Expert Lecture On

Introduction to Statistical Signal Processing with Applications

26th September 2024

IEEE Student Chapter, Dronacharya Group of Institutions, Greater Noida organized an expert lecture on “Introduction to Statistical Signal Processing with Applications” on 26^th September, 2024, Prof. (Dr.) S. D. Joshi, Professor in the Department of Electrical Engineering of IIT Delhi was the keynote speaker of the session.  Faculty members and students of ECE, EEE and ECZ Department attended the lecture.  

The objective of the lecture was to familiarize the students with the Statistical Signal Processing (SSP) and gain the diverse research opportunities for the students and faculty members as well. 

The session was inaugurated by felicitating the Chief Guest by Prof. (Dr.) Arpita Gupta, (Director, DGI), The session was then continued by the Guest Prof. (Dr.) S. D. Joshi.

Prof. (Dr.) S. D. Joshi, gave description about the Statistical Signal Processing (SSP). It involves understanding the probabilistic characteristics of signals and noise, which can affect signal quality. Key concepts include estimation and detection, filtering, spectral analysis, power spectral density (PSD), and fourier transform. Signals are often modeled as random processes or stochastic processes with methods like Maximum Likelihood Estimation and Minimum Mean Square Error. Filters enhance desired signal components while suppressing noise, with linear filters like Wiener Filter being common Statistical Signal Processing (SSP) is used in various applications such as wireless communication systems, radar and sonar, speech and audio processing, biomedical signal processing image and video processing, and finance and economics. SSP techniques include Bayesian methods, Kalman Filtering, adaptive filters, and machine learning. Bayesian methods integrate prior knowledge with observed data, while Kalman Filtering optimizes dynamic system estimation in noisy environments. Adaptive filters adjust parameters in real-time to adapt to changing signal or noise characteristics. Modern SSP incorporates machine learning techniques for tasks like classification, prediction, and feature extraction.

Additionally, he had also explained SSP also provides a robust framework for working with signals in environments affected by randomness and noise. Its wide applications across multiple domains make it a foundational field for those involved in communications, engineering, audio processing, and data analysis. Through techniques like filtering, estimation, and spectral analysis, SSP seeks to enhance signal quality, reduce uncertainty and make better predictions.

He had also shared his knowledge about how the Statistical Signal Processing (SSP) is a crucial discipline that analyses and extracts meaningful information from noisy signals. Over the past decades, SSP has seen significant advancements in algorithm design, machine learning integration and applications in healthcare, biomedical engineering, wireless communication, and quantum computing. Techniques like Bayesian inference, Kalman filters, and Wiener filters have been refined for accurate estimation and detection in uncertain environments. Recent research has also focused on adaptive filtering and machine learning integration which have implications for real-time communications, autonomous systems, and medical diagnostics. Machine learning techniques, particularly deep learning, are increasingly being integrated with traditional SSP approaches, enabling better detection of patterns in noisy signals. SSP has also been used in wireless communication to improve data transmission reliability and efficiency, especially in noisy or congested environments. Future directions for SSP include quantum signal processing, AI integration, and cognitive signal processing systems that can adapt to varying conditions and user demands.

Further he explained the Research in Statistical Signal Processing has continuously shown that the discipline is capable of solving practical problems by combining cutting-edge computational methods with time-tested methods. Its uses in engineering, communications, and healthcare have had revolutionary effects by providing more precise, dependable, and adaptable methods of handling erratic and noisy data. Subsequent investigations are anticipated to concentrate on augmenting computing efficacy, broadening the integration of machine learning, and investigating quantum technologies, so augmenting the function of SSP in developing technologies.
SSP's on-going development will be essential to facilitating breakthroughs in a variety of industries from biomedical engineering to telecommunications and will establish the discipline as a pillar of the contemporary data-driven world.

The session concluded with a heartfelt vote of thanks delivered by Prof. Sanghamitra V. Arora, Head of the Department of ECE, EEE, and ECZ at DGI. The session was widely appreciated by both students and faculty members for its engaging and enthusiastic presentation. It was acknowledged as a highly fruitful event, with numerous research opportunities discussed, leaving a positive impact on all attendees.