Signalverarbeitung mit MATLAB und Künstlicher Intelligenz: Unterschied zwischen den Versionen
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== MindMap == | |||
[[Datei:Signal Processing with MATLAB and Artificial Intelligence MindMap NNew.pdf|800px|frameless|centered|alternativtext=Signal Processing with MATLAB and Artificial Intelligence MindMap]] | |||
== Introductiong == | |||
Traditionally, classical algorithms have been taught to solve engineering problems. This project solves teaching problems using artificial intelligence and compares these classical algorithms. | |||
=== ''Signal Processing'' === | |||
Signal processing is the science of analyzing, modifying, and improving signals. It is essential in many modern applications such as communications, audio and image processing, biomedical signal analysis, and control systems. | |||
==== Key Concepts ==== | |||
Signals: | |||
''A signal is any piece of information that changes over time or space and can be measured. It may be continuous (analog) or discrete (digital).'' | |||
Transforms: | |||
''Techniques like the Fourier Transform and the Discrete Wavelet Transform decompose signals into constituent frequency (or time-frequency) components for easier analysis''. | |||
Filtering: | |||
''The process of removing unwanted noise or extracting useful parts of a signal. Techniques include low-pass, high-pass, band-pass, and adaptive filters.'' | |||
Time-Domain vs. Frequency-Domain Analysis: | |||
''Time-domain analysis examines how a signal changes over time. | |||
''Frequency-domain represents how much of the signal lies within each given frequency band.'''' | |||
== | == Task == | ||
# | # [[Familiarization with MATLAB ® AI toolboxes|Familiarization with MATLAB<sup>®</sup> AI toolboxes]] | ||
# | # [https://wiki.hshl.de/wiki/index.php/Practical_AI_examples Researching practical application examples that can be converted to AI] | ||
# | #Implementation of selected examples | ||
# | #Identify the advantages and disadvantages of AI compared to conventional data processing | ||
# | #Discussion of the results | ||
# Test | #Test and scientific documentation | ||
# | # Providing MATLAB<sup>®</sup>-examples as wiki articles | ||
== Anforderungen == | == Anforderungen == | ||
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== Repository == | == Repository == | ||
Version vom 24. März 2025, 02:09 Uhr
| Autor: | Moye Nyuysoni Glein Perry |
| Art: | Project Work |
| Starttermin: | 14.11.2024 |
| Abgabetermin: | 31.03.2025 |
| Betreuer: | Prof. Dr.-Ing. Schneider |
MindMap
Introductiong
Traditionally, classical algorithms have been taught to solve engineering problems. This project solves teaching problems using artificial intelligence and compares these classical algorithms.
Signal Processing
Signal processing is the science of analyzing, modifying, and improving signals. It is essential in many modern applications such as communications, audio and image processing, biomedical signal analysis, and control systems.
Key Concepts
Signals:
A signal is any piece of information that changes over time or space and can be measured. It may be continuous (analog) or discrete (digital).
Transforms:
Techniques like the Fourier Transform and the Discrete Wavelet Transform decompose signals into constituent frequency (or time-frequency) components for easier analysis.
Filtering:
The process of removing unwanted noise or extracting useful parts of a signal. Techniques include low-pass, high-pass, band-pass, and adaptive filters.
Time-Domain vs. Frequency-Domain Analysis:
Time-domain analysis examines how a signal changes over time.
Frequency-domain represents how much of the signal lies within each given frequency band.''
Task
- Familiarization with MATLAB® AI toolboxes
- Researching practical application examples that can be converted to AI
- Implementation of selected examples
- Identify the advantages and disadvantages of AI compared to conventional data processing
- Discussion of the results
- Test and scientific documentation
- Providing MATLAB®-examples as wiki articles
Anforderungen
Das Projekt erfordert Vorwissen in einigen aber nicht allen nachfolgenden Themengebieten. Sollten Sie die Anforderungen nicht erfüllen, kann die Aufgabenstellung mit Blick auf Ihre Vorkenntnisse individuell angepasst werden.
- MATLAB®/Simulink
- Machine Learning/Deep Learning
- Digitale Signalverarbeitung
- Deep Learning for Image Processing
Anforderungen an die wissenschaftliche Arbeit
- Wissenschaftliche Vorgehensweise (Projektplan, etc.), nützlicher Artikel: Gantt Diagramm erstellen
- Wöchentlicher Fortschrittsberichte (informativ), aktualisieren Sie das Besprechungsprotokoll im Gespräch mit Prof. Schneider
- Projektvorstellung im Wiki
- Tägliche Sicherung der Arbeitsergebnisse in SVN
- Tägliche Dokumentation der geleisteten Arbeitsstunden
- Studentische Arbeiten bei Prof. Schneider
- Anforderungen an eine wissenschaftlich Arbeit
Image Processing
Image processing refers to the use of algorithms and computational techniques to analyse, enhance, and manipulate images. It involves altering or improving images using various methods and tools. The main aim of image processing is to improve image quality. Whether it’s enhancing contrast, adjusting colours, or smoothing edges, the focus is on making the image more visually appealing or suitable for further use. It’s about transforming the raw image into a refined version of itself.
Sample Task: Object Detection Identify in an Image
- Classical Method: Circular Objects Detection
- results:
- AI based method: Detect people, cats and dogs in message using YOLO model.
- Results
Repository
URL: https://svn.hshl.de/svn/MATLAB_Vorkurs/trunk/Signalverarbeitung_mit_Kuenstlicher_Intelligenz
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