Guest Editorial Computational and smart cameras

published_or_final_versio

Design Of Neural Network Circuit Inside High Speed Camera Using Analog CMOS 0.35 ¼m Technology

Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of applications involving industrial as well as consumer appliances. This is particularly the case when low power consumption, small size and/or very high speed are required. This approach exploits the computational features of Neural Networks, the implementation efficiency of analog VLSI circuits and the adaptation capabilities of the on-chip learning feedback schema. High-speed video cameras are powerful tools for investigating for instance the biomechanics analysis or the movements of mechanical parts in manufacturing processes. In the past years, the use of CMOS sensors instead of CCDs has enabled the development of high-speed video cameras offering digital outputs , readout flexibility, and lower manufacturing costs. In this paper, we propose a high-speed smart camera based on a CMOS sensor with embedded Analog Neural Network

Introduction to the Special Section on Social Computing and Social Internet of Things

The papers in this special section focus on social computing and the social Internet of Things (SIoT). SIoT is a new and latest paradigm that extends Internet of Things. This provides an ideal platform for interconnected devices and objects to effectively interact across social platforms for the betterment of the community on a whole. Any Social Internet of things based system means that the data is distributed in nature and focuses on the interest of a larger group of people than a particular individual. Thus social Internet of things have a wide scope and can be used to develop a wide range of applications that involves a group of people or community working towards accomplishing a common objective such as joint ventures, office setup, co-ownerships and so on. Social Computing may be defined as the study of the collaborative behavior of a group of computer users working on some common objectives

Technologies for Data-Driven Interventions in Smart Learning Environments [Editorial]

Smart Learning environments (SLEs) are defined [1] as learning ecologies where students engage in learning activities, or where teachers facilitate such activities with the support of tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners and educators’ actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out. Sources from these data may include learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wide range of automatic, human-mediated, or hybrid interventions, which involve learners and teachers in the decision making behind the interventions.This work was supported in part by the Spanish Ministry of Science and Innovation through Smartlet and the H2OLearn Projects under Grant MICIN/AEI/10.13039/501100011033, and in part by the Fondo Europeo de Desarrollo Regional (FEDER) under Grant TIN2017-85179-C3-1-R, Grant TIN2017-85179-C3-2-R, Grant TIN2017-85179-C3-30R, Grant PID2020-112584RB-C31, Grant PID2020-112584RB C32, and Grant GPID2020-112584RB-C33. The work of Davinia Hernández-Leo (Serra Húnter) was supported by ICREA through the ICREA Academia Program.Publicad

Assessment of inflammation in patients with rheumatoid arthritis using thermography and machine learning: a fast and automated technique

Objectives Sensitive detection of joint inflammation in rheumatoid arthritis (RA) is crucial to the success of the treat-to-target strategy. In this study, we characterise a novel machine learning-based computational method to automatically assess joint inflammation in RA using thermography of the hands, a fast and non-invasive imaging technique. Methods We recruited 595 patients with arthritis and osteoarthritis, as well as healthy subjects at two hospitals over 4 years. Machine learning was used to assess joint inflammation from the thermal images of the hands using ultrasound as the reference standard, obtaining a Thermographic Joint Inflammation Score (ThermoJIS). The machine learning model was trained and tuned using data from 449 participants with different types of arthritis, osteoarthritis or without rheumatic disease (development set). The performance of the method was evaluated based on 146 patients with RA (validation set) using Spearman's rank correlation coefficient, area under the receiver-operating curve (AUROC), average precision, sensitivity, specificity, positive and negative predictive value and F1-score. Results ThermoJIS correlated moderately with ultrasound scores (grey-scale synovial hypertrophy=0.49, p<0.001; and power Doppler=0.51, p<0.001). The AUROC for ThermoJIS for detecting active synovitis was 0.78 (95% CI, 0.71 to 0.86; p<0.001). In patients with RA in clinical remission, ThermoJIS values were significantly higher when active synovitis was detected by ultrasound. Conclusions ThermoJIS was able to detect joint inflammation in patients with RA, even in those in clinical remission. These results open an opportunity to develop new tools for routine detection of joint inflammation

Design Of Neural Network Circuit Inside High Speed Camera Using Analog CMOS 0.35 μm Technology

Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of applications involving industrial as well as consumer appliances. This is particularly the case when low power consumption, small size and/or very high speed are required. This approach exploits the computational features of Neural Networks, the implementation efficiency of analog VLSI circuits and the adaptation capabilities of the on-chip learning feedback schema. High-speed video cameras are powerful tools for investigating for instance the biomechanics analysis or the movements of mechanical parts in manufacturing processes. In the past years, the use of CMOS sensors instead of CCDs has enabled the development of high-speed video cameras offering digital outputs, readout flexibility, and lower manufacturing costs. In this paper, we propose a high-speed smart camera based on a CMOS sensor with embedded Analog Neural Network. 1

Editorial Preface

This annual issue embraces articles from three sets of sources: the first covers the topic Virtual Environments and Interaction Design Research at the University of Saint Joseph, Macao SAR, China works selected and guest edited by Carlos Sena Caires and Gerald Estadieu; the second set are three extended articles from the International Conference on Graphics and Interaction (ICGI’2021), selected and guest edited by Daniel Mendes and Nuno Rodrigues; and, finally, two articles from the regular pipeline.info:eu-repo/semantics/publishedVersio

Indexing of fictional video content for event detection and summarisation

This paper presents an approach to movie video indexing that utilises audiovisual analysis to detect important and meaningful temporal video segments, that we term events. We consider three event classes, corresponding to dialogues, action sequences, and montages, where the latter also includes musical sequences. These three event classes are intuitive for a viewer to understand and recognise whilst accounting for over 90% of the content of most movies. To detect events we leverage traditional filmmaking principles and map these to a set of computable low-level audiovisual features. Finite state machines (FSMs) are used to detect when temporal sequences of specific features occur. A set of heuristics, again inspired by filmmaking conventions, are then applied to the output of multiple FSMs to detect the required events. A movie search system, named MovieBrowser, built upon this approach is also described. The overall approach is evaluated against a ground truth of over twenty-three hours of movie content drawn from various genres and consistently obtains high precision and recall for all event classes. A user experiment designed to evaluate the usefulness of an event-based structure for both searching and browsing movie archives is also described and the results indicate the usefulness of the proposed approach