arXiv subjects

Imran Khan

Publications and source records attributed to Imran Khan.

At least 37 records · Page 2Linked to original sources

arXiv2024

Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case

This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, different methods for monitoring and assessing the water networks, such as offline and online surveys, are used. However, these surveys have several limitations, such as the limited number of participants and low frequency due to the labor involved in conducting such surveys. In this paper, we propose a Natural Language Processing (NLP) framework to automatically collect and analyze water-related posts from social media for data-driven decisions. The proposed framework is composed of two components, namely (i) text classification, and (ii) topic modeling. For text classification, we propose a merit-fusion-based framework incorporating several Large Language Models (LLMs) where different weight selection and optimization methods are employed to assign weights to the LLMs. In topic modeling, we employed the BERTopic library to discover the hidden topic patterns in the water-related tweets. We also analyzed relevant tweets originating from different regions and countries to explore global, regional, and country-specific issues and water-related concerns. We also collected and manually annotated a large-scale dataset, which is expected to facilitate future research on the topic.

cs.SI↗

arXiv2024

An Open, Programmable, Multi-vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface

The transition of fifth generation (5G) cellular systems to softwarized, programmable, and intelligent networks depends on successfully enabling public and private 5G deployments that are (i) fully software-driven and (ii) with a performance at par with that of traditional monolithic systems. This requires hardware acceleration to scale the Physical (PHY) layer performance, end-to-end integration and testing, and careful planning of the Radio Frequency (RF) environment. In this paper, we describe how the X5G testbed at Northeastern University has addressed these challenges through the first 8-node network deployment of the NVIDIA Aerial RAN CoLab (ARC), with the Aerial Software Development Kit (SDK) for the PHY layer, accelerated on Graphics Processing Unit (GPU), and through its integration with higher layers from the OpenAirInterface (OAI) open-source project through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). We discuss software integration, the network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, measuring a cell rate higher than 500 Mbps in downlink and 45 Mbps in uplink.

cs.NI↗

arXiv2024

An Experimental Study of Low-Latency Video Streaming over 5G

Low-latency video streaming over 5G has become rapidly popular over the last few years due to its increased usage in hosting virtual events, online education, webinars, and all-hands meetings. Our work aims to address the absence of studies that reveal the real-world behavior of low-latency video streaming. To that end, we provide an experimental methodology and measurements, collected in a US metropolitan area over a commercial 5G network, that correlates application-level QoE and lower-layer metrics on the devices, such as RSRP, RSRQ, handover records, etc., under both static and mobility scenarios. We find that RAN-side information, which is readily available on every cellular device, has the potential to enhance throughput estimation modules of video streaming clients, ultimately making low-latency streaming more resilient against network perturbations and handover events.

cs.MM↗

arXiv2023

Enhanced target normal sheath acceleration with a grooved hydrocarbon target

The interaction of a high-intensity ultrashort laser pulse with a few microns-thick hydrocarbon target is known to accelerate protons/ions to multi-MeV, on the rear side of the target, via the mechanism of target normal sheath acceleration. Micro-structuring the target front is one of the promising approaches to enhance the cut-off energy as well as to reduce the divergence of accelerated protons/ions. In this paper, the interaction of a normally incident intense laser pulse with targets having single micron-sized grooves, at their front side, of semi-circular, triangular, and rectangular shapes has been studied by using two-dimensional Particle-In-Cell (PIC) simulations. It is observed that as compared to a flat target for targets with a rectangular groove at the front side the focused hot electron beam at the rear side results in an approximately four-fold increase in the cut-off energy of accelerated protons. For triangular and semi-circular groove targets, the cut-off energy remains comparatively lower (higher than the flat target though). The angular divergence of the accelerated protons/ions is also found to be relatively much lower in the case of a rectangular groove.

physics.plasm-ph↗

arXiv2023

Not-so-efficient proton acceleration by an intense laser pulse obliquely incident on a structured target

The interaction of an obliquely incident laser pulse with a flat TNSA target is known to enhance the cut-off energy of protons/ions as compared to a normally incident laser pulse, mostly owing to Brunnel absorption. However, it is not well understood how the oblique incidence of the laser pulse would affect the protons/ion spectra in the case of a micro-structured target. Using two-dimensional particle-in-cell simulations, we show here that the protons/ions cut-off energies are rather reduced in the case of oblique incidence of the laser pulse if the target has a micron-sized groove on its front surface. This is also found to be true for a periodically grooved target.

physics.plasm-ph↗

arXiv2023

Towards Operationalizing Social Bonding in Human-Robot Dyads

With momentum increasing in the use of social robots as long-term assistive and collaborative partners, humans developing social bonds with these artificial agents appears to be inevitable. In human-human dyads, social bonding plays a powerful role in regulating behaviours, emotions, and even health. If this is to extend to human-robot dyads, the phenomenology of such relationships (including their emergence and stability) must be better understood. In this paper, we discuss potential approaches towards operationalizing the phenomenon of social bonding between human-robot dyads. We will discuss a number of biobehavioural proxies of social bonding, moving away from existing approaches that use subjective, psychological measures, and instead grounding our approach in some of the evolutionary, neurobiological and physiological correlates of social bond formation in natural systems: (a) reductions in physiological stress (the ''social buffering'' phenomenon), (b) narrowing of spatial proximity between dyads, and (c) inter-dyad behavioural synchrony. We provide relevant evolutionary support for each proposed component, with suggestions and considerations for how they can be recorded in (real-time) human-robot interaction scenarios. With this, we aim to inspire more robust operationalisation of ''social bonding'' between human and artificial (robotic) agents.

cs.RO↗

arXiv2023

Analyzing Compression Techniques for Computer Vision

Compressing deep networks is highly desirable for practical use-cases in computer vision applications. Several techniques have been explored in the literature, and research has been done in finding efficient strategies for combining them. For this project, we aimed to explore three different basic compression techniques - knowledge distillation, pruning, and quantization for small-scale recognition tasks. Along with the basic methods, we also test the efficacy of combining them in a sequential manner. We analyze them using MNIST and CIFAR-10 datasets and present the results along with few observations inferred from them.

cs.CV↗

arXiv2023

Masses of Four Missing Hadrons having Spin 3/2h Predicted in SU(4) by Standard Model

To study symmetry and its breaking in particle physics, plays very important role in order to get useful information about the nature. The classification and arrangements of subatomic particles is also necessary to study particle physics. Particles which are building blocks of nature are quarks, gluons and leptons. Baryons and Mesons composed of quarks were arranged by Gell-Mann and Okubo in their well-known Eight-Fold way up to SU(3) symmetry. Standard model of particles is composed of these particles. Particles in SU(4) also make some beautiful patron. These make some multiplets. but all the baryons with spin JP= 3/2+ in these multiplets have not been observed till date. In this paper the SU(4) multiplets have been organized and studied in an easy and new way. As a result we obtained some clues about the masses and other characteristics of the unknown hyperons. These approximations about the characteristics of the unidentified baryons have been recorded in this article. Mass formula for the baryon SU(4) multiplets have been obtained.

physics.gen-ph↗

arXiv2023

Low-coherence interferometric measurement of the spectral dependence of the light field backscattered by optical interfaces

In this paper, we show how the combined use of low coherence interferometry, balanced detection and data processing comparable to that used in Fourier transform spectrometry allows us to characterize with ultimate resolutions (sub-ppm in level, 0.2 nm in wavelength and 5 mdeg in angle) the retro-reflection and retro-scattering response of both sides of a 2 mm thick silica wafer.

physics.optics↗

arXiv2022

Low coherence interferometric detection of the spectral dependence of the retro-reflection coefficient of an anti-reflective coated interface

The measurement of very low reflection coefficients of anti-reflective coated interfaces has become a key issue for the realization of precision instruments such as the giant interferometers used for the detection of gravitational waves. We propose in this paper a method, based on low coherence interferometry and balanced detection, which not only allows to obtain the spectral dependence of this reflection coefficient in amplitude and phase, with a sensitivity of the order of 0.1 ppm and a spectral resolution of 0.2 nm, but also to eliminate any spurious influence related to the possible presence of uncoated interfaces. This method also implements a data processing similar to that used in Fourier transform spectrometry. After establishing the formulas that control the accuracy and the signal-to-noise ratio of this method, we present the results that provide a complete demonstration of its successful operation in various experimental conditions.

physics.optics↗

arXiv2022

Supervised Class-pairwise NMF for Data Representation and Classification

Various Non-negative Matrix factorization (NMF) based methods add new terms to the cost function to adapt the model to specific tasks, such as clustering, or to preserve some structural properties in the reduced space (e.g., local invariance). The added term is mainly weighted by a hyper-parameter to control the balance of the overall formula to guide the optimization process towards the objective. The result is a parameterized NMF method. However, NMF method adopts unsupervised approaches to estimate the factorizing matrices. Thus, the ability to perform prediction (e.g. classification) using the new obtained features is not guaranteed. The objective of this work is to design an evolutionary framework to learn the hyper-parameter of the parameterized NMF and estimate the factorizing matrices in a supervised way to be more suitable for classification problems. Moreover, we claim that applying NMF-based algorithms separately to different class-pairs instead of applying it once to the whole dataset improves the effectiveness of the matrix factorization process. This results in training multiple parameterized NMF algorithms with different balancing parameter values. A cross-validation combination learning framework is adopted and a Genetic Algorithm is used to identify the optimal set of hyper-parameter values. The experiments we conducted on both real and synthetic datasets demonstrated the effectiveness of the proposed approach.

cs.LG↗

arXiv2022

An Explainable Regression Framework for Predicting Remaining Useful Life of Machines

Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of machine components. These ML algorithms are generally used as a black box with a total focus on the performance without identifying the potential causes behind the algorithms' decisions and their working mechanism. We believe, the performance (in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the trust of the stakeholders in ML prediction rather more insights on the causes behind the predictions are needed. To this aim, in this paper, we explore the potential of Explainable AI (XAI) techniques by proposing an explainable regression framework for the prediction of machines' RUL. We also evaluate several ML algorithms including classical and Neural Networks (NNs) based solutions for the task. For the explanations, we rely on two model agnostic XAI methods namely Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). We believe, this work will provide a baseline for future research in the domain.

cs.LG↗

arXiv2021

Explainable Event Recognition

The literature shows outstanding capabilities for CNNs in event recognition in images. However, fewer attempts are made to analyze the potential causes behind the decisions of the models and exploring whether the predictions are based on event-salient objects or regions? To explore this important aspect of event recognition, in this work, we propose an explainable event recognition framework relying on Grad-CAM and an Xception architecture-based CNN model. Experiments are conducted on three large-scale datasets covering a diversified set of natural disasters, social, and sports events. Overall, the model showed outstanding generalization capabilities obtaining overall F1-scores of 0.91, 0.94, and 0.97 on natural disasters, social, and sports events, respectively. Moreover, for subjective analysis of activation maps generated through Grad-CAM for the predicted samples of the model, a crowdsourcing study is conducted to analyze whether the model's predictions are based on event-related objects/regions or not? The results of the study indicate that 78%, 84%, and 78% of the model decisions on natural disasters, sports, and social events datasets, respectively, are based onevent-related objects or regions.

cs.CV↗

arXiv2021

Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset

Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community's response to the applications by analyzing information from different sources, such as news and users' reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users' reviews and extracts the evoked sentiments. In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews. In total, we employ eight different methods achieving up to an average F1-Scores 94.8% indicating the feasibility of automatic sentiment analysis of users' reviews on the COVID-19 contact tracing applications. We also highlight the key advantages, drawbacks, and users' concerns over the applications. Moreover, we also collect and annotate a large-scale dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. The presented analysis and the dataset are expected to provide a baseline/benchmark for future research in the domain.

cs.CL↗

arXiv2020

Spectrum of Landau Levels in GaAs Quantum Wells

We have studied the optical and electrical spectra from an n i p LED as a function of magnetic field. This sample incorporated three GaAs quantum wells in the intrinsic region. This device had excess n type doping and as a result. The quantum wells were populated by a two dimension Landau electron gas. The broad B0 field emission band evolved into a series of discrete features in the presence of a magnetic field. These were identified as interband transitions between the different values of l. Landau levels associated with the sub-bands, with the selection rule. An energy splitting between the two polarised components was observed for each Landau level transition. This was found to be equal to the sum of the conduction and valence band spin splittings. We used the know value of electron g factor to determine the valence band spin splittings. Our experimental values were compared to the numerically calculated values and were found to be in reasonable agreement.

cond-mat.mes-hall↗

arXiv2020

Agile and versatile quantum communication: signatures and secrets

Agile cryptography allows for a resource-efficient swap of a cryptographic core in case the security of an underlying classical cryptographic algorithm becomes compromised. Conversely, versatile cryptography allows the user to switch the cryptographic task without requiring any knowledge of its inner workings. In this paper, we suggest how these related principles can be applied to the field of quantum cryptography by explicitly demonstrating two quantum cryptographic protocols, quantum digital signatures (QDS) and quantum secret sharing (QSS), on the same hardware sender and receiver platform. Crucially, the protocols differ only in their classical post-processing. The system is also suitable for quantum key distribution (QKD) and is highly compatible with deployed telecommunication infrastructures, since it uses standard quadrature phase shift keying (QPSK) encoding and heterodyne detection. For the first time, QDS protocols are modified to allow for postselection at the receiver, enhancing protocol performance. The cryptographic primitives QDS and QSS are inherently multipartite and we prove that they are secure not only when a player internal to the task is dishonest, but also when (external) eavesdropping on the quantum channel is allowed. In our first proof-of-principle demonstration of an agile and versatile quantum communication system, the quantum states were distributed at GHz rates. This allows for a one-bit message to be securely signed using our QDS protocols in less than 0.05 ms over a 2 km fiber link and in less than 0.2~s over a 20 km fiber link. To our knowledge, this also marks the first demonstration of a continuous-variable direct QSS protocol.

quant-ph↗

arXiv2020

Distillation of Squeezing using a pulsed engineered PDC source

Hybrid quantum information processing combines the advantages of discrete and continues variable protocols by realizing protocols consisting of photon counting and homodyne measurements. However, the mode structure of pulsed sources and the properties of the detection schemes often require the use optical filters in order to combine both detection methods in a common experiment. This limits the efficiency and the overall achievable squeezing of the experiment. In our work, we use photon subtraction to implement the distillation of pulsed squeezed states originating from a genuinely spatially and temporally single-mode parametric down-conversion source in non-linear waveguides. Due to the distillation, we witness an improvement of $0.17~\mathrm{dB}$ from an initial squeezing value of $-1.648 \pm 0.002~\mathrm{dB}$, while achieving a purity of $0.58$, and confirm the non-Gaussianity of the distilled state via the higher-order cumulants. With this, we demonstrate the source's suitability for scalable hybrid quantum network applications with pulsed quantum light.

quant-ph↗

arXiv2020

Growth and Characterization of Homoepitaxial $β$-Ga$_2$O$_3$ Layers

$β$-Ga$_2$O$_3$ is a next-generation ultra wide bandgap semiconductor (E$_g$ = 4.8 eV to 4.9 eV) that can be homoepitaxially grown on commercial substrates, enabling next-generation power electronic devices among other important applications. Analyzing the quality of deposited homoepitaxial layers used in such devices is challenging, in part due to the large probing depth in traditional x-ray diffraction (XRD) and also due to the surface-sensitive nature of atomic force microscopy (AFM). Here, a combination of evanescent grazing-incidence skew asymmetric XRD and AFM are investigated as an approach to effectively characterize the quality of homoepitaxial $β$-Ga$_2$O$_3$ layers grown by molecular beam epitaxy at a variety of Ga/O flux ratios. Accounting for both structure and morphology, optimal films are achieved at a Ga/O ratio of $\sim$1.15, a conclusion that would not be possible to achieve by either XRD or AFM methods alone. Finally, fabricated Schottky barrier diodes with thicker homoepitaxial layers are characterized by $J-V$ and $C-V$ measurements, revealing an unintentional doping density of 4.3 $\times$ 10$^{16}$ cm$^{-3}$ - 2 $\times$ 10$^{17}$ cm$^{-3}$ in the epilayer. These results demonstrate the importance of complementary measurement methods for improving the quality of the $β$-Ga$_2$O$_3$ homoepitaxial layers used in power electronic and other devices.

physics.app-ph↗