Sound-recognition using Spiking Neural Networks

The development of the Spiking Neural Network (SNN) offers great potential in combination with new types of event-based sensors, by exploiting the embedded temporal information. When combined with dedicated neuromorphic hardware it enables ultra-low power solutions and local on-chip learning. This work implements and presents a viable...

Low power digital baseband architecture for wireless sensor nodes

This thesis presents a digital baseband design for an upcoming wireless standards: IEEE 802.11ah. It is a branch of Wi-Fi (IEEE 802.11) standards. Compared with the previous Wi-Fi standards, this new standard has larger coverage range and consumes less energy. It is particularly suited for energy-constrained sensor applications. In contrast to...

Energy-Efficient Self-Timed Zero-Crossing-Based Incremental Delta-Sigma ADC

The Impact of Low-Power Design Methodology on Digital Libraries

In recent years, exciting new low-power design methods have been introduced, such as: multiple supply voltages, body bias techniques and power shut-off. In order to use these low power design methods, strict requirements for both libraries and tools are needed. An additional challenge is the introduction of more accurate characterization models...

Capacitive Sensor Interface Using an Inverter-Based Period Modulator

This thesis discusses the basic principles, circuit implementation and measurements of an inverter-based capacitive-sensor interface based on period modulation. The interface employs an inverter-based OTA and comparator to increase the current efficiency. Moreover, it applies new circuit techniques to reduce the die size and to reduce noise. A...

Super-regenerative Receiver for UWB-FM

UWB-FM is a low-complexity ultra-wideband (UWB) communication system designed for short-range, low- and medium-data-rate wireless applications such as the personal area network (PAN). These applications often require simple, integrated receivers with low power consumption. Most of previous work utilized delay-line demodulators for UWB-FM...

Low-Power Gesture Recognition Using Convolutional Neural Networks and Ambient Lighting

This paper presents a study focused on developing an efficient signal processing pipeline and identifying suitable machine learning models for real-time gesture recognition using a testbed consisting of an Arduino Nano 33 BLE and three OPT101 photodiodes. Our research aims to address the challenges of limited computational power whilst...