arXiv subjects

Muhammad Irfan

Publications and source records attributed to Muhammad Irfan.

At least 19 recordsLinked to original sources

arXiv2026

Controlled Out-of-Band Device-to-Device Communication in Cellular Networks Using a Backup Channel in Television White Space

In this article, we address the problem of spectrum scarcity in cellular networks (CNs). We propose a backup channel (BuC) for cellular users (CUs) located in the same macro-cell under the control of a single macro base station (eNB). This BuC operates in television white space and is detected by the CUs through a cognitive radio energy-detection channel-sensing technique with a certain probability of success. When all regular channels with the cellular eNB are occupied, the CUs within the same coverage area of the macro eNB can utilize the sensed BuC to establish a controlled out-of-band device-to-device link for communication. The BuC bypasses the eNB for data communication and reduces the burden on the core of the CN. This leads to improved cellular eNB capacity. In the proposed system model, each CU and eNB is equipped with two antennas for communication in two separate bands, i.e., cellular and TV bands. Simulations show significant reductions in the blocking probability and probability of call delay.

eess.SP↗

arXiv2026

Loss-induced nonreciprocal quantum battery

Nonreciprocal quantum batteries offer superior charging performance compared to reciprocal quantum batteries. We consider a charger-battery system comprising two optical cavities that interact independently with a third auxiliary cavity. We show that the nonzero dissipation of the auxiliary cavity induces a nonreciprocal exchange of excitations among the charger-battery system. Therefore, by engineering the loss in the auxiliary cavity, we induce a directional energy flow that enhances the charging efficiency. Using numerical and analytical calculations, we show that the steady-state energy stored in the battery significantly exceeds that in the charger. We compare our results with those of the reciprocal cases and demonstrate that our nonreciprocal quantum battery model exhibits a significant charging advantage. We believe that our proposed scheme represents a step forward in cavity-loss engineering, making it a viable approach for nonreciprocal quantum batteries with existing experimental techniques.

quant-ph↗

arXiv2025

A Physics-Informed Loss Function for Boundary-Consistent and Robust Artery Segmentation in DSA Sequences

Accurate extraction and segmentation of the cerebral arteries from digital subtraction angiography (DSA) sequences is essential for developing reliable clinical management models of complex cerebrovascular diseases. Conventional loss functions often rely solely on pixel-wise overlap, overlooking the geometric and physical consistency of vascular boundaries, which can lead to fragmented or unstable vessel predictions. To overcome this limitation, we propose a novel \textit{Physics-Informed Loss} (PIL) that models the interaction between the predicted and ground-truth boundaries as an elastic process inspired by dislocation theory in materials physics. This formulation introduces a physics-based regularization term that enforces smooth contour evolution and structural consistency, allowing the network to better capture fine vascular geometry. The proposed loss is integrated into several segmentation architectures, including U-Net, U-Net++, SegFormer, and MedFormer, and evaluated on two public benchmarks: DIAS and DSCA. Experimental results demonstrate that PIL consistently outperforms conventional loss functions such as Cross-Entropy, Dice, Active Contour, and Surface losses, achieving superior sensitivity, F1 score, and boundary coherence. These findings confirm that the incorporation of physics-based boundary interactions into deep neural networks improves both the precision and robustness of vascular segmentation in dynamic angiographic imaging. The implementation of the proposed method is publicly available at https://github.com/irfantahir301/Physicsis_loss.

cs.CV↗

arXiv2025

Emotion Recognition with Minimal Wearable Sensing: Multi-domain Feature, Hybrid Feature Selection, and Personalized vs. Generalized Ensemble Model Analysis

Negative emotions are linked to the onset of neurodegenerative diseases and dementia, yet they are often difficult to detect through observation. Physiological signals from wearable devices offer a promising noninvasive method for continuous emotion monitoring. In this study, we propose a lightweight, resource-efficient machine learning approach for binary emotion classification, distinguishing between negative (sadness, disgust, anger) and positive (amusement, tenderness, gratitude) affective states using only electrocardiography (ECG) signals. The method is designed for deployment in resource-constrained systems, such as Internet of Things (IoT) devices, by reducing battery consumption and cloud data transmission through the avoidance of computationally expensive multimodal inputs. We utilized ECG data from 218 CSV files extracted from four studies in the Psychophysiology of Positive and Negative Emotions (POPANE) dataset, which comprises recordings from 1,157 healthy participants across seven studies. Each file represents a unique subject emotion, and the ECG signals, recorded at 1000 Hz, were segmented into 10-second epochs to reflect real-world usage. Our approach integrates multidomain feature extraction, selective feature fusion, and a voting classifier. We evaluated it using a participant-exclusive generalized model and a participant-inclusive personalized model. The personalized model achieved the best performance, with an average accuracy of 95.59%, outperforming the generalized model, which reached 69.92% accuracy. Comparisons with other studies on the POPANE and similar datasets show that our approach consistently outperforms existing methods. This work highlights the effectiveness of personalized models in emotion recognition and their suitability for wearable applications that require accurate, low-power, and real-time emotion tracking.

cs.HC↗

arXiv2025

Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation

Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive nature of RIS elements prevents direct channel acquisition, incurring prohibitive pilot overhead, computational complexity, and energy consumption. To address this, we propose a deep learning (DL)-based channel estimation framework that reduces pilot overhead by grouping RIS elements and reconstructing the cascaded channel from partial pilot observations. Furthermore, conventional DL models trained under single-user settings suffer from poor generalization across new user locations and propagation scenarios. We develop a distributed machine learning (DML) strategy in which the BS and users collaboratively train a shared neural network using diverse channel datasets collected across the network, thereby achieving robust generalization. Building on this foundation, we design a hierarchical DML neural architecture that first classifies propagation conditions and then employs scenario-specific feature extraction to further improve estimation accuracy. Simulation results confirm that the proposed framework substantially reduces pilot overhead and complexity while outperforming conventional methods and single-user models in channel estimation accuracy. These results demonstrate the practicality and effectiveness of the proposed approach for 6G RIS-assisted systems.

eess.SP↗

arXiv2025

First-principles Quantum Insights into Bandgap Engineering, Valley Quantum Hall Effect, and Nonlinear Optical Response of Ge-Doped Graphene for Potential Optoelectronic Applications

The valley in the band structure of materials has gained a lot of attention recently. The promising applications of the valley degree of freedom include the next-generation valleytronic devices, quantum information processing, quantum computing, and optoelectronic devices. Graphene is an ideal quantum material for high-speed valleytronic applications because of its high carrier mobility and convenience of bandgap engineering. Employing first-principles density functional theoretical approach, this study opted bandgap engineering strategy via Germanium doping to open bandgap and enhance valley selectivity in graphene monolayers. The impact of Ge dopant concentration of 2%, 3.125%, 5.5%, and 12.5% is explored on the valleytronic; valley Hall effect, valley transport, and optical properties. The reported results demonstrate that bandgap, valley polarization, and second harmonic generation can be tuned effectively by varying doping concentration of Germanium. The Berry curvature profile is antisymmetric for corresponding K and K' valleys, thus leading to valley-dependent transport properties and a potential valley Hall effect. Finally, the second-order susceptibilities exhibit corresponding optical absorption peaks, indicating efficient second-harmonic generation due to the broken inversion symmetry. These findings highlight the potential of Ge-doped graphene for nonlinear optics and valleytronics applications, while providing novel insights into its topological phase and transport properties.

physics.comp-ph↗

arXiv2025

Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare

Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual's unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL framework BFEL (blockchain enhanced federated edge learning) based on optimised FedCurv for personalised healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through fisher information matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework's high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.

cs.LG↗

arXiv2025

Feedback-enhanced distant entanglement of magnon and phonon modes with atomic ensembles in coupled cavities

The generation and manipulation of distant entanglement between disparate systems is crucial for various quantum technologies. In this work, we investigate a system of coupled cavities comprising an ensemble in cavity-1, a yttrium-iron-garnet (YIG) sphere in cavity-2, and a coherent feedback loop (CFL) that feeds the output of cavity-1 back into cavity-1 through a beam splitter. This system features five excitation modes: cavity-1 photons, atomic ensemble, cavity-2 photons, the magnon, and phonon modes of the YIG sphere. Thus various combinations of bipartite entanglements can be studied. Our main focus is the study of various combinations of distant bipartite entanglements, especially the entanglement of the atomic ensemble and photons in cavity-1 with the magnon and phonon modes of the YIG sphere in cavity-2. Compared to the previously reported results, introducing a CFL significantly enhances all the bipartite entanglements. Besides, the degree of entanglement of various modes, the parameters space, where the strong entanglement exists, is also significantly enhanced due to CFL. Moreover, the entanglement is more robust against thermal noise. We believe our results are important for quantum technologies where the distribution of entanglement on quantum networks is crucial.

quant-ph↗

arXiv2025

Improved Brain Tumor Detection in MRI: Fuzzy Sigmoid Convolution in Deep Learning

Early detection and accurate diagnosis are essential to improving patient outcomes. The use of convolutional neural networks (CNNs) for tumor detection has shown promise, but existing models often suffer from overparameterization, which limits their performance gains. In this study, fuzzy sigmoid convolution (FSC) is introduced along with two additional modules: top-of-the-funnel and middle-of-the-funnel. The proposed methodology significantly reduces the number of trainable parameters without compromising classification accuracy. A novel convolutional operator is central to this approach, effectively dilating the receptive field while preserving input data integrity. This enables efficient feature map reduction and enhances the model's tumor detection capability. In the FSC-based model, fuzzy sigmoid activation functions are incorporated within convolutional layers to improve feature extraction and classification. The inclusion of fuzzy logic into the architecture improves its adaptability and robustness. Extensive experiments on three benchmark datasets demonstrate the superior performance and efficiency of the proposed model. The FSC-based architecture achieved classification accuracies of 99.17%, 99.75%, and 99.89% on three different datasets. The model employs 100 times fewer parameters than large-scale transfer learning architectures, highlighting its computational efficiency and suitability for detecting brain tumors early. This research offers lightweight, high-performance deep-learning models for medical imaging applications.

eess.IV↗

arXiv2025

Vacuum-induced atomic grating

Atom-field interactions, induced by the vacuum of the electromagnetic field, exhibit a variety of fundamental phenomena and effects. In this paper, we study the electromagnetically induced atomic grating due to the vacuum state of the radiation field. Using an ensemble of cold atoms, strongly coupled to an optical cavity, we show that a probe field, propagating through the atomic medium, diffracts to zeroth and first-order diffraction peaks with few photons and even by the electromagnetic vacuum field of the cavity mode. As the number of photons in the cavity increases, the intensity of the first-order diffraction peak initially rises and then exhibits a decreasing trend. Furthermore, we observe that the first-order peak intensity reaches its maximum at resonance for both the vacuum and single-photon cavity state. However, as the number of photons increases further, this peak at resonance transforms into a dip, accompanied by two side peaks at off-resonance positions. This transition from a peak to a dip may potentially be used to distinguish the quantum state of the cavity.

quant-ph↗

arXiv2024

On the Reliability of Radio Frequency Fingerprinting

Radio Frequency Fingerprinting (RFF) offers a unique method for identifying devices at the physical (PHY) layer based on their RF emissions due to intrinsic hardware differences. Nevertheless, RFF techniques depend on the ability to extract information from the PHY layer of the radio spectrum by resorting to Software Defined Radios (SDR). Previous works have highlighted the so-called ``Day-After-Tomorrow'' effect, i.e., an intrinsic issue of SDRs leading to a fingerprint mutation following a radio power cycle. In this work, we extend such a study by demonstrating that fingerprint mutations appear every time a new FPGA image is reloaded, i.e., when the SDR initiates a new communication. In this context, we provide an in-depth analysis of the reliability of RFF over multiple FPGA image reloading operations, highlighting its ephemeral and mutational nature. We introduce a methodology for abstracting fingerprint mutations into a graph and provide a theoretical framework for assessing fingerprint reliability. Our results show that the common assumption of considering the RF fingerprint as unique and always persistent is incorrect. By combining real-world measurements, high-performance SDRs, and state-of-the-art deep learning techniques, we experimentally demonstrate that radio devices feature multiple fingerprints that can be clustered according to shared features. Moreover, we show that the RF fingerprint is a time-independent probabilistic phenomenon, which requires the collection of multiple samples to achieve the necessary reliability.

cs.CR↗

arXiv2024

Cryptographically Secure Pseudo-Random Number Generation (CS-PRNG) Design using Robust Chaotic Tent Map (RCTM)

Chaos, a nonlinear dynamical system, favors cryptography due to their inherent sensitive dependence on the initial condition, mixing, and ergodicity property. In recent years, the nonlinear behavior of chaotic maps has been utilized as a random source to generate pseudo-random number generation for cryptographic services. For chaotic maps having Robust chaos, dense, chaotic orbits exist for the range of parameter space the occurrence of chaotic attractors in some neighborhoods of parameter space and the absence of periodic windows. Thus, the robust chaotic map shows assertive chaotic behavior for larger parameters space with a positive Lyapunov exponent. This paper presents a novel method to generate cryptographically secure pseudo-random numbers (CSPRNG) using a robust chaotic tent map (RCTM). We proposed a new set of equations featuring modulo and scaling operators that achieve vast parameter space by keeping chaotic orbit globally stable and robust. The dynamic behavior of the RCTM is studied first by plotting the bifurcation diagram that shows chaotic behavior for different parameters, which the positive Lyapunov exponent verifies. We iterated the RCTM to generate pseudo-random bits using a simple thresholding method. Various statistical tests are performed that ascertain the randomness of generated secure pseudo-random bits. It includes NIST 800-22 test suite, ENT statistical test suite, TestU01 test suite, key space analysis, key sensitivity analysis, correlation analysis, histogram analysis, and differential analysis. The proposed scheme has achieved larger key space as compared with existing methods. The results show that the proposed PRBG algorithm can generate CSPRNG.

cs.CR↗

arXiv2024

Preventing Radio Fingerprinting through Low-Power Jamming

Radio Frequency fingerprinting enables a passive receiver to recognize and authenticate a transmitter without the need for cryptographic tools. Authentication is achieved by isolating specific features of the transmitted signal that are unique to the transmitter's hardware. Much research has focused on improving the effectiveness and efficiency of radio frequency fingerprinting to maximize its performance in various scenarios and conditions, while little research examined how to protect devices from being subject to radio fingerprinting in the wild. In this paper, we explore a novel point of view. We examine the threat posed by radio frequency fingerprinting, which facilitates the unauthorized identification of wireless devices in the field by malicious entities. We also suggest a method to sanitize the transmitted signal of its fingerprint using a low-power jammer, deployed on purpose to improve devices' anonymity on the channel while still guaranteeing the link's quality of service. Our experimental results and subsequent analysis demonstrate that a low-power jammer can effectively block a malicious eavesdropper from identifying a device without affecting the quality of the wireless link, thereby restoring the privacy of the user when accessing the radio spectrum.

cs.CR↗

arXiv2023

Magnomechanically controlled Goos-H\"{a}nchen shift in cavity QED

Phenomena involving interactions among magnons, phonons, and photons in cavity magnomechanical systems have attracted considerable attention recently, owing to their potential applications in the microwave frequency range. One such important effect is the response of a probe field to such tripartite interaction between photon-magnon-phonon. In this paper, we study Goos-H\"{a}nchen shift (GHS) of a reflected probe field in a cavity magnomechanical system. We consider a YIG sphere positioned within a microwave cavity. A microwave control field directly drives the magnon mode in YIG sphere, whereas the cavity is driven via a weak probe field. Our results show that the GHS can be coherently controlled through magnon-phonon coupling via the control field. For instance, GHS can be tuned from positive to negative by tuning the magnon-phonon coupling. Similarly, the effective cavity detuning is another important controlling parameter for GHS. Furthermore, we observe that the enhancement of GHS occurs when magnon-phonon coupling is weak at resonance, and when the magnon-photon coupling is approximately equal to the loss of microwave photons. Our findings may have potential significance in applications related to microwave switching and sensing.

quant-ph↗

arXiv2023

False Data Injection Attacks in Smart Grids: State of the Art and Way Forward

In the recent years cyberattacks to smart grids are becoming more frequent Among the many malicious activities that can be launched against smart grids False Data Injection FDI attacks have raised significant concerns from both academia and industry FDI attacks can affect the internal state estimation processcritical for smart grid monitoring and controlthus being able to bypass conventional Bad Data Detection BDD methods Hence prompt detection and precise localization of FDI attacks is becomming of paramount importance to ensure smart grids security and safety Several papers recently started to study and analyze this topic from different perspectives and address existing challenges Datadriven techniques and mathematical modelings are the major ingredients of the proposed approaches The primary objective of this work is to provide a systematic review and insights into FDI attacks joint detection and localization approaches considering that other surveys mainly concentrated on the detection aspects without detailed coverage of localization aspects For this purpose we select and inspect more than forty major research contributions while conducting a detailed analysis of their methodology and objectives in relation to the FDI attacks detection and localization We provide our key findings of the identified papers according to different criteria such as employed FDI attacks localization techniques utilized evaluation scenarios investigated FDI attack types application scenarios adopted methodologies and the use of additional data Finally we discuss open issues and future research directions

cs.CR↗

arXiv2023

Zero-photon catalysis based eight-state discrete modulated measurement-device-independent continuous-variable quantum key distribution

Zero-photon catalysis (ZPC) introduces noiseless attenuation and can be implemented by existing technologies in quantum key distribution (QKD) protocols. In this paper, we present a ZPC-based eight-state measurement-device-independent continuous-variable QKD (MDI-CV-QKD) combined with discrete modulation and reverse reconciliation. This ZPC-involved eight-state protocol shows better efficiency in terms of optimal modulation variances, secret key rates, transmission distances, tolerable excess noises, and reconciliation efficiency compared to the eight-state protocol without ZPC, the four-state protocol without ZPC, and the four-state protocol with ZPC, at a low signal-to-noise ratio (SNR).

quant-ph↗

arXiv2023

Multiphoton blockade and antibunching in an optical cavity coupled with dipole-dipole interacting $\Lambda$-type atoms

We study multiphoton blockade effects in a single-mode cavity interacting with two three-level atoms in $\Lambda$-configuration having position-dependent atom-field coupling. We consider the effects of dipole-dipole interaction (DDI) between the three-level atoms and show how the presence of DDI strongly influences the multiphoton blockade. For symmetric coupling of the atoms with the field, the DDI induces an asymmetry in the emission spectra as a function of pump field detuning. At positive detuning, the single-photon blockade gets stronger as a function of DDI strength, leading to photon antibunching. However, it becomes weaker at negative detuning and can also completely vanish. We show that this vanishing single-photon blockade is associated with a strong two-photon blockade, leading to two-photon bunching. Therefore, by just tuning the frequency of the pump field, we can achieve two very distinct features. We also study the effects of DDI when the atoms are asymmetrically coupled with the field and show that the proposed system exhibits two-photon bunching. We believe our results are important for the experimental realization of such systems where DDI may be present.

quant-ph↗

arXiv2022

Entangled atomic ensemble and an yttrium-iron-garnet sphere in coupled microwave cavities

We present a scheme to generate distant bipartite and tripartite entanglement between an atomic ensemble and an yttrium iron garnet (YIG) sphere in coupled microwave cavities. We consider an atomic ensemble in a single-mode microwave cavity which is coupled with a second single-mode cavity having a YIG sphere. Our system, therefore, has five excitation modes namely cavity-1 photons, atomic ensemble, cavity-2 photons, a magnon and a phonon mode in the YIG sphere. We show that significant bipartite entanglement exists between indirectly coupled subsystems in the cavities, which is robust against temperature. Moreover, we present suitable parameters for a significant tripartite entanglement of ensemble, magnon, and phonon modes. We also demonstrate the existence of tripartite entanglement between magnon and phonon modes of the YIG sphere with indirectly coupled cavity photons. Interestingly, this distant tripartite entanglement is of the same order as previously found for a single-cavity system. We show that cavity-cavity coupling strength affects both the degree and transfer of quantum entanglement between various subsystems. Therefore, an appropriate cavity-cavity coupling optimizes the distant entanglement by increasing the entanglement strength and its robustness against temperature.

quant-ph↗