Signal and Information Processing Laboratory (ISI)

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Laboratory Courses

Fachpraktika (Labs)

Please use the online registration

Room

ETF B104.1

Date and Time

Monday, Wednesday and Friday: 13:15 – 16:15

Assistants

(Mondays)
(Wednesdays and Fridays)

 

Fall Semester 2024

SV 1: Analog Filters

In this experiment, an active filter is constructed and measured. Consisting of resistors, capacitors, and an amplifier, it belongs to the class of active RC filters. Properties such as the frequency and time domain's behavior are investigated using a second-order low-pass filter. The experimental circuits are built on a plug-in board with discrete components.

SV 2: Digital Filters

Linear digital filters are designed on the computer and applied to audio signals. The sampling rate is converted and the resulting aliasing is prevented with a filter.

SV 4: Equalization and Adaptive Filters

In wireless and wired data transmission, a signal is sent through a channel. The channel distorts and noises the signal. Equalization of the received signals is a task for which there are many possible solutions.

In this lab exercise, several methods are tested experimentally.

SV 7: K-Means and Spectral Clustering

When confronted with a huge amount of data, we are interested in quickly learning the structure of this data and clustering it by similarity. The goal is to find a smaller representation which still explains quite well our data. Clustering or the art of grouping similar objects together has a plethora of applications in various fields such as vector quantization, grouping proteins or density estimation. In this experiment, we focus on two different clustering algorithms: K-means, for clustering vectors and Spectral Clustering for grouping objects described by a graph. These two algorithms will be implemented in Python and applied to simple examples: clustering data points generated by Gaussian distributions, quantizing colored pixels of an image and clustering research areas. No prior knowledge on Python is required.
 

Spring Semester 2024

SV 1: Analog Filters

In this ex­per­i­ment, an act­ive fil­ter is con­struc­ted and meas­ured. Con­sist­ing of res­ist­ors, ca­pa­cit­ors, and an amp­li­fier, it be­longs to the class of act­ive RC fil­ters. Prop­er­ties such as the fre­quency and time do­main's be­ha­vior are in­vest­ig­ated us­ing a second-​order low-​pass fil­ter. The ex­per­i­mental cir­cuits are built on a plug-​in board with dis­crete com­pon­ents.

SV 5: Error Correcting Codes

In this exercise, error correction codes are introduced. A few codes are demonstrated and their error correction capabilities are compared. A few simple examples describe the basic structure of a code. This is followed by more complex codes, and shows how a picture is completely recovered after being corrupted by errors.

SV 6: Polynomial Regression and Neural Networks

In a test case, polynomial fitting is compared with regression using a neural network. The issue of overfitting is addressed for both methods.

SV 8: Con­tinu­ous Phase Mod­u­la­tion

Many di­gital mod­u­la­tion tech­niques res­ult in a trans­mit­ted sig­nal which - de­pend­ing on the data to be trans­mit­ted - might change ab­ruptly. This leads to det­ri­mental spec­tral char­ac­ter­ist­ics (e.g., poor spec­tral ef­fi­ciency). In con­trast, con­tinu­ous phase mod­u­la­tion (CPM) mod­u­lates the data bits in a con­tinu­ous man­ner and has there­fore a high spec­tral ef­fi­ciency. This is par­tic­u­larly in­ter­est­ing in wire­less com­mu­nic­a­tion where band­width is ex­pens­ive. In fact, CPM is most not­ably used in GSM.

In this ex­per­i­ment you will in­vest­ig­ate a com­mu­nic­a­tion sys­tem that uses CPM mod­u­la­tion. With the aid of SIM­ULINK, you will sim­u­late the mod­u­la­tion and de­cod­ing pro­cesses of CPM sys­tems, and you will ana­lyze key fig­ures such as spec­tral char­ac­ter­ist­ics of the mod­u­lated sig­nal or prob­ab­il­ity of a de­cod­ing er­ror.

 

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