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Dr B Ramprasad Dr B Ramprasad

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Open access Jul 2026

Design and Implementation of a Reconfigurable Hardware Architecture for Adaptive High-Performance VLSI Computing

The increasing complexity of modern digital systems has created a growing demand for hardware platforms capable of adapting to varying computational requirements while maintaining high performance and energy efficiency. Reconfigurable hardware architectures provide a flexible alternative to traditional Application-Specific Integrated Circuits (ASICs) by enabling dynamic modification of hardware functionality after fabrication. Such architectures are widely utilized in field-programmable gate arrays (FPGAs), adaptive computing systems, artificial intelligence accelerators, communication networks, and embedded platforms. This paper presents a reconfigurable hardware architecture designed to improve processing flexibility, resource utilization, and computational efficiency in VLSI systems. The proposed architecture incorporates modular processing elements, dynamic configuration management, and optimized interconnection networks to support multiple computational tasks using the same hardware resources. The design is modeled using Verilog HDL and implemented using Xilinx Vivado. Experimental analysis demonstrates improvements in resource utilization, throughput, scalability, and energy efficiency compared to conventional fixed-function architectures. The proposed system provides an effective solution for next-generation adaptive computing applications. Keywords— Reconfigurable Hardware, FPGA, VLSI Design, Adaptive Computing, Dynamic Reconfiguration, Verilog HDL, Xilinx Vivado, Hardware Optimization.

Radarapu Anjaneyulu Radarapu Anjaneyulu, Neduru Santhosh Neduru Santhosh, Dr B Ramprasad Dr B Ramprasad · 0 citations
Open access Jul 2026

Intelligent Deep Learning-Based Channel Estimation Framework for Next-Generation Wireless Communication Systems

The increasing demand for high-speed wireless communication services, coupled with the deployment of advanced technologies such as Massive Multiple-Input Multiple-Output (MIMO), millimeter-wave communications, Internet of Things (IoT), and Sixth Generation (6G) networks, has significantly increased the complexity of wireless channel environments. Accurate channel estimation plays a critical role in ensuring reliable communication, efficient resource utilization, and high-quality service delivery. Conventional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) often struggle to provide optimal performance in highly dynamic and complex communication environments due to nonlinear channel characteristics, mobility, and interference. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving channel estimation accuracy through intelligent learning and adaptive optimization. This paper presents a comprehensive study of AI-based channel estimation techniques and proposes an Intelligent Deep Learning-Based Channel Estimation Framework (IDL-CEF) designed to enhance wireless communication performance. The proposed framework integrates deep neural networks, machine learning algorithms, adaptive signal processing, and real-time channel prediction mechanisms. Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods. The findings indicate that AI-based channel estimation will become a fundamental component of future intelligent communication systems and 6G wireless networks.

N.Prashanth Kumar N.Prashanth Kumar, A. A. A Akshitha, Dr B Ramprasad Dr B Ramprasad · 0 citations
Jul 2026

Advanced Radar Signal Processing Using Deep Learning for Real-Time Object Detection and Tracking in Autonomous Vehicles

An advanced radar signal processing framework for autonomous vehicles that integrates adaptive preprocessing, target detection, clutter suppression, feature extraction, and object classification to improve perception performance is presented.

Kuruba Theja Kuruba Theja, Dharavath Sunil Dharavath Sunil, Dr B Ramprasad Dr B Ramprasad · 0 citations
Open access Jul 2026

A Smart Predictive Maintenance Architecture for Industrial Equipment Monitoring Using IIoT, Machine Learning, and Digital Twin Models

An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.

Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad · 0 citations