Journal of information and
communication convergence engineering
(J. Inf. Commun. Converg. Eng.)

Vol.22 No.2, June 30, 2024 Article TypeRegular paper

• Regular paper June 30, 2024

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  Harnessing Integration of Symbol-Rate Equalizer and Timing Recovery for Enhanced Stability

  Adrián Francisco Ramírez*, Felipe Pasquevich , and Graciela Corral Briones

  Journal of information and communication convergence engineering 2024; 22(2): 89-97 https://doi.org/10.56977/jicce.2024.22.2.89

  

  

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  Abstract : This research conducted a comparative analysis of two communication systems. The first system utilizes a conventional series configuration consisting of a symbol-rate least mean square (LMS) equalizer followed by a timing recovery loop. The second system introduces an innovative approach that integrates a symbol-rate LMS equalizer and a timing recovery component within a single loop, allowing mutual feedback between the two blocks. In this integrated system, the equalizer also provides timing error information, thereby eliminating the requirement for a separate threshold error detector. This study examines the performance curves of both system configurations. The simulation results revealed that the integrated system may offer improved stability in terms of multiple transmission challenges, including phase and frequency offsets and intersymbol interference. Further analysis and discussion highlight the significant insights and implications of the proposed architecture. Overall, the present findings provide an alternative perspective on the joint implementation of equalization and timing recovery in communication systems.

• Regular paper June 30, 2024

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  Parallel Implementation of Scrypt: A Study on GPU Acceleration for Password-Based Key Derivation Function

  SeongJun Choi , DongCheon Kim , and Seog Chung Seo

  Journal of information and communication convergence engineering 2024; 22(2): 98-108 https://doi.org/10.56977/jicce.2024.22.2.98

  

  

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  Abstract : Scrypt is a password-based key derivation function proposed by Colin Percival in 2009 that has a memory-hard structure. Scrypt has been intentionally designed with a memory-intensive structure to make password cracking using ASICs, GPUs, and similar hardware more difficult. However, in this study, we thoroughly analyzed the operation of Scrypt and proposed strategies to maximize computational parallelism in GPU environments. Through these optimizations, we achieved an outstanding performance improvement of 8284.4% compared with traditional CPU-based Scrypt computations. Moreover, the GPU-optimized implementation presented in this paper outperforms the simple GPU-based Scrypt processing by a significant margin, providing a performance improvement of 204.84% in the RTX3090. These results demonstrate the effectiveness of our proposed approach in harnessing the computational power of GPUs and achieving remarkable performance gains in Scrypt calculations. Our proposed implementation is the first GPU implementation of Scrypt, demonstrating the ability to efficiently crack Scrypt.

• Regular paper June 30, 2024

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  Real-Time CCTV Based Garbage Detection for Modern Societies using Deep Convolutional Neural Network with Person-Identification

  Syed Muhammad Raza , Syed Ghazi Hassan , Syed Ali Hassan , and Soo Young Shin , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(2): 109-120 https://doi.org/10.56977/jicce.2024.22.2.109

  

  

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  Abstract : Trash or garbage is one of the most dangerous health and environmental problems that affect pollution. Pollution affects nature, human life, and wildlife. In this paper, we propose modern solutions for cleaning the environment of trash pollution by enforcing strict action against people who dump trash inappropriately on streets, outside the home, and in unnecessary places. Artificial Intelligence (AI), especially Deep Learning (DL), has been used to automate and solve issues in the world. We availed this as an excellent opportunity to develop a system that identifies trash using a deep convolutional neural network (CNN). This paper proposes a real-time garbage identification system based on a deep CNN architecture with eight distinct classes for the training dataset. After identifying the garbage, the CCTV camera captures a video of the individual placing the trash in the incorrect location and sends an alert notice to the relevant authority.

• Regular paper June 30, 2024

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  Key Management Server Design in Multiuser Environment for Critical File Protection

  Sung-Hwa Han

  Journal of information and communication convergence engineering 2024; 22(2): 121-126 https://doi.org/10.56977/jicce.2024.22.2.121

  

  

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  Abstract : In enterprise environments, file owners are often required to share critical files with other users, with encryption-based file delivery systems used to maintain confidentiality. However, important information might be leaked if the cryptokey used for encryption is exposed. To recover confidentiality, the file owner must then re-encrypt and redistribute the file along with its new encryption key, which requires considerable resources. To address this, we propose a key management server that minimizes the distribution of encryption keys when critical files are compromised, with unique encryption keys assigned for each registered user to access critical files. While providing the targeted functions, the server employs a level of system resources comparable to that of legacy digital rights management. Thus, when implemented in an enterprise environment, the proposed server minimizes cryptokey redistribution while maintaining accessibility to critical files in the event of an information breach.

• Regular paper June 30, 2024

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  Particle Swarm Optimization based Haptic Localization of Plates with Electrostatic Vibration Actuators

  Gwanghyun Jo , Tae-Heon Yang , and Seong-Yoon Shin , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(2): 127-132 https://doi.org/10.56977/jicce.2024.22.2.127

  

  

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  Abstract : Haptic actuators for large display panels play an important role in bridging the gap between the digital and physical world by generating interactive feedback for users. However, the generation of meaningful haptic feedback is challenging for large display panels. There are dead zones with low haptic sensations when a small number of actuators are applied. In contrast, it is important to control the traveling wave generated by the actuators in the presence of multiple actuators. In this study, we propose a particle swarm optimization (PSO)-based algorithm for the haptic localization of plates with electrostatic vibration actuators. We modeled the transverse displacement of a plate under the effect of actuators by employing the Kirchhoff-Love plate theory. In addition, starting with twenty randomly generated particles containing the actuator parameters, we searched for the optimal actuator parameters using a stochastic process to yield localization. The capability of the proposed PSO algorithm is reported and the transverse displacement has a high magnitude only in the targeted region.

• Regular paper June 30, 2024

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  Prediction of Dissolved Oxygen at Anyang-stream using XG-Boost and Artificial Neural Networks

  Keun Young Lee , Bomchul Kim , and Gwanghyun Jo

  Journal of information and communication convergence engineering 2024; 22(2): 133-138 https://doi.org/10.56977/jicce.2024.22.2.133

  

  

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  Abstract : Dissolved oxygen (DO) is an important factor in ecosystems. However, the analysis of DO is frequently rather complicated because of the nonlinear phenomenon of the river system. Therefore, a convenient model-free algorithm for DO variable is required. In this study, a data-driven algorithm for predicting DO was developed by combining XGBoost and an artificial neural network (ANN), called ANN-XGB. To train the model, two years of ecosystem data were collected in Anyang, Seoul using the Troll 9500 model. One advantage of the proposed algorithm is its ability to capture abrupt changes in climate-related features that arise from sudden events. Moreover, our algorithm can provide a feature importance analysis owing to the use of XGBoost. The results obtained using the ANN-XGB algorithm were compared with those obtained using the ANN algorithm in the Results Section. The predictions made by ANN-XGB were mostly in closer agreement with the measured DO values in the river than those made by the ANN.

• Regular paper June 30, 2024

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  Comparison of Fall Detection Systems Based on YOLOPose and Long Short-Term Memory

  Seung Su Jeong , Nam Ho Kim , and Yun Seop Yu , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(2): 139-144 https://doi.org/10.56977/jicce.2024.22.2.139

  

  

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  Abstract : In this study, four types of fall detection systems – designed with YOLOPose, principal component analysis (PCA), convolutional neural network (CNN), and long short-term memory (LSTM) architectures – were developed and compared in the detection of everyday falls. The experimental dataset encompassed seven types of activities: walking, lying, jumping, jumping in activities of daily living, falling backward, falling forward, and falling sideways. Keypoints extracted from YOLOPose were entered into the following architectures: RAW-LSTM, PCA-LSTM, RAW-PCA-LSTM, and PCA-CNN-LSTM. For the PCA architectures, the reduced input size stemming from a dimensionality reduction enhanced the operational efficiency in terms of computational time and memory at the cost of decreased accuracy. In contrast, the addition of a CNN resulted in higher complexity and lower accuracy. The RAW-LSTM architecture, which did not include either PCA or CNN, had the least number of parameters, which resulted in the best computational time and memory while also achieving the highest accuracy.

• Regular paper June 30, 2024

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  Prediction of Closed Quotient During Vocal Phonation using GRU-type Neural Network with Audio Signals

  Hyeonbin Han , Keun Young Lee , Seong-Yoon Shin , Yoseup Kim , Gwanghyun Jo , Jihoon Park , and Young-Min Kim

  Journal of information and communication convergence engineering 2024; 22(2): 145-152 https://doi.org/10.56977/jicce.2024.22.2.145

  

  

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  Abstract : Closed quotient (CQ) represents the time ratio for which the vocal folds remain in contact during voice production. Because analyzing CQ values serves as an important reference point in vocal training for professional singers, these values have been measured mechanically or electrically by either inverse filtering of airflows captured by a circumferentially vented mask or post-processing of electroglottography waveforms. In this study, we introduced a novel algorithm to predict the CQ values only from audio signals. This has eliminated the need for mechanical or electrical measurement techniques. Our algorithm is based on a gated recurrent unit (GRU)-type neural network. To enhance the efficiency, we pre-processed an audio signal using the pitch feature extraction algorithm. Then, GRU-type neural networks were employed to extract the features. This was followed by a dense layer for the final prediction. The Results section reports the mean square error between the predicted and real CQ. It shows the capability of the proposed algorithm to predict CQ values.

• Regular paper June 30, 2024

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  Meta-Analysis of Cognitive and Affective Effects of Arduino- Based Educational Programs

  Bong Seok Jang , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(2): 153-158 https://doi.org/10.56977/jicce.2024.22.2.153

  

  

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  Abstract : This study aims to summarize the effects of Arduino-based educational programs through a meta-analysis. Eleven eligible primary studies were obtained through a systematic literature review and coded accordingly. The results are as follows: The meta-analysis revealed that the overall effect size for all the studies was 0.518. Analysis of the moderator variables indicated statistically significant differences between them. Regarding the learning domains, the results were ranked in descending order of the cognitive and affective domains. Within the cognitive domain, the effect sizes were ranked in descending order as follows: logical thinking, content knowledge, convergence competency, self-efficacy, computational thinking, and creative problem-solving skills. In terms of subject areas, the descending order of effect sizes was agriculture, STEAM, environmental science, practical arts, artificial intelligence, informatics, and computers. Regarding school level, the results were ranked in the following descending order: college, elementary school, middle school, and high school.

• Regular paper June 30, 2024

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  Anomaly Detection System for Solar Power Distribution Panels utilizing Thermal Images

  Kwang-Seong Shin , Jong-Chan Kim , and Seong-Yoon Shin , Member, KIICE

  Journal of information and communication convergence engineering 2024; 22(2): 159-164 https://doi.org/10.56977/jicce.2024.22.2.159

  

  

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  Abstract : This study aimed to develop an advanced anomaly-detection system tailored for solar power distribution panels using thermal imaging cameras to ensure operational stability. It addresses the imperative shift toward digitalized safety management in electrical facilities, transcending the limitations of conventional empirical methodologies. Our proposed system leverages a faster R-CNN-based artificial intelligence model optimized through meticulous hyperparameter tuning to efficiently detect anomalies in distribution panels. Through comprehensive experimentation, we validated the efficacy of the system in accurately identifying anomalies, thereby propelling safety protocols forward during the fourth industrial revolution. This study signifies a significant stride toward fortifying the integrity and resilience of solar power distribution systems, which is pivotal for adapting to emerging technological paradigms and evolving safety standards in the energy sector. These findings offer valuable insights for enhancing the reliability and efficiency of safety management practices and fostering a safer and more sustainable energy landscape.

• Regular paper June 30, 2024

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  Improving Chest X-ray Image Classification via Integration of Self-Supervised Learning and Machine Learning Algorithms

  Tri-Thuc Vo and Thanh-Nghi Do

  Journal of information and communication convergence engineering 2024; 22(2): 165-171 https://doi.org/10.56977/jicce.2024.22.2.165

  

  

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  Abstract : In this study, we present a novel approach for enhancing chest X-ray image classification (normal, Covid-19, edema, mass nodules, and pneumothorax) by combining contrastive learning and machine learning algorithms. A vast amount of unlabeled data was leveraged to learn representations so that data efficiency is improved as a means of addressing the limited availability of labeled data in X-ray images. Our approach involves training classification algorithms using the extracted features from a linear fine-tuned Momentum Contrast (MoCo) model. The MoCo architecture with a Resnet34, Resnet50, or Resnet101 backbone is trained to learn features from unlabeled data. Instead of only fine-tuning the linear classifier layer on the MoCo-pretrained model, we propose training nonlinear classifiers as substitutes for softmax in deep networks. The empirical results show that while the linear fine-tuned ImageNet-pretrained models achieved the highest accuracy of only 82.9% and the linear fine-tuned MoCo-pretrained models an increased highest accuracy of 84.8%, our proposed method offered a significant improvement and achieved the highest accuracy of 87.9%.