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The Applications of Context Awareness Computing and Image Understanding II

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 14174

Special Issue Editors


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Guest Editor
The Department of Computer Science, The University of Aizu, Tsuruga, ikki-machi, Aizu-Wakamatsu City, Fukushima 965-80, Japan
Interests: machine learning-based automatic morphing; induction of compact and high-performance awareness agents; brain modeling and awareness science

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Guest Editor
Faculty of Information Technology, Satya Wacana Christian University, Salatiga 50715, Indonesia
Interests: machine learning; application of artificial intelligence; image and vision computing; pattern recognition; augmented reality and virtual reality; recommendation systems

Special Issue Information

Dear Colleagues,

Context awareness is the ability of a system or system component to gather information about its environment at any given time and adapt behaviors accordingly. Context awareness computing uses software and hardware to collect and analyze data to guide responses automatically. Awareness computing is a state in which a subject perceives the moment when relevant information arrives. It is not only essential for the survival of any living species, but also a fundamental ability leading to higher level intelligence. Therefore, the objective of context awareness computing is to utilize computing technologies to build a system that is aware. More specifically, awareness computing aims to incorporate the latest sensing capabilities of diverse signals with intelligent computing systems to remain in constant observing and awareness states, such as power-aware, location-aware, and context-aware, under a unified computational framework. Image understanding algorithms often enhance system awareness. Robot vision systems aim to analyze objects from the low-level, iconic processes of early vision to the high-level, symbolic methods of recognition and interpretation. In these applications, image understanding provides an intelligent mechanism for computers to recognize, interpret, and analyze images, which is essential when it comes to improving service quality. Many deep learning algorithms are applied in image object recognition. If we can integrate image recognition and context awareness computing, many innovative applications will be achieved.

This Special Issue focuses on related applications based on awareness computing systems and innovative image understanding technologies. The objective is to bring leading scientists and researchers together and create an interdisciplinary platform of computational theories, methodologies, and techniques.

Prof. Dr. Rung-Ching Chen
Prof. Dr. Qiangfu Zhao
Prof. Dr. Hui Yu
Prof. Dr. Hendry Hendry
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • context awareness
  • location awareness
  • image understanding
  • deep learning
  • pattern recognition

Published Papers (7 papers)

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Research

18 pages, 4752 KiB  
Article
Deep Learning Mask Face Recognition with Annealing Mechanism
by Wen-Chang Cheng, Hung-Chou Hsiao and Li-Hua Li
Appl. Sci. 2023, 13(2), 732; https://doi.org/10.3390/app13020732 - 4 Jan 2023
Cited by 5 | Viewed by 1678
Abstract
Face recognition (FR) has matured with deep learning, but due to the COVID-19 epidemic, people need to wear masks outside to reduce the risk of infection, making FR a challenge. This study uses the FaceNet approach combined with transfer learning using three different [...] Read more.
Face recognition (FR) has matured with deep learning, but due to the COVID-19 epidemic, people need to wear masks outside to reduce the risk of infection, making FR a challenge. This study uses the FaceNet approach combined with transfer learning using three different sizes of validated CNN architectures: InceptionResNetV2, InceptionV3, and MobileNetV2. With the addition of the cosine annealing (CA) mechanism, the optimizer can automatically adjust the learning rate (LR) during the model training process to improve the efficiency of the model in finding the best solution in the global domain. The mask face recognition (MFR) method is accomplished without increasing the computational complexity using existing methods. Experimentally, the three models of different sizes using the CA mechanism have a better performance than the fixed LR, step and exponential methods. The accuracy of the three models of different sizes using the CA mechanism can reach a practical level at about 93%. Full article
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11 pages, 4102 KiB  
Article
An Energy Aware Grid-Based Clustering Power Efficient Data Aggregation Protocol for Wireless Sensor Networks
by Neng-Chung Wang, Young-Long Chen, Yung-Fa Huang, Ching-Mu Chen, Wei-Cheng Lin and Chao-Yang Lee
Appl. Sci. 2022, 12(19), 9877; https://doi.org/10.3390/app12199877 - 30 Sep 2022
Cited by 5 | Viewed by 1882
Abstract
A wireless sensor network (WSN) is made up of multiple sensors deployed in a specific sensing area to identify the occurrence of events and quickly transmit useful information back to the base station (BS). In WSNs, schemes to reduce energy consumption are an [...] Read more.
A wireless sensor network (WSN) is made up of multiple sensors deployed in a specific sensing area to identify the occurrence of events and quickly transmit useful information back to the base station (BS). In WSNs, schemes to reduce energy consumption are an important topic of research. A well-designed data transmission scheme can effectively extend the lifetime of a network. In this paper, we propose an energy aware grid-based clustering power efficient data aggregation protocol (GB-PEDAP) for WSNs. The proposed scheme has a two-layer architecture: the inner layer and the outer layer. The inner layer uses direct transmission to collect the data of the cluster (cell), and the outer layer uses a tree structure transmission to collect the data of the cluster head (cell head). In our simulations, the number of rounds executed by GB-PEDAP was approximately 1.2 rounds of TBEEP, 1.3 rounds of GSTEB, and 1.5 rounds of PEDAP. With the initial energy, 0.25 J, the execution rounds of the first node death for GB-PEDAP, TBEEP, GSTEB, and PEDAP were 751, 572, 486, and 339, respectively. The proposed GB-PEDAP can evenly disperse the energy consumption of sensors to avoid the rapid death of sensors, extending the lifetime of a WSN. Full article
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21 pages, 3417 KiB  
Article
Road Segmentation and Environment Labeling for Autonomous Vehicles
by Rung-Ching Chen, Vani Suthamathi Saravanarajan, Long-Sheng Chen and Hui Yu
Appl. Sci. 2022, 12(14), 7191; https://doi.org/10.3390/app12147191 - 17 Jul 2022
Cited by 2 | Viewed by 1515
Abstract
In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify various obstacles present in the environment. The labeling techniques that are currently available are based on pixel-wise segmentation and bounding boxes to detect each object on the road. However, [...] Read more.
In autonomous vehicles (AVs), LiDAR point cloud data are an important source to identify various obstacles present in the environment. The labeling techniques that are currently available are based on pixel-wise segmentation and bounding boxes to detect each object on the road. However, the Avs’ decision on motion control and trajectory path planning depends on the interaction among the objects on the road. The ability of the Avs to understand the moving and non-moving objects is the key to scene understanding. This paper presents a novel labeling method to combine moving and non-moving objects. This labeling technique is named relational labeling. Autoencoders are used to reduce the dimensionality of the data. A K-means model provides pseudo labels by clustering the data in the latent space. Each pseudo label is then converted into unary and binary relational labels. These relational labels are used in the supervised learning methods for labeling and segmenting the LiDAR point cloud data. A backpropagation network (BPN), along with traditional gradient descent-based learning methods, are used for labeling the data. Our study evaluated the labeling accuracy of two as well as three layers of BPN. The accuracy of the two-layer BPN model was found to be better than the three-layer BPN model. According to the experiments, our model showed competitive accuracy of 75% compared to the weakly supervised techniques in a similar area of study, i.e., the accuracy for S3DIS (Area 5) is 48.0%. Full article
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30 pages, 2316 KiB  
Article
A Theoretical Foundation for Context-Aware Cyber-Physical Production Systems
by Fu-Shiung Hsieh
Appl. Sci. 2022, 12(10), 5129; https://doi.org/10.3390/app12105129 - 19 May 2022
Cited by 8 | Viewed by 1301
Abstract
The complex workflows and interactions between heterogeneous entities in Cyber-Physical Production Systems (CPPS) call for the use of context-aware computing technology to operate effectively and meet the order requirements in a timely manner. In addition to the objective to meet the order due [...] Read more.
The complex workflows and interactions between heterogeneous entities in Cyber-Physical Production Systems (CPPS) call for the use of context-aware computing technology to operate effectively and meet the order requirements in a timely manner. In addition to the objective to meet the order due date, due to resource contention between production processes, CPPS may enter undesirable states. In undesirable states, all or part of the production activities are in waiting states or blocked situation due to improper allocation of resources. The capability to meet the order due date and prevent the system from entering an undesirable state poses challenges in the development of context-aware computing applications for CPPS. In this study, we formulate two situation awareness problems, including a Deadline Awareness Problem and a Future States Awareness Problem to address the above issues. In our previous study, we found that Discrete Timed Petri Nets provide an effective tool to model and analyze CPPS. In this paper, we present a relevant theory to support the operation of CPPS by extending the Discrete Timed Petri Nets to lay a foundation for developing context-aware applications of CPPS with deadline awareness and future states awareness capabilities. We illustrate the theory developed in this study by an example and conduct experiments to verify the computational feasibility of the proposed method. Full article
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19 pages, 3414 KiB  
Article
Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data
by Jae Woong Lee and Abdelsalam Helal
Appl. Sci. 2022, 12(5), 2452; https://doi.org/10.3390/app12052452 - 26 Feb 2022
Viewed by 1374
Abstract
In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy [...] Read more.
In the last decade, smart spaces and automatic systems have gained significant popularity and importance. Moreover, as the COVID-19 pandemic continues, the world is seeking remote intervention applications with autonomous and intelligent capabilities. Context-aware computing (CAC) is a key paradigm that can satisfy this need. A CAC-enabled system recognizes humans’ status and situation and provides proper services without requiring manual participation or extra control by humans. However, CAC is insufficient to achieve full automaticity since it needs manual modeling and configuration of context. To achieve full automation, a method is needed to automate the modeling and reasoning of contexts in smart spaces. In this paper, we propose a method that consists of two phases: the first is to instantiate and generate a context model based on data that were previously observed in the smart space, and the second is to discern a present context and predict the next context based on dynamic changes (e.g., user behavior and interaction with the smart space). In our previous work, we defined “context” as a meaningful and descriptive state of a smart space, in which relevant activities and movements of human residents are consecutively performed. The methods proposed in this paper, which is based on stochastic analysis, utilize the same definition, and enable us to infer context from sensor datasets collected from a smart space. By utilizing three statistical techniques, including a conditional probability table (CPT), K-means clustering, and principal component analysis (PCA), we are able to automatically infer the sequence of context transitions that matches the space–state changes (the dynamic changes) in the smart space. Once the contexts are obtained, they are used as references when the present context needs to discover the next context. This will provide the piece missing in traditional CAC, which will enable the creation of fully automated smart-space applications. To this end, we developed a method to reason the current state space by applying Euclidean distance and cosine similarity. In this paper, we first reconsolidate our context models, and then we introduce the proposed modeling and reasoning methods. Through experimental validation in a real-world smart space, we show how consistently the approach can correctly reason contexts. Full article
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14 pages, 4094 KiB  
Article
Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning
by Yeong-Hyeon Byeon, Dohyung Kim, Jaeyeon Lee and Keun-Chang Kwak
Appl. Sci. 2021, 11(22), 10979; https://doi.org/10.3390/app112210979 - 19 Nov 2021
Cited by 2 | Viewed by 1984
Abstract
In modern society, the population has been aging as the lifespan has increased owing to the advancement in medical technologies. This could pose a threat to the economic system and, in serious cases, to the ethics regarding the socially-weak elderly. An analysis of [...] Read more.
In modern society, the population has been aging as the lifespan has increased owing to the advancement in medical technologies. This could pose a threat to the economic system and, in serious cases, to the ethics regarding the socially-weak elderly. An analysis of the behavioral characteristics of the elderly and young adults based on their physical conditions enables silver robots to provide customized services for the elderly to counter aging society problems, laying the groundwork for improving elderly welfare systems and automating elderly care systems. Accordingly, skeleton sequences modeling the changes of the human body are converted into pose evolution images (PEIs), and a convolutional neural network (CNN) is trained to classify the elderly and young adults for a single behavior. Then, a heatmap, which is a contributed portion of the inputs, is obtained using a gradient-weighted class activation map (Grad-CAM) for the classified results, and a skeleton-heatmap is obtained through a series of processes for the ease of analysis. Finally, the behavioral characteristics are derived through the difference matching analysis between the domains based on the skeleton-heatmap and RGB video matching analysis. In this study, we present the analysis of the behavioral characteristics of the elderly and young adults based on cognitive science using deep learning and discuss the examples of the analysis. Therefore, we have used the ETRI-Activity3D dataset, which is the largest of its kind among the datasets that have classified the behaviors of young adults and the elderly. Full article
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16 pages, 2744 KiB  
Article
Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods
by Idongesit Ekerete, Matias Garcia-Constantino, Alexandros Konios, Mustafa A. Mustafa, Yohanca Diaz-Skeete, Christopher Nugent and James McLaughlin
Appl. Sci. 2021, 11(19), 9096; https://doi.org/10.3390/app11199096 - 29 Sep 2021
Cited by 1 | Viewed by 2202
Abstract
This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to [...] Read more.
This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test. Full article
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