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Batteryless IoT devices, powered by energy harvesting, face significant challenges in maintaining operational efficiency and reliability due to intermittent power availability. Traditional checkpointing mechanisms, while essential for…
With the increasing use of smartphones in our daily lives, these devices have become capable of performing many complex tasks. Concerning the need for continuous monitoring of vital signs, especially for the elderly or those with certain…
Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy. By mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating…
We study the problem of finding efficient sampling policies in an edge-based feedback system, where sensor samples are offloaded to a back-end server that processes them and generates feedback to a user. Sampling the system at maximum…
Objective. Reliable, continuous neural sensing on wearable edge platforms is fundamental to long-term health monitoring; however, for electroencephalography (EEG)-based sleep monitoring, dense high-frequency processing is often…
Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge. This paper presents an energy-efficient edge processing…
Superconducting transition-edge sensors (TESs) carried by X-ray telescopes are powerful tools for the study of neutron stars and black holes. Several methods, such as optimal filtering or principal component analysis, have already been…
The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The…
Biosignals offer valuable insights into the physiological states of the human body. Although biosignal modalities differ in functionality, signal fidelity, sensor comfort, and cost, they are often intercorrelated, reflecting the holistic…
Artificial intelligence (AI) has been widely used in bioimage image analysis nowadays, but the efficiency of AI models, like the energy consumption and latency is not ignorable due to the growing model size and complexity, as well as the…
A novel energy-efficient edge computing paradigm is proposed for real-time deep learning-based image upsampling applications. State-of-the-art deep learning solutions for image upsampling are currently trained using either resize or…
Latest developments in VLSI, wireless communications, and biomedical sensing devices allow very small, lightweight, low power, intelligent sensing devices called biosensors. A set of these devices can be integrated into a Wireless…
Iterative algorithms are widely used in digital signal processing applications. With the case study of radio astronomy calibration processing, this work contributes towards revealing and exploiting the intrinsic error resilience of…
Sensory processing at the edge requires ultra-low power stand-alone computing technologies. This is particularly true for modern agriculture and precision irrigation systems which aim to optimize water usage by monitoring key environmental…
The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computational and storage demands, especially in real-time applications…
Biosignals exhibit substantial cross-subject and cross-session variability, inducing severe domain shifts that degrade post-deployment performance for small, edge-oriented AI models. On-device adaptation is therefore essential to both…
Wearable edge AI biomedical devices are increasingly being used for continuous patient health monitoring, enabling real-time insights and extended data collection without the need for prolonged hospital stays. These devices must be energy…
This work aims at the development of signal processing algorithms that can explore the potential of a PICOSEC-MicroMegas detector and offers the ability for online, precise timing. The PICOSEC-MicroMegas detector is a novel gaseous detector…
Biosignals collected from wearable devices are widely utilized in healthcare applications. Machine learning models used in these applications often rely on features extracted from biosignals due to their effectiveness, lower data…
Behaviour biometrics are being explored as a viable alternative to overcome the limitations of traditional authentication methods such as passwords and static biometrics. Also, they are being considered as a viable authentication method for…