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Harnessing Integration of Symbol-Rate Equalizer and Timing Recovery for Enhanced Stability
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 s...
Journal of information and communication convergence engineering 2024; 22(2): 89-97
Parallel Implementation of Scrypt: A Study on GPU Acceleration for Password-Based Key Derivation Function
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-optim...
Journal of information and communication convergence engineering 2024; 22(2): 98-108
Real-Time CCTV Based Garbage Detection for Modern Societies using Deep Convolutional Neural Network with Person-Identification
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 convolu...
Journal of information and communication convergence engineering 2024; 22(2): 109-120
Key Management Server Design in Multiuser Environment for Critical File Protection
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 encryptio...
Journal of information and communication convergence engineering 2024; 22(2): 121-126
Particle Swarm Optimization based Haptic Localization of Plates with Electrostatic Vibration Actuators
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 ...
Journal of information and communication convergence engineering 2024; 22(2): 127-132
Prediction of Dissolved Oxygen at Anyang-stream using XG-Boost and Artificial Neural Networks
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-...
Journal of information and communication convergence engineering 2024; 22(2): 133-138
Comparison of Fall Detection Systems Based on YOLOPose and Long Short-Term Memory
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 th...
Journal of information and communication convergence engineering 2024; 22(2): 139-144
Prediction of Closed Quotient During Vocal Phonation using GRU-type Neural Network with Audio Signals
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 techniqu...
Journal of information and communication convergence engineering 2024; 22(2): 145-152
Meta-Analysis of Cognitive and Affective Effects of Arduino- Based Educational Programs
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 i...
Journal of information and communication convergence engineering 2024; 22(2): 153-158
Anomaly Detection System for Solar Power Distribution Panels utilizing Thermal Images
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 ...
Journal of information and communication convergence engineering 2024; 22(2): 159-164
Improving Chest X-ray Image Classification via Integration of Self-Supervised Learning and Machine Learning Algorithms
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 ...
Journal of information and communication convergence engineering 2024; 22(2): 165-171

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About JICCE

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Background

It has been published since 2003 with a title of International Journal of Maritime Information and Communication Sciences. In 2012, this journal was greatly reformed to further improve the quality of journal, for example, title, research areas and policy of journal etc. The title was changed from the year of 2012 as Journal of Information and Communication Convergence Engineering.

Aims and Scope

Journal of Information and Communication Convergence Engineering (J. Inf. Commun. Converg. Eng., JICCE) is an official English journal of the Korea Institute of Information and Communication Engineering (KIICE). It is an international, peer reviewed, and open access journal that is published quarterly in March, June, September, and December. Its objective is to provide rapid publications of original and significant contributions and it covers all areas related to information and communication convergence engineering including the following areas: communication system and applications, networking and services, intelligent information system, multimedia and digital convergence, semiconductors and communication devices, imaging and biomedical engineering, and computer vision and autonomous vehicles.

Journal of Information and Communication Convergence Engineering Jouranl of information and
communication convergence engineering
(J. Inf. Commun. Converg. Eng.)

eISSN 2234-8883
pISSN 2234-8255