BELLOTTI F.

No description

No description

Editorial of Issue 2 of Volume 11 of the International Journal of Serious Games.

No description

No description

The use of .csv files is very widespread, because of the simplicity of its tabular format and the support by popular editing tools. We propose a novel workflow for enhancing integration of such files with MongoDB storage, and investigate its applicability over a representative sample from the data. world collection. Compared to mongoimport...

This paper presents the Edge Learning Machine (ELM), a machine learning framework for edge devices, which manages the training phase on a desktop computer and performs inferences on microcontrollers. The framework implements, in a platform-independent C language, three supervised machine learning algorithms (Support Vector Machine (SVM) with a...

Microcontroller Units (MCUs) are widely used for industrial field applications, and are now ever more being used also for machine learning on the edge, because of their reliability, low cost, and energy efficiency. Due to the MCU resource limitations, the deployed ML models need to be optimized particularly in terms of memory footprint. In this...

This paper investigated the application of unsupervised learning on a mainstream microcontroller, like the STM32 F4. We focused on the simple K-means technique, which achieved good accuracy levels on the four test datasets. These results are similar to those obtained by training a k-nearest neighbor (K-NN) classifier with the actual labels,...

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and...

Voice assistants are spreading in various environments, such as houses and cars, bringing the possibility of controlling heterogeneous Internet of Things devices with simple voice commands. However, massive use of the cloud connection for speech processing requires an efficient and robust Internet connection and raises concerns in terms of...

The relationship between decision-making and emotions has been extensively studied in both theoretical and empirical research. Game Theory-based paradigms utilizing socio-economic and trust-based contexts have been established to elicit specific emotional responses in autistic individuals. Serious games, incorporating cohesive storylines and...

This Editorial analyzes the manuscripts accepted, after a careful peer-reviewed process, for the Special Issue "Applications in Electronics Pervading Industry, Environment and Society-Sensing Systems and Pervasive Intelligence" of the Sensors MDPI journal [...].

Binarization is a machine learning optimization for limited resource devices that has achieved significant results in edge applications. As microcontrollers are the mainstream platform for field applications in industry, this article investigates memory efficient deployment of binary neural networks (BNNs) to such devices. To this end, we...

The quest for efficient Tiny Machine Learning on Microcontroller Units is increasing rapidly due to the vast application spectrum made possible with the advancement of Tiny ML. One application area that could benefit from such advancement is Electronic Skin systems, that are employed in several domains such as: wearable devices, robotics,...

Data analysis has become a common practice in professional and amateur sport activities, to monitor the player state and enhance performance. In tennis, performance analysis requires detecting and recognizing the different types of shots. With the advances in microcontrollers and machine learning algorithms, this topic becomes ever more...

Online deep reinforcement learning training poses challenges due to its length and instability, despite the development of learning algorithms targeted to overcome these issues. Offline learning has emerged as a potential solution, but it reintroduces the issue of dataset production, which is resource-consuming and challenging even in...

Wearable technology has gained significant attention in research and commercial applications, including sports, where data collection and analysis play a crucial role in improving skills. This study focuses on tennis and the real-time classification of main shots, such as forehand, backhand, and serve. While previous studies have utilized...

This paper presents a novel tool for generating driving scenario datasets, that are a key asset to advance research and development in automated driving and driver assistance systems. The tool relies on the MATLAB. Automated Driving Toolbox and focuses on the overtaking maneuver. It uses simulated vehicular data, without relying on...

Binarization is an extreme quantization technique that is attracting research in the Internet of Things (IoT) field, as it radically reduces the memory footprint of deep neural networks without a correspondingly significant accuracy drop. To support the effective deployment of Binarized Neural Networks (BNNs), we propose CBin-NN, a library of...

No description