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Selected Papers from INNOV 2018

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

Deadline for manuscript submissions: closed (15 March 2019) | Viewed by 50195

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Prof. Dr. Yu-Chen Hu

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Department of Computer Science and Information Management, Providence University, Taichung 43301, Taiwan
Interests: computer vision; digital forensics; information hiding; image and signal processing; data compression; information security; computer network; deep learning; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Dr. Yung-Yao Chen

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Guest Editor

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

Dr. Yu-Hsiu Lin

E-Mail Website
Guest Editor

Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan
Interests: Internet of Things; artificial intelligence/computational intelligence; cloud and edge computing; smart grid technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Seventh International Conference on Communications, Computation, Networks and Technologies (INNOV 2018) will be held 14–18 October, 2018, in Nice, France (https://www.iaria.org/conferences2018/INNOV18.html). INNOV 2018 is intended to serve as a premier and interdisciplinary forum for academic and industrial researchers, scientists, engineers and practitioners throughout the world to present their latest research effort in trend technologies.

Recent breakthrough technologies are trending up for today’s technologically-driven society, from the fundamental constituent of a city (i.e., smart homes/buildings), intelligent factories, to smart cities with intelligent transportation systems. Researchers working on Machine Learning (ML), Computer Vision (CV), and Internet of Things (IoT) have collaborated together to turn the concept of “smart city” from hype to reality. For instance, based on CV and digital image processing techniques with the IoT paradigm, an automatic smart surveillance system consisting of (1) a digital IP camera placed in the frame of a door in a realistic domestic environment, acted as a vision sensor and integrated with an Internet-equipped microcontroller, (2) a cloud-centered IoT server configured with data science analytics and push notification service, and (3) a mobile device alerted with the push notification service is able to recognize the picture of the person who is intending to intrude in the environment. Technical combination of advanced ML, CV, and IoT brings new insights for home surveillance as an example in this modern society. Smart cities promote our daily living by providing complete healthcare, safety, transportation and other smart service solutions.

This Special Issue is dedicated to new research efforts with consolidated and thoroughly evaluated application-oriented research results by authors of outstanding papers from INNOV2018 in the fields of Image and Video Processing with the IoT paradigm for smart home (smart building), smart manufacturing, and/or smart city technologies that are worthy of archival publication in Sensors. Authors recommended for this Special Issue will be invited to submit extended work of their original conference paper to this Special Issue with a peer-reviewed process for publication.

Topics of interest include, but are not limited to:

Image and Video Processing for smart homes/manufacturing/cities
IoT for smart homes/manufacturing/cities
Communication modeling, security, and signal processing for smart homes/manufacturing/cities
Smart sensing and sensor-web infrastructures for smart homes/manufacturing/cities
Security and privacy for IoT, cyber-physical systems, and smart city services

Prof. Dr. Yu-Chen
Assoc. Prof. Dr. Yung-Yao Chen
Assist. Prof. Dr. Yu-Hsiu Lin
Guest Editors

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Keywords

Computer Vision
Image and Video Processing
Internet of Things
Machine Learning
Smart Vision Sensors
Smart Homes
Smart Manufacturing
Smart Cities

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Published Papers (10 papers)

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Research

24 pages, 7916 KiB

Open AccessArticle

An Efficient and Geometric-Distortion-Free Binary Robust Local Feature

by Jing-Ming Guo, Li-Ying Chang and Jiann-Der Lee

Sensors 2019, 19(10), 2315; https://doi.org/10.3390/s19102315 - 20 May 2019

Cited by 3 | Viewed by 2935

Abstract

An efficient and geometric-distortion-free approach, namely the fast binary robust local feature (FBRLF), is proposed. The FBRLF searches the stable features from an image with the proposed multiscale adaptive and generic corner detection based on the accelerated segment test (MAGAST) to yield an optimum threshold value based on adaptive and generic corner detection based on the accelerated segment test (AGAST). To overcome the problem of image noise, the Gaussian template is applied, which is efficiently boosted by the adoption of an integral image. The feature matching is conducted by incorporating the voting mechanism and lookup table method to achieve a high accuracy with low computational complexity. The experimental results clearly demonstrate the superiority of the proposed method compared with the former schemes regarding local stable feature performance and processing efficiency. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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16 pages, 2571 KiB

Open AccessArticle

Smart Fault-Detection Machine for Ball-Bearing System with Chaotic Mapping Strategy

by Shih-Yu Li and Kai-Ren Gu

Sensors 2019, 19(9), 2178; https://doi.org/10.3390/s19092178 - 10 May 2019

Cited by 14 | Viewed by 4262

Abstract

In this paper, a set of smart fault-detection approach with chaotic mapping strategy is developed for an industrial ball-bearing system. There are four main statuses in this ball-bearing system: normal, inner race fault, outer race fault, and ball fault. However, it is hard to simply classify each of them through their vibration signals in time-series. By developing a nonlinear error dynamic system as well as a chaotic mapping strategy, the signals in the time series can be converted into the chaotic domain, which are revealed in 3D phase portraits. Further, through collocation of clustering methods, such as Euclidean distance (ED) and the kernel method of K-means (KM), the proposed 3D phase portraits of each different state can be efficiently identified through checking the autonomously adjusted ranges of feature values. The experiment results show that the proposed smart detection approach is effective and feasible, and the accuracy of detection in the testing stage is close to 100%. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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26 pages, 5505 KiB

Open AccessArticle

Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

by Yung-Yao Chen, Yu-Hsiu Lin, Chia-Ching Kung, Ming-Han Chung and I-Hsuan Yen

Sensors 2019, 19(9), 2047; https://doi.org/10.3390/s19092047 - 2 May 2019

Cited by 145 | Viewed by 12601

Abstract

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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17 pages, 4693 KiB

Open AccessArticle

Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy

by Yu-Hsiu Lin, Chih-Hsien Hsia, Bo-Yan Chen and Yung-Yao Chen

Sensors 2019, 19(9), 1974; https://doi.org/10.3390/s19091974 - 27 Apr 2019

Cited by 14 | Viewed by 4119

Abstract

This study investigates combining the property of human vision system and a 2-phase data hiding strategy to improve the visual quality of data-embedded compressed images. The visual Internet of Things (IoT) is indispensable in smart cities, where different sources of visual data are collected for more efficient management. With the transmission through the public network, security issue becomes critical. Moreover, for the sake of increasing transmission efficiency, image compression is widely used. In order to respond to both needs, we present a novel data hiding scheme for image compression with Absolute Moment Block Truncation Coding (AMBTC). Embedding secure data in digital images has broad security uses, e.g., image authentication, prevention of forgery attacks, and intellectual property protection. The proposed method embeds data into an AMBTC block by two phases. In the intra-block embedding phase, a hidden function is proposed, where the five AMBTC parameters are extracted and manipulated to embed the secret data. In the inter-block embedding phase, the relevance of high mean and low mean values between adjacent blocks are exploited to embed additional secret data in a reversible way. Between these two embedding phases, a halftoning scheme called direct binary search is integrated to efficiently improve the image quality without changing the fixed parameters. The modulo operator is used for data extraction. The advantages of this study contain two aspects. First, data hiding is an essential area of research for increasing the IoT security. Second, hiding in compressed images instead of original images can improve the network transmission efficiency. The experimental results demonstrate the effectiveness and superiority of the proposed method. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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19 pages, 655 KiB

Open AccessArticle

Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology

by Cong Peng and Prashant Goswami

Sensors 2019, 19(8), 1747; https://doi.org/10.3390/s19081747 - 12 Apr 2019

Cited by 39 | Viewed by 6362

Abstract

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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15 pages, 5720 KiB

Open AccessArticle

Single-Image Depth Inference Using Generative Adversarial Networks

by Daniel Stanley Tan, Chih-Yuan Yao, Conrado Ruiz, Jr. and Kai-Lung Hua

Sensors 2019, 19(7), 1708; https://doi.org/10.3390/s19071708 - 10 Apr 2019

Cited by 7 | Viewed by 4345

Abstract

Depth has been a valuable piece of information for perception tasks such as robot grasping, obstacle avoidance, and navigation, which are essential tasks for developing smart homes and smart cities. However, not all applications have the luxury of using depth sensors or multiple cameras to obtain depth information. In this paper, we tackle the problem of estimating the per-pixel depths from a single image. Inspired by the recent works on generative neural network models, we formulate the task of depth estimation as a generative task where we synthesize an image of the depth map from a single Red, Green, and Blue (RGB) input image. We propose a novel generative adversarial network that has an encoder-decoder type generator with residual transposed convolution blocks trained with an adversarial loss. Quantitative and qualitative experimental results demonstrate the effectiveness of our approach over several depth estimation works. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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13 pages, 3355 KiB

Open AccessArticle

Depth Map Upsampling via Multi-Modal Generative Adversarial Network

by Daniel Stanley Tan, Jun-Ming Lin, Yu-Chi Lai, Joel Ilao and Kai-Lung Hua

Sensors 2019, 19(7), 1587; https://doi.org/10.3390/s19071587 - 2 Apr 2019

Cited by 8 | Viewed by 3608

Abstract

Autonomous robots for smart homes and smart cities mostly require depth perception in order to interact with their environments. However, depth maps are usually captured in a lower resolution as compared to RGB color images due to the inherent limitations of the sensors. Naively increasing its resolution often leads to loss of sharpness and incorrect estimates, especially in the regions with depth discontinuities or depth boundaries. In this paper, we propose a novel Generative Adversarial Network (GAN)-based framework for depth map super-resolution that is able to preserve the smooth areas, as well as the sharp edges at the boundaries of the depth map. Our proposed model is trained on two different modalities, namely color images and depth maps. However, at test time, our model only requires the depth map in order to produce a higher resolution version. We evaluated our model both quantitatively and qualitatively, and our experiments show that our method performs better than existing state-of-the-art models. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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15 pages, 4224 KiB

Open AccessArticle

Combining Weighted Contour Templates with HOGs for Human Detection Using Biased Boosting

by Shih-Shinh Huang, Shih-Han Ku and Pei-Yung Hsiao

Sensors 2019, 19(6), 1458; https://doi.org/10.3390/s19061458 - 25 Mar 2019

Cited by 1 | Viewed by 3028

Abstract

This paper proposes a method to detect humans in the image that is an important issue for many applications, such as video surveillance in smart home and driving assistance systems. A kind of local feature called the histogram of oriented gradients (HOGs) has been widely used in describing the human appearance and its effectiveness has been proven in the literature. A learning framework called boosting is adopted to select a set of classifiers based on HOGs for human detection. However, in the case of a complex background or noise effect, the use of HOGs results in the problem of false detection. To alleviate this, the proposed method imposes a classifier based on weighted contour templates to the boosting framework. The way to combine the global contour templates with local HOGs is by adjusting the bias of a support vector machine (SVM) for the local classifier. The method proposed for feature combination is referred to as biased boosting. For covering the human appearance in various poses, an expectation maximization algorithm is used which is a kind of iterative algorithm is used to construct a set of representative weighted contour templates instead of manual annotation. The encoding of different weights to the contour points gives the templates more discriminative power in matching. The experiments provided exhibit the superiority of the proposed method in detection accuracy. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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17 pages, 47365 KiB

Open AccessArticle

Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features

by Shih-Wei Sun, Ting-Chen Mou, Chih-Chieh Fang, Pao-Chi Chang, Kai-Lung Hua and Huang-Chia Shih

Sensors 2019, 19(6), 1425; https://doi.org/10.3390/s19061425 - 22 Mar 2019

Cited by 9 | Viewed by 3927

Abstract

In this paper, a preliminary baseball player behavior classification system is proposed. By using multiple IoT sensors and cameras, the proposed method accurately recognizes many of baseball players’ behaviors by analyzing signals from heterogeneous sensors. The contribution of this paper is threefold: (i) signals from a depth camera and from multiple inertial sensors are obtained and segmented, (ii) the time-variant skeleton vector projection from the depth camera and the statistical features extracted from the inertial sensors are used as features, and (iii) a deep learning-based scheme is proposed for training behavior classifiers. The experimental results demonstrate that the proposed deep learning behavior system achieves an accuracy of greater than 95% compared to the proposed dataset. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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28 pages, 4507 KiB

Open AccessArticle

Reduction of Artefacts in JPEG-XR Compressed Images

by Kai-Lung Hua, Ho Thi Trang, Kathiravan Srinivasan, Yung-Yao Chen, Chun-Hao Chen, Vishal Sharma and Albert Y. Zomaya

Sensors 2019, 19(5), 1214; https://doi.org/10.3390/s19051214 - 9 Mar 2019

Cited by 5 | Viewed by 4279

Abstract

The JPEG-XR encoding process utilizes two types of transform operations: Photo Overlap Transform (POT) and Photo Core Transform (PCT). Using the Device Porting Kit (DPK) provided by Microsoft, we performed encoding and decoding processes on JPEG XR images. It was discovered that when the quantization parameter is >1-lossy compression conditions, the resulting image displays chequerboard block artefacts, border artefacts and corner artefacts. These artefacts are due to the nonlinearity of transforms used by JPEG-XR. Typically, it is not so visible; however, it can cause problems while copying and scanning applications, as it shows nonlinear transforms when the source and the target of the image have different configurations. Hence, it is important for document image processing pipelines to take such artefacts into account. Additionally, these artefacts are most problematic for high-quality settings and appear more visible at high compression ratios. In this paper, we analyse the cause of the above artefacts. It was found that the main problem lies in the step of POT and quantization. To solve this problem, the use of a “uniform matrix” is proposed. After POT (encoding) and before inverse POT (decoding), an extra step is added to multiply this uniform matrix. Results suggest that it is an easy and effective way to decrease chequerboard, border and corner artefacts, thereby improving the image quality of lossy encoding JPEG XR than the original DPK program with no increased calculation complexity or file size. Full article

(This article belongs to the Special Issue Selected Papers from INNOV 2018)

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