Special Issue "Information Technology and Its Applications"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer and Engineering Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: closed (18 January 2019).

Special Issue Editor

Dr. Tzu Chuen Lu
E-Mail Website
Guest Editor
Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
Interests: information hiding; steganography; image processing; interactive game design; 3D modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a forum for presentations and discussions of the recent methodological advances in Information Technology and Its Applications. The Special Issue covers pure research and applications within novel scopes related to multimedia, such as image realated technique, image retrievel, and multimedia applications. In addition, it deals with information technologies such as information hiding, IOT, cloud computing and so on. The topics of this Special Issue include, but are not limited to:

  • Multimedia Applications
  • Image Related
  • Information Hiding
  • Pattern Recognition
  • IOT
  • Cloud Computing
  • Machine Learning
  • Data Mining
  • Neural Network
  • Distributed Systems
  • Software Engineering
  • Bio-informatics
  • Information Technology Related Issues

Dr. Tzu Chuen Lu
Guest Editor

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 papers will be 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. Symmetry is an international peer-reviewed open access monthly 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 1800 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

  • Multimedia Applications
  • Image Related
  • Information Hiding
  • Pattern Recognition
  • IOT
  • Cloud Computing
  • Machine Learning
  • Data Mining
  • Neural Network
  • Distributed Systems
  • Software Engineering
  • Bio-informatics
  • Information Technology Related Issues

Published Papers (16 papers)

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Editorial

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Editorial
Editorial of Special Issue “Information Technology and Its Applications”
Symmetry 2019, 11(1), 109; https://doi.org/10.3390/sym11010109 - 18 Jan 2019
Cited by 1 | Viewed by 911
Abstract
This book contains the successful invited submissions [...] Full article
(This article belongs to the Special Issue Information Technology and Its Applications)

Research

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Article
An Effective Authentication Scheme Using DCT for Mobile Devices
Symmetry 2018, 10(1), 13; https://doi.org/10.3390/sym10010013 - 02 Jan 2018
Cited by 3 | Viewed by 2208
Abstract
This paper proposes an image authentication scheme for mobile devices. The proposed scheme generates an image watermark by using discrete cosine transform (DCT) and hides the watermark in the spatial pixels for image authentication and tamper detection. The hiding operator used in this [...] Read more.
This paper proposes an image authentication scheme for mobile devices. The proposed scheme generates an image watermark by using discrete cosine transform (DCT) and hides the watermark in the spatial pixels for image authentication and tamper detection. The hiding operator used in this paper is very simple in a mobile environment allowing high-speed authentication using a low-power mobile device. The quality of the stego-image and the recovered image becomes excellent as a result of the proposed scheme. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
Symmetry 2018, 10(1), 11; https://doi.org/10.3390/sym10010011 - 29 Dec 2017
Cited by 220 | Viewed by 7495
Abstract
Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing [...] Read more.
Mosaic, Rust, Brown spot, and Alternaria leaf spot are the four common types of apple leaf diseases. Early diagnosis and accurate identification of apple leaf diseases can control the spread of infection and ensure the healthy development of the apple industry. The existing research uses complex image preprocessing and cannot guarantee high recognition rates for apple leaf diseases. This paper proposes an accurate identifying approach for apple leaf diseases based on deep convolutional neural networks. It includes generating sufficient pathological images and designing a novel architecture of a deep convolutional neural network based on AlexNet to detect apple leaf diseases. Using a dataset of 13,689 images of diseased apple leaves, the proposed deep convolutional neural network model is trained to identify the four common apple leaf diseases. Under the hold-out test set, the experimental results show that the proposed disease identification approach based on the convolutional neural network achieves an overall accuracy of 97.62%, the model parameters are reduced by 51,206,928 compared with those in the standard AlexNet model, and the accuracy of the proposed model with generated pathological images obtains an improvement of 10.83%. This research indicates that the proposed deep learning model provides a better solution in disease control for apple leaf diseases with high accuracy and a faster convergence rate, and that the image generation technique proposed in this paper can enhance the robustness of the convolutional neural network model. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Detection of Double-Compressed H.264/AVC Video Incorporating the Features of the String of Data Bits and Skip Macroblocks
Symmetry 2017, 9(12), 313; https://doi.org/10.3390/sym9120313 - 11 Dec 2017
Cited by 9 | Viewed by 2237
Abstract
Today’s H.264/AVC coded videos have a high quality, high data-compression ratio. They also have a strong fault tolerance, better network adaptability, and have been widely applied on the Internet. With the popularity of powerful and easy-to-use video editing software, digital videos can be [...] Read more.
Today’s H.264/AVC coded videos have a high quality, high data-compression ratio. They also have a strong fault tolerance, better network adaptability, and have been widely applied on the Internet. With the popularity of powerful and easy-to-use video editing software, digital videos can be tampered with in various ways. Therefore, the double compression in the H.264/AVC video can be used as a first step in the study of video-tampering forensics. This paper proposes a simple, but effective, double-compression detection method that analyzes the periodic features of the string of data bits (SODBs) and the skip macroblocks (S-MBs) for all I-frames and P-frames in a double-compressed H.264/AVC video. For a given suspicious video, the SODBs and S-MBs are extracted for each frame. Both features are then incorporated to generate one enhanced feature to represent the periodic artifact of the double-compressed video. Finally, a time-domain analysis is conducted to detect the periodicity of the features. The primary Group of Pictures (GOP) size is estimated based on an exhaustive strategy. The experimental results demonstrate the efficacy of the proposed method. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
A Block-Based Division Reversible Data Hiding Method in Encrypted Images
Symmetry 2017, 9(12), 308; https://doi.org/10.3390/sym9120308 - 08 Dec 2017
Cited by 9 | Viewed by 2211
Abstract
Due to the increased digital media on the Internet, data security and privacy protection issue have attracted the attention of data communication. Data hiding has become a topic of considerable importance. Nowadays, a new challenge consists of reversible data hiding in the encrypted [...] Read more.
Due to the increased digital media on the Internet, data security and privacy protection issue have attracted the attention of data communication. Data hiding has become a topic of considerable importance. Nowadays, a new challenge consists of reversible data hiding in the encrypted image because of the correlations of local pixels that are destroyed in an encrypted image; it is difficult to embed secret messages in encrypted images using the difference of neighboring pixels. In this paper, the proposed method uses a block-based division mask and a new encrypted method based on the logistic map and an additive homomorphism to embed data in an encrypted image by histogram shifting technique. Our experimental results show that the proposed method achieves a higher payload than other works and is more immune to attack upon the cryptosystem. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
A Secure Mobility Network Authentication Scheme Ensuring User Anonymity
Symmetry 2017, 9(12), 307; https://doi.org/10.3390/sym9120307 - 08 Dec 2017
Cited by 5 | Viewed by 1891
Abstract
With the rapid growth of network technologies, users are used to accessing various services with their mobile devices. To ensure security and privacy in mobility networks, proper mechanisms to authenticate the mobile user are essential. In this paper, a mobility network authentication scheme [...] Read more.
With the rapid growth of network technologies, users are used to accessing various services with their mobile devices. To ensure security and privacy in mobility networks, proper mechanisms to authenticate the mobile user are essential. In this paper, a mobility network authentication scheme based on elliptic curve cryptography is proposed. In the proposed scheme, a mobile user can be authenticated without revealing who he is for user anonymity, and a session key is also negotiated to protect the following communications. The proposed mobility network authentication scheme is analyzed to show that it can ensure security, user anonymity, and convenience. Moreover, Burrows-Abadi-Needham logic (BAN logic) is used to deduce the completeness of the proposed authentication scheme. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Face Liveness Detection Based on Skin Blood Flow Analysis
Symmetry 2017, 9(12), 305; https://doi.org/10.3390/sym9120305 - 07 Dec 2017
Cited by 16 | Viewed by 4776
Abstract
Face recognition systems have been widely adopted for user authentication in security systems due to their simplicity and effectiveness. However, spoofing attacks, including printed photos, displayed photos, and replayed video attacks, are critical challenges to authentication, and these spoofing attacks allow malicious invaders [...] Read more.
Face recognition systems have been widely adopted for user authentication in security systems due to their simplicity and effectiveness. However, spoofing attacks, including printed photos, displayed photos, and replayed video attacks, are critical challenges to authentication, and these spoofing attacks allow malicious invaders to gain access to the system. This paper proposes two novel features for face liveness detection systems to protect against printed photo attacks and replayed attacks for biometric authentication systems. The first feature obtains the texture difference between red and green channels of face images inspired by the observation that skin blood flow in the face has properties that enable distinction between live and spoofing face images. The second feature estimates the color distribution in the local regions of face images, instead of whole images, because image quality might be more discriminative in small areas of face images. These two features are concatenated together, along with a multi-scale local binary pattern feature, and a support vector machine classifier is trained to discriminate between live and spoofing face images. The experimental results show that the performance of the proposed method for face spoof detection is promising when compared with that of previously published methods. Furthermore, the proposed system can be implemented in real time, which is valuable for mobile applications. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
An Appraisal Model Based on a Synthetic Feature Selection Approach for Students’ Academic Achievement
Symmetry 2017, 9(11), 282; https://doi.org/10.3390/sym9110282 - 18 Nov 2017
Cited by 6 | Viewed by 1963
Abstract
Obtaining necessary information (and even extracting hidden messages) from existing big data, and then transforming them into knowledge, is an important skill. Data mining technology has received increased attention in various fields in recent years because it can be used to find historical [...] Read more.
Obtaining necessary information (and even extracting hidden messages) from existing big data, and then transforming them into knowledge, is an important skill. Data mining technology has received increased attention in various fields in recent years because it can be used to find historical patterns and employ machine learning to aid in decision-making. When we find unexpected rules or patterns from the data, they are likely to be of high value. This paper proposes a synthetic feature selection approach (SFSA), which is combined with a support vector machine (SVM) to extract patterns and find the key features that influence students’ academic achievement. For verifying the proposed model, two databases, namely, “Student Profile” and “Tutorship Record”, were collected from an elementary school in Taiwan, and were concatenated into an integrated dataset based on students’ names as a research dataset. The results indicate the following: (1) the accuracy of the proposed feature selection approach is better than that of the Minimum-Redundancy-Maximum-Relevance (mRMR) approach; (2) the proposed model is better than the listing methods when the six least influential features have been deleted; and (3) the proposed model can enhance the accuracy and facilitate the interpretation of the pattern from a hybrid-type dataset of students’ academic achievement. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Denoising and Feature Extraction Algorithms Using NPE Combined with VMD and Their Applications in Ship-Radiated Noise
Symmetry 2017, 9(11), 256; https://doi.org/10.3390/sym9110256 - 01 Nov 2017
Cited by 32 | Viewed by 3141
Abstract
A new denoising algorithm and feature extraction algorithm that combine a new kind of permutation entropy (NPE) and variational mode decomposition (VMD) are put forward in this paper. VMD is a new self-adaptive signal processing algorithm, which is more robust to sampling and [...] Read more.
A new denoising algorithm and feature extraction algorithm that combine a new kind of permutation entropy (NPE) and variational mode decomposition (VMD) are put forward in this paper. VMD is a new self-adaptive signal processing algorithm, which is more robust to sampling and noise, and also can overcome the problem of mode mixing in empirical mode decomposition (EMD) and ensemble EMD (EEMD). Permutation entropy (PE), as a nonlinear dynamics parameter, is a powerful tool that can describe the complexity of a time series. NPE, a new version of PE, is interpreted as distance to white noise, which shows a reverse trend to PE and has better stability than PE. In this paper, three kinds of ship-radiated noise (SN) signal are decomposed by VMD algorithm, and a series of intrinsic mode functions (IMF) are obtained. The NPEs of all the IMFs are calculated, the noise IMFs are screened out according to the value of NPE, and the process of denoising can be realized by reconstructing the rest of IMFs. Then the reconstructed SN signal is decomposed by VMD algorithm again, and one IMF containing the most dominant information is chosen to represent the original SN signal. Finally, NPE of the chosen IMF is calculated as a new complexity feature, which constitutes the input of the support vector machine (SVM) for pattern recognition of SN. Compared with the existing denoising algorithms and feature extraction algorithms, the effectiveness of proposed algorithms is validated using the numerical simulation signal and the different kinds of SN signal. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Reversible Dual-Image-Based Hiding Scheme Using Block Folding Technique
Symmetry 2017, 9(10), 223; https://doi.org/10.3390/sym9100223 - 12 Oct 2017
Cited by 10 | Viewed by 1999
Abstract
The concept of a dual-image based scheme in information sharing consists of concealing secret messages in two cover images; only someone who has both stego-images can extract the secret messages. In 2015, Lu et al. proposed a center-folding strategy where each secret symbol [...] Read more.
The concept of a dual-image based scheme in information sharing consists of concealing secret messages in two cover images; only someone who has both stego-images can extract the secret messages. In 2015, Lu et al. proposed a center-folding strategy where each secret symbol is folded into the reduced digit to reduce the distortion of the stego-image. Then, in 2016, Lu et al. used a frequency-based encoding strategy to reduce the distortion of the frequency of occurrence of the maximum absolute value. Because the folding strategy can obviously reduce the value, the proposed scheme includes the folding operation twice to further decrease the reduced digit. We use a frequency-based encoding strategy to encode a secret message and then use the block folding technique by performing the center-folding operation twice to embed secret messages. An indicator is needed to identify the sequence number of the folding operation. The proposed scheme collects several indicators to produce a combined code and hides the code in a pixel to reduce the size of the indicators. The experimental results show that the proposed method can achieve higher image quality under the same embedding rate or higher payload, which is better than other methods. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model
Symmetry 2017, 9(10), 216; https://doi.org/10.3390/sym9100216 - 06 Oct 2017
Cited by 14 | Viewed by 3317
Abstract
With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm [...] Read more.
With the rapid development of e-commerce, the contradiction between the disorder of business information and customer demand is increasingly prominent. This study aims to make e-commerce shopping more convenient, and avoid information overload, by an interactive personalized recommendation system using the hybrid algorithm model. The proposed model first uses various recommendation algorithms to get a list of original recommendation results. Combined with the customer’s feedback in an interactive manner, it then establishes the weights of corresponding recommendation algorithms. Finally, the synthetic formula of evidence theory is used to fuse the original results to obtain the final recommendation products. The recommendation performance of the proposed method is compared with that of traditional methods. The results of the experimental study through a Taobao online dress shop clearly show that the proposed method increases the efficiency of data mining in the consumer coverage, the consumer discovery accuracy and the recommendation recall. The hybrid recommendation algorithm complements the advantages of the existing recommendation algorithms in data mining. The interactive assigned-weight method meets consumer demand better and solves the problem of information overload. Meanwhile, our study offers important implications for e-commerce platform providers regarding the design of product recommendation systems. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Polar Bear Optimization Algorithm: Meta-Heuristic with Fast Population Movement and Dynamic Birth and Death Mechanism
Symmetry 2017, 9(10), 203; https://doi.org/10.3390/sym9100203 - 28 Sep 2017
Cited by 68 | Viewed by 3899
Abstract
In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all [...] Read more.
In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO). The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Community Detection Based on Differential Evolution Using Social Spider Optimization
Symmetry 2017, 9(9), 183; https://doi.org/10.3390/sym9090183 - 06 Sep 2017
Cited by 13 | Viewed by 2573
Abstract
Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient [...] Read more.
Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Research on Lower Limb Motion Recognition Based on Fusion of sEMG and Accelerometer Signals
Symmetry 2017, 9(8), 147; https://doi.org/10.3390/sym9080147 - 06 Aug 2017
Cited by 26 | Viewed by 2553
Abstract
Since surface electromyograghic (sEMG) signals are non-invasive and capable of reflecting humans’ motion intention, they have been widely used for the motion recognition of upper limbs. However, limited research has been conducted for lower limbs, because the sEMGs of lower limbs are easily [...] Read more.
Since surface electromyograghic (sEMG) signals are non-invasive and capable of reflecting humans’ motion intention, they have been widely used for the motion recognition of upper limbs. However, limited research has been conducted for lower limbs, because the sEMGs of lower limbs are easily affected by body gravity and muscle jitter. In this paper, sEMG signals and accelerometer signals are acquired and fused to recognize the motion patterns of lower limbs. A curve fitting method based on median filtering is proposed to remove accelerometer noise. As for movement onset detection, an sEMG power spectral correlation coefficient method is used to detect the start and end points of active signals. Then, the time-domain features and wavelet coefficients of sEMG signals are extracted, and a dynamic time warping (DTW) distance is used for feature extraction of acceleration signals. At last, five lower limbs’ motions are classified and recognized by using Gaussian kernel-based linear discriminant analysis (LDA) and support vector machine (SVM) respectively. The results prove that the fused feature-based classification outperforms the classification with only sEMG signals or accelerometer signals, and the fused feature can achieve 95% or higher recognition accuracy, demonstrating the validity of the proposed method. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
Precise Positioning of Marketing and Behavior Intentions of Location-Based Mobile Commerce in the Internet of Things
Symmetry 2017, 9(8), 139; https://doi.org/10.3390/sym9080139 - 02 Aug 2017
Cited by 16 | Viewed by 4329
Abstract
In the complex environment of the IoT (Internet of Things), the amount of information available is enormous and the number of users also increases at a blistering pace. With a huge number of users, e-commerce marketing strategies in the IoT become extremely important [...] Read more.
In the complex environment of the IoT (Internet of Things), the amount of information available is enormous and the number of users also increases at a blistering pace. With a huge number of users, e-commerce marketing strategies in the IoT become extremely important and must be altered accordingly in response to changes in the environment and industry. Hence, the application of IoT technology to mobile commerce allows users to receive integrated information according to time, location, and context using location-based service, and provides them with a more effective shopping experience. The validation results show that external variables indirectly influence behavioral intention through perceived usefulness. The investigation of behavioral intention is used to understand users’ acceptance and using willingness of the store app, which can help narrow the gap between stores and consumers, and help improve operations. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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Article
The Fusion of an Ultrasonic and Spatially Aware System in a Mobile-Interaction Device
Symmetry 2017, 9(8), 137; https://doi.org/10.3390/sym9080137 - 30 Jul 2017
Cited by 1 | Viewed by 1560
Abstract
Over the past four decades, the prophecy from computer pundits and prognosticators pointed to the looming arrival of the paperless office era. However, forty years later, physical paper documents are still playing a significant role due to the ease of use, superior readability, [...] Read more.
Over the past four decades, the prophecy from computer pundits and prognosticators pointed to the looming arrival of the paperless office era. However, forty years later, physical paper documents are still playing a significant role due to the ease of use, superior readability, and availability. The drawbacks of paper sheets are that they are hard to modify and retrieve, have limited space, and are environmentally unfriendly. Augmenting paper documents with digital information from mobile devices extends the two-dimensional space of physical paper documents. Various camera-based recognition and detection devices have been proposed to augment paper documents with digital information. However, there are still some limitations that exist in these systems. This paper presents a novel, low cost, spatially aware, mobile system called Ultrasonic PhoneLens. The Ultrasonic PhoneLens adopts two-dimensional dynamic image presentation and ultrasonic sound positioning techniques. It consists of two ultrasonic sound sensors, one Arduino mini-controller board, and one android mobile device. Based on the location of the mobile device over the physical paper, Ultrasonic PhoneLens can retrieve pre-saved digital information from a mobile database for the object (such as a text, a paragraph, or an image) in a paper document. An empirical study was conducted to evaluate the system performance. The results indicate that our system has a better performance in tasks such as browsing multivalent documents and sharing digital information than the Wiimote PhoneLens system. Full article
(This article belongs to the Special Issue Information Technology and Its Applications)
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