Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems

Active Spectrum Sensing with Sub-Nyquist sampling

At high carriers, enabling low power wireless devices to use opportunistically the available spectrum requires an analog front-end that can sweep different bands quickly, since Nyquist sampling is prohibitively expensive. In this paper we propose a new framework that allows to optimize a sub-Nyquist sampling front-end to combine the benefits of optimum sequential sensing with those of compressi...

Implementation of Sub-Nyquist Sampling System Based on Compressed Sensing

This paper describes the development of a sub-Nyquist sampling system that can digitize high-speed signals using a low-speed analog to digital converter (ADC). The system is implemented by a field programmable gate array (FPGA), and it is possible to make change to the equivalent sampling frequency according to the practical applications. As an application of the compressed sensing (CS) theory,...

Low-Power Wearable Respiratory Sound Sensing

Building upon the findings from the field of automated recognition of respiratory sound patterns, we propose a wearable wireless sensor implementing on-board respiratory sound acquisition and classification, to enable continuous monitoring of symptoms, such as asthmatic wheezing. Low-power consumption of such a sensor is required in order to achieve long autonomy. Considering that the power con...

Adaptive Rate Sampling and Filtering for Low Power Embedded Systems

This work is part of a large project aimed to enhance the signal processing chain required in embedded systems. The motivation is to reduce their size, cost, power consumption, processing noise and electromagnetic emission. This can be done by smartly reorganizing their associated signal processing theory and architecture. The idea is to combine the signal event driven processing with asynchron...

Low Power Technology for Wearable Cognition Systems