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A compressive sampling (CS) photoplethysmographic (PPG) readout with embedded feature extraction to estimate heart rate (HR) directly from compressively sampled data is presented. It integrates a low-power analog front end together with a digital back end to perform feature extraction to estimate the average HR over a 4 s interval directly from compressively sampled PPG data. The application-specified...
The Phoenix1 project aims to develop a new approach to explore unknown environments, based on multiple measurement campaigns carried out by extremely tiny devices, called agents, that gather data through multiple sensors. These low power and low resource agents are configured specifically for each measurement campaign to achieve the exploration goal in the smallest number of iterations. Thus, the...
Wireless acuusuc sensor networks (wasns) are a promising technology for performing acoustic surveillance because of their flexibility and low cost. However, their commercialization is nowadays limited due to their high energy consumption, which is mainly a result of the high data rates required to stream audio data between sensor nodes. In order to improve energy efficiency in WASNs, we explore the...
Analog-to-information converters and Compressed Sampling (CS) sensor front-ends try to only extract the relevant, information-bearing elements of an incoming data stream. Information extraction and recognition tasks can run directly on the compressed data stream without needing full signal reconstruction. The accuracy of the extracted information or classification is strongly determined by the front-end...
State-of-the-art solutions to optical flow fail to jointly offer high density flow estimation, low power consumption and real time operation, rendering them unsuitable for embedded applications. Joint hardware-software scalability at run-time is crucial to achieve these conflicting requirements in one device. This paper therefore presents a scalable Lucas-Kanade optical flow algorithm, together with...
Filter-banks based on a gm-C topology are popular in acoustic sensor systems targeting spectral analysis. Their benefits lie in a very low power consumption and center-frequency scalability through gm-tuning to cover the audio frequency range. However the linear signal swing at the output of the filter is limited due to the inherent non-linearity of the input transistors in a differential pair. This...
Heart rate (HR) and its variability (HRV) provide critical information about an individual's cardiovascular and mental health state. In either application, long-term observation is crucial to arrive at conclusive decisions and provide useful diagnostic feedback [1]. Photoplethysmographic (PPG) estimation of HR and HRV has emerged as an attractive alternative to ECG, as it provides electrode-free operation...
In-band full duplex (IBFD) is a promising technique that allows to potentially double the achievable bi-directional throughput over a given bandwidth. Moreover, it has been estimated that IBFD-equipped wireless networks can be more energy-efficient than half duplex ones due to the reduced energy cost of packet collisions. However, one key challenge for implementing an energy-efficient IBFD system...
The rise of always-listening sensors integrated in energy-scarce devices such as watches and remote-controls increases the need for intelligent scalable interfaces. Contemporary sensor interfaces digitize raw sensor data to extract information with energy-intensive computations, such as FFT, which is inefficient if the end goal is to only extract selective information for classification tasks, e.g...
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