scholarly journals Review on V2X, I2X, and P2X Communications and Their Applications: A Comprehensive Analysis over Time

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2756 ◽  
Author(s):  
José Manuel Lozano Domínguez ◽  
Tomás Jesús Mateo Sanguino
Keyword(s):  
Traffic Accidents ◽  
State Of The Art ◽  
Smart Cities ◽  
Comprehensive Analysis ◽  
Review Article ◽  
Use Case ◽  
The Road ◽  
Different Dimensions ◽  
Future Technologies ◽  
Comprehensive Study

Smart cities are ecosystems where novel ideas and emerging technologies meet to improve economy, environment, governance, living, and mobility. One of the pillars of smart cities is transport, with the improvement of mobility and the reduction of traffic accidents being some of the current key challenges. With this purpose, this manuscript reviews the state-of-the-art of communications and applications in which different actors of the road are involved. Thus, the objectives of this survey are intended to determine who, when, and about what is being researched around smart cities. Particularly, the goal is to situate the focus of scientific and industrial progress on V2X, I2X, and P2X communication to establish a taxonomy that reduces ambiguous acronyms around the communication between vehicles, infrastructure, and pedestrians, as well as to determine what the trends and future technologies are that will lead to more powerful applications. To this end, this literature review article presents a comprehensive study including a representative collection of the 100 most cited papers and patents published in the literature together with a statistical bibliometric analysis of 14,364 keywords over 3422 contributions between 1997 and 2018. As a result, this work provides a technological profile considering different dimensions along the paper, such as the type of communication, use case, country, organization, terminology, and year.

Synthetic Routes to 2-Iminothiazolidines: State-of-the-Art 2006-2020

2020 ◽  
Vol 24 (24) ◽  
pp. 2823-2844
Author(s):  
Aditya Bhattacharyya
Keyword(s):  
State Of The Art ◽  
Biological Significance ◽  
Review Article ◽  
Synthetic Methods ◽  
Pharmacological Properties ◽  
Cyclic Molecules ◽  
Synthetic Routes

: Multiheteroatom-containing small-sized cyclic molecules such as 2- iminothiazolidines are often found to possess beneficial pharmacological properties. In this review article, the biological significance of 2-iminothiazolidines is discussed and the literature reports published in the last 15 years spanning from 2006 to 2020 describing various preparative routes to access 2-iminothiazolidine derivatives have been categorically and chronologically described. The notable synthetic methods discussed here involve ringexpansion transformations of nonactivated and activated aziridines, thiiranes, epoxides, and other miscellaneous reactions.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

scholarly journals A Comprehensive Study of the IoT Cybersecurity in Smart Cities

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 228922-228941
Author(s):  
Roberto Omar Andrade ◽  
Sang Guun Yoo ◽  
Luis Tello-Oquendo ◽  
Ivan Ortiz-Garces
Keyword(s):  
Smart Cities ◽  
Comprehensive Study

scholarly journals Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

2021 ◽  
Author(s):  
Yogesh K. Dwivedi ◽  
Elvira Ismagilova ◽  
Nripendra P. Rana ◽  
Ramakrishnan Raman
Keyword(s):  
Social Media ◽  
Media Use ◽  
State Of The Art ◽  
Relevant Literature ◽  
Comprehensive Analysis ◽  
Social Media Use ◽  
Weight Analysis ◽  
Social Media Tools ◽  
Use Of Social Media ◽  
Business To Business

AbstractSocial media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

Analysis of Vehicle–Pedestrian Interactive Behaviors near Unsignalized Crosswalk

2021 ◽  
pp. 036119812199906
Author(s):  
Byeongjoon Noh ◽  
Dongho Ka ◽  
David Lee ◽  
Hwasoo Yeo
Keyword(s):  
South Korea ◽  
Traffic Accidents ◽  
Road Traffic ◽  
The Road ◽  
Video Footage ◽  
Premature Deaths ◽  
Vehicle Velocity ◽  
Behavioral Features ◽  
Velocity Changes ◽  
On The Road

Road traffic accidents are a leading cause of premature deaths and globally pose a severe threat to human lives. In particular, pedestrians crossing the road present a major cause of vehicle–pedestrian accidents in South Korea, but we lack dense behavioral data to understand the risk they face. This paper proposes a new analytical system for potential pedestrian risk scenes based on video footage obtained by road security cameras already deployed at unsignalized crosswalks. The system can automatically extract the behavioral features of vehicles and pedestrians, affecting the likelihood of potentially dangerous situations after detecting them in individual objects. With these features, we can analyze the movement patterns of vehicles and pedestrians at individual sites, and understand where potential traffic risk scenes occur frequently. Experiments were conducted on four selected behavioral features: vehicle velocity, pedestrian position, vehicle–pedestrian distance, and vehicle–crosswalk distance. Then, to show how they can be useful for monitoring the traffic behaviors on the road, the features are visualized and interpreted to show how they may or may not contribute to potential pedestrian risks at these crosswalks: (i) by analyzing vehicle velocity changes near the crosswalk when there are no pedestrians present; and (ii) analyzing vehicle velocities by vehicle–pedestrian distances when pedestrians are on the crosswalk. The feasibility of the proposed system is validated by applying the system to multiple unsignalized crosswalks in Osan city, South Korea.

scholarly journals Deep Neural Networks for Road Sign Detection and Embedded Modeling Using Oblique Aerial Images

2021 ◽  
Vol 13 (5) ◽  
pp. 879
Author(s):  
Zhu Mao ◽  
Fan Zhang ◽  
Xianfeng Huang ◽  
Xiangyang Jia ◽  
Yiping Gong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Smart Cities ◽  
Geometric Constraints ◽  
Small Object ◽  
The Road ◽  
Road Signs ◽  
Road Sign ◽  
Synthesis Strategy ◽  
Urban Models ◽  
Sign Detection

Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurred texture and high incline angles, road signs cannot be fully reconstructed in oblique photogrammetry, even with state-of-the-art algorithms. The poor reconstruction of road signs commonly leads to less informative guidance and unsatisfactory visual appearance. In this paper, we present a pipeline for embedding road sign models based on deep convolutional neural networks (CNNs). First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy. Second, under the geometric constraints placed by the bounding boxes, we use the scale-invariant feature transform (SIFT) to extract the corresponding points on the road signs. Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal. Least-squares fitting is then applied to the refined point cloud to fit a plane for orientation prediction. Finally, we replace the road signs with computer-aided design models in the 3D urban scene with the predicted location and orientation. The experimental results show that the proposed method achieves a high mAP in road sign detection and produces visually plausible embedded results, which demonstrates its effectiveness for road sign modeling in oblique photogrammetry-based 3D scene reconstruction.

scholarly journals Design, manufacture and testing of a prototype obstacle-detection device at blind corners: an automated highway system technology

2020 ◽  
Author(s):  
Charles Atombo ◽  
Emmanuel Gbey ◽  
Apevienyeku Kwami Holali
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Effective Area ◽  
Obstacle Detection ◽  
The Road ◽  
Vibration Sensors ◽  
Road Signs ◽  
Sensor Module ◽  
Vehicle Path ◽  
Automated Highway

Abstract Traffic accidents on highways are attributed mostly to the "invisibility" of oncoming traffic and road signs. "Speeding" also causes drivers to reduce the effective radius of the vehicle path in the curve, thus trespassing into the lane of the oncoming traffic. The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable, easy to implement and easy to maintain to reduce the risk of road accidents at blind corners. An ultrasonic sensor module with a maximum measuring angle of 15° was used to ensure that a significant portion of the lane was detected at the blind corner. The sensor covered a minimum effective area of 0.5 m2 of the road for obstacle detection. Yellow light was employed to signify caution while negotiating the blind corner. Two photoresistors (PRs) were used as sensors because of the limited number of pins on the microcontroller (Arduino Uno). However, the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users. In real-world applications, the use of piezoelectric accelerometers (vibration sensors) instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

A Kalman filtering fuzzy logic algorithm for recognition of lane departure

2021 ◽  
pp. 1-8
Author(s):  
Kai Ren
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Kalman Filtering ◽  
Traffic Accidents ◽  
Region Of Interest ◽  
Fuzzy Logic Algorithm ◽  
The Road ◽  
Departure Decision ◽  
Lane Departure ◽  
Fusion Analysis

In all kinds of traffic accidents, the unconscious departure of the vehicle from the lane is one of the most important reasons leading to the occurrence of these accidents. In view of the specific problem of lane departure, a lane departure decision-making method is established without calibration relying on the Kalman filtering fuzzy logic algorithm, according to the characteristics of expressway lanes, based on the machine vision and hearing fusion analysis of lane departure, integrating the extraction of the linear lane line model and the region of interest (ROI) in this paper to judge the degree of vehicle departure from the lane by integrating the slope values of the 2 lane lines in the road image. The results show that the system has good lane recognition capabilities and accurate departure decision-making capabilities, and meet the lane departure warning requirements in the expressway environment.

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