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Muhammad Iqbal

Publications and source records attributed to Muhammad Iqbal.

At least 19 recordsLinked to original sources
arXiv2026

Governing What the EU AI Act Excludes: Accountability for Autonomous AI Agents in Smart City Critical Infrastructure

When a traffic signal controller adjusts green phases and a grid manager curtails power on the same corridor, each system may comply with its own obligations. The resident who suffers the combined effect has no single authority to hold accountable and, under the EU AI Act, limited means to obtain an explanation. Annex III, point 2 excludes safety-component AI in critical infrastructure from Article 86 explanation rights and Article 27 fundamental-rights impact assessment. Provider and deployer duties under Articles 9-15 still apply, and residual pathways under the GDPR, NIS2, and tortious liability offer partial coverage. The Act's principal resident-facing accountability instruments are nonetheless narrowed for the autonomous infrastructure systems most likely to interact across agencies. The paper traces this accountability deficit through four residual pathways (GDPR Article 22, GDPR transparency obligations, tortious liability, and NIS2) and shows that each is structurally bounded by individual-controller, individual-decision scope. As a governance response, it presents AgentGov-SC, a three-layer architecture (Agent, Orchestration, City) specifying 25 governance measures with bidirectional traceability to the EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework. Five conflict resolution rules and an autonomy-calibrated activation model complete the design. A scenario analysis traces governance activation through a multi-agent corridor cascade involving three documented UAE smart-city systems, with a contrasting single-system scenario confirming proportional activation. The paper contributes a regulatory gap analysis and governance architecture for an increasingly important class of urban AI deployment that existing frameworks treat as bounded and isolated.

cs.CY↗
arXiv2026

UGAF-ITS: A Standards Harmonization Framework and Validation Tool for Multi-Framework AI Governance in Distributed Intelligent Transportation Systems

Organizations deploying AI-enabled Intelligent Transportation Systems face fragmented governance: ISO/IEC~42001 demands a certifiable management system, the EU AI Act imposes binding high-risk obligations from August~2026, and the NIST AI Risk Management Framework structures voluntary practice. This paper introduces UGAF-ITS, a standards harmonization framework that consolidates 154 source obligations from the three instruments into 12 unified controls across eight governance domains through a reproducible five-phase crosswalk methodology. A three-tier operating model allocates each control to the vehicle, edge, or cloud tier where enforcement and defensible evidence production are feasible. An evidence backbone of 20 versioned artifacts supports a single audit package across all three frameworks without duplicating content. We evaluate UGAF-ITS through an open-source governance engine applied to four architecturally distinct ITS deployment scenarios. The engine encodes the complete crosswalk catalog and executes eight compliance computations. Three-tier deployments achieve 91.7\% average scoped framework coverage with 45.9\% evidence reduction, complete bidirectional traceability, and 80\% of artifacts serving all three frameworks simultaneously. Partial deployments contract predictably, with reduction holding between 25\% and 50\% across archetypes. The siloed baseline is enumerated document by document and computed by the tool at run time. These results establish structural properties of the harmonized catalog under architectural variation. They do not establish operational compliance readiness, which external assessment with practitioners has yet to test. The tool, scenarios, and all reported results are publicly available for independent replication.

cs.CY↗
arXiv2026

Integrating Lagrangian Neural Networks into the Dyna Framework for Reinforcement Learning

Model-based reinforcement learning (MBRL) is sample-efficient but depends on the accuracy of the learned dynamics, which are often modeled using black-box methods that do not adhere to physical laws. Those methods tend to produce inaccurate predictions when presented with data that differ from the original training set. In this work, we employ Lagrangian neural networks (LNNs), which enforce an underlying Lagrangian structure to train the model within a Dyna-based MBRL framework. Furthermore, we train the LNN using stochastic gradient-based and state-estimation-based optimizers to learn the network's weights. The state-estimation-based method converges faster than the stochastic gradient-based method during neural network training. Simulation results are provided to illustrate the effectiveness of the proposed LNN-based Dyna framework for MBRL.

eess.SY↗
arXiv2025

Ethical conundrums: Hacked data in the study of far-right violent extremism

Ethical conduct in digital research is full of grey areas. Disciplinary, institutional and individual norms and conventions developed to support research are challenged, often leaving scholars with a sense of unease or lack of clarity. The growing availability of hacked data is one area. Discussions and debates around the use of these datasets in research are extremely limited. Reviews of the history, culture, or morality of the act of hacking are topics that have attracted some scholarly attention. However, how to undertake research with this data is less examined and provides an opportunity for the generation of reflexive ethical practice. This article presents a case-study outlining the ethical debates that arose when considering the use of hacked data to examine online far-right violent extremism. It argues that under certain circumstances, researchers can do ethical research with hacked data. However, to do so we must proactively and continually engage deeply with ethical quandaries and dilemmas.

cs.CY↗
arXiv2025

Statistical Linear Regression Approach to Kalman Filtering and Smoothing under Cyber-Attacks

Remote state estimation in cyber-physical systems is often vulnerable to cyber-attacks due to wireless connections between sensors and computing units. In such scenarios, adversaries compromise the system by injecting false data or blocking measurement transmissions via denial-of-service attacks, distorting sensor readings. This paper develops a Kalman filter and Rauch--Tung--Striebel (RTS) smoother for linear stochastic state-space models subject to cyber-attacked measurements. We approximate the faulty measurement model via generalized statistical linear regression (GSLR). The GSLR-based approximated measurement model is then used to develop a Kalman filter and RTS smoother for the problem. The effectiveness of the proposed algorithms under cyber-attacks is demonstrated through a simulated aircraft tracking experiment.

eess.SP↗
arXiv2025

Communication-Efficient Distributed Kalman Filtering using ADMM

This paper addresses the problem of optimal linear filtering in a network of local estimators, commonly referred to as distributed Kalman filtering (DKF). The DKF problem is formulated within a distributed optimization framework, where coupling constraints require the exchange of local state and covariance updates between neighboring nodes to achieve consensus. To address these constraints, the problem is transformed into an unconstrained optimization form using the augmented Lagrangian method. The distributed alternating direction method of multipliers (ADMM) is then applied to derive update steps that achieve the desired performance while exchanging only the primal variables. Notably, the proposed method enhances communication efficiency by eliminating the need for dual variable exchange. We show that the design parameters depend on the maximum eigenvalue of the network's Laplacian matrix, yielding a significantly tighter bound compared to existing results. A rigorous convergence analysis is provided, proving that the state estimates converge to the true state and that the covariance matrices across all local estimators converge to a globally optimal solution. Numerical results are presented to validate the efficacy of the proposed approach.

eess.SY↗
arXiv2025

Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate.

eess.SP↗
arXiv2024

Enhancing Object Detection Accuracy in Autonomous Vehicles Using Synthetic Data

The rapid progress in machine learning models has significantly boosted the potential for real-world applications such as autonomous vehicles, disease diagnoses, and recognition of emergencies. The performance of many machine learning models depends on the nature and size of the training data sets. These models often face challenges due to the scarcity, noise, and imbalance in real-world data, limiting their performance. Nonetheless, high-quality, diverse, relevant and representative training data is essential to build accurate and reliable machine learning models that adapt well to real-world scenarios. It is hypothesised that well-designed synthetic data can improve the performance of a machine learning algorithm. This work aims to create a synthetic dataset and evaluate its effectiveness to improve the prediction accuracy of object detection systems. This work considers autonomous vehicle scenarios as an illustrative example to show the efficacy of synthetic data. The effectiveness of these synthetic datasets in improving the performance of state-of-the-art object detection models is explored. The findings demonstrate that incorporating synthetic data improves model performance across all performance matrices. Two deep learning systems, System-1 (trained on real-world data) and System-2 (trained on a combination of real and synthetic data), are evaluated using the state-of-the-art YOLO model across multiple metrics, including accuracy, precision, recall, and mean average precision. Experimental results revealed that System-2 outperformed System-1, showing a 3% improvement in accuracy, along with superior performance in all other metrics.

cs.CV↗
arXiv2024

CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher

Penetration testing, a critical component of cybersecurity, typically requires extensive time and effort to find vulnerabilities. Beginners in this field often benefit from collaborative approaches with the community or experts. To address this, we develop CIPHER (Cybersecurity Intelligent Penetration-testing Helper for Ethical Researchers), a large language model specifically trained to assist in penetration testing tasks. We trained CIPHER using over 300 high-quality write-ups of vulnerable machines, hacking techniques, and documentation of open-source penetration testing tools. Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models. This approach fills a significant gap in traditional cybersecurity Q\&A benchmarks and provides a realistic and rigorous standard for evaluating AI's technical knowledge, reasoning capabilities, and practical utility in dynamic penetration testing scenarios. In our assessments, CIPHER achieved the best overall performance in providing accurate suggestion responses compared to other open-source penetration testing models of similar size and even larger state-of-the-art models like Llama 3 70B and Qwen1.5 72B Chat, particularly on insane difficulty machine setups. This demonstrates that the current capabilities of general LLMs are insufficient for effectively guiding users through the penetration testing process. We also discuss the potential for improvement through scaling and the development of better benchmarks using FARR Flow augmentation results. Our benchmark will be released publicly at https://github.com/ibndias/CIPHER.

cs.CR↗
arXiv2023

Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab-Column Connections

This research aims at comparative analysis of shear strength prediction at slab-column connection, unifying machine learning, design codes and Finite Element Analysis. Current design codes (CDCs) of ACI 318-19 (ACI), Eurocode 2 (EC2), Compressive Force Path (CFP) method, Feed Forward Neural Network (FNN) based Artificial Neural Network (ANN), PSO-based FNN (PSOFNN), and BAT algorithm-based BATFNN are used. The study is complemented with FEA of slab for validating the experimental results and machine learning predictions.In the case of hybrid models of PSOFNN and BATFNN, mean square error is used as an objective function to obtain the optimized values of the weights, that are used by Feed Forward Neural Network to perform predictions on the slab data. Seven different models of PSOFNN, BATFNN, and FNN are trained on this data and the results exhibited that PSOFNN is the best model overall. PSOFNN has the best results for SCS=1 with highest value of R as 99.37% and lowest of MSE, and MAE values of 0.0275%, and 1.214% respectively which are better than the best FNN model for SCS=4 having the values of R, MSE, and MAE as 97.464%, 0.0492%, and 1.43%, respectively.

cs.NE↗
arXiv2023

Adaptive Three Layer Hybrid Reconfigurable Intelligent Surface for 6G Wireless Communication: Trade-offs and Performance

A potential candidate technology for the development of future 6G networks has been recognized as Reconfigurable Intelligent Surface (RIS). However, due to the variation in radio link quality, traditional passive RISs only accomplish a minimal signal gain in situations with strong direct links between user equipment (UE) and base station (BS). In order to get over this fundamental restriction of smaller gain, the idea of active RISs might be a suitable solution. In contrast to current passive RIS, which simply reflects and directs signals without any additional amplification, active RISs have the ability to enhance reflected signals by the incorporation of amplifiers inside its elements. However, with additional amplifiers, apart from the relatively complex attributes of RIS-assisted arrangements, the additional energy consumption of such technologies is often disregarded. So, there might be a tradeoff between the additional energy consumption for the RIS technologies and the overall gain acquired by deploying this potential advancement. The objective of this work is to provide a primary idea of a three-layer hybrid RIS-assisted configuration that is responsive to both active and passive RIS, as well as an additional dormant or inactive state. The single RIS structure should be capable of adjusting its overall configuration in response to fluctuations in transmit power and radio link quality. Furthermore, our fabricated passive RIS-assisted structure verifies a portion of the proposed idea, with simulations highlighting its advantages over standalone passive or active RIS-assisted technologies.

eess.SP↗
arXiv2023

Performance Comparison Between VoLTE and non-VoLTE Voice Calls During Mobility in Commercial Deployment: A Drive Test-Based Analysis

The optimization of network performance is vital for the delivery of services using standard cellular technologies for mobile communications. Call setup delay and User Equipment (UE) battery savings significantly influence network performance. Improving these factors is vital for ensuring optimal service delivery. In comparison to traditional circuit-switched voice calls, VoLTE (Voice over LTE) technology offers faster call setup durations and better battery-saving performance. To validate these claims, a drive test was carried out using the XCAL drive test tool to collect real-time network parameter details in VoLTE and non-VoLTE voice calls. The findings highlight the analysis of real-time network characteristics, such as the call setup delay calculation, battery-saving performance, and DRX mechanism. The study contributes to the understanding of network optimization strategies and provides insights for enhancing the quality of service (QoS) in mobile communication networks. Examining VoLTE and non-VoLTE operations, this research highlights the substantial energy savings obtained by VoLTE. Specifically, VoLTE saves approximately 60.76% of energy before the Service Request and approximately 38.97% of energy after the Service Request. Moreover, VoLTE to VoLTE calls have a 72.6% faster call setup delay than non-VoLTE-based LTE to LTE calls, because of fewer signaling messages required. Furthermore, as compared to non-VoLTE to non-VoLTE calls, VoLTE to non-VoLTE calls offer an 18.6% faster call setup delay. These results showcase the performance advantages of VoLTE and reinforce its potential for offering better services in wireless communication networks.

cs.NI↗
arXiv2021

Implementasi dan Analisis Performa Bonding Interface Mode 802.3ad sebagai Link Redundancy pada Router Mikrotik

Network stability and reliability is an absolute requirement for telecommunications networks. Bonding interface is a technique that can facilitate the network to serve services that require network stability and reliability. This interface bonding system combines two interfaces into a virtual link that is characterized by the use of one IP address. If a frame is sent from the sender to the receiver, but in the process of sending one of the links there is an interruption, then the link that is still connected will be able to maintain the connection so that frame transmission continues, this working system is called link redundancy. To monitor the condition of the link whether it is connected or broken, a mechanism called link-monitoring is used. The monitoring link used in this final project is Media Independent Interface (MII). The test step is to implement a Bonding Interface network for the use of video streaming, VoIP, and file transfer. The analysis carried out is a failover test that uses a connection switching time parameter and a QoS test with packet loss, delay, jitter, and throughput parameters. The results will then be compared with a network that does not use a Bonding Interface, which is a network that is only connected using one link. From the results of the research conducted is that the Bonding Interface system is able to perform a redundancy mechanism when one of the links is broken/down to an active/connected link and the services that are run are not interrupted. Bonding Interface is also able to provide network stability compared to networks that do not use a Bonding Interface, this is characterized by a smaller jitter value than the use of a network with one link.

cs.NI↗
arXiv2020

A New Paradigm for Water Level Regulation using Three Pond Model with Fuzzy Inference System for Run of River Hydropower Plant

The energy generation of a run of river hydropower plant depends upon the flow of river and the variations in the water flow makes the energy production unreliable. This problem is usually solved by constructing a small pond in front of the run of river hydropower plant. However, changes in water level of conventional single pond model results in sags, surges and unpredictable power fluctuations. This work proposes three pond model instead of traditional single pond model. The volume of water in three ponds is volumetrically equivalent to the traditional single pond but it reduces the dependency of the run of river power plant on the flow of river. Moreover, three pond model absorbs the water surges and disturbances more efficiently. The three pond system, modeled as non-linear hydraulic three tank system, is being applied with fuzzy inference system and standard PID based methods for smooth and efficient level regulation. The results of fuzzy inference system are across-the-board improved in terms of regulation and disturbances handling as compared to conventional PID controller.

eess.SY↗
arXiv2018

Design of nanoparticles for generation and stabilization of CO2-in-brine foams with or without added surfactants

Whereas many studies have examined stabilization of emulsions and foams in low salinity aqueous phases with nanoparticles (NPs) with and without added surfactants, interest has grown recently in much higher salinities relevant to subsurface oil and gas applications. It is shown for the first time that NPs grafted with well-defined low molecular weight ligands colloidally stable in concentrated brine (in particular, API brine, 8% NaCl + 2% CaCl2) and are interfacially active at the brine-air interface. These properties were achieved for three types of ligands: a nonionic diol called GLYMO and two short poly(ethylene glycol) (PEG) oligomers with 6-12 EO repeat units. Carbon dioxide-in-water (C/W) foams could be formed only with modified NPs with higher surface pressures at the A/W interface. Furthermore, these ligands were sufficiently CO2-philic that the hydrophilic/CO2-philic balance of silica NPs was low enough for stabilization of CO2-in-water (C/W) foam with API brine. Additionally, NPs with these three ligands formed stable dispersions with various free molecular surfactants in DI water and even API brine (8% NaCl + 2% CaCl2) at room temperature. A wide variety of mixtures of NPs plus anionic, nonionic, or cationic mixtures that formed stable dispersions were also found to stabilize C/W foams in porous media at high salinity. These results provide a basis for future studies of the mechanism of foam stabilization with NPs and NP/surfactant mixtures at high salinity.

cond-mat.soft↗
arXiv2018

Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor

Object retrieval and classification in point cloud data is challenged by noise, irregular sampling density and occlusion. To address this issue, we propose a point pair descriptor that is robust to noise and occlusion and achieves high retrieval accuracy. We further show how the proposed descriptor can be used in a 4D convolutional neural network for the task of object classification. We propose a novel 4D convolutional layer that is able to learn class-specific clusters in the descriptor histograms. Finally, we provide experimental validation on 3 benchmark datasets, which confirms the superiority of the proposed approach.

cs.CV↗
arXiv2018

Local and Global Existence of Solutions to Scalar Equations on Spatially Flat Universe as a Background with Non-minimal Coupling

We prove the wellposedness of scalar wave equations on spatially flat universe as a background with nonminimal coupling with the scalar potential turned on by introducing the $k$-order linear energy and the corresponding energy norm. In the local case, we show that both the $k$-order linear energy and the energy norm are bounded for finite time with initial data in $H^{k+1}\times H^{k}$. Whereas in the global case, we have to add three assumptions related to the nonminimal coupling constant, the scale factor of spacetimes, and the form of the scalar that has to be a polynomial with a small positive parameter. Then, we show that the solution does globally exist with a particular decay estimate that depends on the scale factor of the spacetimes. Finally, we provide some physical models that support our general setup.

math-ph↗