Agenda

CAS MSc Midterm Presentations

Bas Otterloo van (mid)


PRORISC and SAFE 2019

PRORISC and SAFE 2019

PRORISC is an annual conference on Integrated Circuit (IC) design and SAFE is an annual conference on Microsystems, Materials, Technology and RF-devices. Both conferences are organized together within the three technical Dutch universities Twente, Delft and Eindhoven. The conference is organized by PhD students and is intended for PhD candidates to expand their network and share their research ideas, which provides a unique opportunity for future collaborations. Each year, one of the technical universities will be responsible for the organization of the two conferences. In 2019 the PRORISC will be held at at the campus of Delft University of Technology.

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CAS MSc Midterm Presentations

Bishwadeep Das (mid), Rajwade Rajwade (mid), Sherine Brahma (mid)


CAS MSc Midterm Presentations

Joppe Lauriks (mid), Joris Belier (mid)


Signal Processing Seminar

Task-cognizant sparse sensing for inference (ASPIRE)

Pim van der Meulen

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CAS MSc Midterm Presentations

Ye Cui (mid), Metin Calis (mid)


CAS MSc Midterm Presentations

Lantian Kou (mid)


Inauguration Earl McCune and Cicero Vaucher

Who's talking, who's listening?

Earl McCune, Cicero Vaucher
TU Delft

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Signal Processing Seminar

Acoustic signal processing

Jamal Amini

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CAS MSc Midterm Presentations

Kevin vanderMark (mid)


CAS MSc Midterm Presentations

Kostadin Biserkov (mid), Husain Kapadia (mid)


Signal Processing Seminar

Biomedical signal processing

Bahareh Abdikivanani

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CAS MSc Midterm Presentations

Bishwadeep Das (1st), Bas Otterloo van (1st), Yiting Lu (mid), Yajie Tang (mid)


Signal Processing Seminar

sensor networks, rank-constrained optimization, algebraic techniques

Matthew Morency

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MSc ME Thesis Presentation

A Highly Concurrent, Memory-Efficient AER Architecture for Neuro-Synaptic Spike Routing

Joris Coenen

One of the challenges of neuromorphic computing is efficiently routing spikes from neurons to their connected synapses. The aim of this thesis is to design a spike-routing architecture for flexible connections on single-chip neuromorphic systems. A model for estimating area, power consumption, memory, spike latency and link utilisation for neuromorphic spike-routing architecture is described This model leads to the proposal for a new spike-routing architecture with a hybrid addressing scheme and a novel synaptic encoding scheme.

The proposed architecture is implemented in a SystemC simulation tool with a supporting tool for encoding arbitrary SNN topologies for the synapse encoding scheme.

Running the simulations with synthetic benchmarks and a handwriting recognition SNN shows that the proposed architecture is memory-efficient and provides low latency spike-routing with high synaptic activation concurrency.

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MSc CE Thesis Presentation

Area Minimization of DTB Multiplexer - A Chip Component with High Wire Density and Congestion

Reynaldi Canggaputra

DTB Multiplexer is a component within an NXP chip called the BAP3. This component provides a testing functionality for the chip. This component is purely combinational, and requires no clock, however this makes the component wiring-costly. This high wiring requirement leads to the area constraint imposed by the wiring demand rather than cell area, and this also leads to the DTB multiplexer reducing the placement area available for other modules.

In this thesis, the wiring area is going to be estimated as the amount of congestion, which would cause detour in the design which results in extra wiring. In this thesis, DTB multiplexer is placed by external method instead of using the place and route tools usually used by the design team. Instead, the placement is done on MATLAB which is later ported to the place and route tools using script. The placement algorithm implemented in MATLAB is primarily based on two algorithm, Dplace for initial preplacement, which in turn utilizes diffusion preplacement algorithm, and modified C-ECOP for the congestion reduction. More detailed congestion estimation done by using an additional routing estimation algorithm which is based on One-Steiner routing algorithm.

The result indicates that the modified C-ECOP can be used to reduce congestion, thus wiring area when paired with a good initial placement algorithm, but the initial placement algorithm and detailed congestion estimation algorithm with one-steiner could be further improved, and further work is needed to integrate the result with commercial place and route tools.

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MSc SS Thesis Presentation

Localization using Time-of-Arrival Estimation in the LoRa Network

Ming DAI

LoRa (Long Range) is a low-power, long-range and low-cost wireless communication system that can facilitate a wide variety of infrastructures for the Internet of Things (IoT). Current algorithms to locate LoRa tags have a resolution of ca 100 m in practice, and a question is if that can be improved without changing the tags or adding too much to the gateways (basestations).

Conventional delay estimation ranging algorithms extract useful information from the channel frequency response and use this information to estimate delays. In this thesis, three localization techniques are presented: the matched filter, FBCM-MUSIC and TLS-ESPRIT algorithms. Then a multiband architecture is proposed and integrated into the matched filter. These algorithms are implemented in the LoRa system model. The simulations indicate that FBCM-MUSIC and TLS-ESPRIT have better performance than the matched filter in NLOS channels. The results also show that TLS-ESPRIT is more effective and robust compared to MUSIC. The proposed multiband architecture can improve the resolution of TOA estimation and decreases the 90th percentile error by around 40%


Signal Processing Seminar

Network Data Science

Huijuan Wang

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Signal Processing Seminar

Sensor and Machine Learning at The Arizona State University

Andreas Spanias
Arizona State University - SenSIP

This seminar provides a description of the ASU Sensor Signal and Information Processing (SenSIP) center and its application-driven research projects. The center research activities include algorithm development for extracting information from sensors and IoT systems. More specifically center activities are focused on developing signal processing and machine learning methods for various applications including AI-enabled sensing for automotive, IoT solar energy system monitoring, surveillance systems, health monitoring, and sound systems. The center has several industry members that define and monitor research projects typically for Ph.D. student work. SenSIP has also affiliated faculty working on sensor circuits, flexible sensors, radar, smart cameras, motion estimation, secure sensor networks and other systems.

Biography

Andreas Spanias is Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University (ASU). He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (now an NSF I/UCRC site). Member companies of the NSF SenSIP center and industry consortium on sensor information processing include: Intel, National Instruments, LG, NXP, Raytheon, Sprint and several SBIR type companies. He is an IEEE Fellow and he recently received the IEEE Phoenix Section Award for Patents and Innovation. He also received the IEEE Region 6 section award (across 12 states) for education and research in signal processing. He is author of more than 300 papers,15 patents, two text books and several lecture monographs. He served as Distinguished lecturer for the IEEE Signal processing society in 2004.

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Signal Processing Seminar

From matrices to tensors: the power of tensor methods in signal processing

Borbála Hunyadi

In this presentation, I give an overview of the fundamental differences between linear and multilinear algebra. After generalising the basic concepts of matrix rank and matrix SVD to tensors, the richness of tensor representations and decompositions become clear. The richness of tensor algebra, in turn, provides powerful models for signal processing.

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