Physical activity recognition via minimal in-shoes force sensor configuration

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Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

The different receptors in human skin show not only diversity in the stimuli to which they respond, but also variable sensitivity and directionality. This is often determined by their location or morphology, and can play an important role in filtering or a ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...

Depleted Monolithic Active Pixel Sensor (DMAPS) sensors developed in the Tower Semiconductor 180 nm CMOS imaging process have been designed in the context of the ATLAS ITk upgrade Phase-II at the HL-LHC and for future collider experiments. The "MALTA-Czoch ...

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of ...

One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with a single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal (BO) ...

Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estimates in the context of overparametrized neural networks. Several methods, ra ...

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Unstructured neural network pruning algorithms have achieved impressive compression ratios. However, the resulting-typically irregular-sparse matrices hamper efficient hardware implementations, leading to additional memory usage and complex control logic t ...

To achieve the physics goals of future colliders, it is necessary to develop novel, radiation-hard silicon sensors for their tracking detectors. We target the replacement of hybrid pixel detectors with Depleted Monolithic Active Pixel Sensors (DMAPS) that ...

Background: Orthostatic hypotension (OH) in Parkinson's disease (PD) is frequent and associated with impairments in quality of life and reduced activities of daily living. Abdominal binders (AB) and compression stockings (CS) have been shown to be effectiv ...

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Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of acade ...

In this article, we report on SwissSPAD3 (SS3), a 500 x 500 pixel single-photon avalanche diode (SPAD) array, fabricated in 0.18-mu m CMOS technology. In this sensor, we introduce a novel dual-gate architecture with two contiguous temporal windows, or gate ...

Pixelation is common in quantum imaging systems and limit the image spatial resolution. Here, the authors introduce a pixel super-resolution approach based on measuring the full spatially-resolved joint probability distribution of spatially-entangled photo ...

Physical activity recognition via minimal in-shoes force sensor configuration

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