M. I. H. Bhuiyan

Bangladesh University of Engineering and Technology | BUET · Department of Electrical and Electronic Engineering

The presence of albumin in human urine is one of the confirmed early symptoms of kidney dysfunction. A precise urine protein identification process is very important to monitor the kidney’s proper functioning. To identify the presence of albumin in urine, a refractometric protein sensing approach in the photonic crystal fiber (PCF) environment has...

GI tract MRI image segmentation is important for the diagnosis and treatment of many diseases. In case of GI tract cancer or tumor treatment, radio oncologists must apply X-ray beams pointing towards the tumor cell while avoiding the other organs. The traditional segmentation process for the MRI scan is time consuming and labor intensive. A compute...

The COVID-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially chest x-ray and lung Computed Tomography (CT) scans, plays a vital role in the severity analysis of hospitalized COVID-19 patients. However, with an increasing number of patients and a lack of skilled rad...

Deep learning-based automatic classification of breast tumors using parametric imaging techniques from ultrasound (US) B-mode images is still an exciting research area. The Rician inverse Gaussian (RiIG) distribution is currently emerging as an appropriate example of statistical modeling. This study presents a new approach of correlated-weighted co...

Sleep disorders are a common problem that disrupts our regular sleeping patterns. To diagnose sleep disorders, Long-term monitoring of sleep could be useful. In this paper an automated scheme of sleep staging is presented based on Bioradiolocation signals using time and frequency domain feature extraction and Random Forest Classifier. This experime...

Accurate detection and segmentation of lung tumors from volumetric CT scans is a critical area of research for the development of computer aided diagnosis systems for lung cancer. Several existing methods of 2D biomedical image segmentation based on convolutional autoencoders show decent performance for the task. However, it is imperative to make u...

In this article, a hexagonal-shaped porous core and sectored-cladding structured photonic crystal fiber (PCF) is presented for efficient wave propagation in Terahertz (THz) domain. The full vector analysis based finite element method with Comsol software (v. 5.3a) is employed to design and optimize the PCF geometry and evaluate its optical properti...

Introduction: The Covid-19 pandemic has been adversely affecting the patient management systems in hospitals around the world. Radiological imaging, especially the chest x-ray and lung Computed Tomography (CT)-scans, play a vital role in the severity analysis of hospitalized Covid-19 patients. However, with an increasing number of patients and a la...

An efficient blood component detection process is essential for the diagnosis of hematologic diseases. This paper presents refractive index-based terahertz (THz) sensing with photonic crystal fiber (PCF) for the identification of the various blood components. An octagonal hollow-core surrounded by symmetrical air holes structured cladding PCF is de...

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting...

In this paper, an Empirical Mode Decomposition-based method is proposed for the detection of transformer faults from Dissolve gas analysis (DGA) data. Ratio-based DGA parameters are ranked using their skewness. Optimal sets of intrinsic mode function coefficients are obtained from the ranked DGA parameters. A Hierarchical classification scheme empl...

An intrinsic time-scale decomposition (ITD) based method for power transformer fault diagnosis is proposed. Dissolved gas analysis (DGA) parameters are ranked according to their skewness, and then ITD based features extraction is performed. An optimal set of PRC features are determined by an XGBoost classifier. For classification purpose, an XGBoos...

Determination of breast tumors from B-Mode Ultrasound (US) image is a perplexing one. Researches employing statistical modeling such as Nakagami, Normal Inverse Gaussian (NIG) distributed parametric images in this classification task have already explored but experimentation of those statistical models on contourlet transformed coefficient image in...

Automated detection of breast tumor in early stages using B-Mode Ultrasound image is crucial for preventing widespread breast cancer specially among women. This paper is primarily focusing on the classification of breast tumors through statistical modelling such as Rician inverse Gaussian (RiIG) pdf of contourlet transformed B-Mode image of breast...

Detection of blood is very crucial as well as sensitive due to its importance in human body. In this manuscript, a hollow core Topas-based photonic crystal fiber (PCF) biosensor is proposed for sensing in terahertz frequency range. In the hexagonal cladding structure of this proposed biosensor, identical square-shaped air cavities in both the core...

Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightweight convolutional neural network (CNN) architect...

In this paper, a hollow core photonic crystal fiber (PCF)-based THz chemicals sensor has been presented. Hexagonal shaped hollow core and symmetrical hexagonal air lattices have been used in the cladding section to construct the PCF geometry. The developed PCF-based chemical sensor yields high performance in ethanol, methanol, water and benzene det...

Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightweight convolutional neural network (CNN) architect...

Low density lipoprotein (LDL) cholesterol is the leading cause of heart diseases, peripheral artery diseases, and stroke. An accurate, flexible, and efficient detection process is very urgent for identifying the cholesterol. In this context, an octagonal shaped hollow core with eight head star cladding structured photonic crystal fiber (PCF) has be...

In recent years, photonic crystal fiber (PCF) in the THz regime has gained popularity very swiftly for wave guidance and sensing applications. The optical properties of PCF can be controlled by the fine tuning of the geometrical parameters. In this context, PCF geometry has been developed for THz wave propagation as well as for environmental pollut...

A circular sectored core cladding structured photonic crystal fiber (PCF) is presented in this article, where core and cladding are sectored by few number of rectangles. The air fragments of core and cladding are constructed with circular manner that can facilitate the fabrication process. To design the porous core PCF and characterize the properti...

Ferroelectric RAM (FeRAM) is a Non-Volatile Memory (NVM) which offers high endurance, fast speed, low power and high density. Thus, they are suitable to replace conventional memories such as Dynamic RAM (DRAM). However, FeRAM are vulnerable to side channel attack. An adversary can measure the current drawn by the memory during read and write operat...

Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes raw single channel electroencephalogram (Fpz-Cz) signal as input and yields hypnogram annotations for each 30...

Classification of focal and non-focal Electroencephalogram (EEG) signals is an important problem especially for the identification of epileptogenic sites in the brain. However, the number of research works reported in the literature is limited and most of them suffer from validation on a limited scale and moderate accuracy. In this paper, focal and...

Sleep stage classification is an important task for the timely diagnosis of sleep disorders and sleep-related studies. In this paper, automatic scoring of sleep stages using Electrooculogram (EOG) is presented. Single channel EOG signals are analyzed in Discrete Wavelet Transform (DWT) domain employing various statistical features such as Spectral...

Segmentation or lesion boundary detection in classification of Benign and Malignant breast tumors from B-Mode ultrasound image analysis is a challenging one. In this study, first a suitable frame is chosen by strain and velocity imaging from a raw radio frequency (RF) echo of clinical cases. The consequent B-Mode ultrasound (US) image is calculated...

An aftermath of the catastrophic incidence of fire in Tazreen Fashions in 2012 and building collapse at Rana Plaza in 2013 is the introduction of a number of international and national initiatives in order to ameliorate the fire, electrical, and structural safety scenarios in the Ready-Made Garment (RMG) industry of Bangladesh. These initiatives in...

In this paper, a comprehensive analysis for the discrimination of the focal and non-focal electroencephalography (EEG) signals is carried out in the ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) domains. A number of spectral entropy-based features such as the Shannon en...

Sleep stage screening based on visual inspection is burdensome, time-consuming, subjective, and error-prone owing to the large bulk of data which have to be screened. Consequently, automatic sleep scoring is essential for both sleep research and sleep disorder diagnosis. In this work, we present the application of newly proposed tunable-Q factor wa...

Recently electrocorticography (ECoG) has emerged as a potential tool for Brain Computer Interfacing applications. In this paper, a continuous wavelet transform (CWT) based method is proposed for classifying ECoG motor imagery signals corresponding to left pinky and tongue movement. The total experiment is carried out with the publicly available ben...

In this paper, a comprehensive analysis of focal and non-focal electroencephalography is carried out in the empirical mode decomposition and discrete wavelet transform domains. A number of spectral entropy-based features such as the Shannon entropy, log-energy entropy and Renyi entropy are calculated in the empirical mode decomposition and discrete...

Background: Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme. New method: In this work, we decompose sleep-EEG signal segments using...

Effective online processing of electroencephalogram (EEG) signals is a prerequisite of brain computer interfacing (BCI). In this paper, we propose a hybrid method consisting of multivariate empirical mode decomposition (MEMD) and short time Fourier transform (STFT) to identify left and right hand imaginary movements from EEG signals. Experiments ar...

This work analyzes the suitability of spectral features in the Dual Tree Complex Wavelet Transform (DT-CWT) domain for EEG signal analysis by propounding a DT-CWT based feature extraction scheme. Unlike discrete wavelet transform- DT-CWT ensures limited redundancy and provides approximate shift invariance. To demonstrate the efficacy of DT-CWT for...

A portable and wearable yet low-power sleep monitoring system necessitates an automatic sleep scoring algorithm with the use of minimum number of recording channels. Computer-aided sleep staging is also important to eradicate the onus of sleep scorers of analyzing an enormous volume of data. The existing works on sleep scoring are either multichann...

Automated sleep stage classification is essential for alleviating the burden of physicians since a large volume of data have to be analyzed per examination. Most of the existing works in the literature are multichannel based or yield poor classification performance. A single-channel based computerized sleep staging scheme that gives good performanc...

An automatic sleep scoring method based on single channel electroencephalogram (EEG) is essential not only for alleviating the burden of the clinicians of analyzing a high volume of data but also for making a low-power wearable sleep monitoring system feasible. However, most of the existing works are either multichannel or multiple physiological si...

In this paper, a statistical method of classifying Electroencephalogram (EEG) data for automatic detection of epileptic seizure is carried out using a publicly available scalp EEG database. The classification is carried out to distinguish the seizure segments from the non-seizure ones. The higher order moments (specifically variance) have been calc...

In this paper, a feature extraction method based on Dual Tree Complex Wavelet Transform (DTCWT) domain has been proposed to classify left and right hand motor imagery movements from electroencephalogram (EEG) signals. After first performing auto-correlation of the EEG signals to reduce noise and enhance the weak brain signals, the EEG signals are d...

Traditional sleep scoring based on visual inspection of Electroencephalogram (EEG) signals is onerous for sleep scorers because of the gargantuan volume of data that have to be analyzed per examination. Computer-aided sleep staging can alleviate the onus of the sleep scorers. Again, most of the existing works on automatic sleep staging are multicha...

In this paper, Dual Tree Complex Wavelet Trans-form (DTCWT) domain based feature extraction method has been proposed to identify left and right hand motor imagery movements from electroencephalogram (EEG) signals. After first performing auto-correlation of the EEG signals to enhance the weak brain signals and reduce noise, the EEG signals are decom...

In this paper, a statistical method has been proposed to identify motor imagery left and right hand movements from electroencephalogram (EEG) signals in the Dual Tree Complex Wavelet Transform (DTCWT) domain. The total experiment is carried out with the publicly available benchmark BCIcompetition 2003 Graz motor imagery dataset. First, the EEG sign...

In this paper, a method to classify arm movements using statistical features of electroencephalogram (EEG) signals calculated from wavelet packet and Fourier transforms, has been proposed. The EEG signals are analyzed using bi-orthogonal wavelet packet family. Fourier transform is then applied to the corresponding detail coefficients and higher ord...

Speckle noise in medical ultrasound (US) degrades the image quality and reduces its diagnostic value. Reduction of speckle noise is an important pre-processing step for the analysis and processing of medical ultrasound images. Knowledge of the statistics of the log-transformed speckle especially in the multi-resolution transform domain is important...

In this paper, a comprehensive method using symmetric normal inverse Gaussian (NIG) parameters of the sub-bands of EEG signals calculated in the dual-tree complex wavelet transformation domain is proposed for classifying EEG data. The suitability of the NIG probability distribution function is illustrated using statistical measures. A support vecto...

In this paper, a comprehensive analysis of electroencephalogram (EEG) signals is carried out in the empirical mode decomposition (EMD) domain using a publicly available benchmark EEG database. First, the intrinsic mode functions (IMF) are extracted in the EMD domain. Next, normal inverse Gaussian (NIG) probability density function (pdf) is introduc...

In this paper, a statistical analysis of electroencephalogram (EEG) signals is carried out in the empirical mode decomposition (EMD) domain using a publicly available benchmark EEG database. First, the intrinsic mode functions (IMF) are extracted from the EEG signals in the EMD domain. Next, the investigation was carried whether the Bessel k-form (...

In this paper, a sub-band correlation-based method is proposed for the automatic detection of epilepsy and seizure. The analysis is carried out by decomposing the electroencephalogram (EEG) signals, collected from a publicly available EEG database, into the dual tree complex wavelet transform(DT-CWT) domain. An Artificial Neural Network(ANN) is emp...

In this paper, a statistical analysis of EEG signals is carried out in the dual tree complex wavelet transform (DT-CWT) domain. It is shown that Bessel k-form(BKF) pdf can suitably model the DT-CWT sub-bands and the BKF parameters in various DT-CWT sub-bands can discriminate various types of EEG data effectively. Next these parameters are utilized...

In this paper, a statistical method for automatic detection of seizure and epilepsy in the dual-tree complex wavelet transform(DT-CWT) domain is proposed. Variances calculated from the EEG signals and their DT-CWT sub-bands are utilized as features in the classifiers such as artificial neural network(ANN) and support vector machine(SVM). Studies ar...

In this paper, a comprehensive analysis of electroencephalogram (EEG) signals is carried out in the dual tree complex wavelet transform domain using a publicly available EEG database. It is shown that maximum cross-correlation among the sub-bands along with the absolute values of the corresponding correlation coefficient and co-variance can be effe...

Speckle noise is an inherent phenomenon in medical ultrasound (US) images. Since it degrades an ultrasound image quality and reduces its diagnostic value, reduction of speckle noise is a very important pre-processing step in ultrasound image processing. For this purpose, the knowledge of the statistics of speckle noise is necessary; especially in t...