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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 18844

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Prof. Dr. Jin-Woo Jung

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Department of Computer Science and Engineering, Dongguk University, Seoul 04620, Korea
Interests: informatics; computer science; intelligent systems; fuzzy logics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increased need for sensors and data has increased the interest in AI and big data. Sensors and data are the most basic fundamentals needed to design an intelligent system. Futuristic intelligent systems in computer science that require intelligent information processing techniques and big data are based on various types of sensors, including smart sensors, to get meaningful information from various environments. IoT, as well as robotic systems, security, and healthcare applications, are some of the most representative applications for AI and big data based on sensors and data.

This Special Issue aims to publish original technical papers and review papers on recent technologies that focus on sensors and various applications in computer science and intelligent systems.

Potential topics include, but are not limited to, the following:

Sensors and IoT applications in computer science and intelligent systems
Sensors and robotic applications in computer science and intelligent systems
Sensors and security applications in computer science and intelligent systems
Sensors and healthcare applications in computer science and intelligent systems
Sensors and AI applications in computer science and intelligent systems
Sensors and big data applications in computer science and intelligent systems

Prof. Dr. Jin-Woo Jung
Guest Editor

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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 (7 papers)

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19 pages, 4854 KiB

Open AccessArticle

Application of the Model of Spots for Inverse Problems

by Nikolai A. Simonov

Sensors 2023, 23(3), 1247; https://doi.org/10.3390/s23031247 - 21 Jan 2023

Cited by 1 | Viewed by 1494

Abstract

This article proposes the application of a new mathematical model of spots for solving inverse problems using a learning method, which is similar to using deep learning. In general, the spots represent vague figures in abstract “information spaces” or crisp figures with a lack of information about their shapes. However, crisp figures are regarded as a special and limiting case of spots. A basic mathematical apparatus, based on L4 numbers, has been developed for the representation and processing of qualitative information of elementary spatial relations between spots. Moreover, we defined L4 vectors, L4 matrices, and mathematical operations on them. The developed apparatus can be used in Artificial Intelligence, in particular, for knowledge representation and for modeling qualitative reasoning and learning. Another application area is the solution of inverse problems by learning. For example, this can be applied to image reconstruction using ultrasound, X-ray, magnetic resonance, or radar scan data. The introduced apparatus was verified by solving problems of reconstruction of images, utilizing only qualitative data of its elementary relations with some scanning figures. This article also demonstrates the application of a spot-based inverse Radon algorithm for binary image reconstruction. In both cases, the spot-based algorithms have demonstrated an effective denoising property. Full article

(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)

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17 pages, 4115 KiB

Open AccessArticle

BattleSound: A Game Sound Benchmark for the Sound-Specific Feedback Generation in a Battle Game

by Sungho Shin, Seongju Lee, Changhyun Jun and Kyoobin Lee

Sensors 2023, 23(2), 770; https://doi.org/10.3390/s23020770 - 10 Jan 2023

Viewed by 1763

Abstract

A haptic sensor coupled to a gamepad or headset is frequently used to enhance the sense of immersion for game players. However, providing haptic feedback for appropriate sound effects involves specialized audio engineering techniques to identify target sounds that vary according to the game. We propose a deep learning-based method for sound event detection (SED) to determine the optimal timing of haptic feedback in extremely noisy environments. To accomplish this, we introduce the BattleSound dataset, which contains a large volume of game sound recordings of game effects and other distracting sounds, including voice chats from a PlayerUnknown’s Battlegrounds (PUBG) game. Given the highly noisy and distracting nature of war-game environments, we set the annotation interval to 0.5 s, which is significantly shorter than the existing benchmarks for SED, to increase the likelihood that the annotated label contains sound from a single source. As a baseline, we adopt mobile-sized deep learning models to perform two tasks: weapon sound event detection (WSED) and voice chat activity detection (VCAD). The accuracy of the models trained on BattleSound was greater than 90% for both tasks; thus, BattleSound enables real-time game sound recognition in noisy environments via deep learning. In addition, we demonstrated that performance degraded significantly when the annotation interval was greater than 0.5 s, indicating that the BattleSound with short annotation intervals is advantageous for SED applications that demand real-time inferences. Full article

(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)

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18 pages, 72465 KiB

Open AccessArticle

Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane

by Tom Kusznir and Jaroslaw Smoczek

Sensors 2022, 22(1), 339; https://doi.org/10.3390/s22010339 - 03 Jan 2022

Cited by 5 | Viewed by 1945

Abstract

This paper proposes a multi-gene genetic programming (MGGP) approach to identifying the dynamic prediction model for an overhead crane. The proposed method does not rely on expert knowledge of the system and therefore does not require a compromise between accuracy and complex, time-consuming modeling of nonlinear dynamics. MGGP is a multi-objective optimization problem, and both the mean square error (MSE) over the entire prediction horizon as well as the function complexity are minimized. In order to minimize the MSE an initial estimate of the gene weights is obtained by using the least squares approach, after which the Levenberg–Marquardt algorithm is used to find the local optimum for a k-step ahead predictor. The method was tested on both a simulation model obtained from the Euler–Lagrange equation with friction and the experimental stand. The simulation and the experimental stand were trained with varying control inputs, rope lengths and payload masses. The resulting predictor model was then validated on a testing set, and the results show the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)

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13 pages, 2238 KiB

Open AccessArticle

Duty-Cycle-Based Pre-Emption Protocol for Emergency Networks

by Gayoung Kim and Minjoong Rim

Sensors 2022, 22(1), 30; https://doi.org/10.3390/s22010030 - 22 Dec 2021

Cited by 1 | Viewed by 1169

Abstract

This paper proposes a new duty-cycle-based protocol for transmitting emergent data with high priority and low latency in a sensor network environment. To reduce power consumption, the duty cycle protocol is divided into a listen section and a sleep section, and data can only be received when the receiving node is in the listen section. In this paper, high-priority transmission preempts low-priority transmission by distinguishing between high-priority preamble and low-priority preamble. However, even when a high priority transmission preempts a low priority transmission such that the high priority transmission is received first, if the sleep period is very long, the delay may be large. To solve this problem, the high priority short preamble and high priority data reduce receiver sensitivity and increase coverage through repeated transmission. If there are several receiving nodes within a wide coverage, the receiving node that wakes up first can receive the transmission, thus reducing the delay. The delay can also be further reduced by alternately reducing the sleep cycle of one node among the receiving nodes that can receive it. This paper shows that emergent data can be transmitted effectively and reliably by reducing the delay of high-priority data to a minimum through the use of preemption, coverage extension, and an asymmetric sleep cycle. Full article

(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)

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40 pages, 12496 KiB

Open AccessArticle

A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

by Tae-Won Kang, Jin-Gu Kang and Jin-Woo Jung

Sensors 2021, 21(21), 7425; https://doi.org/10.3390/s21217425 - 08 Nov 2021

Cited by 9 | Viewed by 2896

Abstract

This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm’s performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples. Full article

(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)

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17 pages, 2263 KiB

Open AccessPerspective

Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics

by Michael P. McRae, Kritika S. Rajsri, Timothy M. Alcorn and John T. McDevitt

Sensors 2022, 22(17), 6355; https://doi.org/10.3390/s22176355 - 24 Aug 2022

Cited by 6 | Viewed by 4394

Abstract

We are beginning a new era of Smart Diagnostics—integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care. Full article

(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)

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13 pages, 3232 KiB

Open AccessPerspective

Cross Encoder-Decoder Transformer with Global-Local Visual Extractor for Medical Image Captioning

by Hojun Lee, Hyunjun Cho, Jieun Park, Jinyeong Chae and Jihie Kim

Sensors 2022, 22(4), 1429; https://doi.org/10.3390/s22041429 - 13 Feb 2022

Cited by 9 | Viewed by 3760

Abstract

Transformer-based approaches have shown good results in image captioning tasks. However, current approaches have a limitation in generating text from global features of an entire image. Therefore, we propose novel methods for generating better image captioning as follows: (1) The Global-Local Visual Extractor (GLVE) to capture both global features and local features. (2) The Cross Encoder-Decoder Transformer (CEDT) for injecting multiple-level encoder features into the decoding process. GLVE extracts not only global visual features that can be obtained from an entire image, such as size of organ or bone structure, but also local visual features that can be generated from a local region, such as lesion area. Given an image, CEDT can create a detailed description of the overall features by injecting both low-level and high-level encoder outputs into the decoder. Each method contributes to performance improvement and generates a description such as organ size and bone structure. The proposed model was evaluated on the IU X-ray dataset and achieved better performance than the transformer-based baseline results, by 5.6% in BLEU score, by 0.56% in METEOR, and by 1.98% in ROUGE-L. Full article

(This article belongs to the Special Issue Sensors and Applications in Computer Science and Intelligent Systems)

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