arXiv subjects

Sandeep Kumar Singh

Publications and source records attributed to Sandeep Kumar Singh.

17 recordsLinked to original sources

arXiv2026

Generative AI-Driven Phase Control for RIS-Aided Cell-Free Massive MIMO Systems

This work investigates a generative artificial intelligence (GenAI) model to optimize the reconfigurable intelligent surface (RIS) phase shifts in RIS-aided cell-free massive multiple-input multiple-output (mMIMO) systems under practical constraints, including imperfect channel state information (CSI) and spatial correlation. We propose two GenAI based approaches, generative conditional diffusion model (GCDM) and generative conditional diffusion implicit model (GCDIM), leveraging the diffusion model conditioned on dynamic CSI to maximize the sum spectral efficiency (SE) of the system. To benchmark performance, we compare the proposed GenAI based approaches against an expert algorithm, traditionally known for achieving near-optimal solutions at the cost of computational efficiency. The simulation results demonstrate that GCDM matches the sum SE achieved by the expert algorithm while significantly reducing the computational overhead. Furthermore, GCDIM achieves a comparable sum SE with an additional $98\%$ reduction in computation time, underscoring its potential for efficient phase optimization in RIS-aided cell-free mMIMO systems.

cs.IT↗

arXiv2025

A Novel Taxonomy and Classification Scheme for Code Smell Interactions

Code smells are indicators of potential design flaws in source code and do not appear alone but in combination with other smells, creating complex interactions. While existing literature classifies these smell interactions into collocated, coupled, and inter-smell relations, however, to the best of our knowledge, no research has used the existing knowledge of code smells and (or) their relationships with other code smells in the detection of code smells. This gap highlights the need for deeper investigation into how code smells interact with each other and assist in their detection. This would improve the overall comprehension of code smells and how they interact more effectively. This study presents a novel taxonomy and a proposed classification scheme for the possible code smell interactions considering a specific programming language as a domain. This paper has dealt with one scenario called Inter smell detection within the domain. The experiments have been carried out using several popular machine learning (ML) models. Results primarily show the presence of code smell interactions namely Inter-smell Detection within domain. These results are compatible with the available facts in the literature suggesting a promising direction for future research in code smell detection.

cs.SE↗

arXiv2023

Quantum Wrapper Networking

We introduce a new concept of Quantum Wrapper Networking, which enables control, management, and operation of quantum networks that can co-exist with classical networks while keeping the requirements for quantum networks intact. The quantum wrapper networks (QWNs) enable the transparent and interoperable transportation of quantum wrapper datagrams consisting of quantum payloads and, notably, classical headers to facilitate the datagram switching without measuring or disturbing the qubits of the quantum payload. Furthermore, QWNs can utilize the common network control and management for performance monitoring on the classical header and infer the quantum channel quality.

quant-ph↗

arXiv2022

Leveraging Graph and Deep Learning Uncertainties to Detect Anomalous Trajectories

Understanding and representing traffic patterns are key to detecting anomalous trajectories in the transportation domain. However, some trajectories can exhibit heterogeneous maneuvering characteristics despite confining to normal patterns. Thus, we propose a novel graph-based trajectory representation and association scheme for extraction and confederation of traffic movement patterns, such that data patterns and uncertainty can be learned by deep learning (DL) models. This paper proposes the usage of a recurrent neural network (RNN)-based evidential regression model, which can predict trajectory at future timesteps as well as estimate the data and model uncertainties associated, to detect maritime anomalous trajectories, such as unusual vessel maneuvering, using automatic identification system (AIS) data. Furthermore, we utilize evidential deep learning classifiers to detect unusual turns of vessels and the loss of transmitted signal using predicted class probabilities with associated uncertainties. Our experimental results suggest that the graphical representation of traffic patterns improves the ability of the DL models, such as evidential and Monte Carlo dropout, to learn the temporal-spatial correlation of data and associated uncertainties. Using different datasets and experiments, we demonstrate that the estimated prediction uncertainty yields fundamental information for the detection of traffic anomalies in the maritime and, possibly in other domains.

cs.LG↗

arXiv2020

On Detecting and Preventing Jamming Attacks with Machine Learning in Optical Networks

Optical networks are prone to power jamming attacks intending service disruption. This paper presents a Machine Learning (ML) framework for detection and prevention of jamming attacks in optical networks. We evaluate various ML classifiers for detecting out-of-band jamming attacks with varying intensities. Numerical results show that artificial neural network is the fastest (10^6 detections per second) for inference and most accurate (~ 100 %) in detecting power jamming attacks as well as identifying the optical channels attacked. We also discuss and study a novel prevention mechanism when the system is under active jamming attacks. For this scenario, we propose a novel resource reallocation scheme that utilizes the statistical information of attack detection accuracy to lower the probability of successful jamming of lightpaths while minimizing lightpaths' reallocations. Simulation results show that the likelihood of jamming a lightpath reduces with increasing detection accuracy, and localization reduces the number of reallocations required

cs.NI↗

arXiv2020

Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data

The automatic identification system (AIS) reports vessels' static and dynamic information, which are essential for maritime traffic situation awareness. However, AIS transponders can be switched off to hide suspicious activities, such as illegal fishing, or piracy. Therefore, this paper uses real world AIS data to analyze the possibility of successful detection of various anomalies in the maritime domain. We propose a multi-class artificial neural network (ANN)-based anomaly detection framework to classify intentional and non-intentional AIS on-off switching anomalies. The multi-class anomaly framework captures AIS message dropouts due to various reasons, e.g., channel effects or intentional one for carrying illegal activities. We extract position, speed, course and timing information from real world AIS data, and use them to train a 2-class (normal and anomaly) and a 3-class (normal, power outage and anomaly) anomaly detection models. Our results show that the models achieve around 99.9% overall accuracy, and are able to classify a test sample in the order of microseconds.

eess.SP↗

arXiv2018

Computing Exact and Approximate Blocking Probabilities in Elastic Optical Networks

In this paper, we propose the first exact Markov model for connection blocking analysis in elastic optical networks, based on the occupancy status of spectrum slices on all links due to arrivals and departures of various classes of connections in a network. Since the complexity of the exact Markov model grows exponentially with the link capacity, number of links, routes, and classes of demands, we further advance the state-of-the-art in computing approximate blocking probability in elastic optical networks and propose two novel approximations, i.e., load-independent and load-dependent. These approximations are used to compute state-dependent per-class connection setup rates in multi-class elastic optical networks with or without spectrum converters by taking into account the spectrum fragmentation factor in each state. We validate approximation analysis by exact and/or simulation results, and show that load-independent and load-dependent approximations can be more accurately used than previously proposed approximations, under a random-fit (RF) and a first-fit (FF) spectrum allocation policies. The approximate results match closely with the exact model, for smaller networks, and with the simulations under a variety of network scenarios.

cs.NI↗

arXiv2018

Resistivity Anomaly in Weyl Semimetal candidate Molybdenum Telluride

The Weyl semi-metal candidate MoTe$_{2}$ is expected to exhibit a range of exotic electronic transport properties. It exhibits a structural phase transition near room temperature that is evident in the thermal hysteresis in resistivity and thermopower (Seebeck coefficient) as well as large spin-orbit interaction. Here, we also document a resistivity anomaly of up to 13% in the temperature window between 25 and 50 K, which is found to be strongly anisotropic. Based on the experimental data in conjunction with density functional theory calculations, we conjecture that the anomaly can be related to the presence of defects in the system. These findings open opportunities for further investigations and understanding of the transport behavior in these newly discovered semi-metallic layered systems.

cond-mat.mes-hall↗

arXiv2018

Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey

Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

cs.AI↗

arXiv2016

Balancing Data Security and Blocking Performance with Spectrum Randomization in Optical Networks

Data randomization or scrambling has been effectively used in various applications to improve the data security. In this paper, we use the idea of data randomization to proactively randomize the spectrum (re)allocation to improve connections' security. As it is well-known that random (re)allocation fragments the spectrum and thus increases blocking in elastic optical networks, we analyze the tradeoff between system performance and security. To this end, in addition to spectrum randomization, we utilize an on-demand defragmentation scheme every time a request is blocked due to the spectrum fragmentation. We model the occupancy pattern of an elastic optical link (EOL) using a multi-class continuous-time Markov chain (CTMC) under the random-fit spectrum allocation method. Numerical results show that although both the blocking and security can be improved for a particular so-called randomization process (RP) arrival rate, while with the increase in RP arrival rate the connections' security improves at the cost of the increase in overall blocking.

cs.NI↗

arXiv2016

Defragmentation-as-a-Service (DaaS): How beneficial is it?

We analyze Defragmentation-as-a-Service (DaaS) in elastic optical networks and show that the positive effect of defragmentation depends on the rate at which it is performed, load (or, call arrival rates) and the available resources.

cs.NI↗

arXiv2014

Rippling, buckling and melting of single- and multi-layer MoS$_2$

Large-scale atomistic simulations using the reactive empirical bond order force field approach is implemented to investigate thermal and mechanical properties of single-layer (SL) and multi-layer (ML) molybdenum disulfide (MoS$_2$). The amplitude of the intrinsic ripples of SL-MoS$_2$ are found to be smaller than those exhibited by graphene (GE). Furthermore, because of the van der Waals interaction between layers, the out-of-plane thermal fluctuations of ML-MoS$_2$ decreases rapidly with increasing number of layers. This trend is confirmed by the buckling transition due to uniaxial stress which occurs for a significantly larger applied tension as compared to graphene. For SL-MoS$_2$, the melting temperature is estimated to be 3700~K which occurs through dimerization followed by the formation of small molecules consisting of 2 to 5 atoms. When different types of vacancies are inserted in the SL-MoS$_2$ it results in a decrease of both the melting temperature as well as the stiffness.

cond-mat.mtrl-sci↗

arXiv2014

Electronic properties of graphene nano-flakes: Energy gap, permanent dipole, termination effect and Raman spectroscopy

The electronic properties of graphene nano-flakes (GNFs) with different edge passivation is investigated by using density functional theory. Passivation with F and H atoms are considered: C$_{N_c}$ X$_{N_x}$ (X=F or H). We studied GNFs with $10<N_c<56$ and limit ourselves to the lowest energy configurations. We found that: i) the energy difference $Δ$ between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) decreases with $N_c$, ii) topological defects (pentagon and heptagon) break the symmetry of the GNFs and enhance the electric polarization, iii) the mutual interaction of bilayer GNFs can be understood by dipole-dipole interaction which were found sensitive to the relative orientation of the GNFs, iv) the permanent dipoles depend on the edge terminated atom, while the energy gap is independent of it, and v) the presence of heptagon and pentagon defects in the GNFs results in the largest difference between the energy of the spin-up and spin-down electrons which is larger for the H-passivated GNFs as compared to F-passivated GNFs. Our study shows clearly the effect of geometry, size, termination and bilayer on the electronic properties of small GNFs.This study reveals important features of graphene nano-flakes which can be detected using Raman spectroscopy.

cond-mat.mtrl-sci↗

arXiv2013

Melting of Partially Fluorinated Graphene: From Detachment of Fluorine Atoms to Large Defects and Random Coils

The melting of fluorographene is very unusual and depends strongly on the degree of fluorination. For temperatures below 1000 K, fully fluorinated graphene (FFG) is thermo-mechanically more stable than graphene but at T$_m\approx$2800 K FFG transits to random coils which is almost twice lower than the melting temperature of graphene, i.e. 5300 K. For fluorinated graphene (PFG) up to 30 % ripples causes detachment of individual F-atoms around 2000 K while for 40-60 % fluorination, large defects are formed beyond 1500 K and beyond 60% of fluorination F-atoms remain bonded to graphene until melting. The results agree with recent experiments on the dependence of the reversibility of the fluorination process on the percentage of fluorination.

cond-mat.mtrl-sci↗

arXiv2013

Melting of graphene clusters

Density-functional tight-binding and classical molecular dynamics simulations are used to investigate the structural deformations and melting of planar carbon nano-clusters $C_{N}$ with N=2-55. The minimum energy configurations for different clusters are used as starting configuration for the study of the temperature effects on the bond breaking/rotation in carbon lines (N$<$6), carbon rings (5$<$N$<$19) and graphene nano-flakes. The larger the rings (graphene nano-flake) the higher the transition temperature (melting point) with ring-to-line (perfect-to-defective) transition structures. The melting point was obtained by using the bond energy, the Lindemann criteria, and the specific heat. We found that hydrogen-passivated graphene nano-flakes (C$_{N}$H$_M$) have a larger melting temperature with a much smaller dependence on its size. The edges in the graphene nano-flakes exhibit several different meta-stable configurations (isomers) during heating before melting occurs.

cond-mat.mtrl-sci↗

arXiv2013

Thermomechanical properties of a single hexagonal boron nitride sheet

Using atomistic simulations we investigate the thermodynamical properties of a single atomic layer of hexagonal boron nitride (h-BN). The thermal induced ripples, heat capacity, and thermal lattice expansion of large scale h-BN sheets are determined and compared to those found for graphene (GE) for temperatures up to 1000 K. By analyzing the mean square height fluctuations $< h^2>$ and the height-height correlation function $H(q)$ we found that the h-BN sheet is a less stiff material as compared to graphene. The bending rigidity of h-BN: i) is about 16% smaller than the one of GE at room temperature (300 K), and ii) increases with temperature as in GE. The difference in stiffness between h-BN and GE results in unequal responses to external uniaxial and shear stress and different buckling transitions. In contrast to a GE sheet, the buckling transition of a h-BN sheet depends strongly on the direction of the applied compression. The molar heat capacity, thermal expansion coefficient and the Gruneisen parameter are estimated to be 25.2 J\,mol$^{-1}$\,K$^{-1}$, 7.2$\times10^{-6}$K$^{-1}$ and 0.89, respectively.

cond-mat.mes-hall↗

arXiv2013

Vibrational properties of nanographene

The eigenmodes and the vibrational density of states of the ground state configuration of graphene clusters are calculated using atomistic simulations. The modified Brenner potential is used to describe the carbon-carbon interaction and carbon-hydrogen interaction in case of H-passivated edges. For a given configuration of the C-atoms the eigenvectors and eigenfrequencies of the normal modes are obtained after diagonalisation of the dynamical matrix whose elements are the second derivative of the potential energy. The compressional and shear properties are obtained from the divergence and rotation of the velocity field. For symmetric and defective clusters with pentagon arrangement on the edge, the highest frequency modes are shear modes. The specific heat of the clusters is also calculated within the harmonic approximation and the convergence to the result for bulk graphene is investigated.

cond-mat.mtrl-sci↗