Professor Dr. Md Aktaruzzaman was the head of the Biomedical Signal Processing & Machine Learning (BiSPMaL) Lab and the Chairman of the Dept. of Computer Science and Engineering. Currently, he is on study leave for pursuing his postdoctoral fellowship at the KCVRC Indiana University School of Medicine, Indiana since Sep 2022.

Biomedical signal processing, Machine Learning, Sample Entropy, Health Monitoring from Signals Acquired through Wearable Sensors, OCR, Attention Deficit Behavior Disorder Syndromes Analysis

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https://scholar.google.com/citations?user=dp4InSQAAAAJ&hl=en

I am Dr. Md. Robiul Hoque, faculty member of Computer Science and Engineering, Islamic University, Kushtia.

Context-Aware System, Smart Space, Sensor Network, Image and Speech Processing

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https://scholar.google.com/citations?hl=en&user=GGniDIUAAAAJ

Doctor of Philosophy in Computer Applied Technology, University of Chinese Academy of Sciences, China. Dissertation title: “RGB-D Object Detection and Recognition Based on Deep Learning Technique”. Advisor: Professor Ke Lu, School of Engineering Sciences, University of Chinese Academy of Sciences, China.

Computer Vision, Deep Learning, Artificial Intelligence, Pattern Recognition, Machine Learning, Wireless Network

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https://scholar.google.com/citations?user=by41XR8AAAAJ&hl=en

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Dr. Md Shohidul Islam, a distinguished academic from Islamic University, Bangladesh, holds a Doctor of Engineering from the University of Science and Technology of China, specializing in Information and Communication Engineering. His doctoral research titled “Robust Supervised Single Channel Speech Enhancement in the Wavelet Domain” reflects his expertise in speech signal processing. Prior to this, he completed an M.Sc. in Computer Science and Engineering and a B.Sc. in Computer Science and Engineering from Islamic University, Bangladesh. Dr. Islam’s research interests span several domains, including speech-controlled technologies for cardiovascular health engineering, non-invasive continuous blood pressure estimation using physiological signals, and various aspects of speech processing such as enhancement, denoising, and separation. He is also involved in audio watermarking, image processing, video image processing, and medical image processing. Dr. Islam has made significant contributions to science through his publications in peer-reviewed journals and presentations at international conferences. His work on speech enhancement and physiological signals processing has advanced the understanding and application of technology in healthcare and engineering sectors globally.

Physiological Signal Processing like PPG, ABP, Pusle, ECG, EMG and MMG, Speech Enhancement Speech Denoising Speech Dereverberation Blind Source Separation Speech Signal Processing Audio Watermarking Image Processing

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https://scholar.google.com/citations?user=9NlKAOIAAAAJ&hl=en

The process of separating individual sound sources from mono audio is a complex yet essential endeavor in audio signal processing and analysis. This article presents an algorithm tailored for bidirectional transformations aimed at effectively isolating speech from single-channel audio. Leveraging the dual-tree complex wavelet transform (DTCWT) on time-domain signals circumvents limitations inherent in the discrete wavelet transform (DWT), such as its incapacity to manage substantial shifts and inability to discern the correct direction. In this process, a series of subband signals is generated and subjected to the short-time Fourier transform (STFT) to create a complex spectrogram, which is then transformed into its absolute value and input into the Bi-directional Long Short-Term Memory (Bi-LSTM) network with a specified number of layers and units. This network utilizes the bidirectional capabilities of LSTM units to understand both the preceding and subsequent contexts of the input data, enabling the identification of specific speech components, aided by the ideal soft mask components that serve as corresponding labels. The final predicted signal is obtained by element-wise multiplication of the complex spectrogram by the estimated mask produced by the model. Subsequently, the inverse STFT is applied with parameters consistent with the initial transform, followed by the inverse DTCWT on the refined source elements with the same decomposition levels and wavelet filters. The improved efficacy of the proposed method for source separation quality was validated through experimental assessments conducted on the GRID audio–visual and TIMIT databases, considering metrics such as SDR, SIR, SAR, SNR, PESQ, and STOI.

In modern manufacturing industry, Automatic defect detection is becoming an attractive alternative to Human Inspection. Automatic defect detection on object surfaces is a compelling process. For accurate automated inspection and classification, computer vision image processing system has been widely used in manufacturing industries. In this article, we proposed histogram based automatic defect detection that process three objects at a time. In the first step we collect image from the camera, perform preprocessing, segmentation then we used histogram and Spearman’s correlation coefficient to find the defect or non-defect objects. The experimental analysis was evaluated on 300 images including defective and non-defective objects.

Blood pressure (BP), a vital sign in cardiovascular disease (CVD) monitoring, is traditionally measured invasively in critically ill patients or using cuff-based devices. Continuous monitoring of cardiovascular signs is crucial for early intervention and diagnosis, facilitated by miniaturized wearable technologies to mitigate further complications. In this study, we propose a modified UNET architecture with attention-based deep learning (DL) for continuous non-invasive BP estimation using only photoplethysmography (PPG) signals. The model incorporates a 1D convolutional neural network to handle sequential 1D data, enabling the capture and reconstruction of essential features for robust estimation. Attention blocks are employed to selectively collect information at different network stages, combining skip connections from the encoding and decoding paths to focus on significant features while filtering extraneous information. Additionally, the model operates in an autoencoder mode, facilitating feature extraction from intermediate layers and learning compact and meaningful representations of input data. Rigorous evaluation demonstrates that the proposed model achieves mean absolute errors (MAE) of 4.661 mmHg and 2.574 mmHg for systolic BP and diastolic BP, respectively. These results are comparable to existing methods and satisfy international standards, such as the BHS and AAMI guidelines for non-invasive BP monitoring in wearable technology. This research contributes to the advancement of accurate and non-invasive BP estimation, enabling early detection and intervention for improved cardiovascular health monitoring.

Conventional deep learning architectures do not adequately address the requirements of wearable high-precision medical devices such as blood pressure (BP) monitors. This paper presents a novel hybrid deep learning architecture that leverages advancements in sensors and signal processing modules for cuffless and continuous BP monitoring devices, emphasizing enhanced precision in an energy constrained system. The proposed architecture comprises a combination of a convolutional neural network and a bidirectional gated recurrent unit. The proposed model adopts a data-driven end-to-end approach to directly process raw photoplethysmography (PPG) signals, enabling simultaneous estimation of systolic BP and diastolic BP without the need for feature extraction. Performance evaluation was conducted using the Multiparameter Intelligent Monitoring in Intensive Care II dataset, yielding small mean errors of 0.664 mmHg and −0.028 mmHg for the estimated and reference SBP and DBP, respectively.

Separating speech is a challenging area of research, especially when trying to separate the desired source from its combination. Deep learning has arisen as a promising solution, surpassing traditional methods. While prior research has mainly focused on the magnitude, log-magnitude, or a combination of the magnitude and phase portions, a new approach using the Short-time Fourier Transform (STFT), and a deep Convolutional Neural Network named U-NET has been proposed. This method, unlike others, considers both the real and imaginary components for decomposition. During the training stage, the mixed time-domain signal undergoes a transformation into a frequency-domain signal by using STFT, producing a mixed complex spectrogram. The spectrogram’s real and imaginary parts are then divided and combined into a single matrix. The newly formed matrix is fed through U-NET to extract the source components. The same process is repeated at testing. The resulting concatenated matrix for the mixed test signal is passed through the saved model to generate two enhanced concatenated matrices for each source. These matrices are then transformed back into time-domain signals using inverse STFT by extracting the magnitude and phase. The proposed approach has been evaluated using the GRID audio visual corpuses, with results showing improved quality and intelligibility compared to the existing methods, as demonstrated by objective measurement metrics.

Background: Lung cancer is one of the most fatal cancers worldwide, and malignant tumors are characterized by the growth of abnormal cells in the tissues of lungs. Usually, symptoms of lung cancer do not appear until it is already at an advanced stage. The proper segmentation of cancerous lesions in CT images is the primary method of detection towards achieving a completely automated diagnostic system. Method: In this work, we developed an improved hybrid neural network via the fusion of two architectures, MobileNetV2 and UNET, for the semantic segmentation of malignant lung tumors from CT images. The transfer learning technique was employed and the pre-trained MobileNetV2 was utilized as an encoder of a conventional UNET model for feature extraction. The proposed network is an efficient segmentation approach that performs lightweight filtering to reduce computation and pointwise convolution for building more features. Skip connections were established with the Relu activation function for improving model convergence to connect the encoder layers of MobileNetv2 to decoder layers in UNET that allow the concatenation of feature maps with different resolutions from the encoder to decoder. Furthermore, the model was trained and fine-tuned on the training dataset acquired from the Medical Segmentation Decathlon (MSD) 2018 Challenge. Results: The proposed network was tested and evaluated on 25% of the dataset obtained from the MSD, and it achieved a dice score of 0.8793, recall of 0.8602 and precision of 0.93. It is pertinent to mention that our technique outperforms the current available networks, which have several phases of training and testing.

This paper proposes an innovative single-channel supervised speech enhancement (SE) method based on UNET, a convolutional neural network (CNN) architecture that expands on a few changes in the basic CNN architecture. In the training phase, short-time Fourier transform (STFT) is exploited on the noisy time domain signal to build a noisy time-frequency domain signal which is called a complex noisy matrix. We take the real and imaginary parts of the complex noisy matrix and concatenate both of them to form the noisy concatenated matrix. We apply UNET to the noisy concatenated matrix for extracting speech components and train the CNN model. In the testing phase, the same procedure is applied to the noisy time-domain signal as in the training phase in order to construct another noisy concatenated matrix that can be tested using a pre-trained or saved model in order to construct an enhanced concatenated matrix. Finally, from the enhanced concatenated matrix, we separate both the imaginary and real parts to form an enhanced complex matrix. Magnitude and phase are then extracted from the newly created enhanced complex matrix. By using that magnitude and phase, the inverse STFT (ISTFT) can generate the enhanced speech signal. Utilizing the IEEE databases and various types of noise, including stationary and non-stationary noise, the proposed method is evaluated. Comparing the exploratory results of the proposed algorithm to the other five methods of STFT, sparse non-negative matrix factorization (SNMF), dual-tree complex wavelet transform (DTCWT)-SNMF, DTCWT-STFT-SNMF, STFT-convolutional denoising auto encoder (CDAE) and casual multi-head attention mechanism (CMAM) for speech enhancement, we determine that the proposed algorithm generally improves speech quality and intelligibility at all considered signal-to-noise ratios (SNRs). The suggested approach performs better than the other five competing algorithms in every evaluation metric.

Single channel speech separation (SS) is highly significant in many real-world speech processing applications such as hearing aids, automatic speech recognition, control humanoid robots, and cocktail-party issues. The performance of the SS is crucial for these applications, but better accuracy has yet to be developed. Some researchers have tried to separate speech using only the magnitude part, and some are tried to solve complex domains. We propose a dual transform SS method that serially uses the dual-tree complex wavelet transform (DTCWT) and short-term Fourier transform (STFT), and jointly learns the magnitude, real and imaginary parts of the signal applying a generative joint dictionary learning (GJDL). At first, the time-domain speech signal is decomposed by DTCWT, which produces a set of subband signals. Then STFT is connected to each subband signal, which converts each subband signal to the time-frequency domain and builds a complex spectrogram that prepares three parts like real, imaginary and magnitude for each subband signal. Next, we utilize the GJDL approach for making the joint dictionaries, and then the batch least angle regression with a coherence criterion (LARC) algorithm is used for sparse coding. Afterward, computes the initially estimated signals in two different ways, one by considering only the magnitude part and another by considering real and imaginary components. Finally, we apply the Gini index (GI) to the initially estimated signals to achieve better accuracy. The proposed algorithm demonstrates the best performance in all considered evaluation metrics compared to the mentioned algorithms.

Transient interferences such as keystrokes, mouse clicks and hammering pose a significant challenge in the single channel speech enhancement due to their abrupt and non-continuous nature. Traditional noise suppression algorithms and even many non-stationary noise reduction algorithms do not adequately suppress transient interference. Therefore, in this work, we propose a semi-supervised single channel transient noise suppression method to effectively suppress the transient interference without significant audible distortion. The proposed algorithm consists of training and testing stages. In the training stage, the proposed technique first uses the optimally modified-log spectral amplitude (OMLSA) estimator to estimate the transient noise from the noisy speech signal. After that, we eliminate the residual speech components from the estimated noise obtained from OMLSA based on the correlation coefficient, by taking correlation between the estimated noise with the available clean speech data from the dataset passed through the voice activity detector for silence zones removal. Afterwards, we use this noise for training the noise dictionary in sparse non-negative matrix factorization. Clean speech data is used for speech dictionary training. In the enhancement stage, the dictionaries are fixed and concatenated, to obtain the corresponding activation matrices. The clean speech dictionary and the corresponding weight matrix are used to reconstruct the estimated speech. The experimental results reveal that the proposed algorithm provided better performance compared to other existing algorithms in the speech quality evaluation metrics.

In this article, we propose a new source separation method in which the dual-tree complex wavelet transform (DTCWT) and short-time Fourier transform (STFT) algorithms are used sequentially as dual transforms and sparse nonnegative matrix factorization (SNMF) is used to factorize the magnitude spectrum. STFT-based source separation faces issues related to time and frequency resolution because it cannot exactly determine which frequencies exist at what time. Discrete wavelet transform (DWT)-based source separation faces a time-variation-related problem (i.e., a small shift in the time-domain signal causes significant variation in the energy of the wavelet coefficients). To address these issues, we utilize the DTCWT, which comprises two-level trees with different sets of filters and provides additional information for analysis and approximate shift invariance; these properties enable the perfect reconstruction of the time-domain signal. Thus, the time-domain signal is transformed into a set of subband signals in which low- and high-frequency components are isolated. Next, each subband is passed through the STFT and a complex spectrogram is constructed. Then, SNMF is applied to decompose the magnitude part into a weighted linear combination of the trained basis vectors for both sources. Finally, the estimated signals can be obtained through a subband binary ratio mask by applying the inverse STFT (ISTFT) and the inverse DTCWT (IDTCWT). The proposed method is examined on speech separation tasks utilizing the GRID audiovisual and TIMIT corpora. The experimental findings indicate that the proposed approach outperforms the existing methods.

Single-channel speech dereverberation and separation has been a challenging problem and is of high significance in speech processing applications. Many researchers have tried to address this problem separately, either investigating speech dereverberation or separation. Most of the available literature on joint dereverberation and separation research is for multichannel. Moreover, in joint dereverberation and separation research, mostly noise is considered as the other source. Therefore, there is an emergent need to provide an optimal and efficient solution to the single channel speech dereverberation and separation problem. In this paper, we work on speech dereverberation and separation for single-channel, and we do not consider any noise source except the reverberation effects. We combine the two dominant methods, namely robust principal component analysis (RPCA) and sparse non-negative matrix factorization (SNMF), for the dereverberation and separation of the underlying speeches from the speech mixture. Firstly, we use the RPCA algorithm to dereverberate the reverberant mixture, and then we use the SNMF technique to separate the speeches from the speech mixture. We consider unseen RIRs conditions and compare the results with the baseline. The experimental results show that the proposed algorithm improves the speech quality both in evaluation parameters as well as listening.

Alzheimer's disease (AD) is one of the most disabling and burdensome health conditions worldwide and a leading neurodegenerative disease that results in severe dementia. Parkinson's disease (PD) is also a neurodegenerative disease and literature suggests pathogenic links between AD and PD but the molecular mechanisms that underlie this association between AD and PD are not well understood and/or have a limited understanding of the key molecular mechanisms that provoke neurodegeneration. To address this problem, we aimed to identify common molecular biomarkers and pathways in PD and AD that are involved in the progression of these diseases and deliver clues to important pathological mechanisms. We have analyzed the microarray gene expression transcriptomic datasets from control, AD and PD affected individuals. To obtain robust results, we have used combinatorial statistical methods to analyze the datasets. Based on standard statistical criteria, we have identified 111 up-regulated genes overlapping between AD and PD and at the same time we have identified 20 down-regulated overlapping genes between AD and PD. Pathway and Gene Ontology (GO) analyses pointed out that these 111 up-regulated and 20 down-regulated common genes identified several altered molecular pathways and ontological pathways. Further protein-protein interactions (PPI) analysis revealed pathway hub proteins: EGFR; JAK2; MAPK11; EIF3B; WASL; BCL2L1; CDH1; MCM5; RAN; NCOA3; TBL1X; RARA; ARHGEF12; NCOA2 and ESR2.

Transcriptional components were then identified, and significant transcription factors (FOXC1; GATA2; YY1; TFAP2A; E2F1; FOXL1; NFIC; NFKB1; TP53; USF2 and

CREB1 were identified. We have performed protein-drug interaction analysis to reveal drug interaction with proteins. Thus, we identified novel putative links between pathological processes between AD and PD, and possible gene and mechanistic expression links between them.

In this paper, we propose a novel single-channel speech enhancement algorithm that applies dual-domain transforms comprising of dual-tree complex wavelet transform (DTCWT) and short-time Fourier transform (STFT) with a sparse non-negative matrix factorization (SNMF). The first domain belongs to the DTCWT, which is utilized on the time domain signals to conquer the weakness of signal distortions brought about by the downsampling of the discrete wavelet packet transform (DWPT) and delivered a set of subband signals. The second domain alludes to the STFT, which is exploited to each subband signal and built a complex spectrogram. At last, we apply the SNMF to the magnitude spectrogram for extracting speech components. In short, the DTCWT decomposes the time-domain noisy signal into a set of subband signals and afterward applied STFT to each subband signal, and we get nonnegative matrices by taking the absolute value of the complex matrix. From this point forward, we apply SNMF to each nonnegative matrix and identify the speech components. Finally, the estimated signal can be achieved through a subband binary ratio mask (SBRM) by applying the inverse STFT (ISTFT) and, subsequently, the inverse DTCWT (IDTCWT). The proposed approach is assessed utilizing the GRID audio-visual and IEEE databases, and diverse kinds of noises such as stationary, non-stationary, and quasi-stationary. The exploratory outcomes demonstrate that the proposed algorithm improved objective speech quality and intelligibility altogether at all considered signal to noise ratios (SNRs), compared to the other seven speech enhancement methods of STFT-SNMF, STFT-SNMFSE, MLD-STFT-SNMF, STFT-GDL, STFT-CJSR, DTCWT-SNMF, and DWPT-STFT-SNMF.

Wireless sensor nodes have deployed with limited energy sources. The lifetime of a node usually depends on its energy source. The main challenging design issue of the wireless sensor network is to prolong the network lifetime and prevent connectivity degradation by developing an energy-efficient routing protocol. Many research works are done to extend the network lifetime, but still, it is a problem because of the impossibility of recharging. In this paper, we present a hierarchical clustering technique for wireless sensor network called Clustering with Residual Energy and Neighbors (CREN). It is based on two basic parameters, e.g., number of neighbors of a node and its residual energy. We use these properties as a weighted factor to elect a node as a cluster head. A well-known method, LEACH had a high performance in energy saving and the quality of services in the wireless sensor network. Like Low-Energy Adaptive Clustering Hierarchy (LEACH), CREN rotates the cluster head among the sensor nodes to balance the energy consumption. The simulation result shows the proposed technique achieves much higher performance and energy efficiency than LEACH.

In this paper, we propose a novel single channel speech enhancement approach that takes up the Stationary Wavelet Transform (SWT) and Nonnegative Matrix Factorization (NMF) with Concatenated Framing Process (CFP) and proposes Subband Smooth Ratio Mask (ssRM). Due to downsampling process after filtering, Discrete Wavelet Packet Transform (DWPT) suffers the absence of shift-invariance, and for this reason, some errors occur in the signal reconstruction and to mitigate the problem, firstly we use SWT and NMF with KL cost function. Secondly, we exploit the CFP to build each column of the matrix instead of using NMF directly to take advantage of smooth decomposition. Thirdly, we apply the Auto-Regressive Moving Average (ARMA) filtering process to the newly formed matrices for making the speech more stable and standardized. Finally, we propose an ssRM by combing the Standard Ratio Mask (sRM) and Square Root Ratio Mask (srRM) with Normalized Cross-Correlation Coefficients (NCCC) to take the advantages of them (sRM, srRM and NCCC). In short, the SWT divides the time-domain mixing speech signal into a set of subband signals and then framing and taking the absolute value of each subband signal, and we obtain nonnegative matrices. Then, we form the new matrices by applying the CFP where each column of the formed matrix contains five consequent frames of the nonnegative matrix and performs an ARMA filtering operation. After that, we apply NMF to each newly formed matrix and detect the speech components via proposed ssRM. Finally, the estimated signal can be achieved through them by applying inverse SWT. Our approach is evaluated using IEEE corpus and different types of noises. Objective speech quality and intelligibility improve significantly by applying this approach and outperforms related methods such as conventional STFT-NMF and DWPT-NMF.

In this paper, a novel array structure exploiting coprime arrays is proposed which can be very proficient to determine the number of consecutive lags in proportion with the number of array elements. The proposed method comprises novel array structure by configuring three subarrays positioned in alignment with some prescribed values. By increasing array elements in third subarray while keeping other subarrays fixed, explicit number of consecutive lags could be obtained proportionately. The proposed method offers maximization of consecutive lags in remarkable number by calculating the fourth order difference co-array unifying interpolation. The forth order difference co-array is achieved by exploiting the second order difference co-array twice. The consideration of third subarray in addition with two coprime subarrays leads to a novel array structure which can significantly enhance degrees of freedom. An effective interpolation technique nuclear norm minimization is considered to fill the holes subsisting in the virtual co-array in order to exploit full virtual co-array length. This interpolation method uses convex framework which is trackable and very simple to implement yielding a freedom of fixing any predefined extra tuning parameter. The proposed method in this paper is named as VEFODCI which stands for virtual extension of coprime arrays by exploiting fourth order difference co-array with interpolation. The sparse Bayesian learning is used for direction of arrival (DOA) estimation exploiting the proposed novel array structure by imposing interpolation to fill the holes. The array geometry of sensor distributions proves that the proposed method is less susceptible from mutual coupling effect. The simulation results stipulate that the proposed method is performing DOA estimation accurately even in lower angular separation of sources by achieving larger number of consecutive lags than the state of the art.

Doctor of Philosophy in Pattern Recognition and Intelligent Systems from Institute of Automation, Chinese Academy of Sciences under University of Chinese Academy of Sciences, Beijing, China. Dissertation title: “Computational Bioinformatics and Machine Learning Models to identify Diseasome and Neurological Disease Comorbidities”. Advisor: Professor Silong Peng, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Bioinformatics, Health Informatics, Machine Learning, Deep Learning,Pattern Recognition and Data Science.

https://www.researchgate.net/profile/Md_Habibur_Rahman4

https://scholar.google.com/citations?hl=en&user=DzAiISMAAAAJ

SCI, IF: 6.69

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It's test

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Dr. Bappa Sarkar graduated from Islamic University, Kushtia, Bangladesh, with a Bachelor of Science (B.Sc.) in Computer Science and Engineering in 2006 and a Master of Science (M.Sc.) in Computer Science and Engineering in 2007. He completed a PhD degree from Kwangwoon University, South Korea, in 2026. Since 2013, he has served as an assistant professor in the Department of Computer Science and Engineering at the Islamic University, Kushtia, Bangladesh. His areas of interest in research include wearable devices, Point-of-Care (PoC) devices, smart biochips, Biosensors, and AI-based microfluidic control.

wearable devices, Point-of-Care (PoC) devices, Biosensors, and AI-based microfluidic control.

https://www.researchgate.net/profile/Bappa-Sarkar-2?ev=hdr_xprf

https://scholar.google.com/citations?user=7gU2fxgAAAAJ&hl=en

This research introduces a novel moving electrochemical biosensing system designed to significantly improve the sensitivity and reliability of Alzheimer's disease (AD) biomarker detection in blood. The proposed system integrates a laser-induced graphene metal interdigitated array (LIG-MIDA) biosensor modified with multi-walled carbon nanotubes (MWCNTs) and silver nanoparticles (AgNPs). Unlike conventional static electrochemical ELISA (e-ELISA), the biosensor moves vertically within the analyte solution, enhancing analyte mixing, reducing electrode fouling, improving electron transfer, and generating stronger and more stable electrochemical signals.

The sensor movement parameters were optimized to a 4 mm amplitude and 8 mm/s speed, while the electrode composition of 2% MWCNTs and 10% AgNPs provided the highest electrical conductivity and sensing performance. The system was first validated using p-aminophenol (PAP), the electroactive product of the enzyme-substrate reaction in e-ELISA, demonstrating a wide linear detection range from 1 mM to 10 pM with improved signal stability and reproducibility compared with conventional static measurements.

The proposed biosensor was subsequently applied for the detection of the Alzheimer's disease biomarkers amyloid beta-40 (Aβ40) and amyloid beta-42 (Aβ42). The moving sensor achieved exceptionally low detection limits of 0.63 pg/mL for Aβ40 and 0.78 pg/mL for Aβ42, approximately four times lower than those obtained using static electrochemical measurements. Furthermore, the biosensor exhibited excellent linearity, reduced measurement variability, and was successfully validated using human plasma samples, confirming its practical applicability for clinical diagnosis.

Overall, this work presents an innovative and cost-effective electrochemical sensing platform that combines vertical sensor movement, laser-induced graphene technology, and nanomaterial-modified electrodes to substantially enhance biosensing performance. The developed system offers a promising solution for the rapid, accurate, and early detection of Alzheimer's disease and has significant potential for future point-of-care diagnostic applications.

This research presents a high-performance laser-induced graphene (LIG)-based silicon oxide (SiOx) hybrid anode for next-generation lithium-ion batteries (LIBs). The study introduces a single-step CO₂ laser-induced photothermal fabrication process that directly converts a SiOx-coated polyimide substrate into a conductive LIG-SiOx hybrid (LIGSiOxH), simplifying the conventional multi-step manufacturing process. To improve the mechanical stability of the electrode, the researchers further reinforced the hybrid structure with a Super P/Polyacrylic Acid (SP-PAA) coating, which effectively suppresses graphene delamination and accommodates the volume expansion of SiOx during battery cycling.

To overcome the inherently low initial Coulombic efficiency (ICE) of LIG-based anodes, the authors employed an electrochemical prelithiation strategy, increasing the ICE to approximately 93%. The optimized anode delivered a high reversible capacity of 1237 mAh g⁻¹ at 0.1 C and maintained 824 mAh g⁻¹ at 1 C after 250 charge–discharge cycles, corresponding to an impressive 91% capacity retention. Electrochemical analyses, including cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic intermittent titration technique (GITT), confirmed enhanced lithium-ion diffusion kinetics, reduced charge-transfer resistance, and improved electrochemical reversibility

This research introduces a novel Laser-Induced Graphite-Graphene Matrix (LIGGM) anode for high-performance lithium-ion batteries (LIBs). The proposed material is fabricated through a single-step laser engraving process by irradiating a natural graphite-coated polyimide (PI) tape attached to a copper current collector. Compared with conventional graphite and laser-induced graphene (LIG) anodes, the LIGGM anode offers a simpler, faster, and more cost-effective fabrication process while significantly improving electrochemical performance.

The study further incorporates a pre-lithiation strategy to overcome the low Initial Coulombic Efficiency (ICE) commonly observed in graphene-based anodes. Pre-lithiation compensates for lithium loss during the formation of the solid electrolyte interphase (SEI), increasing the ICE from 49% to 92% and enhancing long-term cycling stability. The optimized pre-lithiated LIGGM anode achieves a high specific capacity of 702 mAh g⁻¹, a capacity of 244 mAh g⁻¹ at a high 10C rate, and an 84% capacity retention rate after 250 charge-discharge cycles, outperforming conventional graphite and standard LIG anodes.

This research presents a flexible, wrist-wearable blood flow meter based on a biomimetically structured conductive polydimethylsiloxane (PDMS) composite for real-time cardiovascular monitoring. The proposed sensor consists of two conductive PDMS layers embedded with bio-inspired ciliary microstructures that mimic natural tactile sensing mechanisms. When attached to the wrist, arterial expansion and contraction during each heartbeat cause repeated contact between the two layers, generating electrical signals corresponding to pulse waves. The sensor is designed to be lightweight, low-cost, highly flexible, and suitable for point-of-healthcare (PoHC) applications.

The developed wearable sensor successfully measured real-time wrist pulse waves and pulse wave velocity (PWV) using two sensors positioned 10 mm apart on the wrist. The system accurately estimated a pulse transit time (PTT) of 20 ms and a pulse wave velocity of approximately 5 cm/s, which is consistent with clinically reported radial artery blood flow values. These results demonstrate the sensor's capability for continuous, non-invasive cardiovascular monitoring.

Overall, this work introduces a simple, scalable, and cost-effective wearable sensing platform that combines biomimetic engineering with conductive polymer composites to provide accurate pulse monitoring. The proposed device has significant potential for personalized healthcare, early cardiovascular disease detection, wearable health monitoring, and remote point-of-care diagnostic systems, offering an attractive alternative to conventional bulky and expensive pulse monitoring technologies.

This research presents a novel high-density three-dimensional (3D) graphene anode for lithium-ion batteries by combining reduced graphene oxide (rGO) with laser-induced graphene (LIG) on a polyethersulfone (PES) substrate. The authors introduce a simple, low-cost doctor-blade coating and water peel-off fabrication process, followed by CO₂ laser engraving to produce a highly conductive, porous, and vertically interconnected graphene network. This innovative architecture enhances electron transport, lithium-ion diffusion, and structural stability while eliminating the need for conventional binders and metal current collectors.

Electrochemical evaluation demonstrated an initial reversible specific capacity of 698 mAh g⁻¹, with the anode retaining 91.09% of its initial capacity after 300 charge–discharge cycles and 82.05% after 500 cycles, while maintaining approximately 99.2% coulombic efficiency. Furthermore, a full lithium-ion cell assembled with an NCM811 cathode achieved 92.52% capacity retention after 120 cycles, demonstrating the practical applicability of the proposed electrode for real-world battery systems.

Overall, this work provides a scalable, environmentally friendly, and cost-effective strategy for manufacturing high-performance graphene-based anodes. The proposed 3D PES–rGO–LIG architecture significantly improves battery capacity, cycling stability, and energy density, making it a promising candidate for next-generation lithium-ion batteries used in electric vehicles, portable electronics, and advanced energy storage systems.

This research presents a novel polyethersulfone (PES)-based thick polymer-supported graphene sheet fabricated using laser-induced graphene (LIG) technology as a binder-free, free-standing anode for high-energy-density lithium-ion batteries. Unlike conventional Kapton-based electrodes, the proposed PES sheet is first prepared using a hot-embossing process and then converted into a porous multilayer graphene structure with vertically conductive hexagonal pores, which enhance lithium-ion diffusion, electron transport, and structural stability.

The proposed anode delivered an initial specific capacity of 710 mAh g⁻¹ at 0.1 C, retained 80.7% of its capacity after 200 charge–discharge cycles, and maintained approximately 99% coulombic efficiency, demonstrating excellent cycling stability and rate capability. Furthermore, compared with commercially available Kapton-based laser-induced graphene electrodes, the PES-based graphene sheet achieved approximately 30% higher areal capacity, making it a superior candidate for compact, high-performance energy storage systems.

Overall, this work introduces a simple, scalable, low-cost, and environmentally friendly fabrication approach for producing thick, free-standing graphene anodes. The developed PES-supported graphene architecture offers significant potential for next-generation lithium-ion batteries used in electric vehicles, portable electronics, and other high-energy-density energy storage applications.

This research proposes a novel laser-induced graphene (LIG)-based fabrication technique for developing a binder-free, free-standing, and vertically conductive anode for lithium-ion batteries using commercial Kapton polymer film. The authors designed a unique three-step laser engraving process that creates hexagonal pores within the graphene sheet, enabling efficient vertical pathways for electron and lithium-ion transport while maintaining excellent mechanical stability without requiring a conventional current collector or binder.

This research presents a highly sensitive carbon nanotube (CNT)-metal graphene hybrid (CNT-MGH) nano-interdigitated array (n-IDA) electrochemical biosensor for the early diagnosis of Alzheimer’s disease (AD) using blood samples. The proposed biosensor was fabricated using laser-induced graphene (LIG) technology, where silver nanoparticles (AgNPs), multi-walled carbon nanotubes (MWCNTs), and porous graphene were integrated to create a highly conductive electrode with a large surface area and rapid electron transfer. An innovative 8-well nano-IDA sensor was developed to enable simultaneous electrochemical measurements compatible with standard 96-well ELISA plates.

The sensor was combined with an electrochemical enzyme-linked immunosorbent assay (e-ELISA) to detect the Alzheimer's blood biomarkers phosphorylated Tau-217 (p-Tau217) and phosphorylated Tau-181 (p-Tau181). Instead of conventional optical ELISA detection, the system measured the electrochemical signal produced by the enzymatic by-product p-aminophenol (PAP), significantly improving sensitivity while reducing instrumentation complexity. The optimized sensor demonstrated an exceptional limit of detection (LOD) of 0.1 pM for PAP, 0.16 pg/mL for p-Tau217, and 0.08 pg/mL for p-Tau181, enabling the detection of extremely low biomarker concentrations found in early-stage Alzheimer's disease.

This research presents a deep learning-based system for the automatic recognition of American Sign Language (ASL) alphabets using a Convolutional Neural Network (CNN). The primary objective of the study is to reduce the communication barrier between deaf or speech-impaired individuals and the general population by accurately recognizing hand gesture images representing ASL alphabets. The proposed model was trained and evaluated using the Sign Language MNIST dataset, which contains 34,627 grayscale hand gesture images, including 27,455 training and 7,172 testing samples representing 24 ASL alphabets (excluding J and Z due to their motion-based nature).

The authors first applied image preprocessing and normalization, followed by data augmentation to improve the model's generalization capability. They then designed a custom CNN architecture consisting of eight convolutional layers, three max-pooling layers, two dropout layers, and a fully connected layer to automatically extract discriminative features from hand gesture images and perform multiclass classification. The model was trained using the Adam optimizer and evaluated using standard performance metrics, including accuracy, precision, recall, and F1-score.

Experimental results demonstrate that the proposed CNN model achieved an outstanding 99.78% classification accuracy on unseen test data, outperforming many existing approaches for ASL alphabet recognition. The study concludes that the proposed method provides a highly accurate, reliable, and efficient solution for real-time sign language recognition and has significant potential for developing intelligent assistive technologies that facilitate communication between hearing-impaired individuals and the wider community.

This research presents a semi-automated brain tumor segmentation system that detects and measures tumor regions from Magnetic Resonance Imaging (MRI) scans using morphological image processing techniques. The proposed method performs image preprocessing through grayscale conversion, median filtering, and binary conversion, followed by morphological opening, border clearing, object labeling, and tumor boundary tracing to accurately isolate the tumor region. The segmented tumor area is then calculated by counting the number of pixels, providing an objective measurement of tumor size.

A distinctive feature of the proposed system is its patient observation module, which assigns a unique identification number to each patient and stores previous tumor measurements in a database. During follow-up examinations, the system compares the current MRI results with previous records to determine whether the tumor has increased, decreased, or remained unchanged, thereby assisting physicians in monitoring disease progression and evaluating treatment effectiveness.

The system was implemented in MATLAB and evaluated using MRI images collected from 50 patients. Experimental results demonstrate that the proposed morphological approach effectively segments brain tumors and provides reproducible measurements that reduce dependence on subjective visual assessment. The authors conclude that the method offers a simple, accurate, and computationally efficient tool for computer-aided diagnosis, enabling more reliable tumor detection and longitudinal patient monitoring in clinical practice.

This research presents a deep learning-based face recognition system that combines Convolutional Neural Networks (CNNs) with one-shot learning to accurately identify individuals using only one or a few reference images. The proposed system employs a pre-trained VGG-Face model to extract high-level facial features and uses a Siamese neural network with distance-based similarity measures to compare facial embeddings for recognition. This approach reduces the need for large numbers of training images while maintaining reliable recognition performance.

The paper explains the fundamental concepts of deep learning, including artificial neural networks, convolution, pooling, fully connected layers, dropout, transfer learning, and one-shot learning, and demonstrates how these techniques are integrated into a practical face recognition framework. The implementation was developed in Python using deep learning libraries such as TensorFlow, Keras, OpenCV, and NumPy, with image preprocessing and feature extraction performed before face comparison. Similarity between faces was measured using cosine distance and Euclidean distance, with a predefined threshold used to determine identity matches.

This research proposes a statistical N-gram-based approach for verifying the syntactic correctness of Bangla sentence structures. The system employs bigram, trigram, and quadgram language models trained on a one-million-word Bangla corpus (BdNC01) developed at the Speech and Image Processing Laboratory (SIPL) of Islamic University, Bangladesh. To address the data sparsity problem caused by unseen word sequences, the authors incorporated Witten–Bell smoothing, enabling more reliable probability estimation for sentence verification.

The proposed method was evaluated using 1,000 grammatically correct and 1,000 intentionally incorrect Bangla sentences collected from newspaper sources. Experimental results demonstrated that the system correctly identified valid and invalid sentence structures with an average accuracy of approximately 93.1%. The study also discusses the system's ability to detect both non-word errors and real-word errors, while highlighting that increasing the training corpus size could further improve performance.

This research presents a statistical approach for verifying the correctness of Bangla words using the N-gram language model. The authors developed a system that analyzes the sequence of characters in Bangla words using bigram, trigram, and quadgram models. A corpus containing one million Bangla word tokens, collected from the BdNC01 corpus, was used to train the model. To address the problem of unseen character sequences, the system employed Witten–Bell smoothing, enabling it to estimate probabilities even for rare or previously unseen N-grams.

The proposed system was evaluated using 50,000 correct and 50,000 incorrect Bangla words. Experimental results showed that the system correctly identified valid words with an accuracy of approximately 95.2% and detected incorrect words with an accuracy of about 97.1%, resulting in an overall accuracy of 96.17%.