Information Processing Laboratory Exercises
A tantárgyleírás hatályossága
| Subject name (Hungarian, English) |
Információfeldolgozás laboratórium
Information Processing Laboratory Exercises
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| Subject code | BMEVIMIM322 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 4 | ||||||||||||
| Subject coordinator |
DR. Sujbert László
position: egyetemi docens
contact:
sujbert.laszlo@vik.bme.hu
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| Responsible department |
Mesterséges Intelligencia és Rendszertervezés Tanszék
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| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | http://www.mit.bme.hu/oktatas/targyak/vimim322/ | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Use of the LabView program, steps of the development of virtual instruments. Simple exercises: timing, signal generation, displaying. Implementation of a virtual instrument (chosen from a list). Possible instruments: function generator, spectrum analyzer, oscilloscope, equalizer. The development is supported by built-in functions.
Exercise 2. High-level programming of the “mitmót”
Learning of the VI set given by the hardware, steps of the development of a new project. Simple exercises: thermometer, reaction time meter. Implementation of an embedded system (chosen from a list). Possible systems: temperature control, remote control of a toy-car, sensor network for data acquisition. The development is supported by built-in functions.
Exercise 3. Investigation of adaptive filters
Implementation of the LMS algorithm. Versions of the LMS algorithm, the XLMS algorithm. Investigation of adaptive transversal (FIR) filters. Identification by the LMS algorithm. Adaptive echo cancelation in electronic and acoustic systems.
Exercise 4. Investigation of neural and fuzzy systems
Implementation of a classification system by multi-level processing. Processing of vibration and sound signals: extraction of the main parameters by time domain and frequency domain methods, classification by neural and fuzzy systems. Investigation of parameter setting and learning of neural networks. Investigation of parameter setting of fuzzy systems. Musical sound recognition by neural and fuzzy systems.
Exercise 5. Investigation of distributed systems and sensor networks
Signal transmission on radio channel. Implementation of synchronization of sampling. Use of interpolation techniques. Sampling of acoustic signals by “mitmót”, fusion on DSP. Better exploitation of the bandwidth: compression techniques. Influence of the number of the sensors (the number of the sensors is equal or greater than or less than required). Feedback in sensor networks.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
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Skills
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Attitudes
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Autonomy and responsibility
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Oktatási módszertan
Tanulástámogató anyagok
Online források
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In-term assessments
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Exam-period assessments
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Grade calculation
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Short description
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Detailed description
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