Performance analysis of smart digital signage system based on software-defined IoT and invisible image sensor communication

Hossain, Mohammad Arif; Islam, Amirul; Le, Nam-Tuan; Lee, Yong Tae; Lee, Hyun Woo; Jang, Yeong Min

doi:10.1177/1550147716657926

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…Over the past few years, there have been many advances in camera-based applications and services, such as multimedia, security tracking, localization, broadcasting, 9 and ITS. 10,14 Image sensors can build images pixels projected from various light sources within their field of view (FOV).…”

Section: Architecture Optical Vehicular Communicationmentioning

confidence: 99%

“…Applications of this technology include three-dimensional (3D) localization, kinetic camera-based ranging or distance measurements, various intelligent applications (e.g. virtual or augmented reality), digital or display-signage applications 9 and, more importantly, autonomous vehicular applications. 10,11 OWC operates in three spectrum bands: infrared, visible, and ultra-violet.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional neural networkscheme–based optical camera communication system for intelligent Internet of vehicles

Islam

Hossan

Jang

2018

International Journal of Distributed Sensor Networks

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The evolution of the Internet of vehicles and growing use of mobile devices has created a demand for new wireless communication technologies. Optical camera communication, which uses light-emitting diodes as transmitters and cameras as receivers, has emerged as a promising alternative. Since light-emitting diodes and cameras are already exploring in traffic lights, vehicles, and public lightings, optical camera communication has the potential to intelligently handle transport systems. Although other technologies have been proposed or developed in both academia and industry, they are not yet mature enough to uphold the huge requirements of the Internet of vehicles. This study introduces a new intelligent Internet of vehicles system based on optical camera communication combined with convolutional neural networks. Optical camera communication is a promising candidate for maintaining interference-free and more robust communication, for supporting the Internet of vehicles. Convolutional neural network is introduced for precise detection and recognition of light-emitting diode patterns at long distances and in bad weather conditions. We propose an algorithm to detect the interested light-emitting diode signals (i.e. regions-of-interest), measure the distance using a stereo-vision technique to find out the desired targets, and simulate our proposed scheme using a MATLAB Toolbox. Thus, our system will provide great advantages for next-generation transportation systems.

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Section: Architecture Optical Vehicular Communicationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Convolutional neural networkscheme–based optical camera communication system for intelligent Internet of vehicles

Islam

Hossan

Jang

2018

International Journal of Distributed Sensor Networks

Self Cite

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“…Digital signage refers to a multimedia professional audiovisual system that displays commercial, financial and entertainment information on digital signage terminal display equipment in public places where crowds assemble. As a new medium, digital signage has become an important channel for offline advertisements in modern cities [1], [2]. Compared to traditional television and newspaper advertisements, digital signage advertisements are more flexible and can be The associate editor coordinating the review of this manuscript and approving it for publication was Aysegul Ucar .…”

Section: Introductionmentioning

confidence: 99%

A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network

et al. 2020

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Digital signage is an important outdoor advertising medium in cities. However, advertising on digital signage often lacks pertinence. Thus, it is important to introduce an accurate digital signage audience classification method to facilitate targeted advertising. In this study, a multi-label classification model based on a backpropagation (BP) neural network and the Huff model, referred to as the Huff-BP model, is proposed to investigate digital signage audience classification. A case study is performed on outdoor digital signage within the 6th Ring Road in Beijing, China, and economic census, population census, average housing price, social media check-in and the centrality of traffic networks as research data. The data are divided into 100 × 100-1,000 × 1,000 m normal grids. Multi-label classification modelling factors for various grid scales are constructed. The BP neural network classification algorithm is improved to solve the multilabel classification problem. In addition, an improved Huff model is used to calculate the digital signage influence values between each grid cell and integrated into the improved BP neural network to classify modelling factors at various scales. Finally, four metrics are used to examine the effectiveness of the proposed model. The results show that the Huff-BP-based multi-label classification model achieves relatively good classification results, and the digital signage audiences are mainly concentrated within the 4th Ring Road and near the 5th Ring Road.

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“…Digital signage is a multimedia audio-visual system that releases business, financial, and entertainment information through terminal display devices in public places [1,2]. Compared with traditional TV and newspaper advertisements, digital signage can be used for personalized and customized advertising based on different audiences [3,4].…”

Section: Introductionmentioning

confidence: 99%

Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach

Wang

Zhang

et al. 2020

IJGI

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With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the proposed method has a higher true positive rate and a lower false positive rate than the other three site selection models, which indicates that this method has higher accuracy for site selection. The site results show that areas suitable for digital signage are mainly distributed in Sanlitun, Wangfujing, Financial Street, Beijing West Railway Station, and along the main road network within the Sixth Ring Road. The research provides a reference for integrating geographical features and content data into the site-selection algorithm. It can effectively improve the accuracy and scientific nature of digital signage layouts and the efficiency of digital signage to a certain extent.

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Performance analysis of smart digital signage system based on software-defined IoT and invisible image sensor communication

Cited by 8 publications

References 23 publications

Convolutional neural networkscheme–based optical camera communication system for intelligent Internet of vehicles

Convolutional neural networkscheme–based optical camera communication system for intelligent Internet of vehicles

A Digital Signage Audience Classification Model Based on the Huff Model and Backpropagation Neural Network

Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach

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