Ruchi Tripathi
@new.ggu.ac.in
Assistant Professor, Department of Electronics and Communication
Guru Ghasidas University
EDUCATION
Phd, IIT Kanpur
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering, Signal Processing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ruchi Tripathi and Ketan Rajawat
Institute of Electrical and Electronics Engineers (IEEE)
Recent decades have observed an exponential growth in network traffic, thanks to the increased popularity of real-time applications, such as live video chat and gaming. The resulting growth in the network infrastructure has made it difficult for the service providers to abide by the service level agreements, especially with regards to the quality-of-service guarantees. Predicting network latencies from noisy and missing measurements has therefore emerged as an important problem, and a plethora of solutions have been proposed for the same. Existing network latency predictions rely either on Euclidean embedding or matrix completion methods. This work considers the estimation and prediction of network latencies from a sequence of noisy and incomplete latency matrices collected over time. An adaptive matrix completion algorithm is proposed that can handle streaming data at low computational complexity. The performance of the proposed algorithm is characterized both in theory and using a real dataset, demonstrating its viability as a network monitoring tool.
Harsha Vardhan, Ruchi Tripathi, and Ketan Rajawat
IEEE
This work proposes a dynamic matrix completion (DMC)-based approach for use in the front-end of MIMO radar. The proposed approach is different and complementary to the conventional target tracking algorithms that are widely deployed in the back-end of radar systems. The received signals are modelled as time-varying low-rank matrices and passed through an adaptive singular value thresholding (ASVT) block, resulting in the elimination of noise returns early in the processing chain. When all the antenna elements are not being used and the received signal is only partially observed, the ASVT block imputes the missing entries. Front-end processing results in cleaner signals for the back-end, culminating in fewer range and Doppler bins, increased probability of detection, reduced false alarm rate, and ultimately, improved target tracking performance. Detailed simulation of the radar chain reveal the significant improvements afforded by the proposed algorithm.
Rishabh Dixit, Amrit Singh Bedi, Ruchi Tripathi, and Ketan Rajawat
Institute of Electrical and Electronics Engineers (IEEE)
We consider nondifferentiable dynamic optimization problems such as those arising in robotics and subspace tracking. Given the computational constraints and the time-varying nature of the problem, a low-complexity algorithm is desirable, while the accuracy of the solution may only increase slowly over time. We put forth the proximal online gradient descent (OGD) algorithm for tracking the optimum of a composite objective function comprising of a differentiable loss function and a nondifferentiable regularizer. An online learning framework is considered and the gradient of the loss function is allowed to be erroneous. Both, the gradient error as well as the dynamics of the function optimum or target are adversarial and the performance of the inexact proximal OGD is characterized in terms of its dynamic regret, expressed in terms of the cumulative error and path length of the target. The proposed inexact proximal OGD is generalized for application to large-scale problems where the loss function has a finite sum structure. In such cases, evaluation of the full gradient may not be viable and a variance reduced version is proposed that allows the component functions to be subsampled. The efficacy of the proposed algorithms is tested on the problem of formation control in robotics and on the dynamic foreground–background separation problem in video.
Rishabh Dixit, Amrit Singh Bedi, Ruchi Tripathi, and Ketan Rajawat
IEEE
We consider the minimization of a time-varying function that comprises of a differentiable and a non-differentiable component. Such functions occur in the context of learning and estimation problems, where the loss function is often differentiable and strongly convex, while the regularizer and the constraints translate to a non-differentiable penalty. Dynamic version of the proximal online gradient descent algorithm is designed that can handle errors in the gradient. The performance of the proposed algorithm is analyzed within the online convex optimization framework and bounds on the dynamic regret are developed. These bounds generalize the existing results on non-differentiable minimization. Further, the inexact results are generalized to propose online algorithms for large-scale problems where the full gradient cannot be calculated at every iteration. Instead, we put forth an online proximal stochastic variance reduced gradient descent algorithm that can work with sampled data. Tests on a robot formation control problem demonstrate the efficacy of the proposed algorithms.
Ruchi Tripathi and Ketan Rajawat
IEEE
Last few decades have observed exponential growth in network demands due to increased popularity of real time applications, such as live chat, gaming etc. The resulting infrastructure growth has made it difficult for the service providers to abide by the service level agreements, especially with regards to the quality of service guarantees. Predicting network latencies from noisy and missing measurements has therefore emerged as an important problem, and a plethora of solutions have been proposed for the same. Existing network latency predictions rely either on Euclidean embedding or matrix completion methods. This work considers the estimation and prediction of network latencies from a sequence of noisy and incomplete latency matrices collected over time. An adaptive matrix completion algorithm is proposed that can handle streaming data at low computational complexity. The performance of the proposed algorithm is characterized both in theory and using a real dataset, demonstrating its viability as a network monitoring tool.
Ruchi Tripathi, Boda Mohan, and Ketan Rajawat
Institute of Electrical and Electronics Engineers (IEEE)
The low-rank matrix completion problem is fundamental to a number of tasks in data mining, machine learning, and signal processing. This paper considers the problem of adaptive matrix completion in time-varying scenarios. Given a sequence of incomplete and noise-corrupted matrices, the goal is to recover and track the underlying low rank matrices. Motivated from the classical least-mean square (LMS) algorithms for adaptive filtering, three LMS-like algorithms are proposed for estimating and tracking low-rank matrices. Performance of the proposed algorithms is provided in form of nonasymptotic bounds on the tracking mean-square error. Tracking performance of the algorithms is also studied via detailed simulations over real-world datasets.