, in order to optimally enhance the image contrast. The fuzzy rule-based algorithm requires some threshold comparisons, for which an adaptive implementation, taking into account the frequency content of each block in the compress domain JPEG image is proposed. This guarantees the minimal error implementation at minimum computational cost.

Keywords: Compressed domain processing, Discrete Cosine Transform (DCT), nonlinear operation, fuzzy rule-based contrast enhancement, fuzzy sets, color image enhancement.

Title of the Paper:  Algorithms for Discrete Quadratic Time–Frequency Distributions


Authors: John M. O’ Toole, Mostefa Mesbah, Boualem Boashash

Abstract: Time–frequency distributions (TFDs) are computationally costly to compute. We address this problem by presenting algorithms to reduce the computational load for computing TFDs. Before we can compute the TFDs, however, we first must define a discrete version of the TFD. Defining a discrete TFD (DTFD) is, unfortunately, not a straightforward process—for example, a popular DTFD definition does not satisfy all desirable mathematical properties that are inherent to the continuous TFD. In this paper, we define a new DTFD definition, the DTFDC. This definition is closely related to another DTFD definition which we recently proposed, the DTFD-B. The DTFD-B and DTFD-C satisfy all desirable properties. We provide algorithms for both these definitions and show that the DTFD-C requires only 50% of the computational complexity and memory required to compute the DTFDB.

Keywords: Discrete time–frequency distributions (DTFD), discrete Wigner–Ville distributions (DWVD), discrete-time signal processing (DSP), time–frequency signal analysis, algorithms, computational load, fast Fourier transforms (FFTs)

Title of the Paper:  Image Compression using Neural Networks and Haar Wavelet


Authors: Adnan Khashman, Kamil Dimililer

Abstract: Wavelet-based image compression provides substantial improvements in picture quality at higher compression ratios. Haar wavelet transform based compression is one of the methods that can be applied for compressing images. An ideal image compression system must yield good quality compressed images with good compression ratio, while maintaining minimal time cost. With Wavelet transform based compression, the quality of compressed images is usually high, and the choice of an ideal compression ratio is difficult to make as it varies depending on the content of the image. Therefore, it is of great advantage to have a system that can determine an optimum compression ratio upon presenting it with an image. We propose that neural networks can be trained to establish the non-linear relationship between the image intensity and its compression ratios in search for an optimum ratio. This paper suggests that a neural network could be trained to recognize an optimum ratio for Haar wavelet compression of an image upon presenting the image to the network. Two neural networks receiving different input image sizes are developed in this work and a comparison between their performances in finding optimum Haar-based compression is presented.

Keywords: Optimum Image Compression, Haar Wavelet Transform, Neural Networks

Title of the Paper:  Interference Reduction in ECG using Digital FIR Filters based on Rectangular Window


Authors: Mahesh S. Chavan, R. A. Agarwala, M. D. Uplane

Abstract: Coronary heart disease (CHD) is the leading cause of death for both men and women in the all over the world and India too. CHD is caused by a narrowing of the coronary arteries that supply blood to the heart, and often results in a heart attack. Each year, about millions man kind suffers from heart attack. About maximum of those heart attacks are fatal. About half of those deaths occur within 1 hour of the start of symptoms and before the person reaches the hospital. A heart attack is a medical emergency. Hospitalization is required and possibly intensive care. ECG signal is very important signal in the cardiology. Different artifacts are the reason behind the corruption of the signal care should be taken to avoid the interferences in the ECG. The work is in that direction. Present paper deals with the design of the FIR filter using rectangular window. Basically three filters are designed namely low pass filter high pass filter and notch filter. All the filters are cascaded also. These filters are applied on the ECG signal in the real time manner. For the real time application the 711B add-on card has been used. Results clearly indicate that there is noise reduction in the ECG signal. A Comparative Results are Provided in the paper.

Keywords: Rectangular window, real time ECG processing, mathlab Simulink.

Issue 6, Volume 4, June 2008

Title of the Paper:  Off-Line Cursive Handwritten Tamil Character Recognition


Authors: R. Jagadeesh Kannan, R. Prabhakar

Abstract: In spite of several advancements in technologies pertaining to Optical character recognition, handwriting continues to persist as means of documenting information for day-to-day life. The process of segmentation and recognition pose quiets a lot of challenges especially in recognizing cursive hand-written scripts of different languages. The concept proposed is a solution crafted to perform character recognition of hand-written scripts in Tamil, a language having official status in India, Sri Lanka, and Singapore. The approach utilizes discrete Hidden Markov Models (HMMs) for recognizing off-line cursive handwritten Tamil characters. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database and the precision of the results demonstrates its application on commercial usage. The methodology promises to present a simple and fast scaffold to construct a full OCR system extended with suitable pre-processing.

Keywords: Optical Character Recognition (OCR), Cursive Script Recognition, Handwritten Script Recognition, Segmentation, Offline Recognition, Hidden Markov Model (HMM)

Title of the Paper:  Spline Wavelet Packets Application: Doppler Signal Analysis During Operation Time


Authors: E. Serrano, R. O. Sirne, M. Fabio, A. Viegener, C. E. D' Attellis, J. Guglielmone

Abstract: Wavelet methods play a significant role in signal processing. They are multifaceted tools and many choices and alternatives are open. Particularly, the Discrete Transform leads us to decompose the given signal in a filter bank, or time scale-scheme called multiresolution analysis. Then, the wavelet coefficients reflect the signal information in an efficient structure. Wavelet packets, in a second and deeper analysis, refine the scheme and they give us more frequency precision. In this article, we applied these techniques in a spline framework to process Doppler radar signals. Over-the-horizon-Radars operate in the High Frequency band; they are able to detect targets beyond the horizon and are employed in many applications. The radar operates for long periods of time without interruption; this requires analyzing the echo signal during the time of operation. For this case, we propose an adaptation of Mallat’s algorithm; the method compute the wavelet’s coefficients of consecutive intervals of the signal in a multiresolution analysis framework. The coefficients are calculated and used efficiently to estimate the radial velocity of the target over the time.

Keywords: Wavelets, wavelet packet, multiresolution, spline, radar, signal segmentation.

Title of the Paper:  Performance Evaluation of Motion Estimation in DCT Transform Domain


Authors: Petrescu Catalin-Dumitru, Stefanoiu Dan, Lupu Ciprian

Abstract: Motion estimation is one of the most important steps in video compression algorithms. It reduces temporal redundancy present in frame sequences and allows a better compression of video material. Most of the actual video compression algorithms use “block matching” methods which operate on the bitmap form of the frames. This paper presents a method for computing the values of DCT coefficients of a block of pixels positioned on certain coordinates over four adjacent blocks using only the DCT coefficients of these four blocks. Performance of this method is analyzed for both integer and non-integer displacements. Also, an equivalent of the full-search algorithm translated in 2D-DCT domain is presented.

Keywords: motion estimation, block matching, discrete cosine transform, video compression, match function

Title of the Paper:  Inharmonic Dispersion Tunable Comb Filter Design Using Modified Iir Band Pass Transfer Function


Authors: Varsha Shah, R. S. Patil

Abstract: An excitation/filter system of Inharmonic sound synthesis signal is presented with an application to piano, a stiff string instrument. Specific features of the piano string important in wave propagation, dispersion due to stiffness, frequency dependent losses and presence of phantom partials are included in the proposed model. The modified narrow bandpass filter is used as a basic building block in modeling the vibrating structure. The parallel bank of narrow band pass filters is used to model the dispersion. The center frequencies of narrow bandpass filters can be tuned to follow the same relation as the partial frequencies of piano tone. Novel loss filter is designed to implement frequency dependent losses. The resulting model is called as Inharmonic Dispersion Tunable Comb filter.

Keywords: Bandpass , Bandstop, Dispersion, Inharmonicity, Synthesis

Issue 7, Volume 4, July 2008

Title of the Paper: Slovenian Spontaneous Speech Recognition and Acoustic Modeling of Filled Pauses and Onomatopoeas


Authors: Andrej Zgank, Tomaz Rotovnik, Mirjam Sepesy Maucec

Abstract: This paper is focused on acoustic modeling for spontaneous speech recognition. This topic is still a very challenging task for speech technology research community. The attributes of spontaneous speech can heavily degrade speech recognizer’s accuracy and performance. Filled pauses and onomatopoeias present one of such important attributes of spontaneous speech, which can give considerably worse accuracy. Although filled pauses don’t carry any semantic information, they are still very important from the modeling perspective. A novel acoustic modeling approach is proposed in this paper, where the filled pauses are modeled using the phonetic broad classes, which corresponds with their acoustic-phonetic properties. The phonetic broad classes are language dependent, and can be defined by an expert or in a data-driven way. The new filled pauses modeling approach is compared with three other implicit filled pauses modeling methods. All experiments were carried out using a context-dependent Hidden Markov Models based speech recognition system. For training and evaluation, the Slovenian BNSI Broadcast News speech and text database was applied. The database contains manually transcribed recordings of TV news shows. The evaluation of the proposed acoustic modeling approach was done on a set of spontaneous speech. The overall best filled pauses acoustic modeling approach improved the speech recognizer’s word accuracy for 5.70% relatively in comparison to the baseline system, without influencing the recognition time.

Keywords: speech recognition, acoustic modeling, filled pauses, onomatopoeas, Slovenian spontaneous speech, broadcast news, HMM

Title of the Paper: Fault Characterisation and Classification Using Wavelet and Fast Fourier Transforms


Authors: E. E Ngu, K. Ramar, R. Montano, V. Cooray

Abstract: In order to improve the power quality maintenance and reliability of power supply, different types of faults on the transmission line namely: open-circuit (OC), short-circuit (SC), high impedance faults (HIF) and the fault caused by direct lightning strike (LS) have been investigated in this paper. The disturbances have been modelled and simulated using a well-known transient simulation tool - Alternative Transient Program/ Electromagnetic Transient Program (ATP/EMTP) and the resulting data are then imported into MATLAB for the investigation on the traveling wave (TW) reflection pattern and harmonic behaviour . Study on the characteristics of the faults in terms of their corresponding frequency spectrum, the polarities of the incident-wave and reflected-wave has been performed and the possibility to differentiate the type of fault is explored. For this purpose, the fault on the wave has been created at the moment when the voltage signal reaches its peak and also when it is close to zero. Both, Wavelet Transform (WT) and Fast Fourier Transform (FFT) methods have been used to analyze the transient signals generated by the fault. Model of the network used in this study is taken from [1]-[2].

Keywords: WT, FFT, ATP/EMTP, current reflection pattern, and spectrum analysis

Title of the Paper: 3D Techniques used for Conservation of Museum Patrimony


Authors: A. Chioreanu, N. Paul, A. Vlaicu, B. Orza

Abstract: The paper presents the implementation of a 3D acquisition system intended to be used as a tool in the conservation of folk heritage objects. We developed a computational efficient and cost effective 3D reconstruction system used for acquiring, reconstructing and presenting the 3D shape of heritage objects. The proposed solution for 3D reconstruction is based on a phase shifting fringe projection algorithm. This paper presents a simple analysis of fringe pattern reconstruction models as well as the details of our solution. The result proves that the proposed method is suited for such applications and is more widely applicable.

Keywords: OCC, Panoramic images, Fringe pattern, 3D reconstruction, Digital library

Title of the Paper: Homogeneous Pin-Through-Hole Component Inspection Using Fringe Tracking


Authors: K. Sundaraj

Abstract: Automated visual inspection (AVI) systems are playing important roles in ensuring manufacturing quality in the electronic industry especially in the assemblage of printed circuit boards (PCB). Most existing AVIs that are used for PCB inspection are categorized as non-contact inspection systems consisting of a single overhead camera. Such systems, which can be manual or automated, are incapable of detecting 3D pin-through-hole (PTH) component placement defects and reporting them. By considering an assembled PCB as a textured surface with a predefined depth map, we propose to apply an angled fringe projection to detect defects in PTH component placement. It has been found that angled fringe projection can be used for surface analysis by applying phase shifting and phase unwrapping obtained from several images. However, the turnover time for PCB inspection is very crucial in the electronic industry. In other words, an alternative improved method that speeds up the inspection process is always desirable. This paper describes a method of applying an angled fringe projection for 3D height measurement using a single captured image and a direct triangulation technique. The main focus of this paper has been made on the development of a fringe tracking algorithm and its practical implementation details. This algorithm allows us to obtain the depth map of the surface under analysis with just one image. The simulated data and calibration process of the tracking algorithm are discussed and an experimental result is given for Peripheral Component Interconnect (PCI) component insertion in computer motherboards. With proper system calibration and accurate image processing, we demonstrate the successful manipulation of a structured collimated light source for height measurement using a single captured image.

Keywords: Automated Visual Inspection, Fringe Projection, PCB Inspection.

Issue 8, Volume 4, August 2008

Title of the Paper: Video Target Tracking by using Competitive Neural Networks


Authors: Ernesto Araujo, Cassiano R. Silva, Daniel J. B. S. Sampaio

Abstract: A target tracking algorithm able to identify the position and to pursuit moving targets in video digital sequences is proposed in this paper. The proposed approach aims to track moving targets inside the vision field of a digital camera. The position and trajectory of the target are identified by using a neural network presenting competitive learning technique. The winning neuron is trained to approximate to the target and, then, pursuit it. A digital camera provides a sequence of images and the algorithm process those frames in real time tracking the moving target. The algorithm is performed both with black and white and multi-colored images to simulate real world situations. Results show the effectiveness of the proposed algorithm, since the neurons tracked the moving targets even if there is no pre-processing image analysis. Single and multiple moving targets are followed in real time.

Key–Words: Image motion, Target Tracking, Neural Network, Video Digital Camera, Computational Intelligence.

Title of the Paper: Estimating Parameters of Sinusoids from Noisy Data Using Bayesian Inference with Simulated Annealing


Authors:  Dursun Ustundag, Mehmet Cevri

Abstract: In this paper, we consider Bayesian analysis proposed by Brett horst for estimating parameters of the corrupted signals and incorporate it with a simulated annealing algorithm to obtain a global maximum of the posterior probability density of the parameters. Thus, this analysis offers different approach to find parameter values through a directed, but random, search of the parameter space. For this purpose, we developed a Mathematical code of this Bayesian approach and used it for recovering sinusoids corrupted by random noise. The simulation results support the effectiveness of the method.

Key-Words: Bayesian Statistical Inference, Simulated Annealing, Parameter Estimations, Optimization, Spectral Analysis, Signal Processing.

Title of the Paper: Nonlinear Extension of Inverse Filters for Decomposition of Monotonic Multi-Component Signals


Authors: Vairis Shtrauss

Abstract: - The article is devoted to improving quality of decomposition of monotonic multi-component timeand frequency-domain signals. Decomposition filters operating with data sampled at geometrically spaced times or frequencies (at equally spaced times or frequencies on a logarithmic scale) are combined with artificial neural networks. A nonlinear processing unit, which can be considered as a deconvolution network or a nonlinear decomposition filter, is proposed to be composed from several linear decomposition filters with common inputs, which outputs are nonlinearly transformed, multiplied by weights and summed. One of the fundamental findings of this study is a square activation function, which provides some useful features for the decomposition problem under consideration. First, contrary to conventional activation functions (sigmoid, radial basis functions) the square activation function allows to recover sharper peaks of distributions of time constants (DTC). Second, it ensures physically justified nonnegativity for the recovered DTC. Third, the square activation function transforms the Gaussian input noise into the nonnegative output noise with specific probability distribution having the standard deviation proportional to the variance of input noise, which, in most practical cases when noise level in the data is relatively low, increases radically the noise immunity of the proposed nonlinear algorithms. Practical implementation and application issues are described, such as network training, choice of initial guess, data normalization and smoothing. Some illustrative examples and simulations are presented performed by a developed deconvolution network, which demonstrate improvement of quality of decomposition for a frequency-domain multi-component signal.

Key-Words: - Decomposition, Monotonic Multi-Component Signals, Distribution of Time Constants, Decomposition Filters, Square Activation Function, Deconvolution Networks.

Title of the Paper: Automatic Real Time Localization of Frowning and Smiling Faces under Controlled Head Rotations


Authors: Yulia Gizatdinova, Jouni Erola, Veikko Surakka

Abstract: The aim of the present study was to develop a new method for fast localization of the face from streaming colour video. In the method, the face is located by analyzing local properties of four facial landmarks, namely, regions of eyes, nose, and mouth. The method consists of three stages. First, the face like skin colored image region is segmented from the background and transformed into the grey scale representation. Second, the cropped image is convolved with Sobel operator in order to extract local oriented edges at 16 orientations. The extracted edges are further grouped to form regions of interest representing candidates for facial landmarks. The orientation portraits, which define the distribution of local oriented edges inside the located region, are matched against the edge orientation model to verify the existence of the landmark in the image. The located landmarks are spatially arranged into the face–like constellations. Finally, the best face like constellation of the landmark candidates is defined by a new scoring function. The test results showed that the proposed method located neutral, frowning, and smiling faces with high rates in real time from facial images under controlled head rotation variations.

Key Words : Face localization, Facial landmarks, Sobel edge detection, Frontal view geometrical face model, Facial expressions, Head rotations.

Title of the Paper: An Iterative Algorithm for Automatic Fitting of Continuous Piecewise Linear Models


Authors: Miguel A. Garcia, Francisco Rodriguez

Abstract: Continuous piecewise linear models constitute useful tools to extract the basic features about the patterns of growth in complex time series data. In this work, we present an iterative algorithm for continuous piecewise regression with automatic change-points estimation. The algorithm requires an initial guess about the number and positions of the change-points or hinges, which can be obtained with different methods, and then proceeds by iteratively adjusting these hinges by displacements similar to those of Newton algorithm for function root finding. The algorithm can be applied to high volumes of data, with very fast convergence in most cases, and also allows for sufficiently close hinges to be identified, thus reducing the number of change-points, and so resulting in models of low complexity. Examples of applications to feature extraction from remote sensing vegetation indices time series data are presented.

Key–Words: Continuous piecewise regression, Segmented regression, Multiple change-point models, Remote sensing, NDVI, MODIS.

Title of the Paper: Rules and Feature Extraction for Micro calcifications Detection in Digital Mammograms Using Neuro-Symbolic Hybrid Systems and Undecimated Filter Banks


Authors: Osslan Osiris Vergara Villegas, Humberto De Jesus Ochoa Dominguez, Vianey Guadalupe Cruz Sanchez, Efren David Gutierrez Casas, Gerardo Reyes Salgado

Abstract: - In this paper, we present a Neuro-Symbolic Hybrid System methodology to improve the recognition stage of benignant or malignant micro calcifications in mammography. At the first stage, we use five different undecimated filter banks in order to detect the micro calcifications. The micro calcifications appear as a small number of high intensity pixels compared with their neighbors. Once the microcalcifications were detected, we extract rules in order to obtain the image features. At the end, we can classify the micro calcification in one of three sets: benign, malign, and normal. The results obtained show that there is no a substantial difference in the number of detected micro calcification among the several filter banks used and the NSHS methodology proposed can improve, in the future, the results of micro calcification recognition.

Key-Words: - Breast cancer, Micro calcifications detection, Undecimated filter bank, NSHS.

Title of the Paper: Testing of Image Segmentation Methods


Authors:  I. V. Gribkov, P. P. Koltsov, N. V. Kotovich, A. A. Kravchenko, A. S. Koutsaev, A. S. Osipov, A. V. Zakharov

Abstract: Digital image segmentation is broadly used in various image processing tasks. A large amount of image segmentation methods gives rise to the problem of of method’s choice, most adequate for practical purposes. In this paper, we develop an approach which allows quantitative and qualitative estimation of segmentation programs. It consists in modeling both difficult and typical situations in image segmentation tasks using special sets of artificial test images. The description of test images and testing procedures are given. Our approach clears up specific features and applicability limits of four segmentation methods under examination.

Key-Words: - image processing, energy minimization, image segmentation, ground truth, testing, performance evaluation.

Title of the Paper:  Satellite Sub-Pixel Rainfall Variability


Authors: Eric W. Harmsen, Santa Elizabeth Gomez Mesa, Edvier Cabassa, Nazario D. Ramirez-Beltran, Sandra Cruz Pol, Robert J. Kuligowski, Ramon Vasquez

Abstract: - Rain gauge networks are used to calibrate and validate quantitative precipitation estimation (QPE) methods based on remote sensing, which may be used as data sources for hydrologic models. The typical approach is to adjust (calibrate) or compare (validate) the rainfall in the QPE pixel with the rain gauge located within the pixel. The QPE result represents a mean rainfall over the pixel area, whereas the rainfall from the gauge represents a point, although it is normally assumed to represent some area. In most cases the QPE pixel area is millions of square meter in size. We hypothesize that some rain gauge networks in environments similar to this study (i.e., tropical coastal), which provide only one rain gauge per remote sensing pixel, may lead to error when used to calibrate/validate QPE methods, and that consequently these errors may be propagated throughout hydrologic models. The objective of this paper is to describe a ground-truth rain gauge network located in western Puerto Rico which will be available to test our hypothesis. In this paper we discuss observations from the rain gauge network, but do not present any QPE validation results. In addition to being valuable for validating satellite and radar QPE data, the rain gauge network is being used to test and calibrate atmospheric simulation models and to gain a better understanding of the sea breeze effect and its influence on rainfall.  In this study, a large number of storms (> 60) were evaluated between August 2006 and August 2008. The area covered by the rain gauge network was limited to a single GOES-12 pixel (4 km x 4 km). Five-minute and total storm rainfall amounts were spatially variable at the sub-pixel scale. The average storm rainfall from 20% of the 120 possible rain gauge-pairs was found to be significantly different at the 5% of significance level, indicating significant rainfall variation at the sub-pixel scale. The average coefficient of determination (r2), describing the goodness of fit of a linear model relating rain gauge pairs, was 0.365, further suggesting a significant degree of variability at the satellite sub-pixel scale. Although there were several different storm types identified (localized, upper westerly trough, tropical easterly wave, tropical westerly trough, cold front and localized with cold front), there did not appear to be any relationship between storm type and the correlation patterns among the gauges.

Key-Words: - satellite pixel, rainfall variability, QPE, rain gauge, radar, validation, hydrologic modeling.

Title of the Paper: A Locally Tuned Nonlinear Technique for Color Image Enhancement


Authors:  Saibabu Arigela, Vijayan K. Asari

Abstract: - An innovative technique for the enhancement of digital color images captured under extremely nonuniform lighting conditions is proposed in this paper. The key contributions of this technique are adaptive intensity enhancement, contrast enhancement and color restoration. Simultaneous enhancement of extreme dark and bright intensity regions in an image is performed by a specifically designed Locally Tuned Sine Non- Linear (LTSN) function. The intensity of each pixel’s magnitude is tuned based on its surrounding pixels to accomplish contrast enhancement. Retrieval of the color information from the enhanced intensity image is achieved by a linear color restoration process which is based on the chromatic information of the input image. Experimental evaluations show that the proposed algorithm can be effectively used for improving the visibility of night time surveillance video sequences with frames having extreme bright and dark regions.

 Key-Words: - dynamic range compression, intensity transformation, image enhancement, adaptive intensity enhancement, contrast enhancement, sine nonlinearity

Issue 9, Volume 4, September 2008

Title of the Paper:  New Aspects in Numerical Representations Involved in DNA Repeats Detection


Authors: Petre G. Pop

Abstract: The presence of repeated sequences is a fundamental feature of biological genomes. The detection of tandem repeats is important in biology and medicine as it can be used for phylogenic studies and disease diagnosis. A major difficulty in identification of repeats arises from the fact that the repeat units can be either exact or imperfect, in tandem or dispersed, and of unspecified length. Many of the methods for detecting repeated sequences are part of the digital signal processing field. These methods involve the application of a kind of transformation. Applying a transform technique requires mapping the symbolic domain into the numeric domain in such a way that no additional structure is placed on the symbolic sequence beyond that inherent to it. Therefore the numerical representation of genomic signals is very important. This paper presents results obtained by combining grey level spectrograms with two novel numerical representations to isolate position and length of DNA repeats.

Keywords: Genomic Signal Processing, Sequence Repeats, DNA Representations, Fourier analysis, Spectrograms

Title of the Paper:  Image Transmission through Incoherent Optical Fiber Bundle: Methods for Optimization and Image Quality Improvement


Authors: O. Demuynck, J. M. Menendez

Abstract: Artificial vision, in spite of all theoretical and practical progress accomplished during last thirty years, still cannot be employed technically in some hazardous and strong industrial areas, where conditions are such that cameras would not operate properly. Possible alternatives on such problem are the quite recent IP65 and IP67 industrial cameras, and associated connectors, protected by an anticorrosive, waterproof, and high temperatures resistive carcass employing a dedicated electronic, in addition robust to almost 3G accelerations. Nevertheless, such cameras are still very expensive compared to conventional industrial cameras and would still not be enough in hardest conditions (explosive gas or dust environment) or in electromagnetic interferences environments. A good alternative in extreme conditions is the use of optical fiber bundle. Since such cable only transmits light, they intrinsically have electromagnetic interferences immunity and, depending on the fiber material, could be exposed to very high temperature (over 1000║C for sapphire fibers for example), could be employed in almost all corrosive environment, and are totally submersible. Nevertheless, the coherent optical fiber bundles are very expensive, and for large distances could be non-competitive facing the other hardware solutions (armor plating, electromagnetic interferences isolation). Thus, the best suitable option to develop a competitive system in those particular cases is the use of incoherent optical fiber bundle (IOFB), nowadays just employed for illumination tasks. Image transmission in this case is not direct but require a calibration step that we discuss in this paper. Improvement of the noisy resulting quality image is also exposed here, achieved by experimental post calibration methods. We propose in this work a new calibration method of incoherent optical fiber bundle (IOFB) for image transmission purpose. Firstly, we present the calibration method (an enhancement of previously published calibration methods), some resulting reconstructed images and a discussion on its quality improvement employing simple denoising methods assisted by low pass filters (smooth filter, resizing method,à) . We finally depict the two new post calibration methods, and its associated noise generator physical phenomena we want to treat. Those two post calibration method are: correction of the input plane optical aberrations and extraction of a unique pixel (almost centered) for each fibers. We finally present some resulting images that demonstrate how it efficiently refine the reconstruction Look Up Table (LUT) used for the output image reconstruction improving image quality, image contrast but also reconstruction processing time.

Keywords: Image transmission, Incoherent optical fiber bundle, Hazardous environment, Calibration

Title of the Paper:  Activity Index Variance as an Indicator of the Number of Signal Sources


Authors: Rok Istenic, Damjan Zazula

Abstract: In this paper we introduce a novel technique that can be used as an indicator of the number of active signal sources in convolutive signal mixtures. The technique is designed so that the number of sources is estimated using only recorded signals and some marginal information, such as possible minimum and maximum triggering frequencies of sources, but no information on mixing matrix, other parameters of signal sources, etc. Our research is based on the convolution kernel compensation method (CKC), which is a blind source separation method. First, a correlation matrix of the recorded signals is estimated. Next, a measure of the global activity of the signal sources, called activity index, is introduced. The exact analytical model of the activity index variance was derived for the purpose of the estimation of the number of signal sources. Using the analytical model, the number of active signal sources can be estimated if some a priori marginal information is available. We evaluated these marginal parameter values in extensive simulations of compound signals. The number of sources, their lengths, signal-to-noise ratio, source triggerings, and the number of measurements were randomly combined in preselected ranges. By using the established marginal parameter values and increasing extension factors, the model of the activity index variance was deployed to estimate the number of signal sources. The estimation results using synthetic signal mixtures are very promising.

Keywords: Compound signals, estimation of the number of sources, correlation matrix, convolutive signal mixture, variance model, convolution kernel compensation

Title of the Paper:  Object Detection and Segmentation Algorithm Implemented on a Reconfigurable Embedded Platform Based FPGA


Authors: Slami Saadi, Hamza Mekki, Abderrezak Guessoum

Abstract: In this article, we present a mixed software/hardware Implementation on a Xilinx’s Microblaze Soft core based FPGA platform. The reconfigurable embedded platform designed to support an important algorithm in image processing which is region color image segmentation for detecting objects based on RGB to HSL transformation. The proposed work is implemented and compiled on the embedded development kit EDK6.3i and the synthesis software ISE6.3i available with Xilinx Virtex-II FPGA using C++ language. The basic motivation of our application to radio isotopic images and neutron tomography is to assist physicians in diagnostics by taking out regions of interest. The system is designed to be integrated as an extension to the nuclear imaging system implemented around our nuclear research reactor. The proposed design can significantly accelerate the algorithm and the possible reconfiguration can be exploited to reach a higher performance in the future, and can be used for many image processing applications.

Keywords: Color images, Segmentation, Reconfigurable, Embedded, FPGA

Issue 10, Volume 4, October 2008

Title of the Paper:  An Automatic System for Urban Road Extraction from Satellite and Aerial Images


Authors: S. Idbraim, D. Mammass, D. Aboutajdine, D. Ducrot

Abstract: We present in this paper an automatic system of urban road extraction from satellite and aerial imagery. Our approach is based on an adaptive directional filtering and a watershed segmentation. The first stage consists of an automatic procedure which adapts filtering of each block band to the dominant direction(s) of roads. The choice of the dominant direction(s) is made from a criterion based on the calculation of a factor of direction of detection. The second stage is based on watershed algorithm applied to a Shen-Castan gradient image. This process provides a decision map allowing correcting the errors of the first stage. A ratio of surface on perimeter is used to distinguish among all segments of the image those representing probably roads. Finally, in order to avoid gaps between pieces of roads, the resulting image follows a treatment, based on proximity and colinearity, for linking segments.
The proposed approach is tested on common scenes of Landsat ETM+ and aerial imagery of the city of Agadir in Morocco. The experimental results show satisfactory values of completeness and correctness and are very prominsing.

Keywords: Road extraction; Satellite and aerial imagery; Urban areas; Adaptive directional filtering; Segmentation; Grouping; Evaluation

Title of the Paper:  A New Fast Forecasting Technique using High Speed Neural Networks


Authors: Hazem M. El-Bakry, Nikos Mastorakis

Abstract: Forecasting is an important issue for many different applications. In this paper, a new efficient forecasting technique is presented. Such technique is designed by using fast neural networks (FNNs). The new idea relies on performing cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the proposed fast forecasting technique is less than that needed by conventional neural-based forecasting. Simulation results using MATLAB confirm the theoretical computations. The proposed fast forecasting technique increases the prediction speed and at the same time does not affect the predication accuracy. It is applied professionally for erythemal ultraviolet irradiance prediction.

Keywords: Fast Neural Network, Cross Correlation, Frequency Domain, Combined Neural Classifiers, Information Fusion, erythemal UV irradiance, total ozone

Title of the Paper:  Asymmetric Ratio and FCM based Salient Channel Selection for Human Emotion Detection using EEG


Authors: M. Rizon, M. Murugappan, R. Nagarajan, S. Yaacob

Abstract: Electroencephalogram (EEG) is one of the most reliable physiological signals used for detecting the emotional states of human brain. We propose Asymmetric Ratio (AR) based channel selection for human emotion recognition using EEG. Selection of channels reduces the feature size, computational load requirements and robustness of emotions classification. We address this crisis using Asymmetric Variance Ratio (AVR) and Amplitude Asymmetric Ratio (AAR) as new channel selection methods. Using these methods the 28 homogeneous pairs of EEG channels is reduced to 4 and 2 channel pairs respectively. These methods significantly reduce the number of homogeneous pair of channels to be used for emotion detection. This approach is illustrated with 5 distinct emotions (disgust, happy, surprise, sad, and fear) on 63 channels EEG data recorded from 5 healthy subjects. In this study, we used Multi-Resolution Analysis (MRA) based feature extraction the original and reduced set of channels for emotion classification. These approaches were empirically evaluated by using a simple unsupervised classifier, Fuzzy C-Means clustering with variable clusters. The paper concludes by discussing the impact of reduced channels on emotion recognition with larger number of channels and outlining the potential of the new channel selection method.

Keywords: EEG, Human Emotions, Asymmetric Ratios, Channel selection, Wavelet Transform, Fuzzy C-Means (FCM) clustering

Title of the Paper:  Changes in Fluctuation Waves in Coherent Airflow Structures with Input Perturbation


Authors: Osama A. Marzouk

Abstract: We predict the development and propagation of the fluctuations in a perturbed ideally-expanded air jet. A non-propagating harmonic perturbation in the density, axial velocity, and pressure is introduced at the inflow with different frequencies to produce coherent structures in the airflow, which are synchronized with the applied frequency. Then, the fluctuations and acoustic fields are predicted for each frequency by integrating the axisymmetric linearized Euler equations. We investigate the effect of the perturbation frequency on different characteristics of the pressure and velocity waves. The perturbation frequency can be used to alter the propagation pattern and intensity of the waves and mitigate the noise levels at certain zones.

Keywords: Perturbation, Fluctuation, Waves, Acoustic, Coherent, Air, Jet, Synchronized

Issue 11, Volume 4, November 2008

Title of the Paper:  Perceptible Content Retrieval in DCT Domain and Semi-Fragile Watermarking Technique for Perceptible Content Authentication


Authors: Chamidu Atupelage, Koichi Harada

Abstract: Digital watermarking was commenced to copyright protection and ownership verification of multimedia data. However the evolution of the watermark focused on different security aspects of multimedia data such as integrity and authenticity. Fragile and semi-fragile watermarking schemes were introduced to accomplish these requirements. In this paper, we propose semi-fragile watermarking scheme to authenticate visual content of the image. The proposed method is carried out in DCT domain and authenticating the particular number of low frequency coefficient, it achieves the integrity of the image. Since low frequency coefficients carry the essence of the visual data, authenticating only the low frequency data is adequate. Therefore the proposed technique is efficient than the others, which are processing in all DCT coefficients. Digital signature is generated by following the definition of elliptic curve digital signature algorithm (ECDSA). Thus, the scheme is faster than RSA based authentication definition and it provides high level of security and availability. Additionally our scheme localizes the compromised area of the image. Embedded and visual data are protected from quantization of JPEG by altering the quantization table. However the degradation of compression ratio due to alternation in quantization table has been evaluated. Experimental results show that the watermark does not make any visual artifact in original data and it gives evidence that compression ratio degradation is ignorable for average JPEG quality factors.

Keywords: Semi-fragile watermarking, public key cryptography, discrete cosine transformation, image authentication, imperceptibility, elliptic curve digital signature algorithm, JPEG

Title of the Paper:  Bispectral Resolution and Leakage Effect of the Indirect Bispectrum Estimate for Different Types of 2D Window Functions


Authors: Teofil-Cristian Oroian, Constantin-Iulian Vizitiu, Florin Serban

Abstract: An important practical problem in the area of the higher-order statistical signal processing is to estimate the cumulants and polyspectra of the analyzed signal when a finite sequence of time samples is available. We cannot use the theoretical formula because they are based on the assumption that an infinite sequence of time samples is available, but this is not true in practice. In order to obtain a better estimate for bispectrum of the signal, different types of 2D window functions are used. Also, these windows are investigated in terms of the resolution and leakage effect of the indirect bispectrum estimate.

Keywords: Higher-order statistics, bispectrum estimation, 2D window functions, bispectral resolution

Title of the Paper:  Automatic Sea Floor Characterization based on Underwater Acoustic Image Processing


Authors: Cristian Molder, Mircea Boscoianu, Mihai I. Stanciu, Iulian C. Vizitiu

Abstract: Automatic sea floor characterization is mainly based on the signal or image processing of the data acquired using an active acoustic system called sediment sonar. Each processing method suits a specific type of sonar, such as the monobeam, the multibeam, or the side-scan sonar. Most types of sonar offer a two dimensional view of the sea floor surface. Therefore, a high resolution image results which can be further analyzed. The inconvenient is that the sonar cannot view inside of the sea floor for a deeper analysis. Therefore, lower frequency acoustic systems are used for in-depth sea floor penetration (boomer, sparker, airguns or sub-bottom profilers). In this case, a mono dimensional signal results. Previous studies on the low-frequency systems are mainly based on the visual inspection by a geological human expert. To automatize this process, we propose the use of feature sets based on the transposed expert fuzzy reasoning. Two features are extracted, the first based on the sea floor contour and the second based on the sub-bottom sediment texture.

Keywords: Sedimentology, Underwater Acoustics, Pattern Recognition, Image Processing, Textures, Wavelets

Issue 12, Volume 4, December 2008

Title of the Paper:  On the Use of Kalman Filter for Enhancing Speech Corrupted by Colored Noise


Authors: Boubakir Chabane, Berkani Daoued

Abstract: Kalman filtering is a powerful technique for the estimation of the speech signal observed in additive background noise. This paper presents a contribution in the enhancement of noisy speech with white and colored noise assumption. Some tests were performed with ideal filter parameters, others using the Expectation Maximization (EM) algorithm to iteratively estimate the spectral parameters of the speech and noise. Simulation results show that the application has the best performance evaluated with objective quality scores, observation of the waveforms, as well as informal listening tests in the case of Noizeus database.

Keywords: Speech enhancement, Kalman filtering, colored noise, EM algorithm

Title of the Paper:  Modular Design and Implementation of FPGA-based Tap-selective Maximum-likelihood Channel Estimator


Authors: Jeng-Kuang Hwang, Yuan-Ping Li

Abstract: The modular design of the optimal tap-selective maximum-likelihood (TSML) channel estimator based on field-programmable gate array (FPGA) technology is studied. A novel range reduction algorithm is included in the natural logarithmic function (NLF) emulator based on the coordinate rotation digital computer (CORDIC) methodology and is integrated into the TSML channel estimator system. The low-complexity TSML algorithm, which is employed for sparse multipath channel estimation, is proposed for long-range broadband block transmission systems. Furthermore, the proposed range reduction algorithm aims to solve the limited interval problem in the CORDIC algorithm base on Xilinx’s SG platforms. The modular approach facilitates the reuse of modules.

Keywords: Coordinate rotation digital computer (CORDIC), FPGA design, Maximum-likelihood channel estimation, Range reduction, Logarithm function, Parallel sorting

Title of the Paper:  A Hybrid Noise Cancelling Algorithm with Secondary Path Estimation


Authors: Edgar Lopez-Caudana, Pablo Betancourt, Enrique Cruz, Mariko Nakano-Miyatake, Hector Perez-Meana

Abstract: This paper presents a hybrid active noise canceling (HANC) algorithm to overcome the acoustic feedback present in most ANC system, together with an efficient secondary path estimation scheme. The HANC system provides a solution of two fundamental problems present in these kind of ANC systems: The first consists in a reduction of the acoustic feedback from the cancellation loudspeaker to the input microphone, using two FIR adaptive filters, one with a feedforward configuration an the other with a feedback adaptive filter configuration. To overcome the secondary path modeling problem, a modification of the method proposed by Akhtar is used. Computer simulation results are provide to show the noise cancellation and secondary path estimation performance of presented scheme.

Keywords: Active noise canceling, secondary path estimation, feed-forward ANC, feedback ANC, FxLMS, hybrid structure, Akhtar method


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