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Rashedul Islam

The University of Asia Pacific Dhaka Bangladesh, Department of Computer Science and Engineering, Faculty Member

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Papers by Rashedul Islam

Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis

Applied Sciences, 2019

This paper proposes an online fault diagnosis system for bearings that detect emerging fault mode... more This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function’s cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the k-nearest neighbor (k-NN) classifier. The proposed model is evaluated using acoustic emission s...

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

Future Internet, 2021

Handwriting analysis is playing an important role in user authentication or online writer identif... more Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models sho...

Texture analysis based feature extraction using Gabor filter and SVD for reliable fault diagnosis of an induction motor

International Journal of Information Technology and Management, 2018

This paper presents a texture analysis based feature extraction method using a Gabor filter and s... more This paper presents a texture analysis based feature extraction method using a Gabor filter and singular value decomposition (SVD) for reliable fault diagnosis of an induction motor. This method first converts one-dimensional (1D) vibration signal to a two-dimensional (2D) grey-level texture image for each fault signal. Then, the 2D Gabor filter with optimal frequency and orientation values is used to extract a filtered image with distinctive texture information, and SVD is utilised to decompose the Gabor filtered image and select finer singular values of SVD as discriminative features for multi-fault diagnosis. Finally, one-against-all multiclass support vector machines (OAA-MCSVMs) are used as classifiers. In this study, multiple induction motor faults with different noisy conditions are used to validate the proposed fault diagnosis methodology. The experimental results indicate that the proposed method achieves an average classification accuracy of 99.86% and outperforms conventional fault diagnosis algorithms in the fault classification accuracy.

A Two-Dimensional Fault Diagnosis Model of Induction Motors using a Gabor Filter on Segmented Images

International Journal of Control and Automation, 2016

Image segmentation has received extensive attention due to the use of high-level descriptions of ... more Image segmentation has received extensive attention due to the use of high-level descriptions of image content. This paper proposes a fault diagnosis model using a Gabor filter on segmented two-dimensional (2D) gray-level images. The proposed approach first converts time domain AE signals into 2D gray-level images to exploit texture information from the converted images. 2D discrete wavelet transform (DWT) is then applied to select appropriate (vertical) texture information and reconstructed it into an image. The reconstructed image is segmented into a number of sub-images depending on the segment size and a Gabor filter is applied on each sub-image. Finally, feature vectors are extracted from the Gabor-filtered sub-images and utilized as inputs in a one-against-all multiclass support vector (OAA-MCSVM) to identify each fault in an induction motor. In this study, multiple bearing defects under various segment sizes are utilized to validate the effectiveness of the proposed method. Experimental results indicate that the proposed model outperforms conventional Gabor-filter-based 2D fault diagnosis algorithms in classification accuracy, exhibiting a 97 % average classification accuracy for 64×64 segmented images.

Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

Sensors, 2020

Human activity recognition (HAR) techniques are playing a significant role in monitoring the dail... more Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are t...

A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems

Information Sciences

Feature selection techniques for increasing reliability of fault diagnosis of bearings

2016 9th International Conference on Electrical and Computer Engineering (ICECE), 2016

Features selection (FS) techniques have an apparent need in many complex engineering applications... more Features selection (FS) techniques have an apparent need in many complex engineering applications especially the bearing fault diagnosis of low-speed industrial motor. The main goal of an FS algorithm is to select the most discriminant features subset from a high-dimension features vector that increases the model performance by reducing the redundant and irrelevant fault features. This paper proposes an efficient fault diagnosis model of bearing by incorporating the optimal feature selection approach for increasing the reliability of fault diagnosis of bearing. Also, this paper investigates the feature selection approaches including sequential forward selection (SFS), sequential floating forward selection (SFFS), and genetic algorithm (GA) for identifying the most discriminant subset. The effectiveness of this discriminant features subset is verified with a low-speed bearing fault diagnosis application for identifying bearing failures. The experimental shows up-to-mark diagnosis performance using GA based optimal feature selection method.

Texture Feature Extraction Techniques for Fault Diagnosis of Induction Motors

Journal of Convergence Information Technology, Jul 1, 2014

A Centroid-GPS Model to Improving Positioning Accuracy for a Sensitive Location-Based System

Lecture Notes in Electrical Engineering, 2015

EMG signal decomposition using wavelet transformation with respect to different wavelet and a comparative study

Proceedings of the 2nd International Conference on Interaction Sciences Information Technology, Culture and Human - ICIS '09, 2009

... Shamim Ahmad Associate Prof. University of Rajshahi Rajshahi, Bangladesh +8801713140107 shami... more

Accelerating the formant synthesis of haegeum sounds using a general-purpose graphics processing unit

Multimedia Tools and Applications, 2014

Bangla Hand Written digit recognition using supervised locally linear embedding algorithm and Support Vector Machine

2009 12th International Conference on Computers and Information Technology, 2009

... nizing handwritten digits of the On-Line Handwritten BangIa Numeral Database [1]. Our results... more

Reliable RGB color image watermarking using DWT and SVD

2014 International Conference on Informatics, Electronics & Vision (ICIEV), 2014

This paper proposes a reliable RGB color image watermarking which uses discrete wavelet transform... more This paper proposes a reliable RGB color image watermarking which uses discrete wavelet transform (DWT) and singular value decomposition (SVD) for embedding and extracting watermark. The DWT and SVD applied on the watermark image increase information hiding capacity and perceptual similarity of the watermarked image. In the watermarking embedded stage, the processed watermark information using the proposed method is embedded into three color components (R, G and B) with an optimum watermarking scaling factor (α). In the extraction stage, the resultant watermark is calculated by averaging the three extracted watermarks from R, G and B components. The experimental results show that the proposed method achieves high normalized correlation (NC) of the extracted watermark and high peak signal to noise ratio (PSNR) of the watermarked image after several image processing attacks. In addition, the proposed method outperforms other conventional methods in terms of perceptual similarity, robustness, and detection rate.

Speaker identification system using PCA & eigenface

2009 12th International Conference on Computers and Information Technology, 2009

This paper presents a speech-based speaker identification system and an efficient approach for se... more This paper presents a speech-based speaker identification system and an efficient approach for selection of acoustic parameters closely related to the vocal track shape of the speaker. Speech endpoint detection algorithm is developed in order to discard the room noise and non-speech signal to achieve high accuracy of the system. Windowing and Fast Fourier Transform (FFT) are used to determine the spectrum of the speech signal and peA has been used to extract feature of speech of individual speaker. Eigenface algorithm has been used here as a classification and recognation tool. Eigenspace of individual speaker is generated by the feature of the speech signal. The experimental results show the noticeable performance ofthe proposed system.

Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors

Journal of Sensors, 2016

Optimal feature distribution and feature selection are of paramount importance for reliable fault... more Optimal feature distribution and feature selection are of paramount importance for reliable fault diagnosis in induction motors. This paper proposes a hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method. The hybrid feature selection employs a genetic algorithm- (GA-) based filter analysis to select optimal features and ak-NN average classification accuracy-based wrapper analysis approach that selects the most optimal features. The proposed feature selection model is applied through an offline process, where a high-dimensional hybrid feature vector is extracted from acquired acoustic emission (AE) signals, which represents a discriminative fault signature. The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions. The effectiveness of the proposed feature selection scheme is verified through an online process that diagnose...

An Effective Approach to Improving Low-Cost GPS Positioning Accuracy in Real-Time Navigation

The Scientific World Journal, 2014

Positioning accuracy is a challenging issue for location-based applications using a low-cost glob... more Positioning accuracy is a challenging issue for location-based applications using a low-cost global positioning system (GPS). This paper presents an effective approach to improving the positioning accuracy of a low-cost GPS receiver for real-time navigation. The proposed method precisely estimates position by combining vehicle movement direction, velocity averaging, and distance between waypoints using coordinate data (latitude, longitude, time, and velocity) of the GPS receiver. The previously estimated precious reference point, coordinate translation, and invalid data check also improve accuracy. In order to evaluate the performance of the proposed method, we conducted an experiment using a GARMIN GPS 19xHVS receiver attached to a car and used Google Maps to plot the processed data. The proposed method achieved improvement of 4–10 meters in several experiments. In addition, we compared the proposed approach with two other state-of-the-art methods: recursive averaging and ARMA inte...

Experimental Evaluation of parallelism in real time execution

Parallel programming is a great challenging of modern computing. The main goal of that parallel p... more Parallel programming is a great challenging of modern computing. The main goal of that parallel programming is to improve performance of computer applications. A well-structured parallel application can achieve better performance over sequential execution on existing and upcoming parallel computer architecture. This paper describes the experimental evaluation of parallel application performance with thread-safe data structure in real time execution. Described different performance issues of this paper, help to make efficient parallel application for better performance. Before describing the experimental evaluation, this paper describes some methodologies relevant of parallel programming. The evaluation of parallel applications has been done by experimental result evaluation and performance measurements. The knowledge of proper partitioning, oversubscription, proper work load balancing, way of memory sharing will help to make more efficient parallel application.

Detecting and Learning Unknown Fault States by Automatically Finding the Optimal Number of Clusters for Online Bearing Fault Diagnosis

Applied Sciences, 2019

This paper proposes an online fault diagnosis system for bearings that detect emerging fault mode... more This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function’s cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the k-nearest neighbor (k-NN) classifier. The proposed model is evaluated using acoustic emission s...

User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

Future Internet, 2021

Handwriting analysis is playing an important role in user authentication or online writer identif... more Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models sho...

Texture analysis based feature extraction using Gabor filter and SVD for reliable fault diagnosis of an induction motor

International Journal of Information Technology and Management, 2018

This paper presents a texture analysis based feature extraction method using a Gabor filter and s... more This paper presents a texture analysis based feature extraction method using a Gabor filter and singular value decomposition (SVD) for reliable fault diagnosis of an induction motor. This method first converts one-dimensional (1D) vibration signal to a two-dimensional (2D) grey-level texture image for each fault signal. Then, the 2D Gabor filter with optimal frequency and orientation values is used to extract a filtered image with distinctive texture information, and SVD is utilised to decompose the Gabor filtered image and select finer singular values of SVD as discriminative features for multi-fault diagnosis. Finally, one-against-all multiclass support vector machines (OAA-MCSVMs) are used as classifiers. In this study, multiple induction motor faults with different noisy conditions are used to validate the proposed fault diagnosis methodology. The experimental results indicate that the proposed method achieves an average classification accuracy of 99.86% and outperforms conventional fault diagnosis algorithms in the fault classification accuracy.

A Two-Dimensional Fault Diagnosis Model of Induction Motors using a Gabor Filter on Segmented Images

International Journal of Control and Automation, 2016

Image segmentation has received extensive attention due to the use of high-level descriptions of ... more Image segmentation has received extensive attention due to the use of high-level descriptions of image content. This paper proposes a fault diagnosis model using a Gabor filter on segmented two-dimensional (2D) gray-level images. The proposed approach first converts time domain AE signals into 2D gray-level images to exploit texture information from the converted images. 2D discrete wavelet transform (DWT) is then applied to select appropriate (vertical) texture information and reconstructed it into an image. The reconstructed image is segmented into a number of sub-images depending on the segment size and a Gabor filter is applied on each sub-image. Finally, feature vectors are extracted from the Gabor-filtered sub-images and utilized as inputs in a one-against-all multiclass support vector (OAA-MCSVM) to identify each fault in an induction motor. In this study, multiple bearing defects under various segment sizes are utilized to validate the effectiveness of the proposed method. Experimental results indicate that the proposed model outperforms conventional Gabor-filter-based 2D fault diagnosis algorithms in classification accuracy, exhibiting a 97 % average classification accuracy for 64×64 segmented images.

Enhanced Human Activity Recognition Based on Smartphone Sensor Data Using Hybrid Feature Selection Model

Sensors, 2020

Human activity recognition (HAR) techniques are playing a significant role in monitoring the dail... more Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are t...

A video-based smoke detection using smoke flow pattern and spatial-temporal energy analyses for alarm systems

Information Sciences

Feature selection techniques for increasing reliability of fault diagnosis of bearings

2016 9th International Conference on Electrical and Computer Engineering (ICECE), 2016

Features selection (FS) techniques have an apparent need in many complex engineering applications... more Features selection (FS) techniques have an apparent need in many complex engineering applications especially the bearing fault diagnosis of low-speed industrial motor. The main goal of an FS algorithm is to select the most discriminant features subset from a high-dimension features vector that increases the model performance by reducing the redundant and irrelevant fault features. This paper proposes an efficient fault diagnosis model of bearing by incorporating the optimal feature selection approach for increasing the reliability of fault diagnosis of bearing. Also, this paper investigates the feature selection approaches including sequential forward selection (SFS), sequential floating forward selection (SFFS), and genetic algorithm (GA) for identifying the most discriminant subset. The effectiveness of this discriminant features subset is verified with a low-speed bearing fault diagnosis application for identifying bearing failures. The experimental shows up-to-mark diagnosis performance using GA based optimal feature selection method.

Texture Feature Extraction Techniques for Fault Diagnosis of Induction Motors

Journal of Convergence Information Technology, Jul 1, 2014

A Centroid-GPS Model to Improving Positioning Accuracy for a Sensitive Location-Based System

Lecture Notes in Electrical Engineering, 2015

EMG signal decomposition using wavelet transformation with respect to different wavelet and a comparative study

Proceedings of the 2nd International Conference on Interaction Sciences Information Technology, Culture and Human - ICIS '09, 2009

... Shamim Ahmad Associate Prof. University of Rajshahi Rajshahi, Bangladesh +8801713140107 shami... more

Accelerating the formant synthesis of haegeum sounds using a general-purpose graphics processing unit

Multimedia Tools and Applications, 2014

Bangla Hand Written digit recognition using supervised locally linear embedding algorithm and Support Vector Machine

2009 12th International Conference on Computers and Information Technology, 2009

... nizing handwritten digits of the On-Line Handwritten BangIa Numeral Database [1]. Our results... more

Reliable RGB color image watermarking using DWT and SVD

2014 International Conference on Informatics, Electronics & Vision (ICIEV), 2014

This paper proposes a reliable RGB color image watermarking which uses discrete wavelet transform... more This paper proposes a reliable RGB color image watermarking which uses discrete wavelet transform (DWT) and singular value decomposition (SVD) for embedding and extracting watermark. The DWT and SVD applied on the watermark image increase information hiding capacity and perceptual similarity of the watermarked image. In the watermarking embedded stage, the processed watermark information using the proposed method is embedded into three color components (R, G and B) with an optimum watermarking scaling factor (α). In the extraction stage, the resultant watermark is calculated by averaging the three extracted watermarks from R, G and B components. The experimental results show that the proposed method achieves high normalized correlation (NC) of the extracted watermark and high peak signal to noise ratio (PSNR) of the watermarked image after several image processing attacks. In addition, the proposed method outperforms other conventional methods in terms of perceptual similarity, robustness, and detection rate.

Speaker identification system using PCA & eigenface

2009 12th International Conference on Computers and Information Technology, 2009

This paper presents a speech-based speaker identification system and an efficient approach for se... more This paper presents a speech-based speaker identification system and an efficient approach for selection of acoustic parameters closely related to the vocal track shape of the speaker. Speech endpoint detection algorithm is developed in order to discard the room noise and non-speech signal to achieve high accuracy of the system. Windowing and Fast Fourier Transform (FFT) are used to determine the spectrum of the speech signal and peA has been used to extract feature of speech of individual speaker. Eigenface algorithm has been used here as a classification and recognation tool. Eigenspace of individual speaker is generated by the feature of the speech signal. The experimental results show the noticeable performance ofthe proposed system.

Discriminant Feature Distribution Analysis-Based Hybrid Feature Selection for Online Bearing Fault Diagnosis in Induction Motors

Journal of Sensors, 2016

Optimal feature distribution and feature selection are of paramount importance for reliable fault... more Optimal feature distribution and feature selection are of paramount importance for reliable fault diagnosis in induction motors. This paper proposes a hybrid feature selection model with a novel discriminant feature distribution analysis-based feature evaluation method. The hybrid feature selection employs a genetic algorithm- (GA-) based filter analysis to select optimal features and ak-NN average classification accuracy-based wrapper analysis approach that selects the most optimal features. The proposed feature selection model is applied through an offline process, where a high-dimensional hybrid feature vector is extracted from acquired acoustic emission (AE) signals, which represents a discriminative fault signature. The feature selection determines the optimal features for different types and sizes of single and combined bearing faults under different speed conditions. The effectiveness of the proposed feature selection scheme is verified through an online process that diagnose...

An Effective Approach to Improving Low-Cost GPS Positioning Accuracy in Real-Time Navigation

The Scientific World Journal, 2014

Positioning accuracy is a challenging issue for location-based applications using a low-cost glob... more Positioning accuracy is a challenging issue for location-based applications using a low-cost global positioning system (GPS). This paper presents an effective approach to improving the positioning accuracy of a low-cost GPS receiver for real-time navigation. The proposed method precisely estimates position by combining vehicle movement direction, velocity averaging, and distance between waypoints using coordinate data (latitude, longitude, time, and velocity) of the GPS receiver. The previously estimated precious reference point, coordinate translation, and invalid data check also improve accuracy. In order to evaluate the performance of the proposed method, we conducted an experiment using a GARMIN GPS 19xHVS receiver attached to a car and used Google Maps to plot the processed data. The proposed method achieved improvement of 4–10 meters in several experiments. In addition, we compared the proposed approach with two other state-of-the-art methods: recursive averaging and ARMA inte...

Experimental Evaluation of parallelism in real time execution

Parallel programming is a great challenging of modern computing. The main goal of that parallel p... more Parallel programming is a great challenging of modern computing. The main goal of that parallel programming is to improve performance of computer applications. A well-structured parallel application can achieve better performance over sequential execution on existing and upcoming parallel computer architecture. This paper describes the experimental evaluation of parallel application performance with thread-safe data structure in real time execution. Described different performance issues of this paper, help to make efficient parallel application for better performance. Before describing the experimental evaluation, this paper describes some methodologies relevant of parallel programming. The evaluation of parallel applications has been done by experimental result evaluation and performance measurements. The knowledge of proper partitioning, oversubscription, proper work load balancing, way of memory sharing will help to make more efficient parallel application.