publications

2025

1. VeriLoC: Line-of-Code Level Prediction of Hardware Design Quality from Verilog Code

   Raghu Vamshi Hemadri, Jitendra Bhandari, and  al.

   NeurIPS, Sep 2025

   DOI

2024

1. Detecting anomalies in log messages using code-derived message patterns to guide structured message classification

   Raghu Vamshi Hemadri, Arjun Upadhyay, and  al.

   US Patent, Mar 2024

   DOI
2. AEVBComm: an intelligent communication system based on beta-VAE

   Raghu Vamshi Hemadri, Akshay Rayaluru, Rahul Jashvantbhai Pandya, and 1 more author

   CSI Transactions on ICT, Dec 2024

   Abs DOI

   In recent years, applying Deep Learning (DL) techniques has emerged as a common practice in the communication system, demonstrating promising results. The present paper proposes a new Convolutional Neural Network (CNN) based Variational Autoencoder (VAE) communication system. The VAE (continuous latent space) based communication systems confer unprecedented improvement in the system performance compared to AE (distributed latent space) and other traditional methods. We have introduced an adjustable hyperparameter }}\backslashbeta -}}VAE in the proposed VAE, which is also known as }}\backslashbeta -}}VAE, resulting in extremely disentangled latent space representation. Furthermore, a higher-dimensional representation of latent space is employed, reducing the Block Error Rate (BLER). The CNN based VAE architecture performs the encoding and modulation at the transmitter, whereas decoding and demodulation at the receiver. Finally, to prove that a continuous latent space-based system designated VAE performs better than the other and 4n dimensional representation is better than 2n representation, various simulation results supporting both have been conferred under normal and noisy conditions.

3. PrefixLLM: LLM-aided Prefix Circuit Design

   Weihua Xiao, Venkata Sai Charan Putrevu, Raghu Vamshi Hemadri, and 2 more authors

   Dec 2024

   DOI

2022

1. 

   Iteratively Reweighted Minimax-Concave Penalty Minimization for Accurate Low-rank Plus Sparse Matrix Decomposition

   Raghu Vamshi Hemadri, Praveen Kumar Pokala, and Chandra Sekhar Seelamantula

   IEEE Transactions on Pattern Analysis and Machine Intelligence, Dec 2022

   DOI
2. ChildrEN SafEty and Rescue (CENSER) System for Trafficked Children from Brothels in India

   Raghu Vamshi Hemadri, Amarjot Singh, and Ajeet Singh

   Proceedings of the AAAI Conference on Artificial Intelligence, Jun 2022

   DOI

2021

1. Attendance Management System

   Ravi Kishore Kodali, and Raghu Vamshi Hemadri

   In 2021 International Conference on Computer Communication and Informatics (ICCCI), Jun 2021

   DOI
2. Performance Analysis of Weighted Low Rank Model with Sparse Image Histograms for Face Recognition Under Lowlevel Illumination and Occlusion

   K.V. Sridhar, C Chandravathi, H. Raghuvamshi, and 1 more author

   In 2021 International Conference on Intelligent Technologies (CONIT), Jun 2021

   DOI

2020

1. Jaya Algorithm Based Intelligent Color Reduction

   Raghu Vamshi Hemadri, and Ravi Kumar Jatoth

   In Advanced Engineering Optimization Through Intelligent Techniques, Jun 2020

   Abs

   The purpose of color quantization is to reduce colors in an image with the least parody. Clustering is a popularly used method for color quantization. Color image quantization is an essential action in several applications of computer graphics and image processing. Most of the quantization techniques are mainly based on data clustering algorithms. In this paper, a color reduction hybrid algorithm is proposed by applying Jaya algorithm for clustering. We examine the act of Jaya algorithm in the pre-clustering stage and K-means in the post-clustering phase, and the limitations of both the algorithms are overcome by their combination. The algorithms are compared by MSE and PSNR values of the four images. The MSE values are lower and PSNR values are higher for the proposed algorithm. The results explain that the proposed algorithm is surpassed both the K-means clustering and Jaya algorithm clustering for color reduction method.