5G Phased Arrays

International Summer School on 5G Phased Arrays

Understanding of phased array operation requires multi- disciplinary approach, which is based on the antenna array, microwave circuit and signal processing theories. By bringing these three areas together, the school provides integral approach to phased array front-ends for 5G communication systems.

At the school the phased array foundations will be considered from antenna, RF technology and signal processing points of view. Realization of 5G capabilities such as high data-rate communication link to moving objects will be discussed. The education will be concluded by a design project.

The summer school is open for all young specialists and researchers from both industry and academia. The attendees should have basic knowledge about EM, electrical circuits and signal processing (graduate courses on electromagnetic waves, electrical circuits including microwave (RF) circuits, and signal processing).


  • Foundations of antenna arrays
  • Antenna array topologies for 5G applications
  • Analog and digital beamforming in antenna arrays
  • Front-end architecture and performance
  • 5G applications and system requirements

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Advances in Analog Circuit Design

27th Workshop on Advances in Analog Circuit Design

The AACD workshops are a high-quality series of events held all over the world. They have been held annually since 1992 with the aim of bringing together a large group of people working at the forefront of analog circuit design. The workshops offer the opportunity to discuss new possibilities and future developments whilst networking with key figures from across the analog design community.

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Active Implantable Biomedical Microsystems Course

Active Implantable Biomedical Microsystems Course

Vasiliki Giagka, Virgilio Valente, Christos Strydis, Wouter Serdijn
Delft University of Technology and Erasmus Medical Center

Course on the understanding, design and future developments of active implantable biomedical microsystems, such as cochlear implants, cardiac pacemakers, spinal cord implants, neurostimulators and bioelectronic medicine.

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MSc ME Thesis Presentation

Levar Goossens

Smart Sensor Systems 2018

Smart Sensor Systems 2018

This course addresses the design and development of smart sensor systems. After a general overview, various key aspects of sensor systems are discussed: measurement and calibration techniques, the design of precision sensor interfaces, analog-to-digital conversion techniques, and sensing principles for the measurement of magnetic fields, temperature, capacitance, acceleration and rotation. The state-of-the-art smart sensor systems covered by the course include smart magnetic-field sensors, smart temperature sensors, physical chemosensors, multi-electrode capacitive sensors, implantable smart sensors, DNA microarrays, smart inertial sensors, smart optical microsystems and CMOS image sensors. A systematic approach towards the design of smart sensor systems is presented. The lectures are augmented by case studies and hands-on demonstrations.

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MSc CE Thesis Presentation

Energy Efficient Feature Extraction for Single-Lead ECG Classification Based On Spiking Neural Networks

Eralp Kolagasioglu

Cardiovascular diseases are the leading cause of death in the developed world. Preventing these deaths, require long term monitoring and manual inspection of ECG signals, which is a very time consuming process. Consequently, a wearable system that can automatically categorize beats is essential.

Neuromorphic machines have been introduced relatively recently in the science community. The aim of these machines is to emulate the brain. Their low power design makes them an optimal choice for a low power wearable ECG classifier.

As features are crucial in any machine learning system, this thesis aims at proposing an energy efficient feature extraction algorithm for ECG arrhythmia classification using neuromorphic machines. The feature extraction algorithm proposed in this thesis consists of the merger of a low power feature detection and a feature selection algorithm. Also, different network configurations have been investigated to achieve classification using an LSM architecture. The resulting system can accurately cluster seven beat types, has an overall classification rate of 95.5%, and consumes an estimate of 803.62 nW.

MSc SS Thesis Presentation

The cocktail party problem: GSVD-beamformers in reverberant environments

Derk-Jan Hulsinga

Hearing aids as a form of audio preprocessing is increasingly common in everyday life. The goal of this thesis is to implement a blind approach to the cocktail party problem and challenge some of the regular assumptions made in literature. We approach the problem as wideband FD-BSS. From this field of research, the common assumption of continuous activity is dropped. Instead a number of users detection is implemented as a preprocessing step and ensure the appropriate number of demixing vectors for each time frequency bin. The validity of the standard mixing model used for STFT’s is challenged by looking at the response of a linear array.

Source separation is achieved by demixing vectors based on the GSVD, derived in a model-based approach. While most permutation solvers offer an a posteriori solution for all users, we looked at finding local solutions for a single user. Combining this with the user identification called the alignment step, we conclude that the permutation problem can be reduced to selecting a demixing vector for each discrete time-frequency instance. The correlation coefficient proves to be a sufficient metric to couple reconstructions to the original data as it selects most of the active time-frequency bins.

In simulations, our demixing vectors achieve comparable inteligibility, measured by STOI, as the compared techniques and it is more robust against smaller sample sizes than the theoretically SINR optimal MVDR.

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