Ahmad Javaid

This article presents a comprehensive analysis of AI’s role in human-machine cooperation (HMC), offering an integrated perspective on how machine agents assess and interact with humans. While previous research examined individual aspects like human assessment, trust development, or function allocation separately, we integrate these components into a holistic framework for cooperative systems. We examine two assessment approaches: external methods (observing human cognitive states, intentions, and communications) and internal approaches (using cognitive models to emulate human thinking). Applications in manufacturing and autonomous vehicles demonstrate these concepts systematically. Building on these assessments, we investigate how AI enables machines to develop and calibrate trust in human partners and how it optimizes human-machine interaction through intelligent function allocation and interference management. The article addresses challenges and future research directions in human assessment, machine trust development, transparency, and interaction optimization. This review provides structured insights for utilizing AI in designing effective HMC systems where machines can assess humans, build appropriate trust, maintain transparency, and interact optimally to enable successful cooperation.

Ensuring robust security in near-field Integrated Sensing and Communication (ISAC) systems remains a critical challenge due to dynamic channel conditions, multi-eavesdropper threats, and the high computational burden of real-time optimization at mmWave and THz frequencies. To address these challenges, this paper introduces a novel Bayesian-Stackelberg framework that jointly optimizes sensing, beamforming, and communication. The dual-algorithm design integrates (i) Adaptive Hybrid Node Role Switching between secure transmission and cooperative jamming (ii) Belief-Driven Sensing and Beamforming for confidence based resource allocation. The proposed unified framework significantly improves robustness against attacks while preserving linear computational complexity. Simulation results across carrier frequencies ranging from 28 to 410 GHz demonstrate that the method achieves up to a 35% increase in secrecy rates and a success rate exceeding 98%, outperforming conventional communication systems with minimal runtime overhead. These findings underscore the scalability of belief-driven ISAC security solutions for low-complexity deployment in next generation communications.

This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets.

The authentication of wireless devices through physical layer attributes has attracted a fair amount of attention recently. Recent work in this area has examined various features extracted from the wireless signal to either identify a uniqueness in the channel between the transmitter–receiver pair or more robustly identify certain transmitter behaviors unique to certain devices originating from imperfect hardware manufacturing processes. In particular, the carrier frequency offset (CFO), induced due to the local oscillator mismatch between the transmitter and receiver pair, has exhibited good detection capabilities in stationary and low-mobility transmission scenarios. It is still unclear, however, how the CFO detection capability would hold up in more dynamic time-varying channels where there is a higher mobility. This paper experimentally demonstrates the identification accuracy of CFO for wireless devices in time-varying channels. To this end, a software-defined radio (SDR) testbed is deployed to collect CFO values in real environments, where real transmission and reception are conducted in a vehicular setup. The collected CFO values are used to train machine-learning (ML) classifiers to be used for device identification. While CFO exhibits good detection performance (97% accuracy) for low-mobility scenarios, it is found that higher mobility (35 miles/h) degrades (72% accuracy) the effectiveness of CFO in distinguishing between legitimate and non-legitimate transmitters. This is due to the impact of the time-varying channel on the quality of the exchanged pilot signals used for CFO detection at the receivers.

The integration of nuclear power plants (NPPs) with intermittent renewable energy sources, energy storage systems (ESS), and hydrogen-producing electrolyzers within an integrated energy system (IES) has the potential to enhance both its flexibility and profitability. Optimizing the ESS capacity can further improve the flexibility and the practicability of installing such storage solutions in a nuclear renewable-IES (NR-IES). This paper demonstrates the use of hyperparameter tuning in optimizing ESS capacities within such an envisioned NR-IES. This optimization results in decreased maintenance and installation costs for the ESS as they are operated at optimal capacities, where both the cumulative revenue and overall NPP flexibility in the NR-IES are high. The proposed ESS optimization solution involves a comparative analysis of various search optimization algorithms to determine the most effective hyperparameter tuning algorithm. This paper presents the NR-IES optimization using deep reinforcement learning (DRL) based on a prior study, where an agent is trained via proximal policy optimization (PPO). It demonstrates enhanced results in comparison to the earlier work. Compared to a non-hyperparameter-tuned framework, the ESS capacities tuned by Bayesian optimization (BO) would result in lower expenses and a significant revenue increase of 24.41% over a 120-day period.

The internet has become a significant part of our lives, impacting society in various ways. An increasing number of people are using the internet, bringing both positive and negative aspects. Malicious actors continuously seek to exploit vulnerabilities, leak sensitive information, compromise systems, and manipulate data. Some attacks emphasize the importance of robust privacy features in web browsers, which users frequently neglect. This paper closely examines the security levels of popular web browsers, focusing on elements, such as cookies. There is existing literature on browser security, discussing challenges, security improvements, and the concern of fingerprinting. However, few studies focus on cookies, a crucial feature to online privacy for everyday users. This project assessed browser security by examining cookies and performance. Cookies from diverse websites were tested and analyzed across various browsers, highlighting the need for strong defenses against cookies to safeguard user privacy and enhance our comprehension of browser security.

Interactions with electronic devices are changing in our daily lives. The day-to-day development brings curiosity to recent technology and challenges its use. The gadgets are becoming cumbersome, and their usage frustrates a segment of society. In specific scenarios, the user cannot use the modalities because of the challenges that bring in, e.g., the usage of touch screen devices by elderly people. The idea of multimodality provides easy access to devices of daily use through various modalities. In this paper, we suggest a solution that allows the operation of a microcontroller-based device using voice and speech. The model implemented will learn from the user's behavior and decide based on prior knowledge.