Chowdhury Akram Hossain

Accurate and fast fault detection is essential to ensure the stability and reliability of High Voltage AC (HVAC) transmission systems. Conventional protection methods, including impedance-based and traveling wave techniques, may exhibit reduced performance under noisy operating conditions, system uncertainties, and complex fault scenarios while often requiring separate approaches for fault classification, location detection, and stability assessment. This paper proposes a unified Artificial Neural Network (ANN) based framework for simultaneous fault classification, location detection, and stability assessment using Critical Clearing Time (CCT) within a single HVAC transmission line model. A detailed MATLAB Simulink model is developed to generate a structured dataset comprising twelve fault scenarios, including single-line, double-line, three-phase, and ground faults at different locations along the transmission line. Three-phase voltages and currents, along with zero-sequence components, are used as input features. The ANN model is trained using the Levenberg–Marquardt (LM) optimization algorithm, which was comparatively evaluated against Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) and demonstrated faster convergence, lower prediction error, and higher regression accuracy. To further evaluate the robustness of the proposed framework under high-impedance fault conditions, supplementary simulations were performed using fault resistance values of 10 Ω and 50 Ω in addition to the baseline 0.01 Ω case. The resulting datasets were combined to form an expanded training and evaluation dataset, enabling comprehensive validation of the proposed LM-trained ANN under varying fault resistance conditions. Using the baseline dataset, the proposed framework achieved a high regression coefficient (R = 0.9882) and low mean squared error (MSE = 0.1386), demonstrating accurate fault classification and precise per-kilometer fault location estimation. Furthermore, the integration of fault inception time and duration enables direct computation of CCT, allowing the model to distinguish between stability-critical and non-critical fault conditions. The results confirm that the proposed framework provides a comprehensive and efficient solution for real-time fault analysis by combining classification, localization, temporal analysis, and stability-aware decision support within a single model.

The fast growth of wireless communication systems and the growing need for very high data rates have been the driving force behind the creation of sixth-generation (6G) technologies that operate in the terahertz (THz) frequency region. This research represents the design and analysis of a small compact microstrip patch antenna that works in the terahertz (THz) frequency range for 6G cellular connectivity. The Rogers RT5880 substrate and annealed copper are used in the design of the suggested antenna, which aims for a 593 GHz resonance frequency. A progressive design technique that incorporates slotting and geometric optimization has been used to develop a castle shaped antenna which improve impedance matching and bandwidth to overcome the inherent constraints of traditional microstrip antennas. Excellent impedance matching is shown by the final design's near-ideal voltage standing wave ratio (VSWR) and return loss (S11) of –48.76 dB. It achieves a broad impedance bandwidth of 154.88 GHz, which far outperforms many current systems. The antenna exhibits consistent radiation characteristics in the broadside direction, a gain of 8.005 dBi, a directivity of 8.727 dBi, and an efficiency of around 91.73%. The proposed design performs very well in terms of bandwidth and efficiency, while also preserving compact dimensions and structural simplicity, as shown by a comparative comparison with most current literature. These results validate the suitability of the proposed antenna for high-speed, short-range THz communication systems in future 6G networks.

Low-voltage ride-through (LVRT) capability is essential for grid-connected photovoltaic (PV) systems, especially as rising renewable integration challenges grid stability during voltage disturbances. Existing LVRT methods often target isolated control functions, leading to limited system resilience. This paper presents a unified control strategy integrating DC-link voltage regulation, reactive power injection, and overvoltage mitigation using a coordinated fuzzy logic framework. The proposed architecture employs a cascaded control structure comprising an outer voltage loop and an inner current loop with feed-forward decoupling, synchronized via a Synchronous Reference Frame Phase-Locked Loop (SRF-PLL). At its core is a dual-input, single-output Fuzzy Logic Controller (FLC), featuring optimized membership functions and dynamic rule-based logic to manage multiple control objectives during grid faults. The proposed FLC-based unified LVRT controller for grid-tied PV system was implemented and validated for both symmetrical and asymmetrical fault conditions in MATLAB/Simulink 2023b platform. The proposed FLC-based LVRT controller achieves voltage sag compensation of 97.02% and 98.4% for symmetrical and asymmetrical faults, respectively, outperforming conventional PI control, which achieves 94.02% and 96.5%. The system maintains a stable DC-link voltage of 800 V and delivers up to 78% reactive power support during faults. Fault detection and recovery are completed within 200 ms, complying with Bangladesh grid code requirements. This integrated fuzzy logic approach offers a significant advancement for enhancing grid stability in high-renewable environments and supports reliable renewable utilization, and more sustainable grid operation in developing regions.

Electricity consumption is rising across all industries. Residential electricity use dominates the sector. Solar photovoltaic (PV) systems on residential roofs are increasing quickly, notably in Dhaka, Bangladesh. PV power generation is high at peak sun irradiance. Due to light loads, residential structures use less electricity. PV system surplus electricity may be transmitted to the national grid. Residential customers may sell power to the government, lowering their electricity expense. Traditional energy meters make it difficult to calculate PV system consumption by load and grid injection. This is possible with net metering. Thus, this study presents an internet of things (IoT)-based smart net energy meter for home users to provide surplus solar PV power and consume grid electricity when needed. With the government's new power tariff rate, the net bill will be calculated automatically. A dedicated mobile application is used to monitor all the activities. The billing statement will be generated automatically, and the payment of that bill will be payable using a redirect link with the same mobile application. The suggested smart net energy meter will inform SMS/mobile app users of gas, smoke, and tempering. The suggested meter's performance and efficacy were evaluated using software simulation and hardware analysis.

The telemedicine service concept was mainly established to benefit the underprivileged people from rural areas of a country. However, due to the low literacy and awareness rates among rural population of Bangladesh, the service is not much effective. This paper represents a study on the awareness of the rural population of telemedicine service in Bangladesh and few key findings indicate how the awareness could be increased. The research also suggests that utilizing blockchain technology can enhance the data security and privacy. The research reveals some of the findings which can raise the awareness and popularity of telemedicine service among rural population. We have proposed implementation of blockchain technology which can vastly improve the security issue.

Machine learning is tightening its grasp on many sectors of modern life and medical sector is not an exception. In developing countries like Bangladesh, disease classification process mostly remains manual, time consuming and sometimes erroneous. Designing an E-health system comprised of disease identification model would be a great aid in such circumstances. The automation of identifying the diseases with the help of machine learning will be more accurate and time-saving. In this paper, Decision Tree, Gaussian Naive-Bayes, Random Forest, Logistic Regression, k-NN, MLP, and SVM machine learning techniques are applied for three diseases: Dengue, Diabetes, and Thyroid. MLP for Dengue, Logistic Regression for Diabetes, and Random Forest for Thyroid performed the best with accuracies of 88.3%, 82.5%, and 98.5% respectively. Additionally, a medical specialist recommendation model and a medicine suggestion model are also integrated in the proposed E-Health system.

In recent years, the demand of kiosk devices has increased significantly for relaying information in organizations, institutions, or any other service centers. They have become a better alternative for traditional human assistance or reception desks. However, there are no dynamic operating systems or user interfaces available for kiosk devices. This paper represents a development of an operating system for kiosk devices called ‘Nekray', which was built on Linux Kernel environment. It was designed to be dynamic, fast, user-friendly, and user interactive. The developed operating system avails the option to change the data of its features as per requirement. It also supports plug and play feature and can be installed in any low-cost hardware board. Furthermore, built-in AI is also a part of the developed system that performs its features through image processing. The system maintains the privacy and interactive transition of data to its users on kiosk devices. Request access from your librarian to read this article's full text.