agenda

Signal Processing Seminar

Rob Remis

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Signal Processing Seminar

Jamal Amini

Jamal is going to present his recent research.

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Signal Processing Seminar

Millad Sardarabadi

Millad is going to present his recent research.

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Signal Processing Seminar

3D direction of arrival estimation of multiple audio sources with spherical microphone arrays

Despoina Pavlidi
University of Crete, Department of Computer Science, Heraklion, Crete, Greece

Abstract: Direction of arrival estimation plays a central role in numerous signal-processing applications, such as smart home automation, surveillance systems, etc. Until recently the research community was mainly interested in single-dimensional direction of arrival (DOA) estimation by deploying linear or planar microphone arrays. Nowadays the focus has turned also towards spherical microphone arrays, which enable the more accurate capturing of the acoustic wavefield, hence enabling two-dimensional DOA estimation, i.e., the azimuth and elevation of an active audio source. In this talk we will present our proposed methodologies for DOA estimation in the 3D space. Our first proposed method relies on energetic analysis. We estimate the sound intensity vector on selected time-frequency elements of the spectrum and post-process the estimates utilizing 2D histogram representations. We enhance our approach by applying beamforming around local intensity vector directions. We call our hybrid approach spatially constrained beamforming (SCB). Our second proposed method improves the performance of two grid-based approaches, namely the steered response power (SRP) and the multiple signal classification (MUSIC) algorithm, both formulated in the spherical harmonic domain. We propose to derive local DOA estimates from the power map for SRP and the pseudospectrum for MUSIC. From these local DOA estimates we form a 2D histogram that we process to derive the final multiple sources directions.


Signal Processing Seminar

Shahrzad Naghibzadeh

Shahrzad is going to present her recent research.

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Signal Processing Seminar

Pim van der Meulen

Pim is going to present his recent research.

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Signal Processing Seminar

Image domain gridding for radio astronomy

Bas van der Tol
ASTRON

Fast implementations of a "non-uniform FFT" operation

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MSc CE Thesis Presentation

An Accurate System-Level Device Aging Assessment and Mitigation Simulation Framework

Evelyn Rashmi Jeyachandra

As technology scaling enters the nanometer regime, device aging effects cause quality and reliability issues in CMOS Integrated Circuits (ICs), which in turn shorten its lifetime. Evaluating system aging through circuit simulations is very complex and time consuming. In this thesis, a framework is proposed, which allows for the evaluation of long-term aging effects of ICs and the corresponding measures to counteract premature failure. The focus of this work lies in the abstraction of low-level aging models to system-level models, in order to facilitate swift high-level simulation, without any knowledge of underlying circuit dynamics.

Two major aging mechanisms, namely Negative Bias Temperature Instability (NBTI) and Channel Hot Carrier (CHC) degradation are considered for analysis. System-level aging management is performed with the prototype of a System-on-Chip (SoC) including a Management Unit (MU), which counteracts aging by employing Dynamic Voltage Scaling (DVS), Dynamic Frequency Scaling (DFS), and Adaptive Body Biasing (ABB). The simulation platform prototype is based on System-C and a 65-nm technology library. This SoC simulation computes path delay using characterized models, which represent the aged behaviour of individual circuit elements. Results show that the obtained values are within 2% of circuitlevel simulation values.

Furthermore, the System-C implementation has a shorter execution time with an approximate speedup of 15 times over conventional circuit simulators (e.g. Cadence NCSim).

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Signal Processing Seminar

Bahareh Abdikivanani

Bahareh is going to present her recent research.

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Signal Processing Seminar

Jie Zhang

Jie is going to present his recent research.

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Network topology inference from graph stationary signals

Network topology inference from graph stationary signals

Antonio Garcia Marques (King Juan Carlos University)

We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here permeates benefits from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end we develop efficient inference algorithms stemming from provably-tight convex relaxations of natural nonconvex criteria, particularizing the results for two shifts: the adjacency matrix and the normalized Laplacian. Algorithms and theoretical recovery conditions are developed not only when the templates are perfectly known, but also when the eigenvectors are noisy or when only a subset of them are given. Numerical tests showcase the effectiveness of the proposed algorithms in recovering social, brain, and amino-acid networks.

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MSc Defense of Boliang Xu

Packet loss concealment for speech transmissions in real-time wireless applications

Boliang Xu

Packet communication applications cannot guarantee correct delivery of every packet. Congestions and interferences in the network lead to lost packets. However, real-time applications require timely delivery of data or information and always tolerate packet loss to achieve this aim. When some speech packets are lost, packet loss concealment (PLC) is used to replace the missing speech.

In this thesis, after investigating packet loss characteristics in realistic wireless networks and problems in existing PLC algorithms, we propose a new PLC scheme named adaptive PLC, which is composed of three algorithms: odd-even interpolation, waveform similarity matching and silence substitution. Adaptive PLC adjusts the algorithm to use depending on loss situations. Odd-even interpolation recovers the loss by interpolating odd or even samples in a packet. Waveform similarity matching estimates waveform segments from correctly received or already recovered packets. Silence substitution just fills in the missing part by zeros.

The adaptive PLC achieves improvements in speech quality relative to each single PLC algorithm and other existing PLC algorithms.


Signal Processing Seminar

Thomas Sherson

Thomas is going to present his recent research.

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