arXiv subjects

Rajesh Kumar

Publications and source records attributed to Rajesh Kumar.

At least 73 records · Page 4Linked to original sources

arXiv2024

Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs

The transition to online examinations and assignments raises significant concerns about academic integrity. Traditional plagiarism detection systems often struggle to identify instances of intelligent cheating, particularly when students utilize advanced generative AI tools to craft their responses. This study proposes a keystroke dynamics-based method to differentiate between bona fide and assisted writing within academic contexts. To facilitate this, a dataset was developed to capture the keystroke patterns of individuals engaged in writing tasks, both with and without the assistance of generative AI. The detector, trained using a modified TypeNet architecture, achieved accuracies ranging from 74.98% to 85.72% in condition-specific scenarios and from 52.24% to 80.54% in condition-agnostic scenarios. The findings highlight significant differences in keystroke dynamics between genuine and assisted writing. The outcomes of this study enhance our understanding of how users interact with generative AI and have implications for improving the reliability of digital educational platforms.

cs.CV↗

arXiv2024

Theoretical and Experimental Constraints for the Equation of State of Dense and Hot Matter

This review aims at providing an extensive discussion of modern constraints relevant for dense and hot strongly interacting matter. It includes theoretical first-principle results from lattice and perturbative QCD, as well as chiral effective field theory results. From the experimental side, it includes heavy-ion collision and low-energy nuclear physics results, as well as observations from neutron stars and their mergers. The validity of different constraints, concerning specific conditions and ranges of applicability, is also provided.

nucl-th↗

arXiv2024

Monolithic Germanium Tin on Si Avalanche Photodiodes

We demonstrate monolithically grown germanium-tin (GeSn) on silicon avalanche photodiodes (APDs) for infrared light detection. A relatively thinner Ge buffer design was adopted to allow effective photo carriers to transport from the GeSn absorber to the Si multiplication layer such that clear punch-through behavior and a saturated primary responsivity of 0.3 A/W at 1550 nm were observed before avalanche breakdown in GeSn/Si APDs for the first time. The spectral response covers 1500 to 1700 nm. The measured punch-through and breakdown voltages are 15 and 17 V, respectively. Undisputed multiplication gain was obtained with the maximum value of 4.5 at 77 K, and 1.4 at 250 K, directly in reference to the saturated primary responsivity from the same device rather than a different GeSn p-i-n photodiode in previous reports. A peak responsivity was measured as 1.12 A/W at 1550 nm and 77 K.

cond-mat.mtrl-sci↗

arXiv2024

Medical Image Analysis for Detection, Treatment and Planning of Disease using Artificial Intelligence Approaches

X-ray is one of the prevalent image modalities for the detection and diagnosis of the human body. X-ray provides an actual anatomical structure of an organ present with disease or absence of disease. Segmentation of disease in chest X-ray images is essential for the diagnosis and treatment. In this paper, a framework for the segmentation of X-ray images using artificial intelligence techniques has been discussed. Here data has been pre-processed and cleaned followed by segmentation using SegNet and Residual Net approaches to X-ray images. Finally, segmentation has been evaluated using well known metrics like Loss, Dice Coefficient, Jaccard Coefficient, Precision, Recall, Binary Accuracy, and Validation Accuracy. The experimental results reveal that the proposed approach performs better in all respect of well-known parameters with 16 batch size and 50 epochs. The value of validation accuracy, precision, and recall of SegNet and Residual Unet models are 0.9815, 0.9699, 0.9574, and 0.9901, 0.9864, 0.9750 respectively.

eess.IV↗

arXiv2024

Electrically Injected mid-infrared GeSn laser on Si operating at 140 K

Owing to its true direct bandgap and tunable bandgap energies,GeSn alloys are increasingly attractive as gain media for mid-IR lasers that can be monolithically integrated on Si. Demonstrations of optically pumped GeSn laser at room under pulsed condition and at cryogenic temperature under continuous-wave excitation show great promise of GeSn lasers to be efficient electrically injected light sources on Si. Here we report electrically injected GeSn lasers using Fabry-Perot cavity with 20, 40, and 80 micron ridge widths. A maximum operating temperature of 140 K with lasing threshold of 0.756 kA/cm2 at 77 K and emitting wavelength of 2722 nm at 140 K was obtained. The lower threshold current density compared to previous works was achieved by reducing optical loss and improving the optical confinement. The peak power was measured as 2.2 mW/facet at 77 K.

physics.optics↗

arXiv2024

supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination

The presence of muscles throughout the active parts of the body such as the upper and lower limbs, makes electromyography-based human-machine interaction prevalent. However, muscle signals are stochastic and noisy. These noises can be regular and irregular. Irregular noises due to movements or electrical switching require dynamic filtering. Conventionally, filters are stacked, which trims and delays the signal unnecessarily. This study introduces a decontamination technique involving a supervised rewarding strategy to drive a deep Q-network-based agent (supDQN). It applies one of three filters to decontaminate a 1sec long surface electromyography signal, which is dynamically contaminated. A machine learning agent identifies whether the signal after filtering is clean or noisy. Accordingly, a reward is generated. The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select filter optimally while decontaminating a signal. The proposed filtering strategy is tested on four noise levels (-5 dB, -1 dB, +1 dB, +5 dB). supDQN filters the signal desirably when the signal-to-noise ratio (SNR) is between -5 dB to +1 dB. It filters less desirably at high SNR (+5 dB). A normalized root mean square (nRMSE) is formulated to depict the difference of filtered signal from ground truth. This is used to compare supDQN and conventional methods including wavelet denoising with debauchies and symlet wavelet, high order low pass filter, notch filter, and high pass filter. The proposed filtering strategy gives an average value nRMSE of 1.1974, which is lower than the conventional filters.

eess.SP↗

arXiv2024

MEET: Mixture of Experts Extra Tree-Based sEMG Hand Gesture Identification

Artificial intelligence (AI) has made significant advances in recent years and opened up new possibilities in exploring applications in various fields such as biomedical, robotics, education, industry, etc. Among these fields, human hand gesture recognition is a subject of study that has recently emerged as a research interest in robotic hand control using electromyography (EMG). Surface electromyography (sEMG) is a primary technique used in EMG, which is popular due to its non-invasive nature and is used to capture gesture movements using signal acquisition devices placed on the surface of the forearm. Moreover, these signals are pre-processed to extract significant handcrafted features through time and frequency domain analysis. These are helpful and act as input to machine learning (ML) models to identify hand gestures. However, handling multiple classes and biases are major limitations that can affect the performance of an ML model. Therefore, to address this issue, a new mixture of experts extra tree (MEET) model is proposed to identify more accurate and effective hand gesture movements. This model combines individual ML models referred to as experts, each focusing on a minimal class of two. Moreover, a fully trained model known as the gate is employed to weigh the output of individual expert models. This amalgamation of the expert models with the gate model is known as a mixture of experts extra tree (MEET) model. In this study, four subjects with six hand gesture movements have been considered and their identification is evaluated among eleven models, including the MEET classifier. Results elucidate that the MEET classifier performed best among other algorithms and identified hand gesture movement accurately.

eess.SP↗

arXiv2024

Consciousness Driven Spike Timing Dependent Plasticity

Spiking Neural Networks (SNNs), recognized for their biological plausibility and energy efficiency, employ sparse and asynchronous spikes for communication. However, the training of SNNs encounters difficulties coming from non-differentiable activation functions and the movement of spike-based inter-layer data. Spike-Timing Dependent Plasticity (STDP), inspired by neurobiology, plays a crucial role in SNN's learning, but its still lacks the conscious part of the brain used for learning. Considering the issue, this research work proposes a Consciousness Driven STDP (CD-STDP), an improved solution addressing inherent limitations observed in conventional STDP models. CD-STDP, designed to infuse the conscious part as coefficients of long-term potentiation (LTP) and long-term depression (LTD), exhibit a dynamic nature. The model connects LTP and LTD coefficients to current and past state of synaptic activities, respectively, enhancing consciousness and adaptability. This consciousness empowers the model to effectively learn while understanding the input patterns. The conscious coefficient adjustment in response to current and past synaptic activity extends the model's conscious and other cognitive capabilities, offering a refined and efficient approach for real-world applications. Evaluations on MNIST, FashionMNIST and CALTECH datasets showcase $CD$-STDP's remarkable accuracy of 98.6%, 85.61% and 99.0%, respectively, in a single hidden layer SNN. In addition, analysis of conscious elements and consciousness of the proposed model on SNN is performed.

q-bio.NC↗

arXiv2024

Attention-based Estimation and Prediction of Human Intent to augment Haptic Glove aided Control of Robotic Hand

The letter focuses on Haptic Glove (HG) based control of a Robotic Hand (RH) executing in-hand manipulation of certain objects of interest. The high dimensional motion signals in HG and RH possess intrinsic variability of kinematics resulting in difficulty to establish a direct mapping of the motion signals from HG onto the RH. An estimation mechanism is proposed to quantify the motion signal acquired from the human controller in relation to the intended goal pose of the object being held by the robotic hand. A control algorithm is presented to transform the synthesized intent at the RH and allow relocation of the object to the expected goal pose. The lag in synthesis of the intent in the presence of communication delay leads to a requirement of predicting the estimated intent. We leverage an attention-based convolutional neural network encoder to predict the trajectory of intent for a certain lookahead to compensate for the delays. The proposed methodology is evaluated across objects of different shapes, mass, and materials. We present a comparative performance of the estimation and prediction mechanisms on 5G-driven real-world robotic setup against benchmark methodologies. The test-MSE in prediction of human intent is reported to yield ~ 97.3 -98.7% improvement of accuracy in comparison to LSTM-based benchmark

cs.RO↗

arXiv2024

An Adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis

The vibration analysis of the bearing is very crucial because of its non-stationary nature and low signal-to-noise ratio. Therefore, a novel scheme for detecting bearing defects is put forward based on the extraction of single-valued neutrosophic cross-entropy (SVNCE) to address this issue. Initially, the artificial hummingbird algorithm (AHA) is used to make the feature mode decomposition (FMD) adaptive by optimizing its parameter based on a newly developed health indicator (HI) i.e. sparsity impact measure index (SIMI). This HI ensures full sparsity and impact properties simultaneously. The raw signals are disintegrated into different modes by adaptive FMD at optimal values of its parameters. The energy of these modes is calculated for different health conditions. The energy interval range has been decided based on energy eigen which are then transformed into single-valued neutrosophic sets (SVNSs) for unknown defect conditions. The minimum argument principle employs the least SVNCE values between SVNSs of testing samples (obtained from unknown bearing conditions) and SVNSs of training samples (obtained from known bearing conditions) to recognize the different defects in the bearing. It has been discovered that the suggested methodology is more adept at identifying the various bearing defects.

eess.SP↗

arXiv2024

Semi-Analytical Methods for Population Balance models involving Aggregation and Breakage processes: A comparative study

Population balance models often integrate fundamental kernels, including sum, gelling and Brownian aggregation kernels. These kernels have demonstrated extensive utility across various disciplines such as aerosol physics, chemical engineering, astrophysics, pharmaceutical sciences and mathematical biology for the purpose of elucidating particle dynamics. The objective of this study is to refine the semi-analytical solutions derived from current methodologies in addressing the nonlinear aggregation and coupled aggregation-breakage population balance equation. This work presents a unique semi-analytical approach based on the homotopy analysis method (HAM) to solve pure aggregation and couple aggregation-fragmentation population balance equations, which is an integro-partial differentia equation. By decomposing the non-linear operator, we investigate how to utilize the convergence control parameter to expedite the convergence of the HAM solution towards its precise values in the proposed method.

math.NA↗

arXiv2024

Non-linear collision-induced breakage equation: finite volume and semi-analytical methods

The non-linear collision-induced breakage equation has significant applications in particulate processes. Two semi-analytical techniques, namely homotopy analysis method (HAM) and accelerated homotopy perturbation method (AHPM) are investigated along with the well-known finite volume method (FVM) to comprehend the dynamical behavior of the non-linear system, i.e., the concentration function, the total number and the total mass of the particles in the system. The theoretical convergence analyses of the series solutions of HAM and AHPM are discussed. In addition, the error estimations of the truncated solutions of both methods equip the maximum absolute error bound. To justify the applicability and accuracy of these methods, numerical simulations are compared with the findings of FVM and analytical solutions considering three physical problems.

math.NA↗

arXiv2024

Non-linear collision-induced breakage equation: approximate solution and error estimation

This article aims to provide approximate solutions for the non-linear collision-induced breakage equation using two different semi-analytical schemes, i.e., variational iteration method (VIM) and optimized decomposition method (ODM). The study also includes the detailed convergence analysis and error estimation for ODM in the case of product collisional ($K(ε,ρ)=ερ$) and breakage ($b(ε,ρ,σ)=\frac{2}ρ$) kernels with an exponential decay initial condition. By contrasting estimated concentration function and moments with exact solutions, the novelty of the suggested approaches is presented considering three numerical examples. Interestingly, in one case, VIM provides a closed-form solution, however, finite term series solutions obtained via both schemes supply a great approximation for the concentration function and moments.

math.NA↗

arXiv2024

Optical Screening of Citrus Leaf Diseases Using Label-Free Spectroscopic Tools: A Review

Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.

q-bio.OT↗

arXiv2024

Physical viability of traversable Finslerian wormholes with traceless fluid under conformal symmetry

The current study explores the novel potential of traversable wormhole solutions within the framework of Finsler geometry, incorporating conformal symmetry alongside traceless fluid dynamics. Using the Conformal Killing vector approach, we have discussed the wormholes based on traceless fluid within the intriguing framework of Finsler geometry. The field equations and the associated conformal factor are obtained specifically under the condition of conformal motion in Finsler geometry. Furthermore, we have successfully derived and examined the shape function, considering a range of values for the Finslerian parameter $λ$. Our investigation extends to fundamental physical characteristics such as proper radial distance, active mass function, and total gravitational energy, aiming to understand their influence on the traversability of the wormhole. The observation of energy condition violations provides evidence for the exotic matter's presence near the throat, reinforcing the assertion of the Finslerian wormhole's traversability.

gr-qc↗

arXiv2024

Can LLMs Compute with Reasons?

Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distributed network of SLMs. This network leverages error-based learning and hint incorporation to refine the reasoning capabilities of SLMs. Our goal is to provide a framework that empowers SLMs to approach the level of logic-based applications achieved by high-parameter models, potentially benefiting any language model. Ultimately, this novel concept paves the way for bridging the logical gap between humans and LLMs across various fields.

cs.CL↗

arXiv2024

Effects of hyperon potentials and symmetry energy in quark deconfinement

In this letter we discuss how the results of recent nuclear experiments that correspond to measurements at low densities can affect the equation of state at large densities and temperatures, changing the particle composition and ultimately influencing deconfinement to quark matter. In particular, saturation values of the hyperon potentials affect the hyperon content, while the symmetry energy at saturation directly regulates how the stiffness of the equation of state changes with isospin. We make use of a chiral model that describes nucleons, hyperons, and quarks to show how astrophysical conditions, such as the ones in neutron stars, present the ideal ground to study the effects of these two quantities in dense matter. In this case, for small charge fraction/ large isospin asymmetry, the couplings that reproduce different symmetry energy slopes can significantly modify deconfinement, with quantitative changes in the critical chemical potential depending on the deconfining potential. On the other hand, different values of the parameter that controls the hyperon potentials (kept within a range close to experimental data) do not affect deconfinement significantly.

nucl-th↗

arXiv2024

Traversable wormholes in Finsler geometry under conformal motion

This paper aims to investigate the possibility of physically achievable new wormhole solutions within the context of Finsler geometry by focusing on conformal motion. For this purpose, we study the barotropic linear equation of state (EoS). We scrutinized their geometric features within the EoS model, which includes baryonic and non-baryonic matter. Through derived field equations, shape functions adhering to critical criteria governed by the Finsler parameter $γ$ are explored. By evaluating energy conditions, violations are found, which indicate the potential presence of exotic matter necessary for traversable wormholes. Notably, violations of energy conditions signify the plausibility of traversable wormholes within the conformally transformed Finslerian space observed in both baryonic and non-baryonic scenarios. Additionally, anisotropy exploration uncovers repulsive geometric characteristics within these wormholes.

gr-qc↗