arXiv subjects

Arnab Maity

Publications and source records attributed to Arnab Maity.

At least 19 recordsLinked to original sources

arXiv2026

Robust Model Reference Adaptive Control with Combined Adaptation under Finite Excitation Condition

In adaptive control, parametric uncertainties in linear-in-parameter form consist of unknown parameters and known regressor signals. Convergence of the unknown parameters to their ideal values requires the regressor to satisfy a persistent excitation (PE) condition, which depends on future data and is therefore infeasible to guarantee online. Memory-based parameter update laws address this by enabling ideal parameter convergence under the online-verifiable finite excitation (FE) condition. In this paper, a new algorithm is proposed to construct a memory term via the Modified Gram-Schmidt orthogonalization procedure for a class of multi-input multi-output nonlinear systems with an unknown diagonal control effectiveness matrix and bounded nonparametric uncertainties. Under the finite excitation condition, the constructed memory term yields an identity coefficient matrix in the parameter estimation error dynamics. The identity coefficient matrix eliminates the need for time-varying adaptation gains, enables an explicit ultimate bound on the parameter estimation error, and preserves the structure of the nonparametric uncertainty bound under the memory term. Building on this, a combined adaptation law is developed for controller gain estimation under FE. The closed-loop tracking and estimation errors are shown to decay exponentially to a neighborhood of the origin, characterized by an explicit ultimate bound, with a decay rate that depends solely on user-defined gains and system constants, independent of the level of regressor excitation. This removes the dependence of the convergence rate on the level of regressor excitation, a key limitation of existing approaches such as concurrent learning, memory regressor extension, and DREM.

eess.SY↗

arXiv2026

Design and Performance Evaluation of Secure RF and WiFi-Based Communication in Drone Swarms via Testbed Implementation

Unmanned aerial vehicle (UAV) swarms rely on distributed coordination and cooperative communication to support scalable operations, extended coverage, and applications such as surveillance and real-time data exchange. Wireless technologies such as radio frequency (RF) and WiFi are widely used for UAV-to-UAV and UAV-to-ground control station (GCS) communication but introduce significant security challenges. MAVLink, the predominant communication protocol in UAV systems, provides message integrity and authentication but lacks built-in encryption, leaving telemetry traffic vulnerable to eavesdropping. In our previous work, we proposed MAVShield, a lightweight encryption framework for MAVLink communications. In this paper, MAVShield, AES-CTR, Speck-CTR, ChaCha20, and Rabbit are integrated into four custom-built UAVs to establish secure communication links over RF and WiFi channels. Their performance is evaluated through flight experiments using a UAV swarm testbed. Encrypted telemetry data enable autonomous formation control and collision avoidance during flight. For collision avoidance, we develop a modified artificial potential field (APF) algorithm that computes attractive and repulsive forces directly in geodetic coordinates, eliminating Cartesian transformations and reducing trajectory oscillations while avoiding local-minimum trapping. CPU utilization, memory consumption, and packet delivery ratio (PDR) are measured for each encryption scheme. Results show that MAVShield achieves performance comparable to unencrypted communication while outperforming AES-CTR, Speck-CTR, ChaCha20, and Rabbit in overall efficiency. Algebraic cryptanalysis and Wireshark-based traffic analysis demonstrate resistance to key-recovery attacks and protection of telemetry confidentiality. The results indicate that MAVShield is an efficient and secure solution for UAV swarm communication.

cs.CR↗

arXiv2025

LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification

Latent fingerprint identification remains a challenging task due to low image quality, background noise, and partial impressions. In this work, we propose a novel identification approach called LatentPrintFormer. The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features from latent fingerprints. A spatial attention module is employed to emphasize high-quality ridge regions while suppressing background noise. The extracted features are fused and projected into a unified 512-dimensional embedding, and matching is performed using cosine similarity in a closed-set identification setting. Extensive experiments on two publicly available datasets demonstrate that LatentPrintFormer consistently outperforms three state-of-the-art latent fingerprint recognition techniques, achieving higher identification rates across Rank-10.

cs.CV↗

arXiv2025

Exponentially Stable Combined Adaptive Control under Finite Excitation Condition

The parameter convergence relies on a stringent persistent excitation (PE) condition in adaptive control. Several works have proposed a memory term in the last decade to translate the PE condition to a feasible finite excitation (FE) condition. This work proposes a combined model reference adaptive control for a class of uncertain nonlinear systems with an unknown control effectiveness vector. The closed-loop system is exponentially stable under the FE condition. The exponential rate of convergence is independent of the excitation level of the regressor vector and is lower-bounded in terms of the system parameters and user-designed gains. Numerical simulation is illustrated, validating the results obtained with the proposed adaptive control.

eess.SY↗

arXiv2025

A Novel Cipher for Enhancing MAVLink Security: Design, Security Analysis, and Performance Evaluation Using a Drone Testbed

We present MAVShield, a novel lightweight cipher designed to secure communications in Unmanned Aerial Vehicles (UAVs) using the MAVLink protocol, which by default transmits unencrypted messages between UAVs and Ground Control Stations (GCS). While existing studies propose encryption for MAVLink, most remain theoretical or simulation-based. We implement MAVShield alongside AES-CTR, ChaCha20, Speck-CTR, and Rabbit, and evaluate them on a real drone testbed. A comprehensive security analysis using statistical test suites (NIST and Diehard) demonstrates strong resistance of the novel cipher to cryptanalysis. Performance evaluation across key metrics including memory usage, CPU load, and battery power consumption, demonstrates that MAVShield outperforms existing algorithms and offers an efficient, real-world solution for securing MAVLink communications in UAVs.

cs.CR↗

arXiv2024

Fano resonances in tilted Weyl semimetals in an oscillating quantum well

Considering the low-energy model of tilted Weyl semimetal, we study the electronic transmission through a periodically driven quantum well, oriented in the transverse direction with respect to the tilt. We adopt the formalism of Floquet scattering theory and investigate the emergence of Fano resonances as an outcome of matching between the Floquet sidebands and quasi-bound states. The Fano resonance energy changes linearly with the tilt strength suggesting the fact that tilt-mediated part of quasi-bound states energies depends on the above factor. Given a value of momentum parallel (perpendicular) to the tilt, we find that the energy gap between two Fano resonances, appearing for two adjacent values of transverse (collinear) momentum with respect to the tilt direction, is insensitive (sensitive) to the change in the tilt strength. Such a coupled (decoupled) behavior of tilt strength and the collinear (transverse) momentum can be understood from the tilt-mediated and normal parts of the quasi-bound state energies inside the potential well. We vary the other tilt parameters and chirality of the Weyl points to conclusively verify the exact form of the tilt-mediated part of the quasi-bound state energy that is the same as the tilt term in the static dispersion. The tilt orientation can significantly alter the transport in terms of evolution of Fano resoance energy with tilt momentum. We analytically find the explicit form of the bound state energy that further supports all our numerical findings. Our work paves the way to probe the tilt-mediated part of quasi-bound state energy to understand the complex interplay between the tilt and Fano resonance.

cond-mat.mes-hall↗

arXiv2022

Inference in Functional Linear Quantile Regression

In this paper, we study statistical inference in functional quantile regression for scalar response and a functional covariate. Specifically, we consider a functional linear quantile regression model where the effect of the covariate on the quantile of the response is modeled through the inner product between the functional covariate and an unknown smooth regression parameter function that varies with the level of quantile. The objective is to test that the regression parameter is constant across several quantile levels of interest. The parameter function is estimated by combining ideas from functional principal component analysis and quantile regression. An adjusted Wald testing procedure is proposed for this hypothesis of interest, and its chi-square asymptotic null distribution is derived. The testing procedure is investigated numerically in simulations involving sparse and noisy functional covariates and in a capital bike share data application. The proposed approach is easy to implement and the {\tt R} code is published online at \url{https://github.com/xylimeng/fQR-testing}.

stat.ME↗

arXiv2022

Efficacy of Asynchronous GPS Spoofing Against High Volume Consumer GNSS Receivers

The vulnerability of the Global Positioning System (GPS) against spoofing is known for quite some time. Also, the positioning and navigation of most semi-autonomous and autonomous drones are dependent on Global Navigation Satellite System (GNSS) signals. In prior work, simplistic or asynchronous GPS spoofing was found to be a simple, efficient, and effective cyber attack against L1 GPS or GNSS dependent commercial drones. In this paper, first we make some important observations on asynchronous GPS spoofing attacks on drones presented in prior research literature. Then, we design an asynchronous GPS spoofing attack plan. Next, we test the effectiveness of this attack against GNSS receivers (high volume consumer devices based on Android mobile phones) of different capabilities and a commercial drone (DJI Mavic 2 Pro) under various conditions. Finally, we present several novel insights based on the results of the tests.

cs.CR↗

arXiv2022

PROLIFIC: Projection-based Test for Lack of Importance of Smooth Functional Effect in Crossover Design

Wearable devices for continuous monitoring of electronic health increased attention due to their richness in information. Often, inference is drawn from features that quantify some summary of the data, leading to a loss of information that could be useful when one utilizes the functional nature of the response. When functional trajectories are observed repeated over time, it is termed longitudinal functional data. This work is motivated by the interest to assess the efficacy of a noninflammatory medication, meloxicam, on the daily activity levels of household cats with a pre-existing condition of osteoarthritis under a crossover design. These activity profiles are recorded at a minute level by accelerometer over the entire study period. To this aspect, we propose an orthogonal projection-based test pseudo generalized F test for significance of the functional treatment effect under a functional additive crossover model after adjusting for the carryover effect and other baseline covariates. Under mild conditions, we derive the asymptotic null distribution of the test statistic when the projection function for the underlying Hilbert space is estimated from the data. In finite sample numerical studies, the proposed test maintains the size, is powerful to detect the significance of the smooth effect of meloxicam, and is very efficient compared to bootstrap-based alternatives.

stat.ME↗

arXiv2021

Hypothesis Testing in Nonlinear Function on Scalar Regression with Application to Child Growth Study

We propose a kernel machine based hypothesis testing procedure in nonlinear function-on-scalar regression model. Our research is motivated by the Newborn Epigenetic Study (NEST) where the question of interest is whether a pre-specified group of toxic metals or methylation at any of 9 differentially methylated regions (DMRs) is associated with child growth. We take the child growth trajectory as the functional response, and model the toxic metal measurements jointly using a nonlinear function. We use a kernel machine approach to model the unknown function and transform the hypothesis of no effect to an appropriate variance component test. We demonstrate our proposed methodology using a simulation study and by applying it to analyze the NEST data.

stat.ME↗

arXiv2021

Divide-and-Conquer MCMC for Multivariate Binary Data

The analysis of large scale medical claims data has the potential to improve quality of care by generating insights which can be used to create tailored medical programs. In particular, the multivariate probit model can be used to investigate the correlation between multiple binary responses of interest in such data, e.g. the presence of multiple chronic conditions. Bayesian modeling is well suited to such analyses because of the automatic uncertainty quantification provided by the posterior distribution. A complicating factor is that large medical claims datasets often do not fit in memory, which renders the estimation of the posterior using traditional Markov Chain Monte Carlo (MCMC) methods computationally infeasible. To address this challenge, we extend existing divide-and-conquer MCMC algorithms to the multivariate probit model, demonstrating, via simulation, that they should be preferred over mean-field variational inference when the estimation of the latent correlation structure between binary responses is of primary interest. We apply this algorithm to a large database of de-identified Medicare Advantage claims from a single large US health insurance provider, where we find medically meaningful groupings of common chronic conditions and asses the impact of the urban-rural health gap by identifying underutilized provider specialties in rural areas.

stat.ME↗

arXiv2021

Variational Inference for Shrinkage Priors: The R package vir

We present vir, an R package for variational inference with shrinkage priors. Our package implements variational and stochastic variational algorithms for linear and probit regression models, the use of which is a common first step in many applied analyses. We review variational inference and show how the derivation for a Gibbs sampler can be easily modified to derive a corresponding variational or stochastic variational algorithm. We provide simulations showing that, at least for a normal linear model, variational inference can lead to similar uncertainty quantification as the corresponding Gibbs samplers, while estimating the model parameters at a fraction of the computational cost. Our timing experiments show situations in which our algorithms converge faster than the frequentist LASSO implementations in glmnet while simultaneously providing superior parameter estimation and variable selection. Hence, our package can be utilized to quickly explore different combinations of predictors in a linear model, while providing accurate uncertainty quantification in many applied situations. The package is implemented natively in R and RcppEigen, which has the benefit of bypassing the substantial operating system specific overhead of linking external libraries to work efficiently with R.

stat.CO↗

arXiv2019

A Score Based Test for Functional Linear Concurrent Regression

We propose a novel method for testing the null hypothesis of no effect of a covariate on the response in the context of functional linear concurrent regression. We establish an equivalent random effects formulation of our functional regression model under which our testing problem reduces to testing for zero variance component for random effects. For this purpose, we use a one-sided score test approach, which is an extension of the classical score test. We provide theoretical justification as to why our testing procedure has the right levels (asymptotically) under null using standard assumptions. Using numerical simulations, we show that our testing method has the desired type I error rate and gives higher power compared to a bootstrapped F test currently existing in the literature. Our model and testing procedure are shown to give good performances even when the data is sparsely observed, and the covariate is contaminated with noise. Applications of the proposed testing method are demonstrated on gait study and a dietary calcium absorption data.

stat.ME↗

arXiv2019

Variable Selection in Functional Linear Concurrent Regression

We propose a novel method for variable selection in functional linear concurrent regression. Our research is motivated by a fisheries footprint study where the goal is to identify important time-varying socio-structural drivers influencing patterns of seafood consumption, and hence fisheries footprint, over time, as well as estimating their dynamic effects. We develop a variable selection method in functional linear concurrent regression extending the classically used scalar on scalar variable selection methods like LASSO, SCAD, and MCP. We show in functional linear concurrent regression the variable selection problem can be addressed as a group LASSO, and their natural extension; group SCAD or a group MCP problem. Through simulations, we illustrate our method, particularly with group SCAD or group MCP penalty, can pick out the relevant variables with high accuracy and has minuscule false positive and false negative rate even when data is observed sparsely, is contaminated with noise and the error process is highly non-stationary. We also demonstrate two real data applications of our method in studies of dietary calcium absorption and fisheries footprint in the selection of influential time-varying covariates.

stat.AP↗

arXiv2019

Simultaneous Variable Selection, Clustering, and Smoothing in Function on Scalar Regression

We address the problem of multicollinearity in a function-on-scalar regression model by using a prior which simultaneously selects, clusters, and smooths functional effects. Our methodology groups effects of highly correlated predictors, performing dimension reduction without dropping relevant predictors from the model. We validate our approach via a simulation study, showing superior performance relative to existing dimension reduction approaches in the function-on-scalar literature. We also demonstrate the use of our model on a data set of age specific fertility rates from the United Nations Gender Information database.

stat.AP↗

arXiv2019

The Robust Kernel Association Test

Testing the association between SNP effects and a response is a common task. Such tests are often carried out through kernel machine methods based on least squares, such as the Sequence Kernel Association Test (SKAT). However, these least squares procedures assume a normally distributed response, which is often violated. Other robust procedures such as the Quantile Regression Kernel Machine (QRKM) restrict choice of loss function and only allow inference on conditional quantiles. We propose a general and robust kernel association test with flexible choice of loss function, no distributional assumptions, and has SKAT and QRKM as special cases. We evaluate our proposed robust association test (RobKAT) across various data distributions through simulation study. When errors are normally distributed, RobKAT controls type I error and shows comparable power to SKAT. In all other distributional settings investigated, our robust test has similar or greater power than SKAT. Finally, we apply our robust kernel association test on data from the CATIE clinical trial to detect associations between selected genes on chromosome 6, including the Major Histocompatibility Complex (MHC) region, and neurotrophic herpesvirus antibody levels in schizophrenia patients. RobKAT detected significant association with four SNP-sets (HST1H2BJ, MHC, POM12L2, and SLC17A1), three of which were undetected by SKAT.

stat.ME↗

arXiv2017

A Comparison of Testing Methods in Scalar-on-Function Regression

A scalar-response functional model describes the association between a scalar response and a set of functional covariates. An important problem in the functional data literature is to test the nullity or linearity of the effect of the functional covariate in the context of scalar-on-function regression. This article provides an overview of the existing methods for testing both the null hypotheses that there is no relationship and that there is a linear relationship between the functional covariate and scalar response, and a comprehensive numerical comparison of their performance. The methods are compared for a variety of realistic scenarios: when the functional covariate is observed at dense or sparse grids and measurements include noise or not. Finally, the methods are illustrated on the Tecator data set.

stat.ME↗

arXiv2016

Additive Function-on-Function Regression

We study additive function-on-function regression where the mean response at a particular time point depends on the time point itself as well as the entire covariate trajectory. We develop a computationally efficient estimation methodology based on a novel combination of spline bases with an eigenbasis to represent the trivariate kernel function. We discuss prediction of a new response trajectory, propose an inference procedure that accounts for total variability in the predicted response curves, and construct pointwise prediction intervals. The estimation/inferential procedure accommodates realistic scenarios such as correlated error structure as well as sparse and/or irregular designs. We investigate our methodology in finite sample size through simulations and two real data applications.

stat.ME↗