EVA: Complementary Modules 2021-22

Opened: Saturday, 2 December 2017, 11:20 AM

Complementary modules organized by all institutions participating in the MSE.

Please note:

  • The number of inscriptions is typically restricted
  • Please consider the status field: only modules with "registration open" status can be booked
  • Module inscriptions have to be made via your advisor to the contact person as specified in the offering
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Title: Human Interface Technology (HIT 1)
Short Code: EVA_BFH_MTE7104
ECTS Credits: 3
Organizer Details: Institute HuCE

Oral exam at end of module.

Decision Date: 1 September 2022 
Start Date: 17 September 2022 
End Date: 31 December 2022 
Date Details:

Fall semester, more or less every 2nd/3rd on a Friday (full-day), room HG 4.33 (Biel)


Full day course at 6 Fridays per semester.


English by default, but deviations according to the wishes of the students.

Description (max. 300 characters):

Our research and thus the EVA course HIT1 focuses on technologies „Signal Processing“, „Microelectronics Hardware Algorithms“ and „Computer Architecture“.

Contents and Learning Objectives:

Hardware Algorithms (Prof. Dr. Marcel Jacomet)

The need for higher performance and energy efficiency increase at the same time in portable applications. Hardware algorithms and hardware/software algorithms are the answer of such needs, resulting in system-on-chip microelectronic solutions for signal and data processing. This course block introduces a systematic hardware design approach using the FSMD model. Hardware arithmetic algorithms will be introduced and generic hardware design approaches deduced by the various concepts. The course block is rounded off with an introduction to the unfolding hardware parallelization concept.

Signal Processing (Prof. Dr. Thomas Niederhauser)

Real signals are often disturbed by noise, which requires digital signal processing to extract their information. Applications such as feedback control or autonomous sensor nodes, however, have strongly diverging requirements on the implemented algorithms with respect to energy, space and time constraints. In this course, the students learn how to design and realize adequate frequency-based filters in floating-point and fixed-point arithmetic. Adaptive filters are considered for systems with non-stationary noise sources. The course closes with more advanced multi-rate filter designs.

Image Analysis (Prof. Marcus Hudritsch)

This course block gives a broader insight into digital image analysis. After a short wrap-up over image processing, we start with the classic feature engineering approach where the software engineer first segments an image into desired regions and represents them in processable data structures. After that, we learn different methods to extract meaningful and discriminative features that we can use in the final step to classify the image. The course block closes with machine learning for image analysis where we learn how to use unsupervised and supervise learning with large labeled datasets to classify images.

Admission: Elektro-Ing, Masch-Ing, Micro-Ing oder Informatik-Ing.

50% theory, 50% labs


Prof. Dr. T. Niederhauser thomas.niederhauser@bfh.ch, T: +41 32 321 67 63

Contact Person E-Mail: thomas.niederhauser@bfh.ch
Status: registration open
Specialization: Energy and Environment (EE)

Industrial Technologies (InT)

Information and Communication Technologies (ICT)

Computer Science (CS)

Data Science (DS)

Electrical Engineering (ElE)

Energy & Environment (EnEn)

Mechatronics & Automation (MA)

Medical Engineering (Med)

Photonics (Pho)


[Responsible for this text: Jacomet Marcel]