Subject » BMEVIMIMB05
Embedded Artificial Intelligence Laboratory
Beágyazott mesterséges intelligencia laboratórium
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Beágyazott mesterséges intelligencia laboratórium
Embedded Artificial Intelligence Laboratory
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| Subject code | BMEVIMIMB05 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
Csuka Barna
position: adjunktus
contact:
csuka.barna@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 | — | ||||||||||||
| 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
- Gradient-based investigation of the error backpropagation algorithm of neural networks in an embedded environment, parameterization of the learning factor based on the moment, batch sizes and instantaneous slope.
- The effect of different activation functions on the speed of learning and the performance of the model. Examination of the robustness of neural network models using dropout and ablation methods.
- Examining the bias-variance dilemma in machine learning methods. Identification of the noise component of the training data set and its effect on the best available model performances.
- GPU-based acceleration of neural network architectures, their aspects for both training and query use. Performing matrix operations on SIMD architectures, limitations of parallelizability.
- Teaching convolutional neural networks, increasing model robustness with an augmented (shifted/noisy/rotated/mirrored) sample set.
- Implementation of a classification system with multilevel processing, classification with a neural network and fuzzy approach.
- Design of applied joint time and frequency transformations in a hardware environment. Processing of periodic signals, extraction of main parameters.
- Data analysis workflow (outlier detection, data cleaning, handling incomplete data, knowledge modeling). Examination of the physical measurement ranges of modern embedded sensor platforms (smart, wearable devices).
- Application of Kálmán filters to implement offline sensor fusion. Analysis of the applicability of location and position determination by integrating the gyroscope, magnetometer and accelerometer.
- Possibilities and limitations of implementing real-time sensor fusion on embedded systems. The relationship between model complexity and consumption, examination of computing power available on some hardware architectures.
The laboratory aims to apply the knowledge acquired in the course Embedded Artificial Intelligence in practice. The primary focus of the laboratory is for students to be able to independently apply artificial intelligence methods to real physical measurements and data sets, and to gain practical experience of their advantages and limitations. We implement the algorithms on embedded platforms.
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
A 4-hour weekly laboratory in which students participate in measuring 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)
Curriculum for the embedded artificial intelligence course
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)
Curriculum for the embedded artificial intelligence course
General rules
Requirements:
Participation in the measurements is mandatory. A report must be prepared for each measurement, one for each measurement group. We assign a grade to the reports. The insufficient measurement must be replaced. The midterm mark is the average of the transcript grades. Rounding up from 50 cents.
Additional possibilities:
During the semester, 2 laboratory exercises can be made up, regardless of the reason for the ineffectiveness.
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
Recommended:Embedded artificial intelligence
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.