arXiv subjects

Md. Enamul Haque

Publications and source records attributed to Md. Enamul Haque.

9 recordsLinked to original sources

arXiv2025

DFT based comparative study of the physical properties of MAlB (M = V, Ta, Mo, Nb) MAB compounds

MAB phases have appealing physical features that make them appropriate for a wide range of applications. Motivated by this, we present density functional theory (DFT) calculations of the structural, elastic, bonding, electronic band dispersion, acoustic behavior, phonon spectrum, various thermomechanical and optoelectronic properties of VAlB and TaAlB ternary borides for the first time. The computed ground state lattice parameters of both compounds are very consistent with experimental data. The formation enthalpy, elastic constants, and phonon dispersion calculations indicate that both compounds are chemically, mechanically, and dynamically stable, respectively. The physical parameters of VAlB and TaAlB are studied and compared with those of MoAlB and NbAlB MAB compounds.

cond-mat.mtrl-sci↗

arXiv2025

DFT exploration of pressure dependent physical properties of the recently discovered La3Ni2O7 superconductor

The recent discovery of superconductivity in Ruddlesden-Popper bilayer nickelate La3Ni2O7 under pressure has drawn a lot of interest. La3Ni2O7 is isostructural with cuprates in some respect. Investigation of its properties will undoubtedly provide new insights into high-Tc superconductivity. In the present work, we study structural, mechanical, elastic, optoelectronic, thermophysical properties, and Fermi surface topology of La3Ni2O7 under pressure within the range of 30-40 GPa employing the density functional theory (DFT). The calculated structural parameters agree well with the earlier experimental findings. The structural, mechanical, and thermodynamical stability is justified across the entire pressure range. The computed elastic moduli classify the compound as ductile, and the material's ductility is largely unaffected by pressure. The compound has a high level of machinability index and dry lubricity. The electronic band structure reveals metallic feature of La3Ni2O7. The Debye temperature, thermal conductivity, and melting temperature increase with increasing pressure, but in an anomalous manner. The characteristic peaks in refractive index, reflectivity, and photoconductivity exhibit a small shift towards higher energy for all polarizations of the electric field vector with increasing pressure. The investigated material might be a good ultraviolet radiation absorber and can be used as an anti-reflection system. Moreover, the pressure dependent electronic density of states at the Fermi level, pressure induced negligible variations in the repulsive Coulomb pseudopotential, and the changes in the Debye temperature have been used to explore the effect of pressure on the superconducting transition temperature in this study.

cond-mat.supr-con↗

arXiv2025

Pressure dependent ab initio study of the physical properties of hexagonal BeB2C: a possible high-Tc superconductor

This study uses the Density Functional Theory to explore the pressure dependent properties of hexagonal BeB2C. The metallic nature of BeB2C was substantiated at ambient pressure, with pressure induced alterations in electronic band structure and Fermi surface topology suggesting a potential for tunability across various applications. The phonon dispersion and phonon density of states show the dynamical stability under pressure. The thermophysical properties are also investigated under varying pressure conditions. Finally, the exploration of superconducting properties found that the transition temperature is in good agreement with previously reported values, and illustrated that beB2C holds considerable promise as a high-temperature superconductor, with pressure augmenting its superconducting properties.

cond-mat.mtrl-sci↗

arXiv2023

A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction

The advancement of distributed generation technologies in modern power systems has led to a widespread integration of renewable power generation at customer side. However, the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties. This paper proposes a novel Bayesian probabilistic technique for forecasting renewable solar generation by addressing data and model uncertainties by integrating bidirectional long short-term memory (BiLSTM) neural networks while compressing the weight parameters using variational autoencoder (VAE). Existing Bayesian deep learning methods suffer from high computational complexities as they require to draw a large number of samples from weight parameters expressed in the form of probability distributions. The proposed method can deal with uncertainty present in model and data in a more computationally efficient manner by reducing the dimensionality of model parameters. The proposed method is evaluated using quantile loss, reconstruction error, and deterministic forecasting evaluation metrics such as root-mean square error. It is inferred from the numerical results that VAE-Bayesian BiLSTM outperforms other probabilistic and deterministic deep learning methods for solar power forecasting in terms of accuracy and computational efficiency for different sizes of the dataset.

cs.LG↗

arXiv2022

Classification of Human Monkeypox Disease Using Deep Learning Models and Attention Mechanisms

As the world is still trying to rebuild from the destruction caused by the widespread reach of the COVID-19 virus, and the recent alarming surge of human monkeypox disease outbreaks in numerous countries threatens to become a new global pandemic too. Human monkeypox disease syndromes are quite similar to chickenpox, and measles classic symptoms, with very intricate differences such as skin blisters, which come in diverse forms. Various deep-learning methods have shown promising performances in the image-based diagnosis of COVID-19, tumor cell, and skin disease classification tasks. In this paper, we try to integrate deep transfer-learning-based methods, along with a convolutional block attention module (CBAM), to focus on the relevant portion of the feature maps to conduct an image-based classification of human monkeypox disease. We implement five deep-learning models, VGG19, Xception, DenseNet121, EfficientNetB3, and MobileNetV2, along with integrated channel and spatial attention mechanisms, and perform a comparative analysis among them. An architecture consisting of Xception-CBAM-Dense layers performed better than the other models at classifying human monkeypox and other diseases with a validation accuracy of 83.89%.

eess.IV↗

arXiv2022

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architectures. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption and American electric power (AEP) datasets is conducted to analyze the performance of point and probabilistic forecasting methods. The analysis demonstrates higher accuracy of the long-short term memory (LSTM) models with appropriate hyper-parameter tuning among point forecasting methods especially when sample sizes are larger and involve nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of least pinball score and root mean square error (RMSE).

cs.LG↗

arXiv2019

Efficient Energy Harvesting in Wireless Sensor Networks of Smart Grid

Smart grids are becoming ubiquitous in recent time. With the progress of automation in this arena, it needs to be diagnosed for better performance and less failures. There are several options for doing that but we have seen from the past research that using Wireless Sensor Network (WSN) as the diagnosis framework would be the most promising option due to its diverse benefits. Several challenges such as effect of noise, lower speed, selective node replacement, complexity of logistics, and limited battery lifetime arise while using WSN as the framework. Limited battery lifetime has become one of the most significant issues to focus on to get rid of it. This article provides a model for replenishing the battery charge of the sensor nodes of wireless sensor network. We will use the model for sensor battery recharging in an efficient way so that no nodes become out of service after a while. We will be using mobile charger for this purpose. So, there may be some scope for improving the recharge interval for the mobile charger as well. This will be satisfied using optimum path calculation for each time the charger travels to the nodes. Our main objectives are to maximize the nodes battery utilization, distribute power effectively from the energy harvester, and minimize the distance between power source and cluster head. The simulation results show that the proposed approach successfully maximizes the utilization of the nodes battery while minimizes the waiting time for the sensor nodes to get recharged from the energy harvester.

cs.NI↗

arXiv2019

JPEG Image Compression using the Discrete Cosine Transform: An Overview, Applications, and Hardware Implementation

Digital images are becoming large in size containing more information day by day to represent the as is state of the original one due to the availability of high resolution digital cameras, smartphones, and medical tests images. Therefore, we need to come up with some technique to convert these images into smaller size without loosing much information from the actual. There are both lossy and lossless image compression format available and JPEG is one of the popular lossy compression among them. In this paper, we present the architecture and implementation of JPEG compression using VHDL (VHSIC Hardware Description Language) and compare the performance with some contemporary implementation. JPEG compression takes place in five steps with color space conversion, down sampling, discrete cosine transformation (DCT), quantization, and entropy encoding. The five steps cover for the compression purpose only. Additionally, we implement the reverse order in VHDL to get the original image back. We use optimized matrix multiplication and quantization for DCT to achieve better performance. Our experimental results show that significant amount of compression ratio has been achieved with very little change in the images, which is barely noticeable to human eye.

cs.MM↗

arXiv2014

GPU Accelerated Fractal Image Compression for Medical Imaging in Parallel Computing Platform

In this paper, we implemented both sequential and parallel version of fractal image compression algorithms using CUDA (Compute Unified Device Architecture) programming model for parallelizing the program in Graphics Processing Unit for medical images, as they are highly similar within the image itself. There are several improvement in the implementation of the algorithm as well. Fractal image compression is based on the self similarity of an image, meaning an image having similarity in majority of the regions. We take this opportunity to implement the compression algorithm and monitor the effect of it using both parallel and sequential implementation. Fractal compression has the property of high compression rate and the dimensionless scheme. Compression scheme for fractal image is of two kind, one is encoding and another is decoding. Encoding is very much computational expensive. On the other hand decoding is less computational. The application of fractal compression to medical images would allow obtaining much higher compression ratios. While the fractal magnification an inseparable feature of the fractal compression would be very useful in presenting the reconstructed image in a highly readable form. However, like all irreversible methods, the fractal compression is connected with the problem of information loss, which is especially troublesome in the medical imaging. A very time consuming encoding pro- cess, which can last even several hours, is another bothersome drawback of the fractal compression.

cs.DC↗