Upal Mahbub

A template matching approach of one-shot-learning gesture recognition

This paper proposes a novel approach for gesture recognition from motion depth images based on template matching. Gestures can be represented with image templates, which in turn can be used to compare and match gestures. The proposed method uses a single example of an action as a query to find similar matches and thus termed one-shot-learning gesture recognition. It does not require prior knowledge about actions, foreground/background segmentation, or any motion estimation or tracking. The proposed method makes a novel approach to separate different gestures from a single video. Moreover, this method is based on the computation of space-time descriptors from the query video which measures the likeness of a gesture in a lexicon. These descriptor extraction methods include the standard deviation of the depth images of a gesture as well as the motion history image. Furthermore, two dimensional discrete Fourier transform is employed to reduce the effect of camera shift. The comparison is done based on correlation coefficient of the image templates and an intelligent classifier is proposed to ensure better recognition accuracy. Extensive experimentation is done on a very complicated and diversified dataset to establish the effectiveness of employing the proposed methods.

Synchronous detection and digital control of Shunt Active Power Filter in power quality improvement

Power Quality means to maintain purely sinusoidal current wave form in phase with a purely sinusoidal voltage wave form. Power quality improvement using traditional compensation methods include many disadvantages like electromagnetic interference, possible resonance, fixed compensation, bulkiness etc. So power system and power electronic engineers need to develop adjustable and dynamic solutions using custom power devices. These power conditioning equipments use static power electronic converters to improve the power quality of distribution system customers. The devices include Active Power Filter (APF), dynamic voltage restorer (DVR) and Unified Power Quality Conditioner (UPQC). APF is a compensator used to eliminate the disturbances in current. There are basically two types of APFs: the shunt type and the series type. This paper examines the control of Shunt Active Power Filter (SAPF) from two different aspects: Synchronous Detection Method (SDM) and digital control based on instantaneous power theory (p-q theory). Simulation results using MATLAB SIMULINK demonstrates the application of these methods to the control of APF. Moreover, this work shows that digital control provides better power quality improvement than SDM.

Fuzzy logic driven adaptive PID controller for PWM based buck converter

This paper presents the design and construction of a fuzzy logic based proportional integral derivative (PID) controller for the control of a pulse width modulation (PWM)based dc-dc buck converter working in Continuous Conduction Mode(CCM). The converter operates at a switching frequency of 100 KHz. Computer simulation is done to fuzzify the inputs and their membership functions, express the inference rules linking the input and output variables and defuzzify the output parameter. This approach is simple as no linearized model of the buck converter is required. In the simulation results it is seen that the fuzzy logic based PID control technique provides robust control for non-linear power electronics variable switching structure like the ideal dc-dc buck converter. The practical model of the buck converter is next considered. From extensive experimentations for various parameter set and load disturbances, it is shown that the proposed adaptive Fuzzy logic driven PID controlled buck converter outperforms such non-adaptive PID controlled converters in terms of rise time, settling time and ripple voltage at the output response.

FPGA implementation of Real Time acoustic noise suppression by Spectral Subtraction using Dynamic Moving Average Method

The Paper presents the outlines of the Field Programmable Gate Array (FPGA) implementation of Real Time speech enhancement by Spectral Subtraction of acoustic noise using Dynamic Moving Average Method. It describes an stand alone algorithm for Speech Enhancement and presents a architecture for the implementation. The traditional Spectral Subtraction method can only suppress stationary acoustic noise from speech by subtracting the spectral noise bias calculated during non-speech activity, while adding the unique option of dynamic moving averaging to it, it can now periodically upgrade the estimation and cope up with changes in noise level. Signal to Noise Ratio (SNR) has been tested at different noisy environment and the improvement in SNR certifies the effectiveness of the algorithm. The FPGA implementation presented in this paper, works on streaming speech signals and can be used in factories, bus terminals, Cellular Phones, or in outdoor conferences where a large number of people have gathered. The Table in the Experimental Result section consolidates our claim of optimum resouce usage.

A Single-Channel Acoustic Echo Cancellation Scheme Using Gradient-Based Adaptive Filtering

In this paper, a single-channel acoustic echo cancellation (AEC) scheme is proposed using a gradient-based adaptive least mean squares (LMS) algorithm. Unlike the conventional dual-channel problem, by considering a delayed version of the echo-suppressed signal as a reference, a modified objective function is formulated and thereby an LMS update equation is derived. It is shown that the resulting update equation converges to the optimum Wiener–Hopf solution. Based on the commonly used assumption of negligibility of cross correlation between the reference and the current speech signals, a multi-step stopping criterion is introduced, which not only provides efficient control of the LMS update sequence but also ensures a faster convergence. The proposed control criterion is validated by considering all possible scenarios which arise due to the variation of speech properties at the reference and current samples of the adaptive filter. From extensive experimentation on several real-life echo-corrupted speech signals in different acoustic environments, it is found that the proposed algorithm can efficiently handle the problem of single-channel AEC and provide satisfactory performance in terms of both subjective and objective measures.

An adaptive noise cancellation scheme using particle swarm optimization algorithm

This paper deals with the problem of noise cancellation of speech signals in an acoustic environment. In this regard, generally, different adaptive filter algorithms are employed, many of them may lack the flexibility of controlling the convergence rate, range of variation of filter coefficients, and consistency in error within tolerance limit. In order to achieve these desirable attributes as well as to cancel noise effectively, unlike conventional approaches, we formulate the task of noise cancellation as a coefficient optimization problem whereby we introduce and exploit the particle swarm optimization (PSO) algorithm. In this problem, the PSO is designed to perform the error minimization in frequency domain. The outcomes from extensive experimentations show that the proposed PSO based acoustic noise cancellation method provides high performance in terms of SNR improvements with a satisfactory convergence rate in comparison to that obtained by some of the state-of-the-art methods.

Gradient based adaptive filter algorithm for single channel acoustic echo cancellation in noise

Conventional adaptive echo cancellation schemes are generally suitable for dual channel communication system and their performance degrades significantly in the presence of noise. This paper deals with a challenging task of cancelling both echo and noise in a single channel communication system. In this regard, first, a gradient based single channel adaptive least mean sqares (LMS) algorithm is developed to reduce the effect of echo. Next a spectral subtraction based single channel noise cancellation scheme is employed to reduce the effect of noise and residual echo. In order to overcome the problem of availability of a separate reference signal in single channel communication system, the delayed version of the echo and noise suppressed signal is used as the reference to the proposed adaptive filter algorithm. It is shown that the resulting update equation converges to the optimum Wiener-Hopf solution Several speech signals corrupted by both echo and noise have been tested and it is found that the proposed scheme not only provides an excellent echo cancellation performance in terms of average echo return loss enhancement, but also provides significant improvement in overall signal to distortion ratio.

Single-channel acoustic echo cancellation in noise based on gradient-based adaptive filtering

In this paper, a two-stage scheme is proposed to deal with the difficult problem of acoustic echo cancellation (AEC) in single-channel scenario in the presence of noise. In order to overcome the major challenge of getting a separate reference signal in adaptive filter-based AEC problem, the delayed version of the echo and noise suppressed signal is proposed to use as reference. A modified objective function is thereby derived for a gradient-based adaptive filter algorithm, and proof of its convergence to the optimum Wiener-Hopf solution is established. The output of the AEC block is fed to an acoustic noise cancellation (ANC) block where a spectral subtraction-based algorithm with an adaptive spectral floor estimation is employed. In order to obtain fast but smooth convergence with maximum possible echo and noise suppression, a set of updating constraints is proposed based on various speech characteristics (e.g., energy and correlation) of reference and current frames considering whether they are voiced, unvoiced, or pause. Extensive experimentation is carried out on several echo and noise corrupted natural utterances taken from the TIMIT database, and it is found that the proposed scheme can significantly reduce the effect of both echo and noise in terms of objective and subjective quality measures.

Motion clustering-based action recognition technique using optical flow

A new technique for action clustering-based human action representation on the basis of optical flow analysis and random sample consensus (RANSAC) method is proposed in this paper. The apparent motion of the human subject with respect to the background is detected using optical flow analysis, while the RANSAC algorithm is used to filter out unwanted interested points. From the remaining key interest points, the human subject is localized and the rectangular area surrounding the human body is segmented both horizontally and vertically. Next, the percentage of change of interest points at every small blocks at the intersections of horizontal and vertical segments from frame to frame are accumulated in matrix form for different persons performing the same action. An average of all these matrices is used as a feature vector for that particular action. In addition, the change in the position of the person along X-axis and Y-axis are cumulated for an action and included in the feature vectors. For the purpose of recognition using the extracted feature vectors, a distance-based similarity measure and a support vector machine (SVM)-based classifiers have been exploited. From extensive experimentations upon benchmark motion databases, it is found that the proposed method offers not only a very high degree of accuracy but also computational savings.

Human Action Recognition based on Spectral Domain Features

Abstract In this paper, we propose a novel approach towards human action recognition using spectral domain feature extraction. Action representations can be considered as image templates, which can be useful for understanding various actions or gestures as well as for recognition and analysis. An action recognition scheme is developed based on extracting spectral features from the frames of a video sequence using the two-dimensional discrete Fourier transform (2D-DFT). The proposed spectral feature selection algorithm offers the advantage of very low feature dimensionality and thus lower computational cost. We show that using frequency domain features enhances the distinguishability of different actions, resulting in high within-class compactness and between-class separability of the extracted features, while certain undesirable phenomena, such as camera movement and change in camera distance, are less severe in the frequency domain. Principal component analysis is performed to further reduce the dimensionality of the feature space. Experimental results on a benchmark action recognition database confirm that our proposed method offers not only computational savings but also a high degree of accuracy.