Short Biography

I’m Dr. Md Rashedul Islam. I received the Bachelor of Science (B.Sc) in Computer Science and Engineering at University of Rajshahi, Bangladesh , in 2006, M.Sc. in Informatics from Högskolan i Borås (University of Boras), Sweden in 2011, and Ph.D. in Computer Engineering, University of Ulsan (UoU), South Korea in August 2016. Now I am a visiting researcher in University of Aizu, Japan and Associate Professor (on study leave) of Department of Computer Science and Engineering, University of Asia Pacific (UAP), Bangladesh. I worked as a graduate research assistant in Embedded system lab, University of Ulsan, South Korea. In previous, he worked as a graduate research assistant in Embedded system lab, University of Ulsan, South Korea, Assistant professor in the Department of Computer Science and Engineering, University of Asia Pacific (UAP), Dhaka, Bangladesh, and Lecturer in the Department of Computer Science and Engineering, Leading University, Sylhet, Bangladesh. Also, he has a good experience in professional IT system analysis and development.

I am also serving as: a) Head, Self-Assessment Committee (SAC) of Dept. of CSE Under IQAC, University of ASIA PACIFIC. b) Convener of Software and Hardware Club, Dept. of CSE, University of ASIA PACIFIC. c) Coordinator, Admission Committee, Dept. of CSE, University of ASIA PACIFIC. d) Secretary, Organizing committee, 19th International Conference on Computer and Information Technology 2017 (ICCIT2017). e) Organizing Chair, Organizing committee, ACM-ICPC Dhaka Regional Site 2017. f) Treasurer, Bangladesh Advanced Computing Society.

Education & Training

  • Ph.D. Aug 2016

    Ph.D. in Computer Engineering

    Embedded System Lab, Department of Electrical, Electronics and Computer Engineering,
    University of Ulsan, Ulsan, South Korea
    Thesis: Discriminant Fault Feature Selection and Reliable Online Bearing Fault Diagnosis System using Signal Processing and Machine Learning Techniques

  • M.Sc.2011

    M.Sc. in Informatics

    University of Borås (Högskolan i Borås), Sweden.
    Thesis: Evaluations of the parallel extensions in .NET 4.0

  • B.Sc2006

    B.Sc. in Computer Science and Engineering

    Department of Computer Science and Engineering,
    University of Rajshahi, Bangladesh
    Thesis: Speaker Identification System Using Eigenface Classification Engine Area: Speech Signal Processing

  • HSC 2000

    Higher secondary certificate

    Major in Science, Govt. A. H. College, Bogra under Rajshahi board, Bangladesh.

  • SSC1998

    Secondary school certificate

    Major in Science, Gabtali Pilot high school, Bogra, under Rajshahi board, Bangladesh.

Research Interests

  • Signal & Image processing
  • Machine learning
  • Feature selection & dptimization for supervised and unsupervised learning.
  • Human Computer Interaction
  • Health informatics & disease diagnosis
  • Human activity recognition
  • Data-driven bearing fault diagnosis and prognostics

Ongoing Researchs

  • Parkinson"s disease detection and diagnosis using hand writing and hand movement data analysis.
  • Hand gesture based user authentication system.
  • Gestural Input based non-touch human computer interaction.
  • HCI and Health informatics using EMG and EEG signal and Machine learning
  • Fault detection and diagnosis of induction motor using signal analysis and machine learning.
  • Enhancing supervised classification algorithm
  • Develop optimal feature selection model

Research Projects

  • image

    Parkinson"s disease diagnosis

    Quantities and reliable kinematic feature extraction for disease (i.e. Parkinson's disease) diagnosis using pen-tablet. The purpose of this study is to establish a technological infrastructure with feature extraction and identification algorithm development to generate quantitative, reliable and reasonable evaluation index by utilizing sensor technology, data science and machine/deep learning technology to evaluate cognitive and motor symptoms of neurological diseases..

    --

  • image

    Human activity and abnormality detection using Myo armband

    EMG, accelerometer, and gyroscope data analysis based activity and abnormality detection of human using Myo armband. In this research, the embedded sensor of Myo armband will be used for collecting activity data for analyzing and detecting the abnormality of people. The machine/deep learning techniques is used for analyzing and detecting activity and abnormality.

    --

  • image

    Disease diagnosis using ECG/EEG signal analysis and machine learning

    ECG and EEG are mostly used bio signals which express many artifacts of human body. Based on the feature extracted from those bio signals, disease can be diagnosed using machine learning techniques. Here EEG and ECG signals are analyzing for detecting the abnormalities of human body.

    --

  • image

    Non-touch interface development for HCI

    RGB-D, Leap motion etc sensor based non-touch human computer interaction interface development. The aim of this research is to identify the human body and hand gesture for understanding the gesture command and non-touch input for computer or robot. Dynamic gesture recognition will be done by gesture feature extraction and machine/deep learning models..

    --

  • image

    Fault detection and diagnosis of Induction motor

    Identifying the fault of bearing and induction motor using vibration and acoustic emission signal analysis and machine learning techniques.

    --

  • image

    Image Watermarking and information security

    Hiding secret information into image for image and information security

    --

  • image

    Parallel Processing

    Develop and evaluation the parallel programming structure for many core architectures

    --

  • image

    Human voice Recognition

    Human Identification using voice signal analysis and classification

    --

Thesis Supervisions

  • B.Sc. thesis
    Fall 2018
    Enhanced denoising of sEMG signal for efficient human action recognition
    School of Computer Science and Engineering, University of Aizu
  • B.Sc. Thesis
    Fall 2018
    Brain computer interaction based of EEG signal analysis
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • B.Sc. Thesis
    Fall 2018
    Outlier detection based leaf disease detection and diagnosis using image processing techniques
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • B.Sc. Thesis
    Fall 2018
    Parkinson"s disease detection based on hand writing analysis
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • B.Sc. Thesis
    Spring 2018
    Real time Human computer interaction based on dynamic hand gesture
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • M.Sc Thesis
    Fall 2017
    PCA and ICA Based Feature Extraction for Cardiac Arrhythmia Disease Diagnosis.
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • M.Sc. Thesis
    Fall 2017
    Enhanced Sleep Disorder Detection Method Using DWT and Feature Selection Technique
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • M.Sc Thesis
    Fall 2017
    Feature Extraction from EEG signal Using EMD for Sleep Disorder Detection
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • B.Sc Thesis
    Fall 2017
    Video Based Smoke Detection Using Smoke Growth Analysis for Fire Alarm System
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • B.Sc Thesis
    Fall 2017
    A Fault Diagnosis Model of Bearing using Convolutional Neural Network
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • B.Sc Thesis
    Spring 2017
    Reduction of Gesture Feature Dimension for Improving the Hand Gesture Recognition Performance of Numerical Sign Language
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • M.Sc Thesis
    Spring 2013
    Performance evaluation on Data partitioning and task partitioning in parallelism
    Dept. of Computer Science and Engineering, University of Asia Pacific
  • B.Sc Thesis
    Spring 2013
    Memory Efficient adaptive Distributed database management system using cost function
    Dept. of Computer Science and Engineering, University of Asia Pacific


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International Conference On December-2018
Md. Atiqur Rahman, Syed Islam, Jungpil Shin, Md. Rashedul Islam

Histogram alternation based digital image compression using base-2 coding

Inter. Conf. Digital Image Computing: Techniques and Applications

Abstract

The goal of data compression algorithms is to promote the storage and delivery of big images with excellent compression ratio and least distortion. As the number of internet user is growing day by day, the transfer of data is being another significant concern. The storage and the use of an uncompressed picture are very costly and time-consuming. There are many techniques such as Arithmetic coding, Run-length coding, Huffman coding, Shannon-Fano coding used to compress an image. Compression of a picture using the state-of-the-art techniques has a high impact. However, The compression ratio and transfer speed do not satisfy the current demand. This article proposes a new histogram alternation based lossy image compression using Base-2 coding. It increases the probabilities of an image by doing a little bit of change to its pixels level which helps to reduce code-word. This algorithm uses less storage space and works at high-speed to encode and decode an image. Average code length, compression ratio, mean square error and pick signal to noise ratio are used to estimate this method. The proposed method demonstrates better performance than the state-of-theart techniques.
International Conference On December-2018
Md Rashedul Islam∗, Rasel Ahmed Bhuiyan, Nadeem Ahmed and Md Rajibul Islam

PCA and ICA Based Hybrid Dimension Reduction Model for Cardiac Arrhythmia Disease Diagnosis

IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, Baguio, Philippines

Abstract

Abstract—An arrhythmia is a fluctuation in the continuous beat of the heart (i.e., anomalous rhythm). Arrhythmia is considered a hazardous disease causing genuine medical problems in patients, when left untreated. For saving lives, early diagnosis of arrhythmias would be very conducive. The P-QRS-T wave of the Electrocardiogram (ECG) signal illustrates the cardiac function. However, it is a tough task to extract the discriminant information from a large number of data of ECG signal. In this perspective, this study exhibits a novel approach for diagnosing diseases related to cardiac arrhythmia. In this proposed model, a hybrid dimension reduction model including Independent and Principal Component Analysis (ICA, PCA) are introduced and machine learning features are extracted for disease diagnosis. The original ECG data are splitted into several windows and consider as input of dimension reduction process. After completing the ICA and PCA process, the different components of ICA and PCA are used for feature extraction. Finally, the Multi-Class Support Vector Machine (MCSVM) is used for training and identifying the disease. For evaluating the proposed method, MIT-BIH dataset is used. According to the experiment, the proposed model shows better classification accuracy using the first components of ICA and PCA algorithms, which is 98.67%.
International Conference On November-2018
Md Rashedul Islam, Md Abdur Rahim, Hafeza Akter, Raihan Kabir, Jungpil Shin

Optimal IMF Selection of EMD for Sleep Disorder Diagnosis using EEG Signals

In The 3rd International Conference on Applications in Information Technology (ICAIT’18)

Abstract

Sleep disorders has a vital effect on mental depression and many other diseases of human body. Diagnosing the sleep disorder in an early curable stage may help to provide better treatment and save the life. The EEG (Electroencephalogram) signal is one of the most uses bio signal for capturing brain activities to detect and diagnosis the sleep disorders. Empirical mode decomposition (EMD) is an efficient time-frequency data analysis technique for diagnosing disease by analyzing EEG signal. However, it is a challenging issue to select the optimal intrinsic mode functions (IMFs) of Empirical mode decomposition (EMD) for extracting discriminant properties of EEG signals to diagnosis the sleep disorder. From this point of view, this paper presents a model to select optimal IMF of EMD for diagnosing the sleep disorder using EEG brain signal. In this proposed model, EMD is applied to decompose and analyze EEG signal for extracting biomarker/feature of sleep disorders. During the EMD decomposition process, different levels of IMF are extracted and features, i.e., Shannon Entropy, Spectral Entropy, Standard deviation, Skewness and Kurtosis are calculated from those IMFs for detecting the sleep disorders. In identification process, the multiclass support vector machine (MC-SVM) classification algorithm is used and sleep disorders are classified based on trained knowledge. Finally, the performance of proposed model is evaluated for different IMFs of EMD and find the optimal IMF for sleep disorder diagnosis. For evaluating the proposed model, a benchmark dataset including 4 types of data such as Apnea, REM, PLM and healthy subjects are used in experiment. According to the experimental result, the proposed model achieves the optimal classification performance for IMF 8, i.e., 93.24% average classification accuracy.
International Conference On November-2018
Md Abdur Rahim, Jungpil Shin, and Md Rashedul Islam

Human Machine Interaction based on Hand Gesture Recognition using Skeleton Information of Kinect Sensor

In The 3rd International Conference on Applications in Information Technology (ICAIT’18) (ACM), Aizu-Wakamatsu, Japan

Abstract

The hand gesture provides a natural and intuitive communication medium for the human and machine interaction. Because, it can use in virtual reality, language detection, computer games, and other human-computer or human-machine instruction applications. Currently, the sensor and camera-based application is a field of interest for many researchers. This paper proposes a new hand gesture recognition system using the Kinect sensor's skeleton data, which works in an environment where people do not touch devices or communicate verbally. The proposed model focuses on mainly two modules, namely, hand area and fingertip detection, and hand gesture recognition. The hand area and fingertip are detected by positioning the palm point and find extreme of contour. And, the hand gesture is recognized by measuring the distance between different body indexes of skeleton information. Here, six gestures instructions are considered such as move right to left, move left to right, move up to down, move down to up, open and closed, and also recognize the numeric number using the fingertip. This system is able to detect the presence of hand area and fingers and to recognize different hand gestures. As a result, the average recognition accuracy of different hand gestures and stretched fingers numbers are 95.91% and 96%, respectively.
International Conference On September-2018
Md.Atiqur Rahman, M. M. Fazle Rabbi, Md. Mijanur Rahman, Md. Masudul Islam, Md. Rashedul Islam*

Histogram modification based lossy image compression scheme using Huffman coding

4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018)

Abstract

Medical images produce massive volumes of data, and consequently, suitable image compression procedures require to save expense and time of storage and transportation sequentially. The requirement of transferring or storing satellite images is increasing quickly with the evolution of advanced communications and imaging systems. In the digital world, the size of pictures is a major problem when dealing with the storage and transportation necessities. Compression is one of the most primary procedures to address this difficulty. The intention of data compression is to promote the storage and delivery of big images with excellent compression ratio and least distortion. Moreover, the number of internet user is growing day by day speedily. So, transferring of data is being an another significant concern. This article introduces histogram modification based lossy image compression using Huffman coding. A little bit change of pixel’s value is done which is why the number of probabilities of an original image is decreased and the value of probabilities are increased. As a result, Huffman coding uses very few bits in case of encoding and decoding than that of previous. This process provides higher compression ratio and less average code length keeping the same quality of the corresponding original image. Keywords: Lossy compression, Lossless compression, Huffman Coding, PSNR, Compression ratio, SVD coding.
International Conference On September-2018
Md Rashedul Islam, Rasel Ahmed Bhuiyan, Ummey Kulsum Mitu, Jungpil Shin

Hand Gesture Feature Extraction using Deep Convolutional Neural Network for Recognizing American Sign Language

2018 4th International Conference on Frontiers of Signal Processing (ICFSP 2018) France

Abstract

International Conference On June-2018
Md. Atiqur Rahman, Jungpil Shin, Aloke Kumar Saha, Md. Rashedul Islam*

A Novel Lossless Coding Technique for Image Compression

2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)

Abstract

Advanced imaging technology produces a vast amount of data, particularly from the computed tomography modality. Store the imaging data and/or transfer the data through internet is a significant challenge in terms of cost and time. From this point of view, it is obligatory to compress the imaging data effectively. There are many state of the art coding techniques used to compress data such as Huffman coding. This paper proposes a new coding technique for loss-less imaging data compression. In this proposed method, the probability of image data has been assigned by binary sequence with fewer number of bits which provides an improved result than the state of art techniques in terms of compression ratio and average code length.
International Journal On January-2018
Rashedul Islam, Jia Uddin, Jong-Myon Kim

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

SCOPUS

Abstract

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.
International Journal On December-2017
Dileep K. Appana, Rashedul Islam, Sheraz A. Khan, Jong-Myon Kim

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

Information Sciences, Volumes 418-419, Pages 91-101, ISSN 0020-0255.

SCI | Impact Factor: 4.832

Abstract

Detecting smoke during the initial stages is vital for preventing fire events. This study proposes a video-based approach for alarm systems that detects smoke based on temporal features extracted from optical smoke flow pattern analysis and spatial-temporal energy analysis. To do this, it considers various optical characteristics such as the diffusion, color, and semi-transparency of smoke. In the proposed model, smoke-colored pixels are identified via masking in the HSV color space and a temporal frame difference is applied. To extract the temporal feature vectors, we propose a new method that determines the optical flow of smoke by using distinguished texture information by applying a Gabor filter bank with preferred orientations. In addition, when applied to an image that has been temporal-differenced, the energy of the spatial frequencies is fed as another feature into the feature vector. Finally, these features are fed to a support vector machine (SVM) to discriminate our data more thoroughly and provide accurate detection of smoke. Experiments are carried out with benchmark datasets, which show that the proposed approach can work effectively without false alarms.
International Conference On December-2017
Rasel Ahmed Bhuiyan, Abdul Kawsar Tushar, Akm Ashiquzzaman, Jungpil Shin and Md Rashedul Islam*

Reduction of Gesture Feature Dimension for Improving the Hand Gesture Recognition Performance of Numerical Sign Language

20th International Conference On Computer and Information Technology (ICCIT 2017)

Abstract

A major form of non-touch human-computer interaction (HCI) is hand gesture recognition. This is one of the appealing ways to interact with computers and a natural part of how we communicate. However, as a part of HCI, human hand gesture recognition is a challenging issue. From this point of view, this paper presents an effective hand gesture recognition system with hand feature selection for low cost video acquisition device. In this proposed model, hand features are extracted from video frame using discrete wavelet transformation and singular value decomposition. A genetic algorithm with effective fitness function is used to select optimal hand features by eliminating redundant and irrelevant features for improving the recognition performance. Finally, support vector machine is used to recognize the hand gestures for numerical hand gesture accuracy of American Sign Language. The proposed model is validated using a constructed hand gesture dataset. The proposed model is compared with non-feature selection based models, where the feature selection-embedded model outperforms the traditional hand recognition process.
International Conference On December-2017
Abdul Kawsar Tushar, Akm Ashiquzzaman, and Md. Rashedul Islam*

Faster Convergence and Reduction of Overfitting in Numerical Hand Sign Recognition using DCNN

5th IEEE Region 10 Humanitarian Technology conference 2017

Abstract

Hand signs and signals are the staple form of expression for the hearing and speech impaired people. Human Computer Interaction technology enable people to interact with computer machine using hand gestures. Common sign languages use separate hand signals to communicate different decimals. Recent developments in Deep Convolutional Neural Networks (DCNN) have opened the door to recognize and classify this visual form of gestures more accurately. In this paper, a layer-wise optimized neural network architecture is proposed where batch normalization contributes to faster convergence of training, and introduction of dropout technique mitigates data overfitting. Batch normalization forces each training batch toward zero mean and unit variance, leading to improved flow of gradients through the model and convergence in shorter time. Dropout forces neurons of neural network to regularize, resulting in reduced overfitting. A constructed numerical hand gesture data set is used for validating the claims based on American Sign Language system. The proposed model is shown to surpass other methods in classifying these numerical hand signs successfully.
International Conference On December-2017
Mohammad Sakib Mahmud, Mahbub Arab Majumder, Abdul Kawsar Tushar, Md. Mahtab Kamal, Akm Ashiquzzaman, and Md. Rashedul Islam*

Real-Time Feedback-Centric Nurse Calling System with Archive Monitoring using Raspberry Pi

2017 International Conference on Networking, Systems and Security (NSysS 2017), Dhaka, Bangladesh

Abstract

The relationship between nurse and patient is vital as well as vulnerable on which a significant portion of well-being of patient depends. The recent growth of Internet of Things provides the opportunity to maintain this relationship in a more secure and efficient way. From this point of view, this paper presents a system of real-time nurse calling with focus centered on nurse feedback based on patient condition. The system also includes a robust archive monitoring system for review and trend analysis. In the proposed system, patients are able to call for assistance in time of emergency by pressing a designated button. After this, the device will send a real-time message containing information about the patients bed, room, and floor number to the appropriate nurse station. Nurse will respond as soon as the message is delivered, and optionally call for help by pressing designated buttons in the device. The proposed system reduces the delay in response of nurse. Additionally, the interactions are stored in Raspberry Pi database for future analysis to improve the quality of services. The proposed system uses low-cost hardware like ATMEGA328P microcontroller and ENC28J60 Ethernet controller, and Arduino Uno.
International Conference On September-2017
Akm Ashiquzzaman, Abdul Kawsar Tushar, Md. Rashedul Islam, DongkooShon, Kichang Im, Jeong-Ho Park, Dong-Sun Lim, and Jongmyon Kim

Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network

7th iCatse International Conference on IT Convergence and Security, 2017 (LNEE)

Abstract

Augmented accuracy in prediction of diabetes will open up new frontiers in health prognostics. Data overfitting is a performance-degrading issue in diabetes prognosis. In this study, a prediction system for the disease of diabetes is pre-sented where the issue of overfitting is minimized by using the dropout method. Deep learning neural network is used where both fully connected layers are fol-lowed by dropout layers. The output performance of the proposed neural network is shown to have outperformed other state-of-art methods and it is recorded as by far the best performance for the Pima Indians Diabetes Data Set.
International Conference On September-2017
Hanif Bhuiyan, Jinat Ara, Rajon Bardhan, Md. Rashedul Islam*

Retrieving YouTube Video by Sentiment Analysis on User Comment

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA),Kuching, 2017, pp. 474-478

Abstract

YouTube is one of the comprehensive video information source on the web where video is uploading continuously in real time. It is one of the most popular site in social media, where users interact with sharing, commenting and rating (like/views) videos. Generally the quality, relevancy and popularity of the video is maintained based on this rating. Sometimes irrelevant and low quality videos ranked higher in the search result due to the number of views or likes, which seems untenable. To minimize this issue, we present a Natural Language processing (NLP) based sentiment analysis approach on user comments. This analysis helps to find out the most relevant and popular video of YouTube according to the search. The effectiveness of the proposed scheme has been proved by a data driven experiment in terms of accuracy of finding relevant, popular and high quality video.
International Conference On July-2017
Young-Hun Kim, M M Manjurul Islam, Md. Rashedul Islam, Jong-Myon Kim

Genetic Algorithm Based Discriminant Feature Selection for Improved Fault Diagnosis of Induction Motor

The 19th Int. Conf on Artificial Intelligence ICAI-17 (CSREA Press), Las Vegas, Nevada, USA

Abstract

In this paper, we present an efficient model for reliable fault diagnosis of the induction motor. This is now a growing demand for high classification accuracy in fault diagnosis. However, the system performance is highly dependable on superior feature analysis. But, it’s still crucial and computational complex to select discernment features, thus, a new genetic algorithm (GA) with optimum class separability criteria is utilized to find most discriminate features from a hybrid feature vector. For this approach, wavelet packet decomposition (WPD) is applied on Acoustic Emission (AE) fault signal and hybrid statistical features are extracted from a decomposed wavelet packet coefficient, which has maximum energy. GA and Euclidean distance based novel, optimum class separability (OCS) are used to select the optimal low-dimensional feature set from high dimensional feature set. The efficacy of this proposed model, in terms of classification accuracy, is validated by the knearest neighbor (k-NN) classifier. Experimental results show that the proposed model has a superior classification, yielding an average classification accuracy above 98%.
International Conference On February-2017
Md Rashedul Islam, Abdul Kawsar Tushar and Jong�myon Kim

Efficient Bearing Fault Diagnosis by Extracting Intrinsic Fault Information using Envelope Power Spectrum

2017 IEEE International Conference on Imaging, Vision &Pattern Recognition , Dhaka

Abstract

Early and efficient fault diagnosis of bearing of industrial motor is a modern demand for reducing unexpected breakdown of industrial process. Extracting the intrinsic fault signature in very early stage is important. In this point of view, this paper proposes a fault diagnosis model of industrial bearing including efficient fault signature extraction technique based on narrow band frequency domain analysis of acoustic emission (AE) signal using envelope power spectrum. To do that, AE signals are collected from defective and non-defective bearings under different rotational speeds from industrial-like experimental environment. Envelope power spectrum is calculated from the AE signal and narrow band root mean square (NBRMS) fault features are extracted from defect frequency ranges of the envelope power spectrum. Finally, the k-nearest neighbor (k-NN) classification algorithm is used for identifying the fault of unknown signal and validating the efficiency of the proposed feature extraction model. The experimental result shows that the proposed model outperforms state-of-art algorithms in terms of classification accuracy.
International Journal On January-2017
Manjurul Islam, Rashedul Islam, Jong-Myon Kim

A Hybrid feature selection scheme based on local compactness and global separability for improving roller bearing diagnostic performance

Artificial Life and Computational Intelligence, Volume 10142 of the series Lecture Notes in Computer Science (LNCS) pp 180-192.

LNCS

Abstract

This paper proposes a hybrid feature selection scheme for identifying the most discriminant fault signatures using an improved class separability criteria—the local compactness and global separability (LCGS)—of distribution in feature dimension to diagnose bearing faults. The hybrid model consists of filter based selection and wrapper based selection. In the filter phase, a sequential forward floating selection (SFFS) algorithm is employed to yield a series of suboptimal feature subset candidates using LCGS based feature subset evaluation metric. In the wrapper phase, the most discriminant feature subset is then selected from suboptimal feature subsets based on maximum average classification accuracy estimation of support vector machine (SVM) classifier using them. The effectiveness of the proposed hybrid feature selection method is verified with fault diagnosis application for low speed rolling element bearings under various conditions. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithm when selecting the most discriminate fault feature subset, yielding 1.4% to 17.74% diagnostic performance improvement in average classification accuracy.
International Journal On January-2017
Dileep Appana, Rashedul Islam, Jong-Myon Kim

Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN

Artificial Life and Computational Intelligence,Volume 10142 of the series Lecture Notes in Computer Science (LNCS) pp 193-203

LNCS

Abstract

The k-nearest neighbor (k-NN) method is a simple and highly effective classifier, but the classification accuracy of k-NN is degraded and becomes highly sensitive to the neighborhood size k in multi-classification problems, where the density of data samples varies across different classes. This is mainly due to the method using only a distance-based measure of similarity between different samples. In this paper, we propose a density-weighted distance similarity metric, which considers the relative densities of samples in addition to the distances between samples to improve the classification accuracy of standard k-NN. The performance of the proposed k-NN approach is not affected by the neighborhood size k. Experimental results show that the proposed approach yields better classification accuracy than traditional k-NN for fault diagnosis of rolling element bearings.
Book/Book Chapters On January-2017
Abdul Kawsar Tushar, Akm Ashiquzzaman, Md. Rashedul Islam, Afia Afrin

A Novel Transfer Learning Approach upon Hindi, Arabic, and Bangla Numerals using Convolutional Neural Networks

International Conference On Computational Vision and Bio Inspired Computing (ICCVBIC 2017), 2017 in Tamil Nadu, India

LNAI

image

Abstract

Increased accuracy in predictive models for handwritten character recognition will open up new frontiers for optical character recognition. Major drawbacks of predictive machine learning models are headed by the elongated training time taken by some models, and the requirement that training and test data be in the same feature space and consist of the same distribution. In this study, these obstacles are minimized by presenting a model for transferring knowledge from one task to another. This model is presented for the recognition of handwritten numerals in Indic languages. The model utilizes convolutional neural networks with backpropagation for error reduction and dropout for data overfitting. The output performance of the proposed neural network is shown to have closely matched other state-of-the-art methods using only a fraction of time used by the state-of-the-arts.
International Journal On December-2016
Md. Rashedul Islam, Islam, J. Uddin, J.-M. Kim

Acoustic Emission Sensor Network Based Fault Diagnosis of Induction Motors Using a Gabor Filter and Multiclass Support Vector Machines

Ad Hoc & Sensor Wireless Networks (AHSWN), Vol. 34, pp. 273-287

SCIE | Impact Factor: 1.043

Abstract

Reliable and ef cient fault diagnosis of induction motors is an important issue in industrial environments. This paper proposes a method for reli- able fault diagnosis of induction motors using signal processing of acous- tic emission (AE) data, including Gabor ltering and the use of multiclass support vector machines (MCSVMs), where a ZigBee based wireless sensor network (WSN) model is used for ef ciently transmitting AE sig- nals to a diagnosis server. In the proposed fault diagnosis approach, the induction motor’s different state signals are acquired through proper placement of AE sensors. The AE data are sent to a server through the wireless sensor network and decomposed using discrete wavelet transfor- mation (DWT). An appropriate band is then selected using the maximum energy ratio, and a one-dimensional (1D) Gabor lter with various fre- quencies and orientation angles is applied to reduce abnormalities and extract various statistical parameters for generating features. In addition, principal component analysis (PCA) is applied to the extracted features to select the most dominant feature dimensions. Finally, one-against-one multiclass support vector machines (OAA-MCSVMs) are used to classify multiple fault types of an induction motor, where each SVM individually trains with its own features to increase the fault classi cation accuracy of the induction motor. In experiments, the proposed approach achieved an average classi cation accuracy of 99.80%, outperforming conventional fault diagnosis models.
International Conference On December-2016
Md Rashedul Islam, M M Manjurul Islam and Jong�myon Kim

Feature Selection Techniques for Increasing Reliability of Fault Diagnosis of Bearings

9th International Conference on Electrical and Computer Engineering (ICECE), 2016 ICECE2016, Dhaka

Abstract

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.
Book/Book Chapters On November-2016
Md Rashedul Islam

Power of Parallelism in .NET, ISBN-13: 978-3-659-94849-7, ISBN-10: 3659948497

LAP LAMBERT Academic Publishing

image

Abstract

Parallel programming is being seen as the only cost-effective method to improve performance of computer applications. A well-structured parallel application can achieve better performance in terms of execution speed over the sequential execution on existing and upcoming parallel computer architecture. This book named ? Power of Parallelism in .NET, describes the experimental evaluation of different parallel application performance with thread-safe data structure and parallel constructions in .NET framework. It describes different performance issues of parallel application development. Before describing the experimental evaluation, this book describes some methodologies relevant to parallel programming, such as Parallel Computer Architecture, Memory Architectures, Parallel Programming Models, Decomposition, Threading etc. It describes the different APIs in .NET framework and the way of coding for making an efficient parallel application depends on problem definition. Moreover, this book describes Improper Partitioning, Over-subscription, Improper Workloads and more. The parallel application models and the evolving example of parallel programming is well-illustrated in this book.
International Journal On October-2016
Md. Sharif Uddin, Rashedul Islam, Sheraz Ali Khan, Jaeyoung Kim, Jong-Myon Kim, Seok-Man Sohn and ByeongKeun Choi

Distance and Density Similarity Based Enhanced k-NN Classifier for Improving Fault Diagnosis Performance of Bearings

Shock and Vibration, vol. 2016, Article ID 3843192, 11 pages

SCIE | Impact Factor: 1.857

Abstract

An enhanced -nearest neighbor (-NN) classification algorithm is presented, which uses a density based similarity measure in addition to a distance based similarity measure to improve the diagnostic performance in bearing fault diagnosis. Due to its use of distance based similarity measure alone, the classification accuracy of traditional -NN deteriorates in case of overlapping samples and outliers and is highly susceptible to the neighborhood size, . This study addresses these limitations by proposing the use of both distance and density based measures of similarity between training and test samples. The proposed -NN classifier is used to enhance the diagnostic performance of a bearing fault diagnosis scheme, which classifies different fault conditions based upon hybrid feature vectors extracted from acoustic emission (AE) signals. Experimental results demonstrate that the proposed scheme, which uses the enhanced -NN classifier, yields better diagnostic performance and is more robust to variations in the neighborhood size, .
Book/Book Chapters On August-2016
Md. Rashedul Islam, Md. Sharif Uddin, Sheraz Khan, Jong-Myon Kim, and Cheol-Hong Kim

Multi-core Accelerated Discriminant Feature Selection for Real-time Bearing Fault Diagnosis

Trends in Applied Knowledge-Based Systems and Data Science, LNCS, vol. 9799, pp 645-656, Morioka, Japan.

Springer - Lecture Notes

image

Abstract

This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.
International Journal On May-2016
Myeongsu Kang, Rashedul Islam, JaeYoung Kim, Jong-Myon Kim, Michael Pecht

A Hybrid Feature Selection Scheme for Reducing Diagnostic Performance Deterioration Caused by Outliers in Data-Driven Diagnostics

IEEE Transaction on Industrial Electronics (TIE), VOL. 63, NO. 5

SCI | Impact Factor: 7.05

Abstract

In practice, outliers, defined as data points that are distant from the other agglomerated data points in the same class, can seriously degrade diagnostic performance. To reduce diagnostic performance deterioration caused by outliers in data-driven diagnostics, an outlier-insensitive hybrid feature selection (OIHFS) methodology is developed to assess feature subset quality. In addition, a new feature evaluation metric is created as the ratio of the intraclass compactness to the interclass separability estimated by understanding the relationship between data points and outliers. The efficacy of the developed methodology is verified with a fault diagnosis application by identifying defect-free and defective rolling element bearings under various conditions.
International Journal On January-2016
Md. Rashedul Islam, Md. Sharif Uddin, Sheraz Khan, Jong-Myon Kim, and CheolHong Kim

Multi-core Accelerated Discriminant Feature Selection for Real-time Bearing Fault Diagnosis

Trends in Applied Knowledge-Based Systems and Data Science,vol. 9799, pp 645-656.

LNCS

Abstract

This paper presents a real-time and reliable bearing fault diagnosis scheme for induction motors with optimal fault feature distribution analysis based discriminant feature selection. The sequential forward selection (SFS) with the proposed feature evaluation function is used to select the discriminative feature vector. Then, the k-nearest neighbor (k-NN) is employed to diagnose unknown fault signals and validate the effectiveness of the proposed feature selection and fault diagnosis model. However, the process of feature vector evaluation for feature selection is computationally expensive. This paper presents a parallel implementation of feature selection with a feature evaluation algorithm on a multi-core architecture to accelerate the algorithm. The optimal organization of processing elements (PE) and the proper distribution of feature data into memory of each PE improve diagnosis performance and reduce computational time to meet real-time fault diagnosis.
International Journal On January-2016
Jia Uddin, Md. Rashedul Islam, Jong-Myon Kim

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

International Journal of Control and Automation, Vol. 9, No. 1

SCOPUS

Abstract

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.
International Journal On January-2016
Rashedul Islam, Sheraz Ali Khan, and Jongmyon Kim

Discriminant feature distribution analysis-based hybrid feature selection for online bearing fault diagnosis in induction motors

Journal of Sensors

SCIE | Impact Factor: 2.057

Abstract

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 a -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 diagnoses faults in an unknown AE fault signal by extracting only the selected features and using the -NN classification algorithm to classify the fault condition manifested in the unknown signal. The classification performance of the proposed approach is compared with those of existing state-of-the-art average distance-based approaches. Our experimental results indicate that the proposed approach outperforms the existing methods with regard to classification accuracy.
International Conference On December-2015
Md. Sharif Uddin, Rashedul Islam, Jong-Myon Kim, Cheol-Hong Kim

Many-core Accelerated Local Outlier Factor (LOF) based Classifier in Bearing Fault Diagnosis

18th International Conference on Computer and Information Technology (ICCIT)

Abstract

This paper proposes a feature extraction, selection, and classification based bearing fault diagnosis methodologies using acoustic emission (AE) signal. First, a set of statistical, time-domain, and frequency domain features are extraction from AE signal. Of these features, some features having more informative data to distinguish faults are selected. Finally, a classifier based on local outlier factor (LOF) is used to detect faults of bearing. LOF consists of calculating distances of each input will all the training features, thus having a lot of computations. To reduce execution time, this paper implemented the LOF on a data parallel many-core architecture. Experimental results showed that, many-core implementation of LOF algorithm is more than 923× faster than sequential implementation.
International Journal On March-2015
Md. Rashedul Islam, Jong-Myon Kim

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

Lecture Notes in Electrical Engineering Volume 331

SCOPUS

Abstract

This paper proposes a centroid global positioning system (GPS) model to improve the positioning accuracy of low-cost GPS receivers of a sensitive location-based system. The proposed model estimates the precise movement position by a centroid sum of the individual improved positions of three GPS receivers. Each GPS receiver’s position is improved by using a direction and velocity averaging technique based on combining the vehicle movement direction, velocity averaging, and distance between the waypoints of each GPS receiver using coordinate data (latitude, longitude, time, and velocity). Finally, the precise position is estimated by calculating a triangular centroid sum with distance threshold of the improved positions of three GPS receivers. In order to evaluate the performance of the proposed approach, we used three GARMIN GPS 19x HVS receivers attached to a car and plotted the processed data in Google map. The proposed approach resulted in an improved accuracy of about 2–12 m compared to the original GPS receivers. In addition, we compared the proposed approach to two other state-of-the-art methods. The experimental results show that the proposed approach outperforms the conventional methods in terms of positioning accuracy.
Book/Book Chapters On January-2015
Jong-Myon Kim, Sheraz A. Khan, Md. Rashedul Islam

Maximum class separability-based discriminant feature selection using a GA for reliable fault diagnosis of induction motors

Lecture Notes in Artificial Intelligence LNAI

SCOPUS

Abstract

Reliable fault diagnosis in bearing elements of induction motors, with high classification performance, is of paramount importance for ensuring steady manufacturing. The performance of any fault diagnosis system largely depends on the selection of a feature vector that represents the most distinctive fault attributes. This paper proposes a maximum class separability (MCS) feature distribution analysis-based feature selection method using a genetic algorithm (GA). The MCS distribution analysis model analyzes and selects an optimal feature vector, which consists of the most distinguishing features from a high dimensional feature space, for reliable multi-fault diagnosis in bearings. The high dimensional feature space is an ensemble of hybrid statistical features calculated from time domain analysis, frequency domain analysis, and envelope spectrum analysis of the acoustic emission (AE) signal. The proposed maximum class separability-based objective function using the GA is used to select the optimal feature set. Finally, k-nearest neighbor (k-NN) algorithm is used to validate our proposed approach in terms of the classification performance. The experimental results validate the superior performance of our proposed model for different datasets under different motor rotational speeds as compared to conventional models that utilize (1) the original feature vector and (2) a state-of-the-art average distance-based feature selection method.
International Conference On January-2015
Md. Rashedul Islam, Jia Uddin, Jong-Myon Kim

Fault Diagnosis of an Induction Motor Using a Gabor Filter and Singular Value Decomposition Based Feature Extraction Method

2015 International Conference on Platform Technology and Service (PlatCon-15)

Abstract

This paper presents an efficient fault diagnosis method of an induction motor using two dimensional (2D) feature extraction including a Gabor filter and singular valued decomposition (SVD). Vibration signals are first converted to gray level images, then a 2D Gabor filter and SVD are applied to extract distinctive texture features. Finally, a multiclass support vector machine (MCSVM) is used as a classifier, where each SVM is individually trained with its own feature vector. Experimental results show that the proposed fault diagnosis model outperforms conventional state-of-the-art fault diagnosis algorithms in terms of classification accuracy, yielding an average classification accuracy of 99.86%.
International Conference On December-2014
Jia Uddin, Md Rashedul Islam, Jong-Myon Kim

Reliable Fault Diagnosis of Induction Motors using 2D Signal Processing Techniques

8th International Conference on Electrical and Computer Engineering (ICECE 2014), Dhaka

Abstract

International Journal On October-2014
Myeongsu Kang, Shohidul Islam, Rashedul Islam, and Jong-Myon Kim

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

Multimedia Tools and Applications, Springer

SCIE | Impact Factor: 1054

Abstract

Sound synthesis is recently indispensable with sophisticated audio effects for mimicking rich and natural sounds of the musical instruments, and thus sound synthesis acceleration has been an urgent issue. The formant synthesis is employed to produce the various single notes of the haegeum, a representative traditional Korean bowed string instrument. In this study, the formant synthesis process using multiple pairs of digital resonators and band-pass filters is accelerated with the power of a general-purpose graphics processing unit (GPGPU). This paper compares the performance of the proposed GPGPU-based parallel approach with the CPU-based sequential approach in order to validate the effectiveness of the proposed massively parallel method. Experimental results indicate that the proposed parallel approach achieves at least 79 times speedup over the CPU-based approach by exploiting the massive parallelism inherent in the formant sound synthesis algorithm.
International Conference On July-2014
Md Rashedul Islam, Jong-Myon Kim

A New Cooperative-GPS Approach to Improve GPS Positioning Accuracy for Location Based System

The 2nd FTRA International Conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN-14)

Abstract

International Conference On July-2014
Rashedul Islam, Jia Uddin, Jong-Myon Kim

Reliable Fault Diagnosis of Induction Motors using an Acoustic Emission Sensor and Signal Processing Techniques

The 2nd FTRA International Conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN-14)

Abstract

This paper proposes a reliable fault diagnosis approach of induction motors using an acoustic emission (AE) sensor and signal processing techniques. In the proposed approach, discrete wavelet transform (DWT) is utilized to decompose the AE signal and select the appropriate nodes using the maximum energy ratio (MER). Then, a one-dimensional (1D) Gabor filter with various frequencies and orientation angles is applied to reduce the abnormalities and extract a number of statistical parameters. In addition, the principle component analysis (PCA) employing the extracted features is used to select the most significant feature dimensions. Finally, one against one multi-class support vector machines (OAA-MCSVMs) using the significant feature dimensions is utilized to classify each fault, where SVMs are individually trained with their own feature vectors in order to maximize the fault classification accuracy of induction motors. In the experiment, we utilize four AE fault signals which are directly collected from a R3A AE sensor attached with an induction motor. The experimental results indicate that the proposed approach achieves average classification accuracy of 99.80% and outperforms conventional fault diagnosis models in the classification accuracy.
International Journal On June-2014
Jia Uddin, Rashedul Islam, and Jong-Myon Kim

Texture Feature Extraction Techniques for Fault Diagnosis of Induction Motors

Journal of Convergence

Abstract

This paper presents three texture feature extraction techniques: gray level co-occurrence matrix (GLCM), Gabor filter, and global neighborhood structure (GNS) map, for the fault diagnosis of induction motors. The texture of two- dimensional (2D) gray level images is converted from acoustic emission (AE) fault signals and used for feature extraction of the fault signals. The extracted texture features are used as inputs to a multi-class support vector machine (MCSVM) to classify each fault. The Gaussian radial basis function kernel is used with MCSVM to handle non-linear fault features of acoustic emission (AE) signals. Experimental results with one-second AE signals sampled at 1 MHz showed that the GLCM-based feature extraction method outperformed the Gabor filter and the GNS map in terms of classification accuracy because of its ability to capture the spatial dependence of gray-level texture values.
International Journal On June-2014
Md. Rashedul Islam, Jong-Myon Kim

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

The Science World Journal

SCIE | Impact Factor: 1.73

Abstract

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 interpolation. The experimental results show that the proposed approach outperforms other state-of-the-art methods in terms of positioning accuracy.
Domestic Conference On June-2014
Md. Rashedul Islam, Jong-Myon Kim

Texture Feature Analysis based Fault Diagnosis of Induction Motors

KISPS Summer Conference 2014, pp 153-157, 20-21 June 2014, Degu, South Korea.

Abstract

This paper presents three texture feature extraction techniques: gray level co-occurrence matrix (GLCM), Gabor filter, and global neighborhood structure (GNS) map, for the fault diagnosis of induction motors. The texture of two- dimensional (2D) gray level images is converted from acoustic emission (AE) fault signals and used for feature extraction of the fault signals. The extracted texture features are used as inputs to a multi-class support vector machine (MCSVM) to classify each fault. The Gaussian radial basis function kernel is used with MCSVM to handle non-linear fault features of acoustic emission (AE) signals. Experimental results with one-second AE signals sampled at 1 MHz showed that the GLCM-based feature extraction method outperformed the Gabor filter and the GNS map in terms of classification accuracy because of its ability to capture the spatial dependence of gray-level texture values.
International Conference On May-2014
Rashedul Islam, Jong-Myon Kim

Reliable RGB color image watermarking using DWT and SVD

International Conference on Informatics, Electronics & Vision (ICIEV)

Abstract

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.
International Conference On April-2014
Rashedul Islam, Jong-Myon Kim

Direction Averaging Method for Improving GPS Positioning Accuracy in Real-Time Navigation

The 6th FTRA International Symposium on Advances in Computing, Communications, Security, and Applications (ACSA-14)

Abstract

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 interpolation. The experimental results show that the proposed approach outperforms other state-of-the-art methods in terms of positioning accuracy.
International Conference On April-2014
Md Shohidul Islam, Md Rashedul Islam, Jong-Myon Kim

A accelerating Haegeum’s Sound Synthesis System using Graphics Processing Unit

The 6th FTRA International Symposium on Advances in Computing, Communications, Security, and Applications (ACSA-14)

Abstract

Domestic Conference On January-2014
Md Shohidul Islam, Md Rashedul Islam, Fahmid Al Farid, Jong-Myon Kim

Spectral Modeling Synthesis of Haegeum Using GPU

Winter Conference of the Korea Society of Computer and Information No. 22, Issue 1, 2014

Abstract

This paper presents a parallel approach of formant synthesis method for haegeum on graphics processing units (GPU) using spectral modeling. Spectral modeling synthesis (SMS) is a technique that models time-varying spectra as a combination of sinusoids and a time-varying filtered noise component. A second-order digital resonator by the impulse-invariant transform (IIT) is applied to generate deterministic components and the results are band-pass filtered to adjust magnitude. The noise is calculated by first generating the sinusoids with formant synthesis, subtracting them from the original sound, and then removing some harmonics remained. The synthesized sounds are consequently by adding sinusoids, which are shown to be similar to the original Haegeum sounds. Furthermore, GPU accelerates the synthesis process enabling-real time music synthesis system development, supporting more sound effect, and multiple musical sound compositions.
Domestic Conference On December-2013
Rashedul Islam, Jong-Myon Kim

Direction Averaging Method for Improving GPS Positioning Accuracy in Real Time Navigation

2013 Korea Engineering Arts Society Conference Vol. 11 no. 1, 2013

Abstract

International Journal On October-2012
Md. Rashedul Islam, Aloke Kumar Shah, Md. Rofiqul Islam

Experimental Evaluation of parallelism in real time execution

Research Notes in Information Science

Abstract

International Journal On December-2011
Md. Rashedul Islam, Md. Rofiqul Islam, Tohidul Arafhin Mazumder

Mobile Application and Its Global Impact

International Journal of Engineering & Technology

SCOPUS

Abstract

This paper presents the uses and effect of mobile application in individuals, business and social area. In modern information and communication age mobile application is one of the most concerned and rapidly developing areas. This paper demonstrates that how individual mobile user facilitate using mobile application and the popularity of the mobile application. Here we are presenting the consequence of mobile application in business sector. Different statistical data of the past and present situation of mobile application from different parts of the world has been presented here to express the impact. This paper also presents some effect of mobile application on society from the ethical perspective.
International Journal On April-2011
Md. Shafiul Azam, Md. Rashedul Islam, Md. Omar Faruqe

Determination of the Traveling Speed of a Moving Object of a Video Using Background Extraction and Region Based Segmentation

International Journal of Computer Science and Information Security(IJCSIS)

Abstract

This paper is concerned with the determination of thetraveling speed of a moving object of a video clip based onsubsequent object detection techniques. After preprocessing of the original image sequence, which is sampled from the videocamera, the target moving object is detected with the improvedalgorithm in which the moving object region can be extractedcompletely through several processing of background extractionand region based segmentation such as region-connection, region-merging, and region-clustering methods. Among the multiplemoving objects of the video, the target object has been detectedbased on particular criteria of region that it occupies. Then theresults of these processing can be used to determine the travelingspeed of the target moving object from changes of its coordinateposition from the video frames. Among the different video fileformat, Audio Video Interleaved (AVI) format has been used toexamine our experiments
International Journal On March-2011
Md. Rashedul Islam, Md. Rofiqul Islam, Md. Shariful Alam, Md. Shafiul Azam

Experiences and comparison study of EPC & UML for Business Process & IS Modeling

International Journal of Computer Science and Information Security (IJCSIS)

Abstract

Business process modeling is an approach by which wecan analyze and integrate the business process. Using theBusiness Process Modeling we can represent the current andfuture process of a business/organization/enterprise. The businessprocess modeling is a prerequisite and essential implementing abusiness or making any automation system. In this paper, wepresent our experience in a Business Process Modeling fororganization. This paper presents detailed description aboutbusiness process modeling, details description about the main twomodeling language EPC and UML. This paper presented theuses, advantages, disadvantages of EPC and UML modelinglanguage. Here we tried to express the experience about thosemodeling language. This paper presents a details comparisonbetween two modeling language from the business processmodeling and information system implementation point of view.
International Journal On January-2011
Md. Shariful Alam , Md. Rashedul Islam , Md. Rofiqul Islam

Organizational improvement using Organizational paradigms with the support of people paradigms

International Journal on Computer Science and Engineering (IJCSE)

Abstract

An organization is a vital part of social environment. Different parts of organization have great impact to the environment. On the other hand the different organizational strategy helps to improve the efficiency of organization and customer satisfaction. The people and tools of organization help to organization to work properly. This paper mainly describes about the organizational paradigms and people paradigms also the way how the people paradigms facilitate the organizational paradigms to improve the organizational architecture for better performance. This paper describes the different aspects of organizational like Information system strategy, Information system planning, Business process reengineering etc also End user computing, Knowledge management, Expert system of people paradigms. And finally there is a combination between those.
International Conference On January-2009
Saleh Ahmed, Md. Rashedul lslam, Md. Shafiul Azam

Bangla Hand Written Digit Recognition Using Supervised Locally Linear Embedding Algorithm and Support Vector Machine

12th International Conference on Computers and Information Technology, 2009. ICCIT 09.

Abstract

This paper presents Bangla numeral character recognition system using supervised locally linear embedding algorithm and support vector machine (SVM). The locally linear embedding (LLE) algorithm is an unsupervised technique proposed for nonlinear dimensionality reduction. In this paper, we describe its supervised variant (SLLE). Where class membership information is used to map overlapping high dimensional data into disjoint clusters in the embedded space. we combined it with support vector machine (SVM) for classifying handwritten digits from the on-line handwritten Bangla numeral database.
International Conference On January-2009
Saleh Ahmed, Shamim Ahmad, Md. Omar Faruqe, Md. Rashedul Islam

EMG Signal Decomposition Using Wavelet Transformation with Respect to Different Wavelet and a Comparative Study

2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, Korea, ACM New York, NY, USA ©2009

Abstract

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development and modern Human Computer Interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. For properly analyze EMG signal there need good quality of decomposition so that it can reveal the total characteristics of EMG signals. Because EMG signal is Non-Stationary signal so it needs such a method that can decompose non-stationary signal thus wavelet decomposition is a good choice for this type. There are different types of wavelet available. Henceforth, it is necessary that proper attempt should be taken to choice the best one. Here analyses of EMG Signals were made by Various Wavelet Decomposition method with different types of wavelet and it illustrates the comparative study on best possible energy localization in the time-scale plane in order to show the performance. Thus we can choice the right one. The EMG signals used for this analysis - were found both from locally collected as well as from www.emglab.net[5] which provides EMG signal related raw data and other facilities.
International Conference On January-2009
Md. Rashedul lslam, Md. Shafiul Azam, Saleh Ahmed

Speaker Identification System Using PCA & Eigenface

12th International Conference on Computers and Information Technology, 2009. ICCIT 09

Abstract

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 PCA has been used to extract feature of speech of individual speaker. Eigenface algorithm has been used here as a classification and recognition tool. Eigenspace of individual speaker is generated by the feature of the speech signal. The experimental results show the noticeable performance of the proposed system.
International Conference On January-2006
Md. Rashedul Islam, A.N.K. Zaman, Shamim Ahmad, Md. Khademul Islam Molla

Speaker Identification System Using Eigenface Classification Engine

9th International Conference of Computer and Information Technology (ICCIT 2006)

Abstract

This paper presents a speech-based speaker identification system 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 of individual speaker. Eigenface algorithm is used here as the classification and identification tool. Eigenspace of individual speaker is generated by the spectrum of the speech signal. The experimental results show the noticeable performance of the proposed system.
International Conference On December-2006
Sangeeta Biswas, Md. Rashedul Islam, Shamim Ahmed, Md. Khademul Islam Molla

Text-Dependent Speaker Identification System Using MFCC and HMM

9th International Conference of Computer and Information Technology (ICCIT 2006)

Abstract

Teaching and research experience

  • Present 2018

    Visiting Researcher (Affiliated with Pattern Processing Lab)

    School of Computer Science & Engineering, University of Aizu, Fukushima, Japan
    Duration: 01 April 2018 to now

  • Present 2016

    Associate Professor (on leave)

    Dept. of Computer Science & Engineering, University of Asia Pacific, Dhaka, Bangladesh
    Duration: 16-10-2016 to Now (on leave)

  • 2016 2011

    Assistant Professor

    Dept. of Computer Science & Engineering, University of Asia Pacific, Dhaka, Bangladesh
    Duration: 09-10-2011 to 15-10-2016

  • 2016 2013

    Graduate Research Assistant

    Embedded System Lab, University of Ulsan, South Korea. (http://eucs.ulsan.ac.kr)
    Duration: 01-09-2013 to 22-08-2016

  • 2011 2009

    Senior Lecturer

    Dept. of Computer Science & Engineering, Leading University, Sylhet, Bangladesh
    Duration: 17-09-2009 to 01-10-2011

  • 2009 2007

    Lecturer

    Dept. of Computer Science & Engineering, Leading University, Sylhet, Bangladesh
    Duration: 17-09-2007 to 16-09-2009

Professional experience

  • Present 2016

    Software Engineering Consultant

    Orbit Itech Ltd., Birminghum, UK
    Duration: 01-Feb-2016 to till date

  • 2012 2011

    IT consultant

    Arrowsoft, Sylhet, Bangladesh
    Duration: Jan 2011 to June 2012

  • 2007 2007

    Chief Technical Officer

    Firm: Orbit Solutions Ltd (Complete IT Solution Firm)
    Niloy-60(2nd Floor), Dorga gate, Sylhet.
    Duration: 01-03-2007 To 15-09-2007

  • 2007 2006

    Senior Developer

    Firm: Orbit Solutions Ltd (Complete IT Solution Firm),
    Niloy-60(2nd Floor), Dorga gate, Sylhet.
    Duration: 01-09-2006 To 28-02-2007

Other Experiences

  • 2018 2017

    Head

    Self-Assessment Committee (SAC) of Dept. of CSE Under IQAC

  • 2017

    Secretary

    Organizing committee,19th International Conference on Computer and Information Technology 2017 (ICCIT2017).

  • 2017

    Chair

    Organizing committee, ACM-ICPC Dhaka Regional Site 2017

  • 2017 2016

    Convener

    Software and Hardware Club, Dept. of CSE, University of Asia Pacific

  • 2017 2016

    Coordinator

    Admission Committee, Dept. of CSE, University of Asia Pacific

  • 2018 2017

    Treasurer

    Bangladesh Advanced Computing Society.

  • 2013 2011

    Coordinator

    Master of Science in Computer Science and Engineering (MCSE), University of Asia, Pacific

  • 2013 2011

    Convener

    Research and Publication Unit, Dept of CSE, University of Asia Pacific

  • 2009

    Head (Acting)

    Dept. of CSE, Leading University

  • 2009 2007

    Adviser

    Computer Club, Dept. of CSE, Leading University, Sylhet

Honors, Awards and Grants

  • Nov-2018
    Best Paper Award, 3rd International Conference on Applications in Information Technology (ICAIT"18), 2018
  • 2014
    Best Paper award, Winter Conference of the Korea Society of Computer and Information, South Korea
  • 2014
    Best Paper award, KISPS Summer Conference 2014, South Korea
  • 2014-2016
    BK21 Scholarship, University Ulsan, South Korea, 2014-2016
  • 2013-2016
    Full Scholarship including tuition fees, living expenses for pursuing Ph.D. in Computing Engineering, University Ulsan, South Korea, 2013-2016
  • 2010-2011
    Swedish Govt. awards (full tuition fees) for MSc. in Informatics at Bors (Hgskolan i Bors), Sweden, 2010-2011
  • 2006
    3rd Position, Software and Project Exhibition 2006, Dept. of CSE, University of Rajshai, Bangladesh.
  • 2000-2014
    Merit Scholarship from University of Rajshai, 2000-2004
  • 1996-1997
    Merit Scholarship of Bangladesh Junior Level Scholarship Exam, 1996-1997

Academic Projects

  • 2006
    Vehicle Tracking System Using GPS
    Exhibited in BASIS Softexpo 2008, Dhaka, Bangladesh (Funded By IT company, Japan)
    Language: VB 6.
  • 2005
    Speaker (Human voice) Identification System (AI Based)
    Exhibited in IISP at BASIS Softexpo 2005, Dhaka, Bangladesh
    Language: MATLAB .
  • 2004
    Simulation of 8085 Assembly Language Programming Kit.
    3rd place, Software and Project Exhibition 2006, CSE, RU, Rajshahi.
    And Exhibit in AABISHKARER KHOJE at BASIS Softexpo2007, Dhaka
    Language: VB .
  • 2004
    Web Based ticket booking and reservation System
    Language: PHP,
    Database: MySQL.
  • 2003
    PC Based Home electronic device controller
    Language: Visual C++.

Commercial Web application project management and development (Selected)

  • 2018
    SiliconBasket.com: Online Superstore in Bangladesh (System Architect and team leader)
  • 2017
    Foreverbeaute.com: Perfume E-commerce website in USA. (Team leader)
  • 2016
    Feedbelly.com: Country wide online ordering system for restaurant in UK (System Architect and team leader)
  • 2010
    Cuminclub.se: Online Ordering System for Restaurant, Sweden.
  • 2010
    Restaurangmasahiro.se: Online Ordering System for Restaurant, Sweden.
  • 2007-2012
    Rajputh.co.uk, Currymerchants.co.uk, Purbobaghrestaurant.com, spicehousestroud.co.uk, and more: Online Ordering System for Restaurant, UK.
More Dynamic and Ecommerce Web site for different organization nationally and internationally

Commercial Software project management and development (Selected)

  • 2018
    Restaurant Point of Sell with multiple counter and APPs integration
    Tools: C#.Net, MySQL, Android Apps development.
    Client: Orbit ITech Ltd. UK.
  • 2012
    Perfume wholesale Business Automation with Accounting
    Language: Visual C++.
    Client: B & R Perfume, USA.
  • 2012
    OMR based teachers evaluation system
    Language: C#.Net and MySQL.
    Client: University of Asia Pacific, Bangladesh.
  • 2011
    Supermarket EPOS system
    Language: VB6 and SQL Server.
    Client: Costless Ltd. In Ireland.
  • 2009
    University Automation (Admission, Exam, and Accounts Management Software of Leading University, Client Server Based)
    Language: VB6 and SQL Server.
    Client: Leading University, Bangladesh.
  • 2008
    Examination Script Management System
    Language: VB6 and SQL Server.
    Client: Board of Intermediate and Secondary education, Sylhet (To Manage SSC and HSC exam, Script collection form center All teacher database, automatic Examiner selection, Script Distribution, Script Collection, Examiner Payment etc.)
  • 2007
    Smart Shop
    Language: VB6 and SQL Server.
    Client:
    1) Maha, Nahar Tower, Nayasarak, Sylhet, Bangladesh
    2) Take N Pay, Mirbox Tula, Sylhet, Bangladesh
    3) Top in Town, Mirbox Tula, Sylhet, Bangladesh
    4) Mumme, Nahar Tower, Nayasarak, Sylhet, Bangladesh
    5) Mohona Shopping center, Biany Bazar, Sylhet.
    6) Samana Sopping center, Blue water, Zindabazar, Sylhet
    7) Signature, Kumor para, Sylhet
    8) Golden Man, Zindabazar, Sylhet.
  • 2007
    School Management Software
    Language: VB6 and SQL Server.
    Client:
    1) Sunny Hill International School & College, Sylhet,Bangladesh.
    2) Shahjalal Jamia School & College, Mira Bazar, Sylhet, Bangladesh
  • 2005
    Cold Store Management Software
    Language: VB6 and SQL Server.
    Client:
    1) Biswash Cold Store, Nator, Bangladesh
    2) Uttara Cold Store, Bogra, Bangladesh

At My Office

You can find me at my office located at University of Aizu, Aizu-wakamatsu, Fukushima, Japan

I am at my office weekday from 9:00 am until 5:30 pm, but you may consider a call to fix a time to meet.

Any Query?

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