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4 pages, 190 KiB

Open AccessEditorial

Current Research in Future Information and Communication Engineering 2022

by Yun Seop Yu, Kwang-Baek Kim, Dongsik Jo, Hee-Cheol Kim and Jeongwook Seo

Appl. Sci. 2023, 13(12), 7258; https://doi.org/10.3390/app13127258 - 18 Jun 2023

Cited by 1 | Viewed by 2685

Abstract

The digital revolution has transformed the way we communicate, access information, and interact with technology [...] Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

Research

Jump to: Editorial, Review

13 pages, 3983 KiB

Open AccessArticle

Dependence of the Color Tunability on the H₂Pc Thickness in DC-Voltage-Driven Organic Light-Emitting Diodes

by Tae Jun Ahn, Bum Ho Choi, Jae-Woong Yu and Yun Seop Yu

Appl. Sci. 2023, 13(9), 5315; https://doi.org/10.3390/app13095315 - 24 Apr 2023

Cited by 1 | Viewed by 1306

Abstract

Dependence of the color tunability on the metal free Phthalocyanine (H₂Pc) layer thickness in DC-voltage-driven organic light-emitting diodes (OLEDs) was investigated. A H₂Pc layer was employed as a blue/red emission layer, which was prepared on an Alq₃ green [...] Read more.

Dependence of the color tunability on the metal free Phthalocyanine (H₂Pc) layer thickness in DC-voltage-driven organic light-emitting diodes (OLEDs) was investigated. A H₂Pc layer was employed as a blue/red emission layer, which was prepared on an Alq₃ green emission layer. The thickness of the H₂Pc layer varied from 5 to 30 nm, with a step of 5 nm. The fabricated color-tunable OLEDs (CTOLEDs) were subjected to a thermal treatment layer for 2 min at a temperature of 120 °C to improve the interface properties, especially between H₂Pc and Alq₃. The current density–voltage–luminance characteristics and Commission Internationale de L’Eclairage (CIE) coordinates of the CTOLEDs with and without thermal treatment were measured, and their energy band diagrams were analyzed with respect to the H₂Pc thin film thicknesses. In addition, the recombination rates at the interfaces between the hole transport layer and Alq₃ and the H₂Pc/electron transport layer of the CTOLEDs with and without thermal treatment were theoretically investigated using a technology–computer-aided design (TCAD) program. The experimental and theoretical results showed that the emission color temperature from cool white to warm white at a low voltage can be controlled by adjusting the thickness of the H₂Pc layer in the CTOLED. It was verified that the thermally treated H₂Pc thin film layer acted as a barrier that prevented electrons from being transferred to the Alq₃ at low applied voltages, resulting in white color emission with temperature tunability. The CTOLED with a 20 nm of H₂Pc layer demonstrated the best stable interface state and stability, resulting in the lowest driving voltage, relatively high luminance, and optimal light emission uniformity, respectively. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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23 pages, 33423 KiB

Open AccessArticle

A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation

by Yue Zhang, Seong-Yoon Shin, Xujie Tan and Bin Xiong

Appl. Sci. 2023, 13(3), 1298; https://doi.org/10.3390/app13031298 - 18 Jan 2023

Cited by 2 | Viewed by 1695

Abstract

Deep neural networks (DNNs) have famously been applied in various ordinary duties. However, DNNs are sensitive to adversarial attacks which, by adding imperceptible perturbation samples to an original image, can easily alter the output. In state-of-the-art white-box attack methods, perturbation samples can successfully [...] Read more.

Deep neural networks (DNNs) have famously been applied in various ordinary duties. However, DNNs are sensitive to adversarial attacks which, by adding imperceptible perturbation samples to an original image, can easily alter the output. In state-of-the-art white-box attack methods, perturbation samples can successfully fool DNNs through the network gradient. In addition, they generate perturbation samples by only considering the sign information of the gradient and by dropping the magnitude. Accordingly, gradients of different magnitudes may adopt the same sign to construct perturbation samples, resulting in inefficiency. Unfortunately, it is often impractical to acquire the gradient in real-world scenarios. Consequently, we propose a self-adaptive approximated-gradient-simulation method for black-box adversarial attacks (SAGM) to generate efficient perturbation samples. Our proposed method uses knowledge-based differential evolution to simulate gradients and the self-adaptive momentum gradient to generate adversarial samples. To estimate the efficiency of the proposed SAGM, a series of experiments were carried out on two datasets, namely MNIST and CIFAR-10. Compared to state-of-the-art attack techniques, our proposed method can quickly and efficiently search for perturbation samples to misclassify the original samples. The results reveal that the SAGM is an effective and efficient technique for generating perturbation samples. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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16 pages, 4485 KiB

Open AccessArticle

Auto-Scoring Feature Based on Sentence Transformer Similarity Check with Korean Sentences Spoken by Foreigners

by Aria Bisma Wahyutama and Mintae Hwang

Appl. Sci. 2023, 13(1), 373; https://doi.org/10.3390/app13010373 - 28 Dec 2022

Cited by 2 | Viewed by 2380

Abstract

This paper contains the development of a training service for foreigners to help them increase their ability to speak Korean. The service developed in this paper is implemented in the form of a mobile application that shows specific Korean sentences to the user [...] Read more.

This paper contains the development of a training service for foreigners to help them increase their ability to speak Korean. The service developed in this paper is implemented in the form of a mobile application that shows specific Korean sentences to the user for them to record themselves speaking the sentence. The objective is to generate the score automatically based on how similar the recorded voice with the actual sentence using Speech-To-Text (STT) engines and Sentence Transformers. The application is developed by selecting the four most commonly known STT engines with similar features, which are Google API, Microsoft Azure, Naver Clova, and IBM Watson, which are put into a Rest API along with the Sentence Transformer. The mobile application will record the user’s voice and send it to the Rest API. The STT engines will transcribe the file into a text and then feed it into a Sentence Transformer to generate the score based on their similarity. After measuring the response time and consistency as the performance evaluation by simulating a scenario using an Android emulator, Microsoft Azure with 1.13 s is found to be the fastest STT engine and Naver Clova is found to be the least consistent engine with nine different transcribe results. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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10 pages, 768 KiB

Open AccessArticle

Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis

by Kwang Baek Kim, Hyun Jun Park and Doo Heon Song

Appl. Sci. 2023, 13(1), 351; https://doi.org/10.3390/app13010351 - 27 Dec 2022

Cited by 4 | Viewed by 2212

Abstract

In domains that have complex data characteristics and/or noisy data, any single supervised learning algorithm tends to suffer from overfitting. One way to mitigate this problem is to combine unsupervised learning component as a front end of the main supervised learner. In this [...] Read more.

In domains that have complex data characteristics and/or noisy data, any single supervised learning algorithm tends to suffer from overfitting. One way to mitigate this problem is to combine unsupervised learning component as a front end of the main supervised learner. In this paper, we propose a hierarchical combination of fuzzy C-means clustering component and fuzzy max–min neural network supervised learner for that purpose. The proposed method is evaluated in a noisy domain (Pima Indian Diabetes open database). The proposed combination showed superior result to standalone fuzzy max–min and backpropagation-based neural network. The proposed method also showed better performance than any single supervised learner tested in the same domain in the literature with high accuracy (80.96%) and was at least competitive in other measures such as sensitivity, specificity, and F1 measure. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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13 pages, 3378 KiB

Open AccessArticle

Exploring the Effect of Virtual Environments on Passive Haptic Perception

by Daehwan Kim, Yongwan Kim and Dongsik Jo

Appl. Sci. 2023, 13(1), 299; https://doi.org/10.3390/app13010299 - 26 Dec 2022

Cited by 5 | Viewed by 2351

Abstract

Recent advances in virtual reality (VR) technologies such as immersive head-mounted display (HMD), sensing devices, and 3D printing-based props have become much more feasible for providing improved experiences for users in virtual environments. In particular, research on haptic feedback is being actively conducted [...] Read more.

Recent advances in virtual reality (VR) technologies such as immersive head-mounted display (HMD), sensing devices, and 3D printing-based props have become much more feasible for providing improved experiences for users in virtual environments. In particular, research on haptic feedback is being actively conducted to enhance the effect of controlling virtual objects. Studies have begun to use real objects that resemble virtual objects, i.e., passive haptic, instead of using haptic equipment with motor control, as an effective method that allows natural interaction. However, technical difficulties must be resolved to match transformations (e.g., position, orientation, and scale) between virtual and real objects to maximize the user’s immersion. In this paper, we compare and explore the effect of passive haptic parameters on the user’s perception by using different transformation conditions in immersive virtual environments. Our experimental study shows that the participants felt the same within a certain range, which seems to support the “minimum cue” theory in giving sufficient sensory stimulation. Thus, considering the benefits of the model using our approach, haptic interaction in VR content can be developed in a more economical way. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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11 pages, 7033 KiB

Open AccessArticle

AWGN Removal Using Modified Steering Kernel and Image Matching

by Bong-Won Cheon and Nam-Ho Kim

Appl. Sci. 2022, 12(22), 11588; https://doi.org/10.3390/app122211588 - 15 Nov 2022

Cited by 1 | Viewed by 1167

Abstract

Image noise occurs during acquisition and transmission and adversely affects processes, such as image segmentation and object recognition and classification. Various techniques are being studied for noise removal, and with the recent development of hardware and image processing algorithms, noise removal techniques that [...] Read more.

Image noise occurs during acquisition and transmission and adversely affects processes, such as image segmentation and object recognition and classification. Various techniques are being studied for noise removal, and with the recent development of hardware and image processing algorithms, noise removal techniques that combine non-local techniques are attracting attention. However, one disadvantage of this method is that blurring occurs in the edges and boundary line of the resulting image. In this study, we proposed a modified local steering kernel based on image matching to improve these shortcomings. The proposed technique uses image matching to differentiate the weight obtained by the steering kernel according to the local characteristics of the image and calculates the weight of the filter based on the similarity between the center window and the matching window. The resulting images were quantitatively evaluation and enlargement of images were used and compared with the existing noise removal algorithms. The proposed algorithm showed clearer contrast in the enlarged images and better results than the conventional image restoration techniques in the quantitative evaluation using peak signal-to-noise ratio and structural similarity index. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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18 pages, 2799 KiB

Open AccessArticle

Fall Detection System Based on Simple Threshold Method and Long Short-Term Memory: Comparison with Hidden Markov Model and Extraction of Optimal Parameters

by Seung Su Jeong, Nam Ho Kim and Yun Seop Yu

Appl. Sci. 2022, 12(21), 11031; https://doi.org/10.3390/app122111031 - 31 Oct 2022

Cited by 8 | Viewed by 2375

Abstract

In an aging global society, a few complex problems have been occurring due to falls among the increasing elderly population. Therefore, falls are detected using a pendant-type sensor that can be worn comfortably for fall detection. The sensed data are processed by the [...] Read more.

In an aging global society, a few complex problems have been occurring due to falls among the increasing elderly population. Therefore, falls are detected using a pendant-type sensor that can be worn comfortably for fall detection. The sensed data are processed by the embedded environment and classified by a long-term memory (LSTM). A fall detection system that combines a simple threshold method (STM) and LSTM, the STM-LSTM-based fall detection system, is introduced. In terms of training data accuracy, the proposed STM-LSTM-based fall detection system is compared with the previously reported STM-hidden Markov model (HMM)-based fall detection system. The training accuracy of the STM-LSTM fall detection system is 100%, while the highest training accuracy by the STM-HMM-based one is 99.5%, which is 0.5% less than the best of the STM-LSTM-based system. In addition, in the optimized LSTM fall detection system, this may be overfitted because all data are trained without separating any validation data. In order to resolve the possible overfitting issue, training and validation data are evaluated separately in 4:1, and then in terms of validation data accuracy of the STM-LSTM-based fall detection system, optimal values of the parameters in LSTM and normalization method are found as follows: best accuracy of 98.21% at no-normalization, no-sampling, 128hidden layer nodes, and regularization rate of 0.015. It is also observed that as the number of hidden layer nodes or sampling interval increases, the regularization rate at the highest value of accuracy increases. This means that overfitting can be suppressed by increasing the regularization, and thus an appropriate number of hidden layer nodes and a regularization rate must be selected to improve the fall detection efficiency. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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12 pages, 1372 KiB

Open AccessArticle

Automatic Movie Tag Generation System for Improving the Recommendation System

by Hyogyeong Park, Sungjung Yong, Yeonhwi You, Seoyoung Lee and Il-Young Moon

Appl. Sci. 2022, 12(21), 10777; https://doi.org/10.3390/app122110777 - 24 Oct 2022

Cited by 3 | Viewed by 2894

Abstract

As the content industry develops, the demand for movie content is increasing. Accordingly, the content industry is actively developing super-personalized recommendation systems that match consumers’ tastes. In this paper, we study automatic generation of movie tags to improve the movie recommendation system. We [...] Read more.

As the content industry develops, the demand for movie content is increasing. Accordingly, the content industry is actively developing super-personalized recommendation systems that match consumers’ tastes. In this paper, we study automatic generation of movie tags to improve the movie recommendation system. We extracted background sounds from movie trailer videos, analyzed the sounds using STFT (Short-Time Fourier transform) and major audio attribute features, and created a genre prediction model. The experimental results show that the pre-collected dataset and the data extracted via the model are similar when compared. In this research, we suggest the methodology of an automatic genre prediction system for movie information from trailer videos. This will help to reduce the time and effort for metadata generation for a recommendation system. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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17 pages, 4146 KiB

Open AccessArticle

IoT-Based Intelligent Monitoring System Applying RNN

by Moonsun Shin, Seonmin Hwang, Byungcheol Kim, Sungbo Seo and Junghwan Kim

Appl. Sci. 2022, 12(20), 10421; https://doi.org/10.3390/app122010421 - 15 Oct 2022

Cited by 4 | Viewed by 2527

Abstract

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for [...] Read more.

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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10 pages, 5916 KiB

Open AccessArticle

Effect of Compressed Sensing Rates and Video Resolutions on a PoseNet Model in an AIoT System

by Hye-Min Kwon and Jeongwook Seo

Appl. Sci. 2022, 12(19), 9938; https://doi.org/10.3390/app12199938 - 2 Oct 2022

Cited by 4 | Viewed by 1639

Abstract

To provide an artificial intelligence service such as pose estimation with a PoseNet model in an Artificial Intelligence of Things (AIoT) system, an Internet of Things (IoT) sensing device sends a large amount of data such as images or videos to an AIoT [...] Read more.

To provide an artificial intelligence service such as pose estimation with a PoseNet model in an Artificial Intelligence of Things (AIoT) system, an Internet of Things (IoT) sensing device sends a large amount of data such as images or videos to an AIoT edge server. This causes serious data traffic problems in IoT networks. To mitigate these problems, we can apply compressed sensing (CS) to the IoT sensing device. However, the AIoT edge server may have poor pose estimation accuracy (i.e., pose score), because it has to recover the CS data received from the IoT sensing device and estimate human pose from the imperfectly recovered data according to CS rates. Therefore, in this paper, we analyze the effect of CS rates (from

100 %

to

10 %

) and video resolutions (

1280 \times 720

,

640 \times 480

,

480 \times 360

) in the IoT sensing device on the pose score of the PoseNet model in the AIoT edge server. When only considering the meaningful range of CS rates from

100 %

to

50 %

, we found that the higher the video resolution, the lower the pose score. At the CS rate of

80 %

, we could reduce data traffic by

20 %

despite the degradation in pose score of less than about 0.03 for all video resolutions. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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19 pages, 3131 KiB

Open AccessArticle

Construction of Full-View Data from Limited-View Data Using Artificial Neural Network in the Inverse Scattering Problem

by Sang-Su Jeong, Won-Kwang Park and Young-Deuk Joh

Appl. Sci. 2022, 12(19), 9801; https://doi.org/10.3390/app12199801 - 29 Sep 2022

Cited by 2 | Viewed by 1398

Abstract

Generally, the results of imaging the limited view data in the inverse scattering problem are relatively poor, compared to those of imaging the full view data. It is known that solving this problem mathematically is very difficult. Therefore, the main purpose of this [...] Read more.

Generally, the results of imaging the limited view data in the inverse scattering problem are relatively poor, compared to those of imaging the full view data. It is known that solving this problem mathematically is very difficult. Therefore, the main purpose of this study is to solve the inverse scattering problem in the limited view situation for some cases by using artificial intelligence. Thus, we attempted to develop an artificial intelligence suitable for problem-solving for the cases where the number of scatterers was 2 and 3, respectively, based on CNN (Convolutional Neural Networks) and ANN (Artificial Neural Network) models. As a result, when the ReLU function was used as the activation function and ANN consisted of four hidden layers, a learning model with a small mean square error of the output data through the ground truth data and this learning model could be developed. In order to verify the performance and overfitting of the developed learning model, limited view data that were not used for learning were newly created. The mean square error between output data obtained from this and ground truth data was also small, and the data distributions between the two data were similar. In addition, the locations of scatterers by imaging the out data with the subspace migration algorithm could be accurately found. To support this, data related to artificial neural network learning and imaging results using the subspace migration algorithm are attached. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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19 pages, 3719 KiB

Open AccessArticle

A Performance Evaluation of the Alpha-Beta (α-β) Filter Algorithm with Different Learning Models: DBN, DELM, and SVM

by Junaid Khan and Kyungsup Kim

Appl. Sci. 2022, 12(19), 9429; https://doi.org/10.3390/app12199429 - 20 Sep 2022

Cited by 12 | Viewed by 2926

Abstract

In this paper, we present a new Multiple learning to prediction algorithm model that used three different combinations of machine-learning methods to improve the accuracy of the α-β filter algorithm. The parameters of α and β were tuned in dynamic conditions instead of [...] Read more.

In this paper, we present a new Multiple learning to prediction algorithm model that used three different combinations of machine-learning methods to improve the accuracy of the α-β filter algorithm. The parameters of α and β were tuned in dynamic conditions instead of static conditions. The proposed system was designed to use the deep belief network (DBN), the deep extreme learning machine (DELM), and the SVM as three different learning algorithms. Then these learned parameters were trained by the machine-learning algorithms tuned to the α-β filter algorithm as a prediction module, and they gave the final predicted results. The MAE and RMSE were used to evaluate the performance of the proposed α-β filter with different learning algorithms. Each algorithm recorded different best-case accuracy results; for the DBN, we achieved 3.60 and 2.61; for the DELM, we obtained the best-case result of 3.90 and 2.81; and finally, for the SVM, 4.0 and 3.21 were attained in terms of the RMSE and MAE, respectively, as compared to 5.21 and 3.95. When assessed in comparison with the typical alpha–beta filter algorithm, the proposed system provided results with better accuracy. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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12 pages, 1765 KiB

Open AccessArticle

Long Short-Term Memory (LSTM)-Based Dog Activity Detection Using Accelerometer and Gyroscope

by Ali Hussain, Khadija Begum, Tagne Poupi Theodore Armand, Md Ariful Islam Mozumder, Sikandar Ali, Hee Cheol Kim and Moon-Il Joo

Appl. Sci. 2022, 12(19), 9427; https://doi.org/10.3390/app12199427 - 20 Sep 2022

Cited by 9 | Viewed by 3467

Abstract

Dog owners are extremely driven to comprehend the activity and health of their dogs and to keep tabs on their well-being. Dogs’ health and well-being, whether as household pets or service animals, are critical issues that are addressed seriously for moral, psychological, and [...] Read more.

Dog owners are extremely driven to comprehend the activity and health of their dogs and to keep tabs on their well-being. Dogs’ health and well-being, whether as household pets or service animals, are critical issues that are addressed seriously for moral, psychological, and economical reasons. Evaluations of a dog’s welfare depend on quantitative assessments of the frequency and variability of certain behavioral features, which are sometimes challenging to make in a dog’s normal environment. While it is challenging to obtain dogs’ behavioral patterns, it is nearly impossible to directly identify one distinct behavior when they are roaming around at will. Applications for automatic pet monitoring include real-time surveillance and monitoring systems that accurately identify pets using the most recent methods for the classification of pet activities. The suggested method makes use of a long short-term memory (LSTM)-based method to detect and classify the activities of dogs based on sensor data (i.e., accelerometer and gyroscope). The goal of this study is to use wearable sensor data and examine the activities of dogs using recurrent neural network (RNN) technology. We considered 10 pet behaviors, which include walking, sitting, down, staying, feeding, sideways, leaping, running, shaking, and nose work. As dog activity has a wider diversity, experimental work is performed on the multi-layer LSTM framework to have a positive influence on performance. In this study, data were collected from 10 dogs of various ages, sexes, breeds, and sizes in a safe setting. Data preprocessing and data synchronization were performed after the collection of data. The LSTM model was trained using the preprocessed data and the model’s performance was evaluated by the test dataset. The model showed good accuracy and high performance for the detection of 10 activities of dogs. This model will be helpful for the real-time monitoring of dogs’ activity, thus improving the well-being of dogs. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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11 pages, 3238 KiB

Open AccessArticle

AnyPlace: Automatic Gaze Alignment of the Teleported Avatar for MR Collaborative Environments

by Jin-Ho Chung and Dongsik Jo

Appl. Sci. 2022, 12(18), 9154; https://doi.org/10.3390/app12189154 - 13 Sep 2022

Cited by 3 | Viewed by 1472

Abstract

Tele-conference systems are widely used as a form of communication media between remote sites. In order to overcome the limitations of video-based tele-conference systems with the continued technological innovations in mixed reality (MR), the use of a three-dimensional teleported avatar, in which a [...] Read more.

Tele-conference systems are widely used as a form of communication media between remote sites. In order to overcome the limitations of video-based tele-conference systems with the continued technological innovations in mixed reality (MR), the use of a three-dimensional teleported avatar, in which a remote participant is teleported into a local environment, would be an effective future tele-conference system that would allow natural movement and interaction in the same location. However, technical difficulties must be resolved to enable control of the teleported avatar adapted to the environmental differences of the remote location and the user’s situation. This paper presents a novel method to adjust automatic gaze alignment of the teleported avatar with matching in the local site for MR collaborative environments. We ran comparative validation experiments to measure spatial accuracy of the gaze and evaluate the user’s communication efficiency using our method. In a quantitative experiment, the degree of gaze matching error in various environments was found to form a mirror-symmetrical U-shape, and the necessity of gaze matching gain was also recognized. Additionally, our experimental study showed that participants felt a greater co-presence during communication than in an idle situation without conversation. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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12 pages, 1925 KiB

Open AccessArticle

MSSI-Based Dispersion-Managed Link Configured by Randomly-Distributed RDPS Only in Former Half Section

by Jae-Pil Chung and Seong-Real Lee

Appl. Sci. 2022, 12(18), 8970; https://doi.org/10.3390/app12188970 - 7 Sep 2022

Cited by 2 | Viewed by 1153

Abstract

The weakness of the dispersion-managed link, which is combined with optical phase conjugation to compensate for optical signal distortion caused by chromatic dispersion and the nonlinear Kerr effect of the standard single mode fiber is, its limited structural flexibility. We propose a dispersion [...] Read more.

The weakness of the dispersion-managed link, which is combined with optical phase conjugation to compensate for optical signal distortion caused by chromatic dispersion and the nonlinear Kerr effect of the standard single mode fiber is, its limited structural flexibility. We propose a dispersion map that can simultaneously compensate for the distorted wavelength division multiplexed signal while increasing the configurational flexibility. Each residual dispersion per span (RDPS) in the former half of the proposed link is randomly determined, and in the latter half, the arrangement order of RDPS is the same as or inverted in the former half. We confirm that the dispersion maps in which the RDPS distribution pattern in the latter half is opposite to the arrangement order in the former half are more effective in compensation, and the compensation effect is better than in the dispersion map of the conventional scheme. The notable result of this study is that the flexibility can be increased by randomly arranging RDPS in the former half, and compensation improvement can be achieved by inversing the order of RDPS arrangement of the former half in the latter half, which makes the dispersion profile of each half link roughly symmetric with respect to the midway optical phase conjugator. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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20 pages, 5478 KiB

Open AccessArticle

Multi-Population Differential Evolution Algorithm with Uniform Local Search

by Xujie Tan, Seong-Yoon Shin, Kwang-Seong Shin and Guangxing Wang

Appl. Sci. 2022, 12(16), 8087; https://doi.org/10.3390/app12168087 - 12 Aug 2022

Cited by 3 | Viewed by 1828

Abstract

Differential evolution (DE) is a very effective stochastic optimization algorithm based on population for solving various real-world problems. The quality of solutions to these problems is mainly determined by the combination of mutation strategies and their parameters in DE. However, in the process [...] Read more.

Differential evolution (DE) is a very effective stochastic optimization algorithm based on population for solving various real-world problems. The quality of solutions to these problems is mainly determined by the combination of mutation strategies and their parameters in DE. However, in the process of solving these problems, the population diversity and local search ability will gradually deteriorate. Therefore, we propose a multi-population differential evolution (MUDE) algorithm with a uniform local search to balance exploitation and exploration. With MUDE, the population is divided into multiple subpopulations with different population sizes, which perform different mutation strategies according to the evolution ratio, i.e., DE/rand/1, DE/current-to-rand/1, and DE/current-to-pbest/1. To improve the diversity of the population, the information is migrated between subpopulations by the soft-island model. Furthermore, the local search ability is improved by way of the uniform local search. As a result, the proposed MUDE maintains exploitation and exploration capabilities throughout the process. MUDE is extensively evaluated on 25 functions of the CEC 2005 benchmark. The comparison results show that the MUDE algorithm is very competitive with other DE variants and optimization algorithms in generating efficient solutions. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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15 pages, 936 KiB

Open AccessArticle

An Intelligent DOA Estimation Error Calibration Method Based on Transfer Learning

by Min Zhang, Chenyang Wang, Wenli Zhu and Yi Shen

Appl. Sci. 2022, 12(15), 7636; https://doi.org/10.3390/app12157636 - 28 Jul 2022

Cited by 5 | Viewed by 1830

Abstract

Affected by various error factors in the actual environment, the accuracy of the direction of arrival (DOA) estimation algorithm will greatly decrease during an application. To address this issue, in this paper, we propose an intelligent DOA estimation error calibration method based on [...] Read more.

Affected by various error factors in the actual environment, the accuracy of the direction of arrival (DOA) estimation algorithm will greatly decrease during an application. To address this issue, in this paper, we propose an intelligent DOA estimation error calibration method based on transfer learning, which learns error knowledge from a small number of actual signal samples and improves the DOA estimation accuracy in the real application. We constructed a deep convolutional neural network (CNN)-based intelligent DOA estimation model to learn the mapping between the input signals and their azimuths. We generated a large number of ideal simulation signal samples to train the CNN model and used it as the pretrained model. Then, we fine-tuned the CNN model with a small number of actual signal samples collected in the actual environment. We demonstrate the effectiveness of the proposed method through simulation experiments. The experimental results indicate that the proposed method can effectively improve the accuracy of DOA estimation in the actual environment. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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11 pages, 2245 KiB

Open AccessArticle

Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering

by Kwang Baek Kim, Doo Heon Song and Hyun Jun Park

Appl. Sci. 2022, 12(11), 5753; https://doi.org/10.3390/app12115753 - 6 Jun 2022

Cited by 5 | Viewed by 2225

Abstract

Accurate diagnosis of acute appendicitis from abdominal ultrasound is a challenging task, since traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, and wall thickness, rely on complete identification or visualization of the appendix and the diagnosis is frequently operator subjective. In [...] Read more.

Accurate diagnosis of acute appendicitis from abdominal ultrasound is a challenging task, since traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, and wall thickness, rely on complete identification or visualization of the appendix and the diagnosis is frequently operator subjective. In this paper, we propose a robust automatic segmentation method for inflamed appendix identification to mitigate abovementioned difficulties. We use outlier rejection fuzzy c-means clustering (FCM) algorithm within a double-layered learning structure to extract the target inflamed appendix area. The proposed method extracts the target appendix in 98 cases out of 100 test images, which is far better than traditional FCM, standard outlier FCM, and double-layered learning with FCM in correct extraction rate. Furthermore, we investigate the outlier rejection effect and double layered learning effect by comparing our proposed method with standard double-layered FCM and the standard outlier-rejection FCM. In this comparison, the proposed method exhibits robust segmentation results in accuracy, precision, and recall by 2.5~5.6% over two standard methods in quality with human pathologists’ marking as the ground truth. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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19 pages, 1983 KiB

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Metaverse for Digital Anti-Aging Healthcare: An Overview of Potential Use Cases Based on Artificial Intelligence, Blockchain, IoT Technologies, Its Challenges, and Future Directions

by Md Ariful Islam Mozumder, Tagne Poupi Theodore Armand, Shah Muhammad Imtiyaj Uddin, Ali Athar, Rashedul Islam Sumon, Ali Hussain and Hee-Cheol Kim

Appl. Sci. 2023, 13(8), 5127; https://doi.org/10.3390/app13085127 - 20 Apr 2023

Cited by 40 | Viewed by 5731

Abstract

Metaverse is the buzz technology of the moment raising attention both from academia and industry. Many stakeholders are considering an extension of their existing applications into the metaverse environment for more usability. The healthcare industry is gradually making use of the metaverse to [...] Read more.

Metaverse is the buzz technology of the moment raising attention both from academia and industry. Many stakeholders are considering an extension of their existing applications into the metaverse environment for more usability. The healthcare industry is gradually making use of the metaverse to improve quality of service and enhance living conditions. In this paper, we focus on the potential of digital anti-aging healthcare in the metaverse environment. We show how we can use metaverse environment to enhance healthcare service quality and increase the life expectancy of patients through more confident processes, such as chronic disease management, fitness, and mental health control, in the metaverse. The convergence of artificial intelligence (AI), blockchain (BC), Internet of Things (IoT), immersive technologies, and digital twin in the metaverse environment presents new scopes for the healthcare industry. By leveraging these technologies, healthcare providers can improve patient outcomes, reduce healthcare costs, and create new healthcare experiences for a better life, thus facilitating the anti-aging process. AI can be used to analyze large-scale medical data and make personalized treatment plans, while blockchain can create a secure and transparent healthcare data ecosystem. As for IoT devices, they collect real-time data from patients, which is necessary for treatment. Together, these technologies can transform the healthcare industry and improve the lives of patients worldwide. The suggestions highlighted in this paper are worthy to undergo implementation and create more benefits that will promote a digital anti-aging process for its users for a longer life experience. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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34 pages, 6627 KiB

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Prospects of Structural Similarity Index for Medical Image Analysis

by Vicky Mudeng, Minseok Kim and Se-woon Choe

Appl. Sci. 2022, 12(8), 3754; https://doi.org/10.3390/app12083754 - 8 Apr 2022

Cited by 38 | Viewed by 6857

Abstract

An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with [...] Read more.

An image quality matrix provides a significant principle for objectively observing an image based on an alteration between the original and distorted images. During the past two decades, a novel universal image quality assessment has been developed with the ability of adaptation with human visual perception for measuring the difference of a degraded image from the reference image, namely a structural similarity index. Structural similarity has since been widely used in various sectors, including medical image evaluation. Although numerous studies have reported the use of structural similarity as an evaluation strategy for computer-based medical images, reviews on the prospects of using structural similarity for medical imaging applications have been rare. This paper presents previous studies implementing structural similarity in analyzing medical images from various imaging modalities. In addition, this review describes structural similarity from the perspective of a family’s historical background, as well as progress made from the original to the recent structural similarity, and its strengths and drawbacks. Additionally, potential research directions in applying such similarities related to medical image analyses are described. This review will be beneficial in guiding researchers toward the discovery of potential medical image examination methods that can be improved through structural similarity index. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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