Search Results for author: Bhaskar D. Rao

Found 22 papers, 3 papers with code

Vector Quantization Methods for Access Point Placement in Cell-Free Massive MIMO Systems

no code implementations23 Nov 2022 Govind R. Gopal, Bhaskar D. Rao

We examine the problem of uplink cell-free access point (AP) placement in the context of optimal throughput.

Quantization

Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions

1 code implementation13 Oct 2022 Kuan-Lin Chen, Harinath Garudadri, Bhaskar D. Rao

When the number of pieces is unknown, we prove that, in terms of the number of distinct linear components, the neural complexities of any CPWL function are at most polynomial growth for low-dimensional inputs and factorial growth for the worst-case scenario, which are significantly better than existing results in the literature.

Maximum Likelihood-based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL with Grid Refinement

no code implementations7 Oct 2022 Rohan R. Pote, Bhaskar D. Rao

The proposed optimization problem is non-convex, and we propose a majorization-minimization based iterative procedure to estimate the structured matrix; each iteration solves a semidefinite program.

Direction of Arrival Estimation

A Generalized Proportionate-Type Normalized Subband Adaptive Filter

no code implementations17 Nov 2021 Kuan-Lin Chen, Ching-Hua Lee, Bhaskar D. Rao, Harinath Garudadri

Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems.

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

1 code implementation NeurIPS 2021 Kuan-Lin Chen, Ching-Hua Lee, Harinath Garudadri, Bhaskar D. Rao

To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i. e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets.

A Novel Bayesian Approach for the Two-Dimensional Harmonic Retrieval Problem

no code implementations17 Feb 2021 Rohan R. Pote, Bhaskar D. Rao

Sparse signal recovery algorithms like sparse Bayesian learning work well but the complexity quickly grows when tackling higher dimensional parametric dictionaries.

Retrieval

General Total Variation Regularized Sparse Bayesian Learning for Robust Block-Sparse Signal Recovery

no code implementations13 Feb 2021 Aditya Sant, Markus Leinonen, Bhaskar D. Rao

Block-sparse signal recovery without knowledge of block sizes and boundaries, such as those encountered in multi-antenna mmWave channel models, is a hard problem for compressed sensing (CS) algorithms.

Modified Vector Quantization for Small-Cell Access Point Placement with Inter-Cell Interference

no code implementations5 Nov 2020 Govind R. Gopal, Elina Nayebi, Gabriel Porto Villardi, Bhaskar D. Rao

In this paper, we explore the small-cell uplink access point (AP) placement problem in the context of throughput-optimality and provide solutions while taking into consideration inter-cell interference.

Fairness Quantization

RE-MIMO: Recurrent and Permutation Equivariant Neural MIMO Detection

1 code implementation30 Jun 2020 Kumar Pratik, Bhaskar D. Rao, Max Welling

Each iterative unit is a neural computation module comprising of 3 sub-modules: the likelihood module, the encoder module, and the predictor module.

Multimodal Sparse Bayesian Dictionary Learning

no code implementations10 Apr 2018 Igor Fedorov, Bhaskar D. Rao

This paper addresses the problem of learning dictionaries for multimodal datasets, i. e. datasets collected from multiple data sources.

Dictionary Learning

Re-Weighted Learning for Sparsifying Deep Neural Networks

no code implementations5 Feb 2018 Igor Fedorov, Bhaskar D. Rao

This paper addresses the topic of sparsifying deep neural networks (DNN's).

Relevance Subject Machine: A Novel Person Re-identification Framework

no code implementations30 Mar 2017 Igor Fedorov, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen

We propose a novel method called the Relevance Subject Machine (RSM) to solve the person re-identification (re-id) problem.

Person Re-Identification

A GAMP Based Low Complexity Sparse Bayesian Learning Algorithm

no code implementations8 Mar 2017 Maher Al-Shoukairi, Philip Schniter, Bhaskar D. Rao

In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into Expectation-Maximization (EM)-based sparse Bayesian learning (SBL).

Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition

no code implementations6 May 2016 Igor Fedorov, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen

In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers.

Face Recognition

A Unified Framework for Sparse Non-Negative Least Squares using Multiplicative Updates and the Non-Negative Matrix Factorization Problem

no code implementations7 Apr 2016 Igor Fedorov, Alican Nalci, Ritwik Giri, Bhaskar D. Rao, Truong Q. Nguyen, Harinath Garudadri

We show that the proposed framework encompasses a large class of S-NNLS algorithms and provide a computationally efficient inference procedure based on multiplicative update rules.

Type I and Type II Bayesian Methods for Sparse Signal Recovery using Scale Mixtures

no code implementations17 Jul 2015 Ritwik Giri, Bhaskar D. Rao

In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors currently in use for sparse signal recovery (SSR).

Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals

no code implementations21 Apr 2014 Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Zhouyue Pi, Bhaskar D. Rao

Particularly, the proposed algorithm ensured that the BCI classification and the drowsiness estimation had little degradation even when data were compressed by 80%, making it very suitable for continuous wireless telemonitoring of multichannel signals.

Data Compression EEG

Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities

no code implementations15 Nov 2013 Zhilin Zhang, Bhaskar D. Rao, Tzyy-Ping Jung

As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices.

EEG

Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning

no code implementations7 May 2012 Zhilin Zhang, Tzyy-Ping Jung, Scott Makeig, Bhaskar D. Rao

The design of a telemonitoring system via a wireless body-area network with low energy consumption for ambulatory use is highly desirable.

Data Compression

Extension of SBL Algorithms for the Recovery of Block Sparse Signals with Intra-Block Correlation

no code implementations4 Jan 2012 Zhilin Zhang, Bhaskar D. Rao

We examine the recovery of block sparse signals and extend the framework in two important directions; one by exploiting signals' intra-block correlation and the other by generalizing signals' block structure.

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