Signalverarbeitung mit MATLAB und Künstlicher Intelligenz - Versionsgeschichte

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Sample Task: Object Detection Identify in an Image */

2025-04-16T08:57:27Z

Sample Task: Object Detection Identify in an Image

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	* Classical Method: Circular Objects Detection		* Classical Method: Circular Objects Detection
	[[Datei:Data.jpg\|300px\|frameless\|centered]]		[[Datei:Data.jpg\|300px\|frameless\|centered]]
			Fig. 6: Image comprised of shapes
	* results:		* results:
	[[Datei:Results1.png\|700px\|frameless\|centered]]		[[Datei:Results1.png\|700px\|frameless\|centered]]
			Fig. 7: Circles Detected with Classical ALgorithm

	* AI based method: Detect people, cats and dogs in message using YOLO model.		* AI based method: Detect people, cats and dogs in message using YOLO model.
	[[Datei:Sample.jpg\|400px\|frameless\|centered]]		[[Datei:Sample.jpg\|400px\|frameless\|centered]]
	Fig. 6: ~~Image comprised of shapes~~		Fig. 8: Sample image to use for Object Detection
	*Results		*Results
	[[Datei:Results2.jpg\|700px\|frameless\|centered]]		[[Datei:Results2.jpg\|700px\|frameless\|centered]]
	Fig. 7: ~~Circles Detected with Classical ALgorithm~~		Fig. 9: Results of AI detection

	== Results ==		== Results ==

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Sample Task: Object Detection Identify in an Image */

2025-04-16T08:55:17Z

Sample Task: Object Detection Identify in an Image

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	* AI based method: Detect people, cats and dogs in message using YOLO model.		* AI based method: Detect people, cats and dogs in message using YOLO model.
	[[Datei:Sample.jpg\|400px\|frameless\|centered]]		[[Datei:Sample.jpg\|400px\|frameless\|centered]]
			Fig. 6: Image comprised of shapes
	*Results		*Results
	[[Datei:Results2.jpg\|700px\|frameless\|centered]]		[[Datei:Results2.jpg\|700px\|frameless\|centered]]
			Fig. 7: Circles Detected with Classical ALgorithm

	== Results ==		== Results ==

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Image Processing */

2025-04-16T08:53:44Z

Image Processing

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	[[Datei:What-is-image.png\|300px\|frameless\|centered]]		[[Datei:What-is-image.png\|300px\|frameless\|centered]]
			Fig. 5: Sample Image

	== Sample Task: Object Detection Identify in an Image ==		== Sample Task: Object Detection Identify in an Image ==

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Discussion */

2025-04-16T08:52:37Z

Discussion

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	This study confirms that hybridizing classical signal processing with AI methods yields stronger results than using either in isolation. AI methods reduced manual intervention and improved adaptability, especially in real-time tasks like lane tracking.		This study confirms that hybridizing classical signal processing with AI methods yields stronger results than using either in isolation. AI methods reduced manual intervention and improved adaptability, especially in real-time tasks like lane tracking.

			==== What are the applications of Noise Removal and Visual Relationship in the Lab ====
			'''Noise Reduction:'''

			It helps in improving object tracking for someone who is on a project that has to do with that, as well as helping students build autonomous systems that navigate the lane. image noise reduction helps their cameras and sensors interpret the environment better.

			'''Visual Relation:'''

			Tool and part monitoring:
			Track which components are being used and how.

			Detect if required tools are missing from a workstation (useful for automation or alerts).
			Understand movement patterns within the lane.

	==== Challenges included: ====		==== Challenges included: ====

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Conclusion */

2025-04-16T08:21:13Z

Conclusion

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	== Conclusion ==		== Conclusion ==
	In this project, we combined classical signal processing with the learning capabilities of AI. This study achieved effective solutions to lane tracking, object detection, and signal interpretation problems.We were able to familiarize ourselves		In this project, we combined classical signal processing with the learning capabilities of AI. This study achieved effective solutions to lane tracking, object detection, and signal interpretation problems. We were able to familiarize ourselves
	with MATLAB® AI toolboxes and Research practical application examples that can be converted to AI, and we implemented 3 selected examples. We also identified the advantages and disadvantages of AI compared to conventional data processing		with MATLAB® AI toolboxes and Research practical application examples that can be converted to AI, and we implemented 3 selected examples. We also identified the advantages and disadvantages of AI compared to conventional data processing. During my study, I was able to achieve certifications in Image Processing, Signal Processing, and Matlab from Mathworks, in which I acquired knowledge I used in the project.

	== Outlook ==		== Outlook ==

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Conclusion */

2025-04-16T08:15:03Z

Conclusion

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	== Conclusion ==		== Conclusion ==
	~~By combining~~ classical signal processing with the learning capabilities of AI~~, this~~ study achieved effective solutions to lane tracking, object detection, and signal interpretation problems. ~~CNNs~~ and ~~YOLO models~~, ~~in particular, showed strong promise in automating traditionally manual signal tasks~~.		In this project, we combined classical signal processing with the learning capabilities of AI. This study achieved effective solutions to lane tracking, object detection, and signal interpretation problems.We were able to familiarize ourselves
			with MATLAB® AI toolboxes and Research practical application examples that can be converted to AI, and we implemented 3 selected examples. We also identified the advantages and disadvantages of AI compared to conventional data processing

	== Outlook ==		== Outlook ==

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Results */

2025-04-15T14:15:40Z

Results

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	== Results ==		== Results ==
	Lane Tracking: CNN models successfully simulated lane following behavior with moderate accuracy. ~~Initial data~~ had limitations ~~(e.g.~~, ~~curve bias)~~, ~~requiring additional data~~ and ~~normalization~~.		Lane Tracking: CNN models successfully simulated lane following behavior with moderate accuracy. Initially, I had limitations as I focused just on classification,I got more data, and tried other methods.

	Object Detection: YOLO models identified multiple objects with high accuracy after fine-tuning and proper data labeling.		Object Detection: YOLO models identified multiple objects with high accuracy after fine-tuning and proper data labeling.

Moye-nyuysoni.glein-perry@stud.hshl.de: /* Image Processing */

2025-04-15T14:11:56Z

Image Processing

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	== Image Processing ==		== 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~~.		Image processing refers to the application of computational techniques to analyze, enhance, and manipulate digital images. According to Gonzalez and Woods, it involves operations that transform an image into another image, typically to improve its visual quality or to extract meaningful information. This can include tasks such as noise reduction, contrast enhancement, edge detection, and color correction. The primary objective is often to make the image more suitable for a specific application—whether that’s for visual interpretation, machine analysis, or further processing. In essence, it's about converting raw image data into a form that is easier to understand or more visually effective.

Moye-nyuysoni.glein-perry@stud.hshl.de: /* References */

2025-04-15T14:10:23Z

References

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	== References==		== References==
	<ref>mathworks.com/</ref>		<ref>mathworks.com</ref>
	<ref>https://support.apple.com/guide/logicpro-ipad/cutoff-frequency-lpip5e73bf83/ipados</ref>		<ref>https://support.apple.com/guide/logicpro-ipad/cutoff-frequency-lpip5e73bf83/ipados</ref>

Moye-nyuysoni.glein-perry@stud.hshl.de: /* References */

2025-04-15T14:10:10Z

References

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	== References==		== References==
			<ref>mathworks.com/</ref>
	<ref>https://support.apple.com/guide/logicpro-ipad/cutoff-frequency-lpip5e73bf83/ipados</ref>		<ref>https://support.apple.com/guide/logicpro-ipad/cutoff-frequency-lpip5e73bf83/ipados</ref>

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	<ref>Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-Time Signal Processing. Pearson.</ref>		<ref>Oppenheim, A. V., & Schafer, R. W. (2009). Discrete-Time Signal Processing. Pearson.</ref>
	<ref>V. A. Ardeti, V. R. Kolluru, G. T. Varghese, and R. K. Patjoshi, “An overview on state-of-the-art electrocardiogram signal processing methods: Traditional to AI-based approaches,” Expert Systems with Applications, vol. 217, p. 119561, 2023.</ref>		<ref>V. A. Ardeti, V. R. Kolluru, G. T. Varghese, and R. K. Patjoshi, “An overview on state-of-the-art electrocardiogram signal processing methods: Traditional to AI-based approaches,” Expert Systems with Applications, vol. 217, p. 119561, 2023.</ref>
			<ref>Digital Image Processing" by Rafael C. Gonzalez and Richard E. Woods</ref>

	== Appendices ==		== Appendices ==