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Advanced Sensing for Intelligent Transport Systems and Smart Society

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 25470

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Special Issue Editors

Prof. Dr. Kai-Lung Hua

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Guest Editor

Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
Interests: multimedia; big data; deep learning; computer vision; pattern recognition; data science; machine learning; mobile multimedia applications
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chao-Lung Yang

E-Mail Website
Guest Editor

Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106335, Taiwan
Interests: data mining; big data analytics; machine learning; Industry 4.0 process improvemen; AI human action recognition; social network analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Yi-Ling Chen

E-Mail Website
Guest Editor

Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
Interests: data mining; machine learning; pervasive computing; social network analysis; smart society

Dr. Shih-Shinh Huang

E-Mail Website
Guest Editor

Department of Computer and Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811532, Taiwan
Interests: intelligent transport system; computer vision; pattern recognition; intelligent vehicles
Special Issues, Collections and Topics in MDPI journals

Dr. Yung-Yao Chen

E-Mail Website
Guest Editor

Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
Interests: vision-based automation; pattern recognition; imaging systems; smart vehicles; smart city
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current era of technologically driven society, the integration of advanced sensing techniques has been leveraged in the areas of intelligent transport systems (ITSs) and smart society. For the former case, an ITS requires various sensors to construct an intelligent transportation infrastructure. For example, a smart traffic signal control system can control traffic flow by planning the signal arrangement at individual road intersections or pedestrian crossings. To automatically determine the optimal signal arrangement, the central control system first requires complete information about vehicle and pedestrian counters, which rely on the cooperation of different sensors and artificial intelligence (AI) techniques. However, accurately predicting the world under different conditions, such as rain, dirt, and darkness, is challenging. To address this problem, advanced sensing technologies and machine learning/deep learning algorithms can be combined for accurate prediction. For the latter case, smart societies are human-centered, with various social challenges balanced by integrating several technological innovations such as smart sensing, artificial intelligence, Internet of Things (IoT), and so on. ITSs play an essential role in smart societies. For example, ITSs can augment public transportation (especially resolving the issue of lack of public transportation, which typically occurs in underpopulated rural areas) by introducing self-driving taxis and automatic guided vehicles. Incorporating advanced sensing technologies into intelligent transport systems and smart society can make people’s lives more conformable and sustainable.

This Special Issue investigates novel methodologies and applications related to intelligent transport systems and smart society. Both reviews and original research articles are welcome. The topics of interest for this Special Issue include (but are not limited to):

Intelligent sensing systems;
IoT-oriented intelligent multi-sensing in intelligent transport systems;
Intelligent sensing applications in smart societies/smart cities/smart factories;
AI, data mining, and machine learning in intelligent sensing;
Security and privacy enhancement for future IoT-based ITSs and smart societies;
Advanced image processing, such as high dynamic range imaging and superpixel processing;
Methodologies for analyzing sensor data;
New application scenarios for intelligent sensing;
Smart vehicle-routing algorithms in intelligent transport systems;
Intelligent logistics networks in intelligent transport systems;
Human–robot interactions in intelligent transport systems;
Spatio-temporal data analysis in intelligent transport systems;
Sensor applications for smart city development.

Prof. Dr. Kai-Lung Hua
Dr. Chao-Lung Yang
Dr. Yi-Ling Chen
Dr. Shih-Shinh Huang
Dr. Yung-Yao Chen
Guest Editors

Manuscript Submission Information

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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.

Keywords

intelligent transport system
smart society
intelligent sensing systems
multi-sensory data
multi-modal data fusion
smart city
advanced signal and image processing
IoT-oriented sensing
artificial intelligence applications
sensor data analysis

Published Papers (7 papers)

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Research

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22 pages, 8960 KiB

Open AccessArticle

A New Photographic Reproduction Method Based on Feature Fusion and Virtual Combined Histogram Equalization

by Yu-Hsiu Lin, Kai-Lung Hua, Yung-Yao Chen, I-Ying Chen and Yun-Chen Tsai

Sensors 2021, 21(18), 6038; https://doi.org/10.3390/s21186038 - 09 Sep 2021

Viewed by 1513

Abstract

A desirable photographic reproduction method should have the ability to compress high-dynamic-range images to low-dynamic-range displays that faithfully preserve all visual information. However, during the compression process, most reproduction methods face challenges in striking a balance between maintaining global contrast and retaining majority of local details in a real-world scene. To address this problem, this study proposes a new photographic reproduction method that can smoothly take global and local features into account. First, a highlight/shadow region detection scheme is used to obtain prior information to generate a weight map. Second, a mutually hybrid histogram analysis is performed to extract global/local features in parallel. Third, we propose a feature fusion scheme to construct the virtual combined histogram, which is achieved by adaptively fusing global/local features through the use of Gaussian mixtures according to the weight map. Finally, the virtual combined histogram is used to formulate the pixel-wise mapping function. As both global and local features are simultaneously considered, the output image has a natural and visually pleasing appearance. The experimental results demonstrated the effectiveness of the proposed method and the superiority over other seven state-of-the-art methods. Full article

(This article belongs to the Special Issue Advanced Sensing for Intelligent Transport Systems and Smart Society)

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20 pages, 10512 KiB

Open AccessArticle

Photographic Reproduction and Enhancement Using HVS-Based Modified Histogram Equalization

by Yung-Yao Chen, Kai-Lung Hua, Yun-Chen Tsai and Jun-Hua Wu

Sensors 2021, 21(12), 4136; https://doi.org/10.3390/s21124136 - 16 Jun 2021

Cited by 6 | Viewed by 2022

Abstract

Photographic reproduction and enhancement is challenging because it requires the preservation of all the visual information during the compression of the dynamic range of the input image. This paper presents a cascaded-architecture-type reproduction method that can simultaneously enhance local details and retain the naturalness of original global contrast. In the pre-processing stage, in addition to using a multiscale detail injection scheme to enhance the local details, the Stevens effect is considered for adapting different luminance levels and normally compressing the global feature. We propose a modified histogram equalization method in the reproduction stage, where individual histogram bin widths are first adjusted according to the property of overall image content. In addition, the human visual system (HVS) is considered so that a luminance-aware threshold can be used to control the maximum permissible width of each bin. Then, the global tone is modified by performing histogram equalization on the output modified histogram. Experimental results indicate that the proposed method can outperform the five state-of-the-art methods in terms of visual comparisons and several objective image quality evaluations. Full article

(This article belongs to the Special Issue Advanced Sensing for Intelligent Transport Systems and Smart Society)

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25 pages, 8725 KiB

Open AccessArticle

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium’s Niagara Framework for Residential Demand-Side Management

by Yung-Yao Chen, Ming-Hung Chen, Che-Ming Chang, Fu-Sheng Chang and Yu-Hsiu Lin

Sensors 2021, 21(8), 2883; https://doi.org/10.3390/s21082883 - 20 Apr 2021

Cited by 17 | Viewed by 4944

Abstract

Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework^® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable. Full article

(This article belongs to the Special Issue Advanced Sensing for Intelligent Transport Systems and Smart Society)

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18 pages, 3453 KiB

Open AccessArticle

Identification and Analysis of Weather-Sensitive Roads Based on Smartphone Sensor Data: A Case Study in Jakarta

by Chao-Lung Yang, Hendri Sutrisno, Arnold Samuel Chan, Hendrik Tampubolon and Budhi Sholeh Wibowo

Sensors 2021, 21(7), 2405; https://doi.org/10.3390/s21072405 - 31 Mar 2021

Cited by 3 | Viewed by 2023

Abstract

Weather change such as raining is a crucial factor to cause traffic congestion, especially in metropolises with the limited sewer system infrastructures. Identifying the roads which are sensitive to weather changes, defined as weather-sensitive roads (WSR), can facilitate the infrastructure development. In the literature, little research focused on studying weather factors of developing countries that might have deficient infrastructures. In this research, to fill the gap, the real-world data associating with Jakarta, Indonesia, was studied to identify WSR based on smartphone sensor data, real-time weather information, and road characteristics datasets. A spatial-temporal congestion speed matrix (STC) was proposed to illustrate traffic speed changes over time. Under the proposed STC, a sequential clustering and classification framework was applied to identify the WSR in terms of traffic speed. In this work, the causes of WSR were evaluated based on the variables’ importance of the classification method. The experimental results show that the proposed method can cluster the roads according to the pattern changes in the traffic speed caused by weather change. Based on the results, we found that the distances to shopping malls, mosques, schools, and the roads’ altitude, length, width, and the number of lanes are highly correlated to WSR in Jakarta. Full article

(This article belongs to the Special Issue Advanced Sensing for Intelligent Transport Systems and Smart Society)

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24 pages, 6614 KiB

Open AccessArticle

Understanding Malicious Accounts in Online Political Discussions: A Multilayer Network Approach

by Nhut-Lam Nguyen, Ming-Hung Wang, Yu-Chen Dai and Chyi-Ren Dow

Sensors 2021, 21(6), 2183; https://doi.org/10.3390/s21062183 - 20 Mar 2021

Cited by 4 | Viewed by 2630

Abstract

Online social media platforms play an important role in political communication where users can freely express and exchange their political opinion. Political entities have leveraged social media platforms as essential channels to disseminate information, interact with voters, and even influence public opinion. For this purpose, some organizations may create one or more accounts to join online political discussions. Using these accounts, they could promote candidates and attack competitors. To avoid such misleading speeches and improve the transparency of the online society, spotting such malicious accounts and understanding their behaviors are crucial issues. In this paper, we aim to use network-based analysis to sense influential human-operated malicious accounts who attempt to manipulate public opinion on political discussion forums. To this end, we collected the election-related articles and malicious accounts from the prominent Taiwan discussion forum spanning from 25 May 2018 to 11 January 2020 (the election day). We modeled the discussion network as a multilayer network and used various centrality measures to sense influential malicious accounts not only in a single-layer but also across different layers of the network. Moreover, community analysis was performed to discover prominent communities and their characteristics for each layer of the network. The results demonstrate that our proposed method can successfully identify several influential malicious accounts and prominent communities with apparent behavior differences from others. Full article

(This article belongs to the Special Issue Advanced Sensing for Intelligent Transport Systems and Smart Society)

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18 pages, 767 KiB

Open AccessArticle

Adaptive Traffic Signal Control: Game-Theoretic Decentralized vs. Centralized Perimeter Control

by Maha Elouni, Hossam M. Abdelghaffar and Hesham A. Rakha

Sensors 2021, 21(1), 274; https://doi.org/10.3390/s21010274 - 03 Jan 2021

Cited by 7 | Viewed by 2840

Abstract

This paper compares the operation of a decentralized Nash bargaining traffic signal controller (DNB) to the operation of state-of-the-art adaptive and gating traffic signal control. Perimeter control (gating), based on the network fundamental diagram (NFD), was applied on the borders of a protected urban network (PN) to prevent and/or disperse traffic congestion. The operation of gating control and local adaptive controllers was compared to the operation of the developed DNB traffic signal controller. The controllers were implemented and their performance assessed on a grid network in the INTEGRATION microscopic simulation software. The results show that the DNB controller, although not designed to solve perimeter control problems, successfully prevents congestion from building inside the PN and improves the performance of the entire network. Specifically, the DNB controller outperforms both gating and non-gating controllers, with reductions in the average travel time ranging between

21 %

and

41 %

, total delay ranging between

40 %

and

55 %

, and emission levels/fuel consumption ranging between

12 %

and

20 %

. The results demonstrate statistically significant benefits of using the developed DNB controller over other state-of-the-art centralized and decentralized gating/adaptive traffic signal controllers. Full article

(This article belongs to the Special Issue Advanced Sensing for Intelligent Transport Systems and Smart Society)

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Review

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14 pages, 611 KiB

Open AccessReview

PC5-Based Cellular-V2X Evolution and Deployment

by Lili Miao, John Jethro Virtusio and Kai-Lung Hua

Sensors 2021, 21(3), 843; https://doi.org/10.3390/s21030843 - 27 Jan 2021

Cited by 34 | Viewed by 8357

Abstract

C-V2X (Cellular Vehicle-to-Everything) is a state-of-the-art wireless technology used in autonomous driving and intelligent transportation systems (ITS). This technology has extended the coverage and blind-spot detection of autonomous driving vehicles. Economically, C-V2X is much more cost-effective than the traditional sensors that are commonly used by autonomous driving vehicles. This cost-benefit makes it more practical in a large scale deployment. PC5-based C-V2X uses an RF (Radio Frequency) sidelink direct communication for low latency mission-critical vehicle sensor connectivity. Over the C-V2X radio communications, the autonomous driving vehicle’s sensor ability can now be largely enhanced to the distances as far as the network covers. In 2020, 5G is commercialized worldwide, and Taiwan is at the forefront. Operators and governments are keen to see its implications in people’s daily life brought by its low latency, high reliability, and high throughput. Autonomous driving class L3 (Conditional Automation) or L4 (Highly Automation) are good examples of 5G’s advanced applications. In these applications, the mobile networks with URLLC (Ultra-Reliable Low-Latency Communication) are perfectly demonstrated. Therefore, C-V2X evolution and 5G NR (New Radio) deployment coincide and form a new ecosystem. This ecosystem will change how people will drive and how transportation will be managed in the future. In this paper, the following topics are covered. Firstly, the benefits of C-V2X communication technology. Secondly, the standards of C-V2X and C-V2X applications for automotive road safety system which includes V2P/V2I/V2V/V2N, and artificial intelligence in VRU (Vulnerable Road User) detection, object recognition and movement prediction for collision warning and prevention. Thirdly, PC5-based C-V2X deployment status in global, especially in Taiwan. Lastly, current challenges and conclusions of C-V2X development. Full article

(This article belongs to the Special Issue Advanced Sensing for Intelligent Transport Systems and Smart Society)

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