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Recent Developments and Challenges in Artificial Intelligence and Deep Learning in Advanced Sensing Systems

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

Deadline for manuscript submissions: 25 January 2025 | Viewed by 2587

Special Issue Editors

Prof. Dr. Kah Phooi Seng

E-Mail Website
Guest Editor
1. School of AI & Advanced Computing, Xi’an Jiaotong Liverpool University, Suzhou 215123, China
2. School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: artificial intelligence; deep learning; machine learning and data analytics; intelligent systems; Internet of Things and edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Li-Minn Ang

E-Mail Website
Guest Editor
School of Science and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Interests: electrical and computer engineering; intelligent and smart sensor systems; IoT and data analytics for environmental sensing; innovative technologies for real world systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing technologies can benefit sensing processes in many research fields and applications. Significant advances in artificial intelligence (AI) have given rise to advanced intelligent sensor technologies and sensing systems. The emergence of high-end AI sensor technology is a cornerstone of the new era of technological advancements and transformation in satellites, military applications, artificial intelligent Internet of Things, neuromorphic computing systems, and the next generation of robots and Industry 5.0. These sensing systems have advanced capabilities as they can use multiple sensors capable of detecting and processing multidimensional information. Moreover, low-end AI sensor technology can also provide cost-effective solutions for a wide range of applications such as smart buildings, smart homes, and individual healthcare.

Furthermore, recent advances in deep learning techniques have also led to significant progress in sensing systems. Due to improvements in computation power and speed and graphics processing units (GPUs), the design and development of new deep learning algorithms has been accelerated. Several fundamental deep learning frameworks, such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), and transformer architectures, have been successfully applied in the fields of image processing, natural language processing, automatic speech recognition, and healthcare informatics. They have also contributed to great achievements in image, speech, and text modalities and have continuously emerged in different architectures to provide better accuracy for targeted tasks.

This Special Issue focuses on AI sensors, advanced AI sensing systems, and new or improved deep learning architectures or techniques to address a broad range of sensor- and sensing-related issues in intelligent systems. Contributions addressing the design and development of novel deep learning frameworks, specifically focusing on their effectiveness, intelligence, and reliability in achieving superior performance, are also encouraged. This Special Issue is dedicated to theoretical innovations, research, and developments for real-world applications. The potential topics of this Special Issue include, but are not limited to, the following:

  • Emerging AI sensors or advanced sensing systems;
  • New deep learning techniques and applications;
  • AI sensing technology for smart cities, health monitoring and medical applications, industrial sensing, and urban environmental monitoring and sensing;
  • AI sensing technology in marine sensing, oceans, coastal zones, and inland waters;
  • Wearable AI sensors and self-powered sensor systems;
  • AI and deep learning techniques in sensing, smart cities, and remote sensing applications;
  • AI Internet of Things and Internet of Vehicles for sensing and smart cities;
  • AI and situational awareness for natural disaster monitoring;
  • AI sensors or sensing for robots and autonomous systems;
  • Big data processing using deep learning and sensing;
  • New deep learning and image processing techniques and applications;
  • AI and deep learning in embedded systems.

Prof. Dr. Kah Phooi Seng
Prof. Dr. Li-Minn Ang
Guest Editors

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.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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.

Keywords

  • AI sensors
  • intelligent sensing
  • advanced sensing
  • deep learming
  • AI internet of things
  • AI wearables

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Published Papers (3 papers)

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Research

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18 pages, 2702 KiB  
Article
Active Disturbance Rejection Control via Neural Networks for a Lower-Limb Exoskeleton
by Karina I. Espinosa-Espejel, Yukio Rosales-Luengas, Sergio Salazar, Ricardo Lopéz-Gutiérrez and Rogelio Lozano
Sensors 2024, 24(20), 6546; https://doi.org/10.3390/s24206546 - 11 Oct 2024
Viewed by 619
Abstract
This article presents the design of a control algorithm based on Artificial Neural Networks (ANNs) applied to a lower-limb exoskeleton, which is aimed to carry out walking trajectories during lower-limb rehabilitation. The interaction between the patient and the exoskeleton leads to model uncertainties [...] Read more.
This article presents the design of a control algorithm based on Artificial Neural Networks (ANNs) applied to a lower-limb exoskeleton, which is aimed to carry out walking trajectories during lower-limb rehabilitation. The interaction between the patient and the exoskeleton leads to model uncertainties and external disturbances that are always present. For this reason, the proposed control considers that the non-linear part of the model is unknown and is perturbed by external disturbances, which are estimated by an active disturbance rejection control via Artificial Neural Networks. To validate the proposed approach, a numerical simulation and an experimental implementation of the ANN-Controller are developed. Full article
(This article belongs to the Special Issue Recent Developments and Challenges in Artificial Intelligence and Deep Learning in Advanced Sensing Systems)
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21 pages, 2219 KiB  
Article
Deep Learning Based Over-the-Air Training of Wireless Communication Systems without Feedback
by Christopher P. Davey, Ismail Shakeel, Ravinesh C. Deo and Sancho Salcedo-Sanz
Sensors 2024, 24(10), 2993; https://doi.org/10.3390/s24102993 - 8 May 2024
Viewed by 1142
Abstract
In trainable wireless communications systems, the use of deep learning for over-the-air training aims to address the discontinuity in backpropagation learning caused by the channel environment. The primary methods supporting this learning procedure either directly approximate the backpropagation gradients using techniques derived from [...] Read more.
In trainable wireless communications systems, the use of deep learning for over-the-air training aims to address the discontinuity in backpropagation learning caused by the channel environment. The primary methods supporting this learning procedure either directly approximate the backpropagation gradients using techniques derived from reinforcement learning, or explicitly model the channel environment by training a generative channel model. In both cases, over-the-air training of transmitter and receiver requires a feedback channel to sound the channel environment and obtain measurements of the learning objective. The use of continuous feedback not only demands extra system resources but also makes the training process more susceptible to adversarial attacks. Conversely, opting for a feedback-free approach to train the models over the forward link, exclusively on the receiver side, could pose challenges to reliably end the training process without intermittent testing over the actual channel environment. In this article, we propose a novel method for the over-the-air training of wireless communication systems that does not require a feedback channel to train the transmitter and receiver. Random samples are transmitted through the channel environment to train a mixture density network to approximate the channel distribution on the receiver side of the network. The transmitter and receiver models are trained with the resulting channel model, and the transmitter can be deployed after training. We show that the block error rate measurements obtained with the simulated channel are suitable for monitoring as a stopping criterion during the training process. The resulting method is demonstrated to have equivalent performance to the end-to-end autoencoder training on small message sequences. Full article
(This article belongs to the Special Issue Recent Developments and Challenges in Artificial Intelligence and Deep Learning in Advanced Sensing Systems)
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Review

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21 pages, 2496 KiB  
Review
Transportation Mode Detection Using Learning Methods and Self-Contained Sensors: Review
by Ilhem Gharbi, Fadoua Taia-Alaoui, Hassen Fourati, Nicolas Vuillerme and Zebo Zhou
Sensors 2024, 24(22), 7369; https://doi.org/10.3390/s24227369 - 19 Nov 2024
Viewed by 322
Abstract
Due to increasing traffic congestion, travel modeling has gained importance in the development of transportion mode detection (TMD) strategies over the past decade. Nowadays, recent smartphones, equipped with integrated inertial measurement units (IMUs) and embedded algorithms, can play a crucial role in such [...] Read more.
Due to increasing traffic congestion, travel modeling has gained importance in the development of transportion mode detection (TMD) strategies over the past decade. Nowadays, recent smartphones, equipped with integrated inertial measurement units (IMUs) and embedded algorithms, can play a crucial role in such development. In particular, obtaining much more information on the transportation modes used by users through smartphones is very challenging due to the variety of the data (accelerometers, magnetometers, gyroscopes, proximity sensors, etc.), the standardization issue of datasets and the pertinence of learning methods for that purpose. Reviewing the latest progress on TMD systems is important to inform readers about recent datasets used in detection, best practices for classification issues and the remaining challenges that still impact the detection performances. Existing TMD review papers until now offer overviews of applications and algorithms without tackling the specific issues faced with real-world data collection and classification. Compared to these works, the proposed review provides some novelties such as an in-depth analysis of the current state-of-the-art techniques in TMD systems, relying on recent references and focusing particularly on the major existing problems, and an evaluation of existing methodologies for detecting travel modes using smartphone IMUs (including dataset structures, sensor data types, feature extraction, etc.). This review paper can help researchers to focus their efforts on the main problems and challenges identified. Full article
(This article belongs to the Special Issue Recent Developments and Challenges in Artificial Intelligence and Deep Learning in Advanced Sensing Systems)
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