Agenda

CAS Christmas dinner

Christmas celebration with food and drinks from all corners of the world.


Signal Processing Seminar

Wangyang Yu

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Signal Processing Seminar

The Quest for Fast Learning from Few Examples

Andreas Loukas

Though the data in our disposal are numerous and diverse, deriving meaning from them is often non trivial. This talk centers on two key challenges of data analysis, relating to the sample complexity (how many examples suffice to learn something with statistical significance) and computational complexity (how long does the computation take) of learning algorithms. In particular, we are going to consider two famous unsupervised algorithms, principal component analysis and spectral clustering, and ask what can they learn when given very few examples or a fraction of the computation time.


MSc ME Thesis Presentation

FPGA based real time detection and signal, processing of electric nanosecond Partial Discharge (PD) pulses to extract parameters facilitating PD classication.

Ayush Joshi

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Signal Processing Seminar

Mario Coutiño Minguez


Signal Processing Seminar

Jamal Amini

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Signal Processing Seminar

Jiani Liu

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Signal Processing Seminar

Farnaz Nassirinia

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Signal Processing Seminar

Blind calibration of radio astronomical phased arrays

Stefan Wijnholds
ASTRON

Radio astronomical phased arrays are usually calibrated under the assumption that the observed scene is known. That assumption may not hold if a new frequency window is opened (for example in the case of the currently planned antenna arrays in space observing below 10 MHz) or when there are unexpected source signals such as transients or radio frequency interference (RFI). In this talk, I present recent work on blind calibration of radio astronomical phased assuming that the observed scene is sparse. The resulting method applies sparse reconstruction techniques to the measured array covariance matrices instead of time series data. I discuss the computation speed-up provided by this shift from signal domain to power domain and explain how phase transition diagrams need to be reinterpreted in this context 

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Signal Processing Seminar

Tuomas Aittomäki

With the increasing demand for wireless communication with high data rates, more and more spectral resources are allocated for communication systems. This has lead to the risk of decreasing spectrum allocated exclusively for radars. As the use of radars is likely to increase in future, it is necesary to look at how radar and communication systems could co-exist. This talk is an overview of developments in shared use of spectral and hardware resources between these systems.

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Signal Processing Seminar

Accurate Calculation of the Mean Strain of Non-uniform Strain Fields Using a Conventional FBG Sensor

Aydin Rajabzadeh

In the past few decades fibre Bragg grating (FBG) sensors have gained a lot of attention in the field of distributed point strain measurement. One of the most interesting properties of these sensors is the presumed linear relationship between the strain and the peak wavelength shift of FBG reflection spectra. However, subjecting sensors to a non-uniform stress field will in general result in a strain estimation error when using this linear relationship, which is due to the difference between the average strain value over the length of the sensor and the point strain value based on the peak wavelength shift of the FBG reflected spectra. In this presentation, we will first introduce a new formulation for analysis of FBG reflected spectra under an arbitrary strain distribution. The presented method is an approximation of the classic transfer matrix model, and will be called the approximated transfer matrix model or ATMM. Using the properties of this new formulation, a new method will be presented that compensates for the mean strain estimation error, and it will be validated using simulations and experimental FBG measurements.

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Signal Processing Seminar

Graph Sampling for Covariance Estimation

Geert Leus

In this talk, the focus is on subsampling as well as reconstructing the second-order statistics of signals residing on nodes of arbitrary undirected graphs. Second-order stationary graph signals may be obtained by graph filtering zero-mean white noise and they admit a well-defined power spectrum whose shape is determined by the frequency response of the graph filter. Estimating the graph power spectrum forms an important component of stationary graph signal processing and related inference tasks such as Wiener prediction or inpainting on graphs. The central result is that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the second-order statistics of the graph signal from the subsampled observations, and more importantly, without any spectral priors. To this end, both a nonparametric approach as well as parametric approaches are considered. The results specialize for undirected circulant graphs in that the graph nodes leading to the best compression rates are given by the so-called minimal sparse rulers.

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