arXiv subjects

Anirban Ghosh

Publications and source records attributed to Anirban Ghosh.

At least 19 recordsLinked to original sources

arXiv2026

Cover time statistics of one-dimensional Brownian motion under stochastic resetting

We investigate the effect of stochastic resetting on the statistics of the cover time of a one-dimensional Brownian motion with a diffusion constant $D$, confined to a finite interval of length $L$. The cover time $t_c$, defined as the minimum time required for the particle to visit every point of the interval at least once, exhibits a non-monotonic dependence on the scaled reset rate $\rho = rL^2/4D$. The scaled mean cover time $4D\langle t_c\rangle/L^2$ initially decreases with increasing $\rho$, attains a minimum at an optimal value $\rho^*$, and then increases with $\rho$, indicating an optimal resetting rate that minimizes the search duration. Furthermore, we derive an exact analytical expression for the cover time distribution, including its asymptotic limits, which agree well with numerical simulations. These results demonstrate that stochastic resetting serves as an efficient mechanism for optimizing cover-time processes in confined geometries.

cond-mat.stat-mech↗

arXiv2026

Topo-ADV: Generating Topology-Driven Imperceptible Adversarial Point Clouds

Deep neural networks for 3D point cloud understanding have achieved remarkable success in object classification and recognition, yet recent work shows that these models remain highly vulnerable to adversarial perturbations. Existing 3D attacks predominantly manipulate geometric properties such as point locations, curvature, or surface structure, implicitly assuming that preserving global shape fidelity preserves semantic content. In this work, we challenge this assumption and introduce the first topology-driven adversarial attack for point cloud deep learning. Our key insight is that the homological structure of a 3D object constitutes a previously unexplored vulnerability surface. We propose Topo-ADV, an end-to-end differentiable framework that incorporates persistent homology as an explicit optimization objective, enabling gradient-based manipulation of topological features during adversarial example generation. By embedding persistence diagrams through differentiable topological representations, our method jointly optimizes (i) a topology divergence loss that alters persistence, (ii) a misclassification objective, and (iii) geometric imperceptibility constraints that preserve visual plausibility. Experiments demonstrate that subtle topology-driven perturbations consistently achieve up to 100% attack success rates on benchmark datasets such as ModelNet40, ShapeNet Part, and ScanObjectNN using PointNet and DGCNN classifiers, while remaining geometrically indistinguishable from the original point clouds, beating state-of-the-art methods on various perceptibility metrics.

cs.CV↗

arXiv2026

Forecasting of Multiple Seasonal Categorical Time Series Using Fourier Series with Application to AQI Data of Kolkata

Multiple seasonalities have been widely studied in continuous time series using models such as TBATS, for instance in electricity demand forecasting. However, their treatment in categorical time series, such as air quality index (AQI) data, remains limited. Categorical AQI often exhibits distinct seasonal patterns at multiple frequencies, which are not captured by standard models. In this paper, we propose a framework that models multiple seasonalities using Fourier series and indicator functions, inspired by the TBATS methodology. The approach accommodates the ordinal nature of AQI categories while explicitly capturing daily, weekly and yearly seasonal cycles. Simulation studies demonstrate the empirical consistency of parameter estimates under the proposed model. We further illustrate its applicability using real categorical AQI data from Kolkata and compare forecasting performance with Markov models and machine learning methods. Results indicate that our approach effectively captures complex seasonal dynamics and provides improved predictive accuracy. The proposed methodology offers a flexible and interpretable framework for analyzing categorical time series exhibiting multiple seasonal patterns, with potential applications in air quality monitoring, energy consumption and other environmental domains.

stat.ME↗

arXiv2025

Wafer-Scale Integration of Piezo- and Ferroelectric Al0.64Sc0.36N Thin Films by Reactive Sputtering

Large-area deposition of Aluminium-Scandium-Nitride (Al1-xScxN) thin films with higher Sc content (x) remains challenging due to issues such as abnormal orientation growth, stress control, and the undesired crystal phase. These anomalies across the wafer hinder the development of high scandium-content AlScN films, which are critical for microelectromechanical systems applications. In this study, we report the sputter deposition of Al0.64Sc0.36N thin films from a 300 mm Al0.64Sc0.36 alloy target on 200 mm Si(100) wafers, achieving an exceptionally high deposition rate of 8.7 {\mu}m/h with less than 1% AOGs and controllable stress tuning. Comprehensive microstructural and electrical characterizations confirm the superior growth of high-quality Al0.64Sc0.36N films with exceptional wafer-average piezoelectric coefficients (d33,f =15.62 pm/V and e31,f = -2.9 C/m2) owing to low point defects density and grain mosaicity. This was accomplished through the implementation of an optimized seed layer and a refined electrode integration strategy, along with optimal process conditions. The wafer yield and device failure rates are analysed and correlated with the average stress of the films and their stress profiles along the diameter. The resulting films show excellent uniformity in structural, compositional, and piezoelectric properties across the entire 200 mm wafer, underscoring their strong potential for next-generation MEMS applications.

cond-mat.mtrl-sci↗

arXiv2025

TACO-Net: Topological Signatures Triumph in 3D Object Classification

3D object classification is a crucial problem due to its significant practical relevance in many fields, including computer vision, robotics, and autonomous driving. Although deep learning methods applied to point clouds sampled on CAD models of the objects and/or captured by LiDAR or RGBD cameras have achieved remarkable success in recent years, achieving high classification accuracy remains a challenging problem due to the unordered point clouds and their irregularity and noise. To this end, we propose a novel state-of-the-art (SOTA) 3D object classification technique that combines topological data analysis with various image filtration techniques to classify objects when they are represented using point clouds. We transform every point cloud into a voxelized binary 3D image to extract distinguishing topological features. Next, we train a lightweight one-dimensional Convolutional Neural Network (1D CNN) using the extracted feature set from the training dataset. Our framework, TACO-Net, sets a new state-of-the-art by achieving $99.05\%$ and $99.52\%$ accuracy on the widely used synthetic benchmarks ModelNet40 and ModelNet10, and further demonstrates its robustness on the large-scale real-world OmniObject3D dataset. When tested with ten different kinds of corrupted ModelNet40 inputs, the proposed TACO-Net demonstrates strong resiliency overall.

cs.CV↗

arXiv2025

Finite size effect in the persistence probability of the Edwards-Wilkinson model of surface growth and effect of non-linearity

The dynamical evolution of the surface height is controlled by either a linear or a nonlinear Langevin equation, depending on the underlying microscopic dynamics, and is often done theoretically using stochastic coarse-grained growth equations. The persistence probability $p(t)$ of stochastic models of surface growth that are constrained by a finite system size is examined in this work. We focus on the linear Edwards-Wilkinson model (EW) and the nonlinear Kardar-Parisi-Zhang model, two specific models of surface growth. The persistence exponents in the continuum version of these two models have been widely investigated. Krug et al.[Phys. Rev. E , 56:2702-2712, (1997)] and Kallabis et al. [EPL (Europhysics Letters) , 45(1):20, 1999] had shown that, the steady-state persistence exponents for both these models are related to the growth exponent $\beta$ as $\theta=1-\beta$. It is numerically found that the values of persistence exponents for both these models are close to the analytically predicted values. While the results of the continuum equations of the surface growth are well known, we focus to study the persistence probability expressions for discrete models with a finite size effect. In this article, we have investigated the persistence probabilities for the linear Edwards-Wilkinson(EW) model and for the non-linear Kardar-Parisi-Zhang(KPZ) model of surface growth on a finite one-dimensional lattice. The interesting phenomenon which is found in this case is that the known scenario of $p(t)$ of the following algebraic decay vanishes as we introduce a finite system size.

cond-mat.stat-mech↗

arXiv2025

TopoRec: Point Cloud Recognition Using Topological Data Analysis

Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting a meaningful global descriptor from a query point cloud that can be matched with the descriptors of the database point clouds is a challenging problem. Furthermore, when the query point cloud is noisy or has been transformed (e.g., rotated), it adds to the complexity. To this end, we propose a novel methodology, named TopoRec, which utilizes Topological Data Analysis (TDA) for extracting local descriptors from a point cloud, thereby eliminating the need for resource-intensive GPU-based machine learning training. More specifically, we used the ATOL vectorization method to generate vectors for point clouds. To test the quality of the proposed TopoRec technique, we have implemented it on multiple real-world (e.g., Oxford RobotCar, NCLT) and realistic (e.g., ShapeNet) point cloud datasets for large-scale place and object recognition, respectively. Unlike existing learning-based approaches such as PointNetVLAD and PCAN, our method does not require extensive training, making it easily adaptable to new environments. Despite this, it consistently outperforms both state-of-the-art learning-based and handcrafted baselines (e.g., M2DP, ScanContext) on standard benchmark datasets, demonstrating superior accuracy and strong generalization.

cs.RO↗

arXiv2025

Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases

Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular network of the retina as a reflection of the cardiovascular system, alongwith the dynamic cardiac insights from ECG, we sought to provide a holistic diagnostic perspective. Initially, a Fast Fourier Transform (FFT) was applied to both the ECG and fundus images, transforming the data into the frequency domain. Subsequently, the Earth Mover's Distance (EMD) was computed for the frequency-domain features of both modalities. These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT's spectral insights and EMD's capability to capture nuanced data differences, offers a robust representation for CVD classification. Preliminary tests yielded a commendable accuracy of 84 percent, underscoring the potential of this combined diagnostic strategy. As we continue our research, we anticipate refining and validating the model further to enhance its clinical applicability in resource limited healthcare ecosystems prevalent across the Indian sub-continent and also the world at large.

eess.IV↗

arXiv2025

Vehicle to vehicle path loss modeling at millimeter wave band for crossing cars

Fifth generation (5G) new radio is now offering sidelink capability, which allows direct vehicle-to-vehicle (V2V) communication. Millimeter wave (mmWave) enables low-latency mission-critical V2V communications, such as forward crash warning, between two vehicles crossing on a road without dividers. In this article, we present a measurement-based path loss (PL) model for V2V links operating at 59.6 GHz mmWave when two vehicles approach from opposite sides and cross each other. Our model outperforms other existing PL models and can reliably model both approaching and departing vehicle scenarios.

eess.SP↗

arXiv2025

Synthesis of omnidirectional path loss model based on directional model and multi-elliptical geometry

Millimeter wave (mmWave) technology offers high throughput but has a limited radio range, necessitating the use of directional antennas or beamforming systems such as massive MIMO. Path loss (PL) models using narrow-beam antennas are known as directional models, while those using omnidirectional antennas are referred to as omnidirectional models. To standardize the analysis, omnidirectional PL models for mmWave ranges have been introduced, including TR 38.901 by 3GPP, which is based on measurements from directional antennas. However, synthesizing these measurements can be complex and time-consuming. This study proposes a numerical approach to derive an omnidirectional model from directional data using multi-elliptical geometry. We assessed the effectiveness of this method against existing PL models for mmWaves that are available in the literature.

eess.SP↗

arXiv2025

Variability of radio signal attenuation by single deciduous tree versus reception angle at 80 GHz

Vegetation significantly affects radio signal attenuation, influenced by factors such as signal frequency, plant species, and foliage density. Existing attenuation models typically address specific scenarios, like single trees, rows of trees, or green spaces, with the ITU-R P.833 recommendation being a widely recognized standard. Most assessments for single trees focus on the primary radiation direction of the transmitting antenna. This paper introduces a novel approach to evaluating radio signal scattering by a single deciduous tree. Through measurements at 80 GHz and a bandwidth of approximately 2 GHz, we analyze how total signal attenuation varies with the reception angle relative to the transmitter-tree axis. The findings from various directional measurements contribute to a comprehensive attenuation model applicable to any reception angle and also highlight the impact of bandwidth on the received signal level.

eess.SP↗

arXiv2025

Power angular spectrum versus Doppler spectrum -- Measurements and analysis

In this paper, we present an empirical verification of the method of determining the Doppler spectrum (DS) from the power angular spectrum (PAS). Measurements were made for the frequency of 3.5 GHz, under non-line-of-sight conditions in suburban areas characteristic of a university campus. In the static scenario, the measured PAS was the basis for the determination of DSs, which were compared with the DSs measured in the mobile scenario. The obtained results show that the proposed method gives some approximation to DS determined with the classic methods used so far.

eess.SP↗

arXiv2025

Spectral efficiency for mmWave downlink with beam misalignment in urban macro scenario

In this paper, we analyze the spectral efficiency for millimeter wave downlink with beam misalignment in urban macro scenario. For this purpose, we use a new approach based on the modified Shannon formula, which considers the propagation environment and antenna system coefficients. These factors are determined based on a multi-ellipsoidal propagation model. The obtained results show that under non-line-of-sight conditions, the appropriate selection of the antenna beam orientation may increase the spectral efficiency in relation to the direct line to a user.

eess.SP↗

arXiv2025

MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI

Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems.

cs.AR↗

arXiv2025

Anisotropic active Brownian particle in two dimensions under stochastic resetting

We analytically investigate the dynamic behavior of an an-isotropic active Brownian particle under various stochastic resetting protocols in two dimensions. The motion of shape-asymmetric active Brownian particles in two dimensions leads to an-isotropic diffusion at short times, whereas rotational diffusion causes the transport to become isotropic at longer times. We have considered three different resetting protocols: (a) complete resetting, when both position and orientation are reset to their initial states, (b) only position is reset to its initial state, (c) only orientation is reset to its initial state. We reveal that orientation resetting sustains asymmetry even at late times. When both the spatial position and orientation are subject to resetting, a complex position probability distribution forms in the steady state. All the analytical findings are thoroughly validated by corresponding simulation results.

cond-mat.stat-mech↗

arXiv2024

THz Channels for Short-Range Mobile Networks: Multipath Channel Behavior and Human Body Shadowing Effects

The THz band (0.1-10 THz) is emerging as a crucial enabler for sixth-generation (6G) mobile communication systems, overcoming the limitations of current technologies and unlocking new opportunities for low-latency and ultra-high-speed communications by utilizing several tens of GHz transmission bandwidths. However, extremely high spreading losses and various interaction losses pose significant challenges to establishing reliable communication coverage, while human body shadowing further complicates maintaining stable communication links. Although point-to-point (P2P) fixed wireless access in the THz band has been successfully demonstrated, realizing fully mobile and reliable wireless access via highly directional communication remains a challenge. This paper addresses the key challenges faced by THz mobile networks, focusing particularly on the behavior of multipath channels and the impact of human body shadowing (HBS). It presents the environment-dependent characteristics of multipath clusters through empirical measurements at 300~GHz using a consistent setup, highlighting the need to account for environmental factors in system design. In addition, it presents a motion capture-based approach for precise measurement and prediction of HBS to enable proactive path scheduling and enhances link reliability, offering key insights for robust THz communication systems in future 6G networks.

eess.SP↗

arXiv2024

Edge state behavior of interacting Bosons in a Su-Schrieffer-Heeger lattice

In the low momentum regime, the Su-Schrieffer-Heeger (SSH) model's key characteristics are encapsulated by a Dirac-type Hamiltonian in continuum space, i.e., the localized states emerge at the boundaries. Building on this, we have developed an effective Hamiltonian to model ultra cold interacting Bosons on an SSH like lattice through variational minimization under the mean field approximation. To pinpoint the boundary states, we have developed an algorithm by generalizing the imaginary time propagator, where a initial state evolves under the squared Hamiltonian to converge to the targeted state. This algorithm has broader applicability, enabling the identification of specific eigenstates in various contexts. Furthermore, we draw a parallel to an experimentally physical setup involving a gas of ultra cold Bosons confined to an array of potential wells with alternating depths. By establishing the system's analogy with the SSH system, we apply our algorithm to investigate boundary states in the presence of interaction, demonstrating how these findings align with those of the continuous system.

cond-mat.quant-gas↗

arXiv2024

Switching of post quench reflection asymmetry in an embedded non-Hermitian Su-Schrieffer-Heeger system

A quench in a Su-Schrieffer-Heeger lattice across the topological boundary initialized with an edge state leads to transport across the chain. We consider such a quench in an effective model in which non-Hermitian components are embedded in an SSH lattice. We find that the transport arising as a result of quench is asymmetric in the sense that there is imbalance in reflection in transport from left and right and this imbalance switches from higher right reflection to higher left reflection as the configuration to which the system is quenched varies in parameter space. We discuss the switching as emergence from the underlying phenomenon of a partial reorganization of bulk states in terms of localization and energy, the intricacies of which depends upon the configuration of the system and the symmetries present.

quant-ph↗