Ali A. Nasir

Y. Shi, H. D. Tuan, A. A. Nasir et al.

In this paper, phasor measurement unit (PMU) placement for power grid state estimation under different degrees of observability is studied. Observability degree is the depth of the buses' reachability by the placed PMUs and thus constitutes an important characteristic for PMU placement. However, the sole observability as addressed in many works still does not guarantee a good estimate for the grid state. Some existing works also considered the PMU placement for minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state. However, they ignore the observability requirements for computational tractability and thus potentially lead to artificial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state, under grid observability constraints. The provided solution is free from the mentioned fundamental drawbacks in the existing PMU placement designs. The problems are posed as binary nonlinear optimization problems, for which this paper develops efficient algorithms for computational solutions. The performance of the proposed algorithms is analyzed in detail through numerical examples on large-scale IEEE power networks.

Khaoula Khaled, Muhammad Afaq, Ali Arshad Nasir et al.

Unmanned aerial vehicle (UAV) can provide on-demand, high-capacity connectivity in disaster and normal situation. However, it faces a challenge of curse of dimensionality in trajectory optimization, where interference-limited environments and vast search spaces make real-time coordination computationally expensive. To overcome this challenge, we propose the Rate-Aware Quantum-Annealed Graph Condensation (RA-QAGC) scheme, which combines rate-aware graph abstraction with decentralized reinforcement learning to enable scalable, interference-aware UAV coordination. By identifying high throughput locations and guiding UAV trajectory adaptation toward throughput-optimal regions, RA-QAGC effectively balances network capacity by maintaining quality-of-service (QoS) requirements. Simulation results demonstrate the proposal outperformed over existing schemes by achieving 59.4 Mbps total throughput and 23.9 Mbps priority-user throughput, representing gains of approximately 15% and 34%, respectively, over the baseline schemes.

Abbas Anwar, Mohammad Shullar, Ali Arshad Nasir et al.

Images captured in challenging environments often experience various forms of degradation, including noise, color cast, blur, and light scattering. These effects significantly reduce image quality, hindering their applicability in downstream tasks such as object detection, mapping, and classification. Our transformer-based diffusion model was developed to address image restoration tasks, aiming to improve the quality of degraded images. This model was evaluated against existing deep learning methodologies across multiple quality metrics for underwater image enhancement, denoising, and deraining on publicly available datasets. Our findings demonstrate that the diffusion model, combined with transformers, surpasses current methods in performance. The results of our model highlight the efficacy of diffusion models and transformers in improving the quality of degraded images, consequently expanding their utility in downstream tasks that require high-fidelity visual data.

Omar H. Salim, Wei Xiang, Ali A. Nasi et al.

Previous studies in the literature for video transmission over wireless communication systems focused on combating the effects of additive channel noise and fading channels without taking the impairments in the physical layer such as phase noise (PHN) into account. Oscillator phase noise impairs the performance of multi-input multi-output- orthogonal frequency division multiplexing (MIMO-OFDM) systems in providing high data rates for video applications and may lead to decoding failure. In this paper, we propose a light field (LF) video transmission system in wireless channels, and analyze joint data detection and phase mitigation in MIMO-OFDM systems for LF video transmission. The signal model and rate-distortion (RD) model for LF video transmission in the presence of multiple PHNs are discussed. Moreover, we propose an iterative algorithm based on the extended Kalman filter for joint data detection and PHN tracking. Numerical results show that the proposed detector can significantly improve the average bit-error rate (BER) and peak-to-noise ratio (PSNR) performance for LF video transmission compared to existing algorithms. Moreover, the BER and PSNR performance of the proposed system is closer to that of the ideal case of perfect PHN estimation. Finally, it is demonstrated that the proposed system model and algorithm are well suited for LF video transmission in wireless channels.