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Title: HBSP: Hardware Based Signal Processing
Short Code: MTE7903
ECTS Credits: 2
Organizer Details: BFH HuCE
Evaluation: Final exam 2 weeks after the last course session. The lecture will provide additional information at the beginning of the course.
Decision Date: 14 September 2022 
Start Date: 28 October 2022 
End Date: 13 January 2023 
Date Details:
SW Date CW Morning (4L) Afternoon (4L)
6 28.10.2022 43 HBSP (TN)
18.11.2022 46 HBSP (TN)
25.11.2022 47 HBSP (TN) HBSP (AH)
11 2.12.2022 48 HBSP (TN) HBSP (AH)
12 09.12.2022 49 Self-study
13 16.12.2022 50 Self-study
14 23.12.2022 51 Self-study
15 13.01.2023 2 Exam HBSP

Type: Full day course at 6 Fridays per semester + 2.5 days individual preparation for the exam.

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

Description (max. 300 characters):

The EVA course is part of a cluster of courses carried out by the institute HuCE. The subjects of this EVA are „Hardware Algorithms“ and „Signal Processing“.

Contents and Learning Objectives:

Hardware Algorithms (Prof. Andreas Habegger)

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.

Admission: Write an E-Mail to : with CC to EVA contact person. Students who are not enrolled at BFH must first register with IS-A:!formInscrs.connection?ww_c_formulaire=FORMULAIRE_ERASMUS

50% theory and 50% labs


Prof. Dr. Thomas Niederhauser and Prof. Andreas Habegger

Contact Person E-Mail:
Status: finished
Specialization: Industrial Technologies (InT)

Computer Science (CS)

Electrical Engineering (ElE)

Mechatronics & Automation (MA)

Medical Engineering (Med)


[Responsible for this text: Niederhauser Thomas]