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Computational Intelligence and Cyberphysical Systems in Sensing
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Computational Intelligence and Cyberphysical Systems in Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 6835

Special Issue Editor

Prof. Dr. Manuel Graña

E-Mail Website
Guest Editor
Computational Intelligence Group, UPV/EHU, University of the Basque Country, 48940 Leioa, Spain
Interests: computational approaches to artificial perception
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue covers the exciting emerging areas of computational intelligence and cyber physical systems that are at the core of innovative sensing and data capturing for the modeling of physical and virtual worlds, encompassing industrial applications and social systems via increasingly autonomous systems that combine breakthrough physical electromechanical designs, data processing and artificial intelligence. Increasing the control and monitoring capabilities of everyday activities as well as industrial and healthcare processes is being facilitated by such innovative hybrid artificial systems. Topics for the Special Issue include improved control of nonlinear systems, applications in precision agriculture, analysis of medical imaging data for improved diagnosis assistance of dementia- and neural-based diseases, image and signal processing in human activity monitoring, social network content control for fake news and disinformation spread countenance, industrial process anomaly prediction and management via digital twins, advanced cognitive systems for new generation robotic systems that combine perception and intelligence in innovative ways. 

Prof. Dr. Manuel Graña
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (4 papers)

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Research

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16 pages, 5430 KiB  
Article
Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics
by Ricardo Muñoz-Cancino, Sebastián A. Ríos and Manuel Graña
Sensors 2023, 23(11), 5165; https://0-doi-org.brum.beds.ac.uk/10.3390/s23115165 - 29 May 2023
Viewed by 1283
Abstract
The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such [...] Read more.
The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics. Full article
(This article belongs to the Special Issue Computational Intelligence and Cyberphysical Systems in Sensing)
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14 pages, 408 KiB  
Article
SLRProp: A Back-Propagation Variant of Sparse Low Rank Method for DNNs Reduction
by Asier Garmendia-Orbegozo, Jose David Nuñez-Gonzalez and Miguel Angel Anton
Sensors 2023, 23(5), 2718; https://0-doi-org.brum.beds.ac.uk/10.3390/s23052718 - 02 Mar 2023
Viewed by 1138
Abstract
Application of deep neural networks (DNN) in edge computing has emerged as a consequence of the need of real time and distributed response of different devices in a large number of scenarios. To this end, shredding these original structures is urgent due to [...] Read more.
Application of deep neural networks (DNN) in edge computing has emerged as a consequence of the need of real time and distributed response of different devices in a large number of scenarios. To this end, shredding these original structures is urgent due to the high number of parameters needed to represent them. As a consequence, the most representative components of different layers are kept in order to maintain the network’s accuracy as close as possible to the entire network’s ones. To do so, two different approaches have been developed in this work. First, the Sparse Low Rank Method (SLR) has been applied to two different Fully Connected (FC) layers to watch their effect on the final response, and the method has been applied to the latest of these layers as a duplicate. On the contrary, SLRProp has been proposed as a variant case, where the relevances of the previous FC layer’s components were weighed as the sum of the products of each of these neurons’ absolute values and the relevances of the neurons from the last FC layer that are connected with the neurons from the previous FC layer. Thus, the relationship of relevances across layer was considered. Experiments have been carried out in well-known architectures to conclude whether the relevances throughout layers have less effect on the final response of the network than the independent relevances intra-layer. Full article
(This article belongs to the Special Issue Computational Intelligence and Cyberphysical Systems in Sensing)
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19 pages, 3798 KiB  
Article
Coordinated Navigation of Two Agricultural Robots in a Vineyard: A Simulation Study
by Chris Lytridis, Christos Bazinas, Theodore Pachidis, Vassilios Chatzis and Vassilis G. Kaburlasos
Sensors 2022, 22(23), 9095; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239095 - 23 Nov 2022
Cited by 2 | Viewed by 1645
Abstract
The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires appropriate coordination strategies to be developed to ensure safe, effective, [...] Read more.
The development of an effective agricultural robot presents various challenges in actuation, localization, navigation, sensing, etc., depending on the prescribed task. Moreover, when multiple robots are engaged in an agricultural task, this requires appropriate coordination strategies to be developed to ensure safe, effective, and efficient operation. This paper presents a simulation study that demonstrates a robust coordination strategy for the navigation of two heterogeneous robots, where one robot is the expert and the second robot is the helper in a vineyard. The robots are equipped with localization and navigation capabilities so that they can navigate the environment and appropriately position themselves in the work area. A modular collaborative algorithm is proposed for the coordinated navigation of the two robots in the field via a communications module. Furthermore, the robots are also able to position themselves accurately relative to each other using a vision module in order to effectively perform their cooperative tasks. For the experiments, a realistic simulation environment is considered, and the various control mechanisms are described. Experiments were carried out to investigate the robustness of the various algorithms and provide preliminary results before real-life implementation. Full article
(This article belongs to the Special Issue Computational Intelligence and Cyberphysical Systems in Sensing)
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12 pages, 738 KiB  
Perspective
A Perspective on Lifelong Open-Ended Learning Autonomy for Robotics through Cognitive Architectures
by Alejandro Romero, Francisco Bellas and Richard J. Duro
Sensors 2023, 23(3), 1611; https://0-doi-org.brum.beds.ac.uk/10.3390/s23031611 - 02 Feb 2023
Cited by 1 | Viewed by 2121
Abstract
This paper addresses the problem of achieving lifelong open-ended learning autonomy in robotics, and how different cognitive architectures provide functionalities that support it. To this end, we analyze a set of well-known cognitive architectures in the literature considering the different components they address [...] Read more.
This paper addresses the problem of achieving lifelong open-ended learning autonomy in robotics, and how different cognitive architectures provide functionalities that support it. To this end, we analyze a set of well-known cognitive architectures in the literature considering the different components they address and how they implement them. Among the main functionalities that are taken as relevant for lifelong open-ended learning autonomy are the fact that architectures must contemplate learning, and the availability of contextual memory systems, motivations or attention. Additionally, we try to establish which of them were actually applied to real robot scenarios. It transpires that in their current form, none of them are completely ready to address this challenge, but some of them do provide some indications on the paths to follow in some of the aspects they contemplate. It can be gleaned that for lifelong open-ended learning autonomy, motivational systems that allow finding domain-dependent goals from general internal drives, contextual long-term memory systems that all allow for associative learning and retrieval of knowledge, and robust learning systems would be the main components required. Nevertheless, other components, such as attention mechanisms or representation management systems, would greatly facilitate operation in complex domains. Full article
(This article belongs to the Special Issue Computational Intelligence and Cyberphysical Systems in Sensing)
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