arXiv subjects

Muhammad Farooq

Publications and source records attributed to Muhammad Farooq.

At least 19 recordsLinked to original sources

arXiv2026

Neutral-current neutrino-nucleus scattering off I (127) and Cs (133): Coherent and incoherent contributions with electroweak refinements for odd-A nuclei

Calculations of neutral-current neutrino-nucleus scattering cross sections are important for interpreting low- and intermediate-energy neutrino data, where terrestrial measurements remain limited. The first observation of coherent elastic neutrino-nucleus scattering (CEvNS) in 2017 with a CsI[Na] detector at the Spallation Neutron Source reported results consistent with Standard Model expectations, motivating improved theoretical descriptions beyond the purely coherent regime. In this work, we calculate neutral-current scattering cross sections for 127I and 133Cs within a unified analytical framework that consistently incorporates coherent elastic, incoherent excitation, and spin-dependent axial contributions relevant for odd-A nuclei. A consistent distinction between nuclear and nucleon form factors is maintained throughout the formalism. The incoherent contribution is evaluated using structure-function methods, providing a physically motivated decomposition of the total cross section. Electroweak effects are included through momentum-transfer-dependent sin^2(theta_W) corrections in the MS-bar scheme together with flavor-dependent neutrino charge-radius contributions implemented consistently in the vector couplings. Cross sections are presented as functions of neutrino energy and for decay-at-rest neutrino spectra in the low-to-intermediate energy region where elastic and quasi-elastic processes dominate. Inclusion of incoherent and axial contributions enhances the total cross section near Enu ~ 10 MeV, while the incoherent component becomes dominant around Enu ~ 50 MeV. Expected interaction rates for decay-at-rest neutrinos are of order 0.1 events kg^-1 yr^-1 near a 40 keV recoil threshold. The results provide a systematic assessment of subleading contributions relevant for CsI-based detectors and astrophysical neutrino applications.

hep-ph↗

arXiv2026

Long Range Outlook for Short-Range Correlations

Short range correlated (SRC) N N pairs are pairs of nucleons with high relative momentum (prel > kF where kF ~ 250 MeV/c is the Fermi momentum in medium to heavy nuclei) and lower center of mass momentum. The motivation for studying SRC pairs ranges from a desire to achieve a more comprehensive understanding of the many-body nuclear wave-function at high-resolution to searching for explicit QCD-dynamics effects within the nuclear medium, not to mention connections to many other open problems in nuclear physics. Exploring short-range correlations was one of the physics motivations for building CEBAF (now Jefferson Lab). Scientists used the high luminosity and high energy of this cutting-edge machine to find kinematics that cleanly showed the signals of short-range correlations. This paved the way in the last two decades for tremendous progress understanding these correlations. This paper reviews recent progress and highlights outstanding questions and areas that need further study.

nucl-ex↗

arXiv2025

Realizing the Scientific Program with Polarized Ion Beams at EIC

Polarized ion beams at the Electron Ion Collider are essential to address some of the most important open questions at the twenty-first century frontiers of understanding of the fundamental structure of matter. Here, we summarize the science case and identify polarized $^2$H, $^3$He, $^6$Li and $^7$Li ion beams as critical technology that will enable experiments which address the most important science. Further, we discuss the required ion polarimetry and spin manipulation in EIC. The current EIC accelerator design is presented. We identify a significant R\&D effort involving both national laboratories and universities that is required over about a decade to realize the polarized ion beams and estimate (based on previous experience) that it will require about 20 FTE over 10 years (or a total of about 200 FTE-years) of personnel, including graduate students, postdoctoral researchers, technicians and engineers. Attracting, educating and training a new generation of physicists in experimental spin techniques will be essential for successful realization. AI/ML is seen as having significant potential for both acceleration of R\&D and amplification of discovery in optimal realization of this unique quantum technology on a cutting-edge collider. The R\&D effort is synergistic with research in atomic physics and fusion energy science.

nucl-ex↗

arXiv2025

High-dimensional covariance matrix regularization using informative targets

The sample covariance matrix becomes non-invertible in high-dimensional settings, making classical multivariate statistical methods inapplicable. Various regularization techniques address this issue by imposing a structured target matrix to improve stability and invertibility. While diagonal matrices are commonly used as targets due to their simplicity, more informative target matrices can enhance performance. This paper explores the use of such targets and estimates the underlying correlation parameter using maximum likelihood. The proposed method is analytically straightforward, computationally efficient, and more accurate than recent regularization techniques when targets are correctly specified. Its effectiveness is demonstrated through extensive simulations and a real-world application.

stat.ME↗

arXiv2024

An Adaptive System Architecture for Multimodal Intelligent Transportation Systems

Multimodal intelligent transportation systems (M-ITS) encompass a range of transportation services that utilise various modes of transport and incorporate intelligent technologies for enhanced efficiency and user experience. There are several challenges in M-ITS including data integration, Interoperability, scalability, user experience, etc. To address these challenges, such a system requires an adaptive system architecture that enables M-ITS to operate as an integrated ecosystem. In this paper, we provide an adaptive, user-centric, and layered architecture for multimodal transportation systems. The proposed architecture ensures scalability for seamless interactions of various subcomponents, that are often managed by different stakeholders. Concurrently, the data architecture is detailed, covering diverse data sources, advanced analytics, and stringent governance, providing a robust basis for intelligent decision-making. We provide two example use cases of the proposed architecture, showing how the data architecture and the system architecture can be fused and serve multimodal intelligent transport services.

eess.SY↗

arXiv2024

On the Sum Secrecy Rate Maximisation for Wireless Vehicular Networks

Wireless communications form the backbone of future vehicular networks, playing a critical role in applications ranging from traffic control to vehicular road safety. However, the dynamic structure of these networks creates security vulnerabilities, making security considerations an integral part of network design. We address these security concerns from a physical layer security aspect by investigating achievable secrecy rates in wireless vehicular networks. Specifically, we aim to maximize the sum secrecy rate from all vehicular pairs subject to bandwidth and power resource constraints. For the considered problem, we first propose a solution based on the successive convex approximation (SCA) method, which has not been applied in this context before. To further reduce the complexity of the SCA-based method, we also propose a low-complexity solution based on a fast iterative shrinkage-thresholding algorithm (FISTA). Our simulation results for SCA and FISTA show a trade-off between convergence and runtime. While the SCA method achieves better convergence, the FISTA-based approach is at least 300 times faster than the SCA method.

eess.SY↗

arXiv2023

Robustness for Free: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits

Soft robotics aims to develop robots able to adapt their behavior across a wide range of unstructured and unknown environments. A critical challenge of soft robotic control is that nonlinear dynamics often result in complex behaviors hard to model and predict. Typically behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. More recently, optimization algorithms such as Genetic Algorithms (GA) have been used to discover gaits, but these behaviors are often optimized for a single environment or terrain, and can be brittle to unplanned changes to terrain. In this paper we demonstrate how Quality Diversity Algorithms, which search of a range of high-performing behaviors, can produce repertoires of gaits that are robust to changing terrains. This robustness significantly out-performs that of gaits produced by a single objective optimization algorithm.

cs.NE↗

arXiv2023

Inferior Myocardial Infarction Detection from lead II of ECG: A Gramian Angular Field-based 2D-CNN Approach

This paper presents a novel method for inferior myocardial infarction (MI) detection using lead II of electrocardiogram (ECG). We evaluate our proposed method on a public dataset, namely, Physikalisch Technische Bundesanstalt (PTB) ECG dataset from Physionet. Under our proposed method, we first clean the noisy ECG signals using db4 wavelet, followed by an R-peak detection algorithm to segment the ECG signals into beats. We then translate the ECG timeseries dataset to an equivalent dataset of gray-scale images using Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) operations. Subsequently, the gray-scale images are fed into a custom two-dimensional convolutional neural network (2D-CNN) which efficiently differentiates between a healthy subject and a subject with MI. Our proposed approach achieves an average classification accuracy of 99.68%, 99.80%, 99.82%, and 99.84% under GASF dataset with noise and baseline wander, GADF dataset with noise and baseline wander, GASF dataset with noise and baseline wander removed, and GADF dataset with noise and baseline wander removed, respectively. Most importantly, this work opens the floor for innovation in wearable devices to measure lead II ECG (e.g., by a smart watch worn on right wrist, along with a smart patch on left leg), in order to do accurate, real-time and early detection of inferior wall MI.

eess.SP↗

arXiv2022

Mirror Prox Algorithm for Large-Scale Cell-Free Massive MIMO Uplink Power Control

We consider the problem of max-min fairness for uplink cell-free massive multiple-input multiple-output (MIMO) subject to per-user power constraints. The standard framework for solving the considered problem is to separately solve two subproblems: the receiver filter coefficient design and the power control problem. While the former has a closed-form solution, the latter has been solved using either second-order methods of high computational complexity or a first-order method that provides an approximate solution. To deal with these drawbacks of the existing methods, we propose a mirror prox based method for the power control problem by equivalently reformulating it as a convex-concave problem and applying the mirror prox algorithm to find a saddle point. The simulation results establish the optimality of the proposed solution and demonstrate that it is more efficient than the known methods. We also conclude that for large-scale cell-free massive MIMO, joint optimization of linear receive combining and power control provides significantly better user fairness than the power control only scheme in which receiver coefficients are fixed to unity.

eess.SP↗

arXiv2022

On the Achievable Rate of IRS-Assisted Multigroup Multicast Systems

Intelligent reflecting surfaces (IRSs) have shown huge advantages in many potential use cases and thus have been considered a promising candidate for next-generation wireless systems. In this paper, we consider an IRS-assisted multigroup multicast (IRS-MGMC) system in a multiple-input single-output (MISO) scenario, for which the related existing literature is rather limited. In particular, we aim to jointly design the transmit beamformers and IRS phase shifts to maximize the sum rate of the system under consideration. In order to obtain a numerically efficient solution to the formulated non-convex optimization problem, we propose an alternating projected gradient (APG) method where each iteration admits a closed-form and is shown to be superior to a known solution that is derived from the majorization-minimization (MM) method in terms of both achievable sum rate and required complexity, i.e., run time. In particular, we show that the complexity of the proposed APG method grows linearly with the number of IRS tiles, while that of the known solution in comparison grows with the third power of the number of IRS tiles. The numerical results reported in this paper extend our understanding on the achievable rates of large-scale IRS-assisted multigroup multicast systems.

cs.IT↗

arXiv2022

Massive MIMO for Serving Federated Learning and Non-Federated Learning Users

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a promising learning framework for beyond 5G wireless networks. It is anticipated that future wireless networks will jointly serve both FL and downlink non-FL user groups in the same time-frequency resource. While in the downlink of each FL iteration, both groups jointly receive data from the base station in the same time-frequency resource, the uplink of each FL iteration requires bidirectional communication to support uplink transmission for FL users and downlink transmission for non-FL users. To overcome this challenge, we present half-duplex (HD) and full-duplex (FD) communication schemes to serve both groups. More specifically, we adopt the massive multiple-input multiple-output technology and aim to maximize the minimum effective rate of non-FL users under a quality of service (QoS) latency constraint for FL users. Since the formulated problem is highly nonconvex, we propose a power control algorithm based on successive convex approximation to find a stationary solution. Numerical results show that the proposed solutions perform significantly better than the considered baselines schemes. Moreover, the FD-based scheme outperforms the HD-based scheme in scenarios where the self-interference is small or moderate and/or the size of FL model updates is large.

eess.SP↗

arXiv2022

Serving Federated Learning and Non-Federated Learning Users: A Massive MIMO Approach

Federated learning (FL) with its data privacy protection and communication efficiency has been considered as a promising learning framework for beyond-5G/6G systems. We consider a scenario where a group of downlink non-FL users are jointly served with a group of FL users using massive multiple-input multiple-output technology. The main challenge is how to utilise the resource to optimally serve both FL and non-FL users. We propose a communication scheme that serves the downlink of the non-FL users (UEs) and the uplink of FL UEs in each half of the frequency band. We formulate an optimization problem for optimizing transmit power to maximize the minimum effective data rates for non-FL users, while guaranteeing a quality-of-service time of each FL communication round for FL users. Then, a successive convex approximation-based algorithm is proposed to solve the formulated problem. Numerical results confirm that our proposed scheme significantly outperforms the baseline scheme.

eess.SP↗

arXiv2022

Power Control for Multigroup Multicast Cell-Free Massive MIMO Downlink

We consider a multigroup multicast cell-free multiple-input multiple-output (MIMO) downlink system with short-term power constraints. In particular, the normalized conjugate beamforming scheme is adopted at each access point (AP) to keep the downlink power strictly under the power budget regardless of small scale fading. In the considered scenario, APs multicast signals to multiple groups of users whereby users in the same group receive the same message. Under this setup, we are interested in maximizing the minimum achievable rate of all groups, commonly known as the max-min fairness problem, which has not been studied before in this context. To solve the considered problem, we first present a bisection method which in fact has been widely used in previous studies for cell-free massive MIMO, and then propose an accelerated projected gradient (APG) method. We show that the proposed APG method outperforms the bisection method requiring lesser run time while still achieving the same objective value. Moreover, the considered power control scheme provides significantly improved performance and more fairness among the users compared to the equal power allocation scheme.

eess.SP↗

arXiv2022

Accelerated Projected Gradient Method for the Optimization of Cell-Free Massive MIMO Downlink

We consider the downlink of a cell-free massive multiple-input multiple-output (MIMO) system where large number of access points (APs) simultaneously serve a group of users. Two fundamental problems are of interest, namely (i) to maximize the total spectral efficiency (SE), and (ii) to maximize the minimum SE of all users. As the considered problems are non-convex, existing solutions rely on successive convex approximation to find a sub-optimal solution. The known methods use off-the-shelf convex solvers, which basically implement an interior-point algorithm, to solve the derived convex problems. The main issue of such methods is that their complexity does not scale favorably with the problem size, limiting previous studies to cell-free massive MIMO of moderate scales. Thus the potential of cell-free massive MIMO has not been fully understood. To address this issue, we propose an accelerated projected gradient method to solve the considered problems. Particularly, the proposed solution is found in closed-form expressions and only requires the first order information of the objective, rather than the Hessian matrix as in known solutions, and thus is much more memory efficient. Numerical results demonstrate that our proposed solution achieves far less run-time, compared to other second-order methods.

eess.SP↗

arXiv2020

A Low-Complexity Approach for Max-Min Fairness in Uplink Cell-Free Massive MIMO

We consider the problem of max-min fairness for uplink cell-free massive multiple-input multiple-output which is a potential technology for beyond 5G networks. More specifically, we aim to maximize the minimum spectral efficiency of all users subject to the per-user power constraint, assuming linear receive combining technique at access points. The considered problem can be further divided into two subproblems: the receiver filter coefficient design and the power control problem. While the receiver coefficient design turns out to be a generalized eigenvalue problem, and thus, admits a closed-form solution, the power control problem is numerically troublesome. To solve the power control problem, existing approaches rely on geometric programming (GP) which is not suitable for large-scale systems. To overcome the high-complexity issue of the GP method, we first reformulate the power control problem intro a convex program, and then apply a smoothing technique in combination with an accelerated projected gradient method to solve it. The simulation results demonstrate that the proposed solution can achieve almost the same objective but in much lesser time than the existing GP-based method.

eess.SP↗

arXiv2020

Utility Maximization for Large-Scale Cell-Free Massive MIMO Downlink

We consider the system-wide utility maximization problem in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system whereby a very large number of access points (APs) simultaneously serve a group of users. Specifically, four fundamental problems with increasing order of user fairness are of interest: (i) to maximize the average spectral efficiency (SE), (ii) to maximize the proportional fairness, (iii) to maximize the harmonic-rate of all users, and lastly (iv) to maximize the minimum SE of all users, subject to a sum power constraint at each AP. As the considered problems are non-convex, existing solutions normally rely on successive convex approximation to find a sub-optimal solution. More specifically, these known methods use off-the-shelf convex solvers, which basically implement an interior-point algorithm, to solve the derived convex problems. The main issue of such methods is that their complexity does not scale favorably with the problem size, limiting previous studies to cell-free massive MIMO of moderate scales. Thus the potential of cell-free massive MIMO has not been fully understood. To address this issue, we propose a unified framework based on an accelerated projected gradient method to solve the considered problems. Particularly, the proposed solution is found in closed-form expressions and only requires the first order oracle of the objective, rather than the Hessian matrix as in known solutions, and thus is much more memory efficient. Numerical results demonstrate that our proposed solution achieves the same utility performance but with far less run-time, compared to other second-order methods. Simulation results for large-scale cell-free massive MIMO show that the four utility functions can deliver nearly uniformed services to all users. In other words, user fairness is not a great concern in large-scale cell-free massive MIMO.

eess.SP↗

arXiv2020

Analysis and Prediction of COVID-19 Pandemic in Pakistan using Time-dependent SIR Model

The current outbreak is known as Coronavirus Disease or COVID-19 caused by the virus SAR-COV-2 which continues to wreak havoc across the globe. The World Health Organization (WHO) has declared the outbreak a Public Health Emergency of International Concern. In Pakistan, the spread of the virus is on the rise with the number of infected people and causalities rapidly increasing. In the absence of proper vaccination and treatment, to reduce the number of infections and casualties, the only option so far is to educate people regarding preventive measures and to enforce countrywide lock-down. Any strategy about the preventive measures needs to be based upon detailed analysis of the COVID-19 outbreak and accurate scientific predictions. In this paper, we conduct mathematical and numerical analysis to come up with reliable and accurate predictions of the outbreak in Pakistan. The time-dependent Susceptible-Infected-Recovered (SIR) model is used to fit the data and provide future predictions. The turning point of the peak of the pandemic is defined as the day when the transmission rate becomes less than the recovering rate. We have predicted that the outbreak will reach its maximum peak occurring from late May to 9 June with unrecovered number of Infectives in the range 20000-47000 and the cumulative number of infected cases in the range of 57500-153100. The number of Infectives will remain at the lower end in the lock-down scenario but can rapidly double or triple if the spread of the epidemic is not curtailed and localized. The uncertainty on single day projection in our analysis after April 15 is found to be within 5\%.

q-bio.PE↗

arXiv2020

On the existence-uniqueness and exponential estimate for solutions to stochastic functional differential equations driven by G-L\'evy process

The existence-uniqueness theory for solutions to stochastic dynamic systems is always a significant theme and has received a huge attention. The objective of this article is to study the mentioned theory for stochastic functional differential equations (SFDEs) driven by G-L\'evy process. The existence-uniqueness theorem for solutions to SFDEs driven by G-L\'evy process has been determined. The error estimation between the exact solution and Picard approximate solutions has been shown. In addition, the exponential estimate has been derived.

math.PR↗