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All Journal JAREE (Journal on Advanced Research in Electrical Engineering) Scientific Journal of Computer Science
Saeed, Zubair
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Azimuth Angle and Magnetic Declination to Maximize Solar Panel Efficiency (Solar Tracking System) Saeed, Zubair; Shahzad, Waseem; Ur Rehman, Asad; Ali, Syeda Zuriat e Zehra; Zaman, Shah; Shehzad, Faheem
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 8, No 1 (2024): January
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v8i1.357

Abstract

This study presents the idea of power production through the solar which depends on the light intensity that falls on the solar panel. This project utilizes dual axis solar tracking system based on intensity of light using Arduino UNO. The hardware implementation includes an Arduino UNO and four LDR sensor for sensing the maximum intensity of light. Two DC motors are used, one motor is used for horizontal rotation and the second motor is used for the vertical rotation. This system includes the implementation of MPPT. It controls the charge and stores it in a battery. Battery provides 12V to inverter where DC voltages convert into AC voltages. SPWM inverter is designed over push pull topology. In conclusion, the proposed system operates on low input power and delivers high efficiency in output.
Load Frequency Control by Quadratic Regulator Approach with Compensating Pole using SIMULINK Saeed, Zubair; Ur Rehman, Haseeb; Haseeb, Abdul; Taseen, Rabia; Shah, Muhammad Shahzaib; Shaikh, Inam Ul Hasan; Haider Ali, Muhammad Zulqarnain
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 7, No 2 (2023): July
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v7i2.356

Abstract

In this research, for the load frequency control (LFC) challenge, we provide a few possible approaches to building an optimum PID controller. This scheme employs the Quadratic Regulator Approach with Compensating Pole (QRAWCP) approach. In both multi-area and single-area power systems, this control law is used to solve load frequency concerns. And the other scenario that is considerable, the controller's robustness is evaluated on the same systems in terms of non-linearities, external disturbances, and parametric uncertainty such as the Governor Dead Band (GDB) as well as the Generation Rate Constraint (GRC). The performance of the control method is evaluated using Simulink simulations.
Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review Ishtiaq, Arslan; Saeed, Zubair; Khan, Misha Urooj; Samer, Aqsa; Shabbir, Mamoona; Ahmad, Waqar
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 6, No 2 (2022): October
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v6i2.323

Abstract

Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.
Exploring Quantum Machine Learning in Solving Complex Optimization Problems: Algorithms and Insights Nawaz, Uzma; Saeed, Zubair; Atif, Kamran
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.396

Abstract

Optimization problems across domains such as logistics, finance, and artificial intelligence often involve complex and NP-hard formulations that are computationally challenging for classical algorithms due to scalability and efficiency limitations. The study aims to systematically investigate the role of Quantum Machine Learning (QML) in addressing complex optimization problems and to analyze its advantages over traditional optimization techniques. A comprehensive survey and comparative analysis of key QML algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Neural Networks (QNNs), and Quantum Support Vector Machines (QSVMs), is conducted by examining their working principles, optimization capabilities, and real-world applications. The findings indicate that QML algorithms demonstrate significant potential in exploring large solutions spaces efficiently, achieving faster convergence, and providing improved optimization performance compared to classical approaches, although challenges such as quantum noise, scalability, and hardware limitations remain. The novelty of this study lies in providing a unified and critical comparative framework that integrates multiple QML optimization algorithms, highlights their practical feasibility, and identifies key research gaps hindering their real-world deployment. The implications of this research provide valuable insights for researchers and practitioners in selecting appropriate QML techniques and emphasize the need for advancements in hybrid quantum -classical systems, algorithms design, and quantum hardware to enable practical large-scale optimization.
Co-Authors Ahmad, Waqar Ali, Syeda Zuriat e Zehra Atif, Kamran Haider Ali, Muhammad Zulqarnain Haseeb, Abdul Ishtiaq, Arslan Khan, Misha Urooj Nawaz, Uzma Samer, Aqsa Shabbir, Mamoona Shah, Muhammad Shahzaib Shahzad, Waseem Shaikh, Inam Ul Hasan Shehzad, Faheem Taseen, Rabia Ur Rehman, Asad Ur Rehman, Haseeb Zaman, Shah