arXiv subjects

Muhammad Tayyab

Publications and source records attributed to Muhammad Tayyab.

16 recordsLinked to original sources

arXiv2026

Development of Thomson parabola spectrometer for diagnostics of ions driven by ultrahigh intensity laser: Simulations and numerical analysis

A Thomson parabola ion spectrometer (TPIS) has been designed and developed for diagnostics of laseraccelerated ion beams in the MeV energy range. The TPIS has been validated by ion acceleration experiment at petawatt laser facility. Necessary simulations to evaluate the electric and magnetic field distributions have been performed with the help of a numerical simulation software to aid the selection of the spectrometer geometry while minimising fringe-field effects. Analytical dispersion expressions have been formulated from the simulations that take into account the spatial variation in the electromagnetic field profiles. The ion deflections obtained from these expressions demonstrate an improved agreement with experimentally measured proton trajectories compared to the case when constant fields are considered. The TPIS hence fabricated in-house has been subject to magnetic field measurements, which are in excellent agreement with the simulated field profile. The TPIS has the provision to change the field-free drift region, showcasing flexibility to be employed over a broad energy range and with different experimental setups. The spectrometer has been subsequently used for detecting laser-accelerated ion beams from thin aluminum foil targets. These experiments have demonstrated the capability of the spectrometer to resolve multiple ion species with sufficient separation between them. The developed TPIS provides a compact, flexible and accurate diagnostic for high-energy laser-plasma experiments.

physics.plasm-ph↗

arXiv2025

What Works for 'Lost-in-the-Middle' in LLMs? A Study on GM-Extract and Mitigations

The diminishing ability of large language models (LLMs) to effectively utilize long-range context-the "lost-in-the-middle" phenomenon-poses a significant challenge in retrieval-based LLM applications. To study the impact of this phenomenon in a real-world application setting, we introduce GM-Extract, a novel benchmark dataset meticulously designed to evaluate LLM performance on retrieval of control variables. To accurately diagnose failure modes, we propose a simple yet elegant evaluation system using two distinct metrics: one for spatial retrieval capability (Document Metric) and the other for semantic retrieval capability (Variable Extraction Metric). We conduct a systematic evaluation of 7-8B parameter models on two multi-document tasks (key-value extraction and question-answering), demonstrating a significant change in retrieval performance simply by altering how the data is represented in the context window. While a distinct U-shaped curve was not consistently observed, our analysis reveals a clear pattern of performance across models, which we further correlate with perplexity scores. Furthermore, we perform a literature survey of mitigation methods, which we categorize into two distinct approaches: black-box and white-box methods. We then apply these techniques to our benchmark, finding that their efficacy is highly nuanced. Our evaluation highlights scenarios where these strategies successfully improve performance, as well as surprising cases where they lead to a negative impact, providing a comprehensive understanding of their utility in a practical context.

cs.CL↗

arXiv2025

Orbital-Engineered Spin Asymmetry and Multifunctionality in Eu-Activated CaAlSiN$_3$: A First-Principles Roadmap to Optical-Thermoelectric Fusion

Rare-earth-doped nitride phosphors are promising materials for solid-state lighting and photonic applications due to their thermal stability, sharp emission lines, and strong UV-blue absorption. In this work, we present a first-principles density functional theory (DFT) study, using the GGA+U approach, of pristine and Eu3+-doped CaAlSiN3 at doping levels of 8.5% and 17%. Electronic structure calculations show that Eu incorporation introduces localized 4f states within the band gap, leading to band-gap narrowing and enabling red photoluminescence through the 5D0 -> 7F2 transition. Spin-polarized density of states and spin density mapping confirm the magnetic nature of Eu3+, while charge density, Bader analysis, and electron localization function (ELF) indicate mixed ionic-covalent bonding and charge transfer from Eu to neighboring N and Al atoms, stabilizing the doped lattice. Optical spectra, including dielectric function, absorption, refractive index, and reflectivity, reveal red-shifted absorption edges and enhanced visible-range light-matter interactions, consistent with experimental red to near-infrared emission. Formation energy analysis confirms the thermodynamic feasibility of Eu substitution, while elastic constants and Pugh's ratio indicate mechanical robustness and ductility. Thermoelectric transport properties, obtained using WIEN2k and BoltzTraP, suggest that moderate Eu3+ doping improves the power factor and reduces lattice thermal conductivity through disorder scattering. These results establish Eu-doped CaAlSiN3 as a stable and efficient red-emitting phosphor for white light-emitting diodes (WLEDs) and provide theoretical insights for crystal site engineering in advanced optoelectronic materials.

cond-mat.mtrl-sci↗

arXiv2025

Illuminating Stability and Spectral Shifts: A DFT+U Study of Eu-Doped ZnWO$_4$ for Visible-Light Optoelectronics

Tungstate-based oxides have attracted significant attention owing to their excellent structural stability, chemical robustness, and versatile optical properties, making them suitable for next-generation optoelectronic and phosphor applications. Among these, ZnWO$_4$ has emerged as a promising host matrix; however, the role of europium (Eu) substitution in modulating its optoelectronic behavior remains underexplored. In this work, we employ spin-polarized density functional theory (DFT) within the GGA+U framework to investigate the structural, electronic, and optical properties of pristine ZnWO$_4$ and Eu-doped ZnWO4 systems. Phonon dispersion analysis confirms dynamical stability for both pristine and doped structures. Eu doping reduces the bandgap, introduces new localized states near the Fermi level, and significantly alters the density of states, thereby enhancing electronic transitions. The optical response reveals a broadened dielectric function, red-shifted absorption edge, and intensified extinction coefficient, consistent with the presence of Eu 4f states. Additionally, reflectivity and energy-loss spectra indicate improved photon-phonon coupling and optical tunability upon doping. These findings highlight that Eu incorporation not only stabilizes the ZnWO$_4$ lattice but also tailors its optoelectronic features, positioning Eu-doped ZnWO4 as a potential candidate for white-light-emitting diodes (w-LEDs) and related optoelectronic technologies.

cond-mat.mtrl-sci↗

arXiv2025

Strong overlap of deterministic and stochastic dynamics in a super-diffusive regime

We consider deterministic dynamics, known as the slicer map (SM), which exhibits normal and anomalous diffusion by varying a single parameter. The statistics of the position moments and the low-order position autocorrelation function (PACF) of the SM closely overlap with those of a stochastic process called the L\'evy-Lorentz gas (LLg), particularly in the normal and strongly superdiffusive anomalous regimes. However, matching low-order statistics alone cannot fully characterize the microscopic dynamics or distinguish underlying process classes. To demonstrate how these dynamics strongly overlap, we focus on the scaling of higher-order PACF, which provides a more detailed characterization. In this paper, we analytically derive the generalized PACF of the SM and explore its scaling forms under different temporal relationships. Specifically, we derive several scalings of the 3-point PACF by analyzing intriguing relations between three times. We compare these scalings with the power-law tails of the numerically estimated 3-point PACF of the LLg. This comparison provides a detailed description of the correlation scalings of the SM, demonstrating that the SM shares key features with the LLg. Our findings establish the SM as a deterministic analog of the LLg, enabling efficient prediction of multi-time position correlations in superdiffusive systems.

cond-mat.stat-mech↗

arXiv2024

Generalized autocorrelation function in the family of deterministic and stochastic anomalous diffusion processes

We investigate the observables of the one-dimensional model for anomalous transport in semiconductor devices where diffusion arises from scattering at dislocations at fixed random positions, known as L\'evy-Lorentz gas. To gain insight into the microscopic properties of such a stochastically complex system, deterministic dynamics known as the Slicer Map and Fly-and-Die dynamics are used. We analytically derive the generalized position auto-correlation function of these dynamics and study the special case, the $3$-point position correlation function. For this, we derive single parameter-dependent scaling and compare it with the numerically estimated $3$-point position auto-correlation of the L\'evy-Lorentz gas, for which the analytical expression is still an open question. Here we obtained a remarkable agreement between them, irrespective of any functional relationship with time. Moreover, we demonstrate that the position moments and the position auto-correlations of these systems scale in the same fashion, provided the times are large enough and far enough apart. Other observables, such as velocity moments and correlations, are reported to distinguish the systems.

cond-mat.stat-mech↗

arXiv2023

Ambient IoT: A missing link in 3GPP IoT Devices Landscape

Ambient internet of things (IoT) is the network of devices which harvest energy from ambient sources for powering their communication. After decades of research on operation of these devices, Third Generation Partnership Project (3GPP) has started discussing energy harvesting technology in cellular networks to support massive deployment of IoT devices at low operational cost. This article provides a timely update on 3GPP studies on ambient energy harvesting devices including device types, use cases, key requirements, and related design challenges. Supported by link budget analysis for backscattering energy harvesting devices, which are a key component of this study, we provide insight on system design and show how this technology will require a new system design approach as compared to New Radio (NR) system design in 5G.

cs.NI↗

arXiv2022

An Integrated Approach for Energy Efficient Handover and Key Distribution Protocol for Secure NC-enabled Small Cells

Future wireless networks must serve dense mobile networks with high data rates, keeping energy requirements to a possible minimum. The small cell-based network architecture and device-to-device (D2D) communication are already being considered part of 5G networks and beyond. In such environments, network coding (NC) can be employed to achieve both higher throughput and energy efficiency. However, NC-enabled systems need to address security challenges specific to NC, such as pollution attacks. All integrity schemes against pollution attacks generally require proper key distribution and management to ensure security in a mobile environment. Additionally, the mobility requirements in small cell environments are more challenging and demanding in terms of signaling overhead. This paper proposes a blockchain-assisted key distribution protocol tailored for MAC-based integrity schemes, which combined with an uplink reference signal (UL RS) handover mechanism, enables energy efficient secure NC. The performance analysis of the protocol during handover scenarios indicates its suitability for ensuring high level of security against pollution attacks in dense small cell environments with multiple adversaries being present. Furthermore, the proposed scheme achieves lower bandwidth and signaling overhead during handover compared to legacy schemes and the signaling cost reduces significantly as the communication progresses, thus enhancing the network's cumulative energy efficiency.

cs.NI↗

arXiv2021

Compressing Deep CNNs using Basis Representation and Spectral Fine-tuning

We propose an efficient and straightforward method for compressing deep convolutional neural networks (CNNs) that uses basis filters to represent the convolutional layers, and optimizes the performance of the compressed network directly in the basis space. Specifically, any spatial convolution layer of the CNN can be replaced by two successive convolution layers: the first is a set of three-dimensional orthonormal basis filters, followed by a layer of one-dimensional filters that represents the original spatial filters in the basis space. We jointly fine-tune both the basis and the filter representation to directly mitigate any performance loss due to the truncation. Generality of the proposed approach is demonstrated by applying it to several well known deep CNN architectures and data sets for image classification and object detection. We also present the execution time and power usage at different compression levels on the Xavier Jetson AGX processor.

cs.CV↗

arXiv2021

Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review

Biological cells, by definition, are the basic units which contain the fundamental molecules of life of which all living things are composed. Understanding how they function and differentiating cells from one another therefore is of paramount importance for disease diagnostics as well as therapeutics. Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care (POC) solutions with each passing day. Furthermore, Machine Learning has allowed for enhancement in analytical capabilities of these various biosensing modalities, especially the challenging task of classification of cells into various categories using a data-driven approach rather than physics-driven. In this review, we provide an account of how Machine Learning has been applied explicitly to sensors that detect and classify cells. We also provide a comparison of how different sensing modalities and algorithms affect the classifier accuracy and the dataset size required.

eess.SP↗

arXiv2019

Analytical models of Energy and Throughput for Caches in MPSoCs

General trends in computer architecture are shifting more towards parallelism. Multicore architectures have proven to be a major step in processor evolution. With the advancement in multicore architecture, researchers are focusing on finding different solutions to fully utilize the power of multiple cores. With an ever-increasing number of cores on a chip, the role of cache memory has become pivotal. An ideal memory configuration should be both large and fast, however, in fact, system architects have to strike a balance between the size and access time of the memory hierarchy. It is important to know the impact of a particular cache configuration on the throughput and energy consumption of the system at design time. This paper presents an enhanced version of previously proposed cache energy and throughput models for multicore systems. These models use significantly a smaller number of input parameters as compared to other models. This paper also validates the proposed models through cycle accurate simulator and a renowned processor power estimator. The results show that the proposed energy models provide accuracy within a maximum error range of 10% for single-core processors and around 5% for MPSoCs, and the throughput models result in a maximum error of up to 11.5% for both single and multicore architectures.

cs.AR↗

arXiv2019

BasisConv: A method for compressed representation and learning in CNNs

It is well known that Convolutional Neural Networks (CNNs) have significant redundancy in their filter weights. Various methods have been proposed in the literature to compress trained CNNs. These include techniques like pruning weights, filter quantization and representing filters in terms of a basis functions. Our approach falls in this latter class of strategies, but is distinct in that that we show both compressed learning and representation can be achieved without significant modifications of popular CNN architectures. Specifically, any convolution layer of the CNN is easily replaced by two successive convolution layers: the first is a set of fixed filters (that represent the knowledge space of the entire layer and do not change), which is followed by a layer of one-dimensional filters (that represent the learned knowledge in this space). For the pre-trained networks, the fixed layer is just the truncated eigen-decompositions of the original filters. The 1D filters are initialized as the weights of linear combination, but are fine-tuned to recover any performance loss due to the truncation. For training networks from scratch, we use a set of random orthogonal fixed filters (that never change), and learn the 1D weight vector directly from the labeled data. Our method substantially reduces i) the number of learnable parameters during training, and ii) the number of multiplication operations and filter storage requirements during implementation. It does so without requiring any special operators in the convolution layer, and extends to all known popular CNN architectures. We apply our method to four well known network architectures trained with three different data sets. Results show a consistent reduction in i) the number of operations by up to a factor of 5, and ii) number of learnable parameters by up to a factor of 18, with less than 3% drop in performance on the CIFAR100 dataset.

cs.LG↗

arXiv2019

Displacement Autocorrelation Functions for Strong Anomalous Diffusion: A Scaling Form, Universal Behavior, and Corrections to Scaling

Strong anomalous diffusion is characterized by asymptotic power-law growth of the moments of displacement, with exponents that do not depend linearly on the order of the moment. The exponents concerning small-order moments are dominated by random motion, while higher-order exponents grow by faster trajectories, such as ballistic excursions or "light fronts". Often such a situation is characterized by two linear dependencies of the exponents on their order. Here, we introduce a simple exactly solvable model, the Fly-and-Die (FnD) model, that sheds light on this behavior and on the consequences of light fronts on displacement autocorrelation functions in transport processes. We present analytical expressions for the moments and derive a scaling form that expresses the long-time asymptotics of the autocorrelation function $\langle x(t_1)\,x(t_2)\rangle$ in terms of the dimensionless time difference $(t_2-t_1)/t_1$. The scaling form provides a faithful collapse of numerical data for vastly different systems. This is demonstrated here for the Lorentz gas with infinite horizon, polygonal billiards with finite and infinite horizon, the L\'evy-Lorentz gas, the Slicer Map, and L\'evy walks. Our analysis also captures the system-specific corrections to scaling.

cond-mat.stat-mech↗

arXiv2018

Linking Received Packet to the Transmitter Through Physical-Fingerprinting of Controller Area Network

The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time- and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.

cs.CR↗

arXiv2017

Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram

The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host millions of pilgrims every year. During Hajj, the movement of large number of people has a unique spatial and temporal constraints, which makes Hajj one of toughest challenges for crowd management. In this paper, we propose a computer vision based framework that automatically analyses video sequence and computes important measurements which include estimation of crowd density, identification of dominant patterns, detection and localization of congestion. In addition, we analyze helpful statistics of the crowd like speed, and direction, that could provide support to crowd management personnel. The framework presented in this paper indicate that new advances in computer vision and machine learning can be leveraged effectively for challenging and high density crowd management applications. However, significant customization of existing approaches is required to apply them to the challenging crowd management situations in Masjid Al Haram. Our results paint a promising picture for deployment of computer vision technologies to assist in quantitative measurement of crowd size, density and congestion.

cs.CV↗

arXiv2017

Equivalence of position-position auto-correlations in the Slicer Map and the L\'evy-Lorentz gas

The Slicer Map is a one-dimensional non-chaotic dynamical system that shows sub-, super-, and normal diffusion as a function of its control parameter. In a recent paper [Salari et al., CHAOS 25, 073113 (2015)] it was found that the moments of the position distributions as the Slicer Map have the same asymptotic behaviour as the L\'evy-Lorentz gas, a random walk on the line in which the scatterers are randomly distributed according to a L\'evy-stable probability distribution. Here we derive analytic expressions for the position-position correlations of the Slicer Map and, on the ground of this result, we formulate some conjectures about the asymptotic behaviour of position-position correlations of the L\'evy-Lorentz gas, for which the information in the literature is minimal. The numerically estimated position-position correlations of the L\'evy-Lorentz show a remarkable agreement with the conjectured asymptotic scaling.

cond-mat.stat-mech↗