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Information Processing Laboratory Exercises

Információfeldolgozás laboratórium
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
Subject name (Hungarian, English)
Információfeldolgozás laboratórium
Information Processing Laboratory Exercises
Subject code BMEVIMIM322
Subject type
Training Level
Course types and hours (weekly/semester)
Course type lecture tutorial laboratory
hours (weekly) 0 0 3
type (linked/independent) autonomous course
Assessment type félévközi érdemjegy
Credits 4
Subject coordinator
DR. Sujbert László
position: egyetemi docens
Responsible department
Mesterséges Intelligencia és Rendszertervezés Tanszék
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

Programme
Exercise 1. Development of virtual instruments

 

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.

The aim of the measurements is to learn in detail some information processing algorithms and their software tools frequently used in embedded systems. During the measurements the students utilize the elementary signal processing tools (e.g. averaging, filtering, discrete Fourier transform), but the aim is the development and investigation of complex systems. The subject consists of 5 measurements, each one is 8 hours long. The measurements are based on signal processing boards (equipped by Analog Devices DSPs), and the „mitmót”, the modular microcontroller-based platform developed at the Department of Measurement and Information Systems. Most of the exercises are based on real physical systems or their model. The software background is provided by LabView, Matlab, and the Visual DSP++ development system.

Learning outcomes

Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:

Knowledge

No learning outcomes recorded.

Skills

No learning outcomes recorded.

Attitudes

No learning outcomes recorded.

Autonomy and responsibility

No learning outcomes recorded.

Oktatási módszertan

4 hours/week laboratory exercises. One measurement consists of 2 consecutive 4 hours exercises. The students work in groups.

Tanulástámogató anyagok

Online források
Measurement guides.

Recommended preliminary knowledge for completing the subject

Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
nincs
General rules
Requirements: ·        Attendance on the exercises is obligatory. ·        Each student group has to write a measurement report on each measurement. The reports are evaluated by marks. Failed measurements are to be repeated. ·        The final mark is the average of the marks of the measurement reports. Rounding is up to the next integer from 0.50. Additional possibilities: At most 2 exercises can be additionally accomplished, independently from the origin of the failure. In case of more failed exercises (e.g. serious illness), accomplishment is to be discussed with the owner of the subject.
Assessment methods
In-term assessments

No detailed assessments provided.

Weight of in-term assessments

No weights provided.

Exam-period assessments

No detailed assessments provided.

Weight of exam elements

No weights provided.

Grade calculation

No grade thresholds provided.

Attendance requirements

No attendance requirements provided.

Rules for retake and resubmission

Not provided.

Short description

Not provided.

Detailed description

Not provided.

Recommended courses
Signal processing and programming
Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements
Requirements valid from:
Requirements valid until:
Curriculum placement

No curriculum placements recorded for this subject version.