Signal Processing and AI Integration Internship Program
in EC & EEEAbout this course
Signal Processing & AI Integration: 6-Week Structured Learning and Experience
Introduction
Signal processing plays a fundamental role in various fields, from communications and biomedical applications to artificial intelligence and automation. This program provides hands-on experience in signal analysis, filtering, feature extraction, and AI-driven techniques. Participants will explore theoretical concepts and implement practical projects using Python, MATLAB, and deep learning frameworks.
Week 1: Fundamentals of Signal Processing
· Introduction to Signal Processing
o Task: Research and write a report on the fundamentals of signal processing.
o Outcome: A 500-word report explaining key concepts, history, and applications.
· Types of Signals and Their Characteristics
o Task: Compare different types of signals (analog vs. digital, deterministic vs. random, etc.).
o Outcome: A comparative table with examples and characteristics of each type.
· Fourier Transform in Signal Processing
o Task: Explain the importance of the Fourier Transform in analyzing signals.
o Outcome: A 5-slide presentation with mathematical explanations and real-world applications.
Week 2: Filters and AI Integration
· Filtering in Signal Processing
o Task: Research different types of filters (low-pass, high-pass, band-pass) and their uses.
o Outcome: A report with diagrams explaining each filter type and its significance.
· Introduction to AI in Signal Processing
o Task: Explore how AI is transforming signal processing in various industries.
o Outcome: A case study highlighting AI applications in speech, image, and biomedical signals.
· Designing a Basic Digital Filter
o Task: Implement a simple FIR or IIR filter in Python or MATLAB.
o Outcome: A working script with input/output signals and filter response graphs.
Week 3: Advanced Analysis Techniques
· Feature Extraction in Signal Processing
o Task: Research key features used in AI-based signal analysis (e.g., frequency, amplitude, phase).
o Outcome: A structured report with real-world examples of feature extraction techniques.
· Time-Frequency Analysis Using Wavelets
o Task: Study the wavelet transform and its advantages over the Fourier Transform.
o Outcome: A visual demonstration of wavelet decomposition using MATLAB/Python.
· AI-Based Noise Reduction in Signals
o Task: Implement a noise removal algorithm using AI techniques like deep learning.
o Outcome: A Python script showcasing a before-and-after comparison of noisy signals.
Week 4: AI in Speech and Image Processing
· Speech Signal Processing Using AI
o Task: Explore AI applications in speech recognition and enhancement.
o Outcome: A report on techniques such as MFCCs, spectrogram analysis, and neural networks.
· Image Processing and AI Integration
o Task: Implement basic image enhancement techniques using OpenCV and AI models.
o Outcome: A working Python script applying filters and transformations to images.
· Biomedical Signal Processing with AI
o Task: Research how AI is used in analyzing ECG, EEG, and other biomedical signals.
o Outcome: A case study with real-world applications in healthcare.
Week 5: Specialized AI Applications in Signal Processing
· EEG Signal Classification Using AI
o Task: Implement a simple AI model for classifying EEG signal patterns.
o Outcome: A Python notebook with dataset preprocessing, model training, and accuracy results.
· Music Genre Classification Using AI
o Task: Use machine learning to classify music based on audio features.
o Outcome: A project with data preprocessing, model training, and genre prediction results.
· Radar Signal Processing with AI
o Task: Study AI’s role in radar signal interpretation and object detection.
o Outcome: A report explaining AI applications in defense and automotive radar systems.
Week 6: Future Trends and Final Project
· Data Compression Techniques in Signal Processing
o Task: Explore different compression techniques (lossy vs. lossless) in signal processing.
o Outcome: A structured report with practical examples in audio, image, and video compression.
· AI for Anomaly Detection in Signals
o Task: Implement an AI-based anomaly detection system for time-series signals.
o Outcome: A Python project demonstrating real-time anomaly detection.
· Emotion Recognition from Speech Signals
o Task: Study AI techniques used to recognize emotions in speech.
o Outcome: A research report with an overview of datasets, models, and applications.
· AI-Driven Music Composition using Signal Processing
o Task: Research how AI generates music using signal processing techniques.
o Outcome: A case study with examples of AI-generated music compositions.
· Future Trends in AI and Signal Processing
o Task: Analyze the latest advancements and predict future developments in the field.
o Outcome: A presentation with insights from research papers, patents, and expert opinions.
Final Project: AI-Integrated Signal Processing Application
· Task: Develop a real-world AI-based signal processing project (e.g., real-time speech enhancement, ECG anomaly detection, or AI-powered audio classification).
· Outcome: A working prototype with a report detailing the methodology, results, and future scope.
Expected Outcomes
By the end of this program, participants will:
· Understand the fundamentals of signal processing and AI-based analysis techniques.
· Implement digital filtering, Fourier and wavelet transforms, and feature extraction.
· Develop AI-driven applications for speech, image, biomedical, and radar signals.
· Work with real-world datasets and deep learning models for classification and enhancement.
· Gain hands-on experience with Python, MATLAB, OpenCV, and AI frameworks.
· Build an AI-integrated signal processing project as a final demonstration of their skills.
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To understand the fundamental principles of signal processing, including its historical development, key concepts, and practical applications in various fields.
To classify different types of signals and analyze their unique characteristics through comparison.
To understand the significance of the Fourier Transform in analyzing and processing signals.
To explore different types of filters used in signal processing and their applications.
To explore the impact of Artificial Intelligence (AI) on signal processing and analyze its applications in various domains.
To understand the design and implementation of digital filters by creating a simple FIR or IIR filter using Python or MATLAB.
To explore how key features such as frequency, amplitude, and phase are extracted and utilized in AI-based signal analysis.
To understand the wavelet transform and how it provides better time-frequency analysis compared to the Fourier Transform.
To implement a noise removal technique using AI-based methods, such as deep learning, for signal enhancement.
To explore AI applications in speech recognition and enhancement through feature extraction and neural network-based techniques.
To explore the integration of AI in image processing by applying enhancement techniques using OpenCV and deep learning models.
To analyze how AI techniques are applied in biomedical signal processing for healthcare applications.
To implement an AI-based model for classifying EEG signal patterns.
To apply machine learning techniques for classifying music genres based on extracted audio features.
To explore AI’s impact on radar signal processing for defense, automotive, and surveillance applications.
To explore and compare various data compression techniques used in signal processing, including lossy and lossless methods.
To develop an AI-based system for detecting anomalies in time-series signals, such as financial data, ECG signals, or sensor readings.
To study AI techniques used in recognizing emotions from speech signals using machine learning and deep learning.
To explore how AI generates music using signal processing techniques and machine learning.
To analyze the latest advancements in AI-driven signal processing and predict future developments in the field.
