Search results for: orthogonal decomposition

Authors: P. C. Saxena, D. K. Tayal

Abstract:

The join dependency provides the basis for obtaining lossless join decomposition in a classical relational schema. The existence of Join dependency shows that that the tables always represent the correct data after being joined. Since the classical relational databases cannot handle imprecise data, they were extended to fuzzy relational databases so that uncertain, ambiguous, imprecise and partially known information can also be stored in databases in a formal way. However like classical databases, the fuzzy relational databases also undergoes decomposition during normalization, the issue of joining the decomposed fuzzy relations remains intact. Our effort in the present paper is to emphasize on this issue. In this paper we define fuzzy join dependency in the framework of type-1 fuzzy relational databases & type-2 fuzzy relational databases using the concept of fuzzy equality which is defined using fuzzy functions. We use the fuzzy equi-join operator for computing the fuzzy equality of two attribute values. We also discuss the dependency preservation property on execution of this fuzzy equi- join and derive the necessary condition for the fuzzy functional dependencies to be preserved on joining the decomposed fuzzy relations. We also derive the conditions for fuzzy join dependency to exist in context of both type-1 and type-2 fuzzy relational databases. We find that unlike the classical relational databases even the existence of a trivial join dependency does not ensure lossless join decomposition in type-2 fuzzy relational databases. Finally we derive the conditions for the fuzzy equality to be non zero and the qualification of an attribute for fuzzy key.

Keywords: Fuzzy - equi join, fuzzy functions, fuzzy join dependency, type-1 fuzzy relational database, type-2 fuzzy relational database.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2038

Authors: A. Benyahia, M. Zergoug, M. Amir, M. Fodil

Abstract:

The main objective of this work is to enhance the Pulsed Eddy Current (PEC) response from the aluminum structure using signal processing. Cracks and metal loss in different structures cause changes in PEC response measurements. In this paper, time-frequency analysis is used to represent PEC response, which generates a large quantity of data and reduce the noise due to measurement. Power Spectral Density (PSD) after Wavelet Decomposition (PSD-WD) is proposed for defect detection. The experimental results demonstrate that the cracks in the surface can be extracted satisfactorily by the proposed methods. The validity of the proposed method is discussed.

Keywords: NDT, pulsed eddy current, continuous wavelet transform, Mexican hat wavelet mother, defect detection, power spectral density.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 765

Authors: V. Piscopo

Abstract:

In the traditional buckling analysis of rectangular plates the classical thin plate theory is generally applied, so neglecting the plating shear deformation. It seems quite clear that this method is not totally appropriate for the analysis of thick plates, so that in the following the two variable refined plate theory proposed by Shimpi (2006), that permits to take into account the transverse shear effects, is applied for the buckling analysis of simply supported isotropic rectangular plates, compressed in one and two orthogonal directions. The relevant results are compared with the classical ones and, for rectangular plates under uniaxial compression, a new direct expression, similar to the classical Bryan-s formula, is proposed for the Euler buckling stress. As the buckling analysis is a widely diffused topic for a variety of structures, such as ship ones, some applications for plates uniformly compressed in one and two orthogonal directions are presented and the relevant theoretical results are compared with those ones obtained by a FEM analysis, carried out by ANSYS, to show the feasibility of the presented method.

Keywords: Buckling analysis, Thick plates, Biaxial stresses

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2626

Authors: Justin Leo Cheang Loong, Khazaimatol S Subari, Rosli Besar, Muhammad Kamil Abdullah

Abstract:

In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.

Keywords: biometric, ecg, linear predictive coding, wavelet packet decomposition

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2899

Authors: Naoki Yamamoto, Jun Murakami, Chiharu Okuma, Yutaro Shigeto, Satoko Saito, Takashi Izumi, Nozomi Hayashida

Abstract:

Multi-dimensional principal component analysis (PCA) is the extension of the PCA, which is used widely as the dimensionality reduction technique in multivariate data analysis, to handle multi-dimensional data. To calculate the PCA the singular value decomposition (SVD) is commonly employed by the reason of its numerical stability. The multi-dimensional PCA can be calculated by using the higher-order SVD (HOSVD), which is proposed by Lathauwer et al., similarly with the case of ordinary PCA. In this paper, we apply the multi-dimensional PCA to the multi-dimensional medical data including the functional independence measure (FIM) score, and describe the results of experimental analysis.

Keywords: multi-dimensional principal component analysis, higher-order SVD (HOSVD), functional independence measure (FIM), medical data, tensor decomposition

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2502

Authors: Wendong Yang, Changhai Wang, Valeria Arrighi

Abstract:

Low ink sintering temperature is desired for flexible electronics, as it would widen the application of the ink on temperature-sensitive substrates where the selection of silver precursor is very critical. In this paper, four types of organic silver precursors, silver carbonate, silver oxalate, silver tartrate and silver itaconate, were synthesized using an ion exchange method, firstly. Various characterization methods were employed to investigate their physical phase, chemical composition, morphologies and thermal decomposition behavior. It was found that silver oxalate had the ideal thermal property and showed the lowest decomposition temperature. An ink was then formulated by complexing the as-prepared silver oxalate with ethylenediamine in organic solvents. Results show that a favorable conductive film with a uniform surface structure consisting of silver nanoparticles and few voids could be produced from the ink at a sintering temperature of 150 °C.

Keywords: Conductive ink, electrical property, film, organic silver.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1091

Authors: A.O. Boudraa, J.C. Cexus, Z. Saidi

Abstract:

This paper introduces a new signal denoising based on the Empirical mode decomposition (EMD) framework. The method is a fully data driven approach. Noisy signal is decomposed adaptively into oscillatory components called Intrinsic mode functions (IMFs) by means of a process called sifting. The EMD denoising involves filtering or thresholding each IMF and reconstructs the estimated signal using the processed IMFs. The EMD can be combined with a filtering approach or with nonlinear transformation. In this work the Savitzky-Golay filter and shoftthresholding are investigated. For thresholding, IMF samples are shrinked or scaled below a threshold value. The standard deviation of the noise is estimated for every IMF. The threshold is derived for the Gaussian white noise. The method is tested on simulated and real data and compared with averaging, median and wavelet approaches.

Keywords: Empirical mode decomposition, Signal denoisingnonstationary process.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4001

Authors: M. Kidouche, H. Habbi, M. Zelmat, S. Grouni

Abstract:

In analyzing large scale nonlinear dynamical systems, it is often desirable to treat the overall system as a collection of interconnected subsystems. Solutions properties of the large scale system are then deduced from the solution properties of the individual subsystems and the nature of the interconnections. In this paper a new approach is proposed for the stability analysis of large scale systems, which is based upon the concept of vector Lyapunov functions and the decomposition methods. The present results make use of graph theoretic decomposition techniques in which the overall system is partitioned into a hierarchy of strongly connected components. We show then, that under very reasonable assumptions, the overall system is stable once the strongly connected subsystems are stables. Finally an example is given to illustrate the constructive methodology proposed.

Keywords: Comparison principle, First integral, Large scale system, Lyapunov stability.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1526

Authors: Wahiba Mohguen, Raïs El’hadi Bekka

Abstract:

This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE).

Keywords: Customized thresholding, ECG signal, EMD, hard thresholding, Soft-thresholding.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1084

Authors: Sajad Farokhi, Siti Mariyam Shamsuddin, Jan Flusser, Usman Ullah Sheikh

Abstract:

Although face recognition seems as an easy task for human, automatic face recognition is a much more challenging task due to variations in time, illumination and pose. In this paper, the influence of time-lapse on visible and thermal images is examined. Orthogonal moment invariants are used as a feature extractor to analyze the effect of time-lapse on thermal and visible images and the results are compared with conventional Principal Component Analysis (PCA). A new triangle square ratio criterion is employed instead of Euclidean distance to enhance the performance of nearest neighbor classifier. The results of this study indicate that the ideal feature vectors can be represented with high discrimination power due to the global characteristic of orthogonal moment invariants. Moreover, the effect of time-lapse has been decreasing and enhancing the accuracy of face recognition considerably in comparison with PCA. Furthermore, our experimental results based on moment invariant and triangle square ratio criterion show that the proposed approach achieves on average 13.6% higher in recognition rate than PCA.

Keywords: Infrared Face recognition, Time-lapse, Zernike moment invariants

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1784

Authors: Hazem M. El-Bakry

Abstract:

Neural processors have shown good results for detecting a certain character in a given input matrix. In this paper, a new idead to speed up the operation of neural processors for character detection is presented. Such processors are designed based on cross correlation in the frequency domain between the input matrix and the weights of neural networks. This approach is developed to reduce the computation steps required by these faster neural networks for the searching process. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately by using a single faster neural processor. Furthermore, faster character detection is obtained by using parallel processing techniques to test the resulting sub-images at the same time using the same number of faster neural networks. In contrast to using only faster neural processors, the speed up ratio is increased with the size of the input image when using faster neural processors and image decomposition. Moreover, the problem of local subimage normalization in the frequency domain is solved. The effect of image normalization on the speed up ratio of character detection is discussed. Simulation results show that local subimage normalization through weight normalization is faster than subimage normalization in the spatial domain. The overall speed up ratio of the detection process is increased as the normalization of weights is done off line.

Keywords: Fast Character Detection, Neural Processors, Cross Correlation, Image Normalization, Parallel Processing.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1536

Authors: V. Sudha, D. Sriram Kumar

Abstract:

Orthogonal Frequency Division Multiplexing (OFDM) has been used in many advanced wireless communication systems due to its high spectral efficiency and robustness to frequency selective fading channels. However, the major concern with OFDM system is the high peak-to-average power ratio (PAPR) of the transmitted signal. Some of the popular techniques used for PAPR reduction in OFDM system are conventional partial transmit sequences (CPTS) and clipping. In this paper, a parallel combination/hybrid scheme of PAPR reduction using clipping and CPTS algorithms is proposed. The proposed method intelligently applies both the algorithms in order to reduce both PAPR as well as computational complexity. The proposed scheme slightly degrades bit error rate (BER) performance due to clipping operation and it can be reduced by selecting an appropriate value of the clipping ratio (CR). The simulation results show that the proposed algorithm achieves significant PAPR reduction with much reduced computational complexity.

Keywords: CCDF, OFDM, PAPR, PTS.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1368

Authors: S. Srinivasa Moorthy, K. Manonmani

Abstract:

The new, polymer composites consisting of e-glass fiber reinforcement with titanium oxide filler in the double bonded unsaturated polyester resin matrix were made. The glass fiber and titanium oxide reinforcement composites were made in three different fiber lengths (3cm, 5cm, and 7cm), filler content (2 wt%, 4 wt%, and 6 wt%) and fiber content (20 wt%, 40 wt%, and 60 wt%). 27 different compositions were fabricated and a sequence of experiments were carried out to determine tensile strength and impact strength. The vital influencing factors fiber length, fiber content and filler content were chosen as 3 factors in 3 levels of Taguchi’s L₉ orthogonal array. The influences of parameters were determined for tensile strength and impact strength by Analysis of variance (ANOVA) and S/N ratio. Using Artificial Neural Network (ANN) an expert system was devised to predict the properties of hybrid reinforcement GFRP composites. The predict models were experimentally proved with the maximum coincidence.

Keywords: Analysis of variance (ANOVA), Artificial neural network (ANN), Polymer composites, Taguchi’s orthogonal array.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2400

Authors: Atif Elahi, Ijaz Mansoor Qureshi, Mehreen Atif, Noor Gul

Abstract:

Orthogonal frequency division multiplexing (OFDM) is one of the best candidates for dynamic spectrum access due to its flexibility of spectrum shaping. However, the high sidelobes of the OFDM signal that result in high out-of-band radiation, introduce significant interference to the users operating in its vicinity. This problem becomes more critical in cognitive radio (CR) system that enables the secondary users (SUs) users to access the spectrum holes not used by the primary users (PUs) at that time. In this paper, we present a generalized OFDM framework that has a capability of describing any sidelobe suppression techniques, despite of whether one or a number of techniques are used. Based on that framework, we propose cancellation carrier (CC) technique in conjunction with the generalized sidelobe canceller (GSC) to reduce the out-of-band radiation in the region where the licensed users are operating. Simulation results show that the proposed technique can reduce the out-of-band radiation better when compared with the existing techniques found in the literature.

Keywords: Cognitive radio, cancellation carriers, generalized sidelobe canceller, out-of-band radiation, orthogonal frequency division multiplexing.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1198

Authors: I. S. Jung, W. H. Jung, S. H. Baek, S. Kang

Abstract:

This paper describes the shape optimization of impeller blades for a anti-heeling bidirectional axial flow pump used in ships. In general, a bidirectional axial pump has an efficiency much lower than the classical unidirectional pump because of the symmetry of the blade type. In this paper, by focusing on a pump impeller, the shape of blades is redesigned to reach a higher efficiency in a bidirectional axial pump. The commercial code employed in this simulation is CFX v.13. CFD result of pump torque, head, and hydraulic efficiency was compared. The orthogonal array (OA) and analysis of variance (ANOVA) techniques and surrogate model based optimization using orthogonal polynomial, are employed to determine the main effects and their optimal design variables. According to the optimal design, we confirm an effective design variable in impeller blades and explain the optimal solution, the usefulness for satisfying the constraints of pump torque and head.

Keywords: Bidirectional axial flow pump, Impeller blade, CFD, Analysis of variance, Polynomial surrogate model

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3777

Authors: P.L.D.N.M. de Silva, S.G. Edirisinghe, R. Weerasuriya

Abstract:

High Peak-to-Average Power Ratio (PAPR) is a concern of Orthogonal Frequency Division Multiplexing (OFDM) based Visible Light Communication (VLC) systems. Discrete Fourier Transform spread (DFT-s) OFDM is an alternative single carrier modulation scheme which would address this concern. Employing channel coding techniques is another mechanism to reduce the PAPR. In this study, the improvement which can be harnessed by hybridizing these two techniques for VLC system is being studied. Within the study, efficient techniques such as Hamming coding and Convolutional coding have been studied. Thus, we present the impact of the hybrid of DFT-s OFDM and Channel coding (Hamming coding and Convolutional coding) on PAPR in VLC systems, using MATLAB simulations.

Keywords: Convolutional Coding, Discrete Fourier Transform spread Orthogonal Frequency Division Multiplexing (DFT-s OFDM), Hamming Coding, Peak-to-Average Power Ratio (PAPR), Visible Light Communications (VLC).

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 513

Authors: Chen Wu, Lijuan Wang

Abstract:

Relation between tolerance class and indispensable attribute and knowledge dependency in rough set model with tolerance relation is explored. After giving definitions and concepts of knowledge dependency and knowledge dependency degree for incomplete information system in tolerance rough set model by distinguishing decision attribute containing missing attribute value or not, the result of maintaining reflectivity, transitivity, augmentation, decomposition law and merge law for complete knowledge dependency is proved. Knowledge dependency degrees (not complete knowledge dependency degrees) only satisfy some laws after transitivity, augmentation and decomposition operations. An algorithm to solve attribute reduction in an incomplete decision table is designed. The correctness is checked by an example.

Keywords: Incomplete information system, rough set, tolerance relation, knowledge dependence, attribute reduction.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 728

Authors: Liping Li, Changchun Liu, Ke Li, Chengyu Liu

Abstract:

Non-stationary trend in R-R interval series is considered as a main factor that could highly influence the evaluation of spectral analysis. It is suggested to remove trends in order to obtain reliable results. In this study, three detrending methods, the smoothness prior approach, the wavelet and the empirical mode decomposition, were compared on artificial R-R interval series with four types of simulated trends. The Lomb-Scargle periodogram was used for spectral analysis of R-R interval series. Results indicated that the wavelet method showed a better overall performance than the other two methods, and more time-saving, too. Therefore it was selected for spectral analysis of real R-R interval series of thirty-seven healthy subjects. Significant decreases (19.94±5.87% in the low frequency band and 18.97±5.78% in the ratio (p<0.001)) were found. Thus the wavelet method is recommended as an optimal choice for use.

Keywords: empirical mode decomposition, heart rate variability, signal detrending, smoothness priors, wavelet

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2068

Authors: Mehmet Bozca

Abstract:

Singular value decomposition based optimisation of geometric design parameters of a 5-speed gearbox is studied. During the optimisation, a four-degree-of freedom torsional vibration model of the pinion gear-wheel gear system is obtained and the minimum singular value of the transfer matrix is considered as the objective functions. The computational cost of the associated singular value problems is quite low for the objective function, because it is only necessary to compute the largest and smallest singular values (μmax and μmin) that can be achieved by using selective eigenvalue solvers; the other singular values are not needed. The design parameters are optimised under several constraints that include bending stress, contact stress and constant distance between gear centres. Thus, by optimising the geometric parameters of the gearbox such as, the module, number of teeth and face width it is possible to obtain a light-weight-gearbox structure. It is concluded that the all optimised geometric design parameters also satisfy all constraints.

Keywords: Singular value, optimisation, gearbox, torsional vibration.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1946

Authors: H K Lakshminarayana, J S Bhat, H M Mahesh

Abstract:

A new estimator for evolutionary spectrum (ES) based on short time Fourier transform (STFT) and modified group delay function (MGDF) by signal decomposition (SD) is proposed. The STFT due to its built-in averaging, suppresses the cross terms and the MGDF preserves the frequency resolution of the rectangular window with the reduction in the Gibbs ripple. The present work overcomes the magnitude distortion observed in multi-component non-stationary signals with STFT and MGDF estimation of ES using SD. The SD is achieved either through discrete cosine transform based harmonic wavelet transform (DCTHWT) or perfect reconstruction filter banks (PRFB). The MGDF also improves the signal to noise ratio by removing associated noise. The performance of the present method is illustrated for cross chirp and frequency shift keying (FSK) signals, which indicates that its performance is better than STFT-MGDF (STFT-GD) alone. Further its noise immunity is better than STFT. The SD based methods, however cannot bring out the frequency transition path from band to band clearly, as there will be gap in the contour plot at the transition. The PRFB based STFT-SD shows good performance than DCTHWT decomposition method for STFT-GD.

Keywords: Evolutionary Spectrum, Modified Group Delay, Discrete Cosine Transform, Harmonic Wavelet Transform, Perfect Reconstruction Filter Banks, Short Time Fourier Transform.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1610

Authors: Shengman Zhao, Yang Yu, Shenghui Cui

Abstract:

Using logarithmic mean Divisia decomposition technique, this paper analyzes the change in industrial energy intensity of Fujian Province in China, based on data sets of added value and energy consumption for 35 selected industrial sub-sectors from 1999 to 2009. The change in industrial energy intensity is decomposed into intensity effect and structure effect. Results show that the industrial energy intensity of Fujian Province has achieved a reduction of 51% over the past ten years. The structural change, a shift in the mix of industrial sub-sectors, made overwhelming contribution to the reduction. The impact of energy efficiency’s improvement was relatively small. However, the aggregate industrial energy intensity was very sensitive to both the changes in energy intensity and in production share of energy-intensive sub-sectors, such as production and supply of electric power, steam and hot water. Pathway to reduce industrial energy intensity for energy conservation in Fujian Province is proposed in the end.

Keywords: Decomposition analysis, energy intensity, Fujian Province, industry.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1388

Authors: Vijay Kumar Nath, Deepika Hazarika, Anil Mahanta,

Abstract:

Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.

Keywords: Lapped orthogonal transform, Lapped biorthogonal transform, Image compression, KS test,

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1605

Authors: Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

Abstract:

In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., “Car", “Building", “Mountain", “Farm" and “Coast". This classification is performed without any segmentation processes. To verify the learning capabilities of the proposed method, we compare our MSNN model with the traditional Sigmoidal Neural Network (SNN) model. Results of comparison have shown that the MSNN model performs better than the traditional SNN model in the context of training run time and classification rate. Both color moments and multi-level wavelets decomposition technique are used to extract features from images. The proposed method has been tested on a variety of real and synthetic images.

Keywords: Image classification, multi-level neural networks, feature extraction, wavelets decomposition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1647

Authors: Mourad Moussa, Ali Douik, Hassani Messaoud

Abstract:

In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.

Keywords: Image segmentation, objects tracking, Parzen window, singular value decomposition, target recognition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1984

Authors: Muhammad Kamil Abdullah, Khazaimatol S Subari, Justin Leo Cheang Loong, Nurul Nadia Ahmad

Abstract:

This paper discusses the effectiveness of the EEG signal for human identification using four or less of channels of two different types of EEG recordings. Studies have shown that the EEG signal has biometric potential because signal varies from person to person and impossible to replicate and steal. Data were collected from 10 male subjects while resting with eyes open and eyes closed in 5 separate sessions conducted over a course of two weeks. Features were extracted using the wavelet packet decomposition and analyzed to obtain the feature vectors. Subsequently, the neural networks algorithm was used to classify the feature vectors. Results show that, whether or not the subjects- eyes were open are insignificant for a 4– channel biometrics system with a classification rate of 81%. However, for a 2–channel system, the P4 channel should not be included if data is acquired with the subjects- eyes open. It was observed that for 2– channel system using only the C3 and C4 channels, a classification rate of 71% was achieved.

Keywords: Biometric, EEG, Wavelet Packet Decomposition, NeuralNetworks

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3026

Authors: Preetam Kumar

Abstract:

The number of users supported in a DS-CDMA cellular system is typically less than spreading factor (N), and the system is said to be underloaded. Overloading is a technique to accommodate more number of users than the spreading factor N. In O/O overloading scheme, the first set is assigned to the N synchronous users and the second set is assigned to the additional synchronous users. An iterative multistage soft decision interference cancellation (SDIC) receiver is used to remove high level of interference between the two sets. Performance is evaluated in terms of the maximum number acceptable users so that the system performance is degraded slightly compared to the single user performance at a specified BER. In this paper, the capacity of CDMA based O/O overloading scheme is evaluated with SDIC receiver. It is observed that O/O scheme using orthogonal Gold codes provides 25% channel overloading (N=64) for synchronous DS-CDMA system on an AWGN channel in the uplink at a BER of 1e-5.For a Rayleigh faded channel, the critical capacity is 40% at a BER of 5e-5 assuming synchronous users. But in practical systems, perfect chip timing is very difficult to maintain in the uplink.. We have shown that the overloading performance reduces to 11% for a timing synchronization error of 0.02Tc for a BER of 1e-5.

Keywords: DS-CDMA, Interference Cancellation, MultiuserDetection, Orthogonal codes, Overloading.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1716

Authors: Assma Azeroual, Karim Afdel, Mohamed El Hajji, Hassan Douzi

Abstract:

Key frame extraction methods select the most representative frames of a video, which can be used in different areas of video processing such as video retrieval, video summary, and video indexing. In this paper we present a novel approach for extracting key frames from video sequences. The frame is characterized uniquely by his contours which are represented by the dominant blocks. These dominant blocks are located on the contours and its near textures. When the video frames have a noticeable changement, its dominant blocks changed, then we can extracte a key frame. The dominant blocks of every frame is computed, and then feature vectors are extracted from the dominant blocks image of each frame and arranged in a feature matrix. Singular Value Decomposition is used to calculate sliding windows ranks of those matrices. Finally the computed ranks are traced and then we are able to extract key frames of a video. Experimental results show that the proposed approach is robust against a large range of digital effects used during shot transition.

Keywords: Key Frame Extraction, Shot detection, FSDWT, Singular Value Decomposition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2520

Authors: F. Esmaeilzadeh, M. E. Zeighami, J. Fathi

Abstract:

This paper describes a one-dimensional numerical model for natural gas production from the dissociation of methane hydrate in hydrate-capped gas reservoir under depressurization and thermal stimulation. Some of the hydrate reservoirs discovered are overlying a free-gas layer, known as hydrate-capped gas reservoirs. These reservoirs are thought to be easiest and probably the first type of hydrate reservoirs to be produced. The mathematical equations that can be described this type of reservoir include mass balance, heat balance and kinetics of hydrate decomposition. These non-linear partial differential equations are solved using finite-difference fully implicit scheme. In the model, the effect of convection and conduction heat transfer, variation change of formation porosity, the effect of using different equations of state such as PR and ER and steam or hot water injection are considered. In addition distributions of pressure, temperature, saturation of gas, hydrate and water in the reservoir are evaluated. It is shown that the gas production rate is a sensitive function of well pressure.

Keywords: Hydrate reservoir, numerical modeling, depressurization, thermal stimulation, gas generation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2053

Authors: Samba Raju, Chiluveru, Manoj Tripathy

Abstract:

Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methods

Keywords: Discrete Wavelet Transform, speech intelligibility, STOI, standard deviation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 693

Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

Abstract:

Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: Time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1570