arXiv subjects

Seokjoo Shin

Publications and source records attributed to Seokjoo Shin.

9 recordsLinked to original sources

arXiv2025

Visual Event Detection over AI-Edge LEO Satellites with AoI Awareness

Non terrestrial networks (NTNs), particularly low Earth orbit (LEO) satellite systems, play a vital role in supporting future mission critical applications such as disaster relief. Recent advances in artificial intelligence (AI)-native communications enable LEO satellites to act as intelligent edge nodes capable of on board learning and task oriented inference. However, the limited link budget, coupled with severe path loss and fading, significantly constrains reliable downlink transmission. This paper proposes a deep joint source-channel coding (DJSCC)-based downlink scheme for AI-native LEO networks, optimized for goal-oriented visual inference. In the DJSCC approach, only semantically meaningful features are extracted and transmitted, whereas conventional separate source-channel coding (SSCC) transmits the original image data. To evaluate information freshness and visual event detection performance, this work introduces the age of misclassified information (AoMI) metric and a threshold based AoI analysis that measures the proportion of users meeting application specific timeliness requirements. Simulation results show that the proposed DJSCC scheme provides higher inference accuracy, lower average AoMI, and greater threshold compliance than the conventional SSCC baseline, enabling semantic communication in AI native LEO satellite networks for 6G and beyond.

cs.IT↗

arXiv2024

Deep Learning in Physical Layer: Review on Data Driven End-to-End Communication Systems and their Enabling Semantic Applications

Deep learning (DL) has revolutionized wireless communication systems by introducing datadriven end-to-end (E2E) learning, where the physical layer (PHY) is transformed into DL architectures to achieve peak optimization. Leveraging DL for E2E optimization in PHY significantly enhances its adaptability and performance in complex wireless environments, meeting the demands of advanced network systems such as 5G and beyond. Furthermore, this evolution of data-driven PHY optimization has also enabled advanced semantic applications across various modalities, including text, image, audio, video, and multimodal transmissions. These applications elevate communication from bit-level to semantic-level intelligence, making it capable of discerning context and intent. Although the PHY, as a DL architecture, plays a crucial role in enabling semantic communication (SemCom) systems, comprehensive studies that integrate both E2E communication and SemCom systems remain significantly underexplored. This highlights the novelty and potential of these integrative fields, marking them as a promising research domain. Therefore, this article provides a comprehensive review of the emerging field of data-driven PHY for E2E communication systems, emphasizing their role in enabling semantic applications across various modalities. It also identifies key challenges and potential research directions, serving as a crucial guide for future advancements in DL for E2E communication and SemCom systems.

cs.NI↗

arXiv2023

Review of Deep Learning-based Malware Detection for Android and Windows System

Differentiating malware is important to determine their behaviors and level of threat; as well as to devise defensive strategy against them. In response, various anti-malware systems have been developed to distinguish between different malwares. However, most of the recent malware families are Artificial Intelligence (AI) enable and can deceive traditional anti-malware systems using different obfuscation techniques. Therefore, only AI-enabled anti-malware system is robust against these techniques and can detect different features in the malware files that aid in malicious activities. In this study we review two AI-enabled techniques for detecting malware in Windows and Android operating system, respectively. Both the techniques achieved perfect accuracy in detecting various malware families.

cs.LG↗

arXiv2022

Channel model for end-to-end learning of communications systems: A survey

The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance of the system. Recently, end-to-end learning of communications systems through machine learning (ML) have been proposed to optimize the system metrics jointly over all components. These methods show performance improvements but has a limitation that it requires a differentiable channel model. In this study, we have summarized the existing approaches that alleviates this problem. We believe that this study will provide better understanding of the topic and an insight into future research in this field.

cs.LG↗

arXiv2022

Just-Noticeable-Difference Based Edge Map Quality Measure

The performance of an edge detector can be improved when assisted with an effective edge map quality measure. Several evaluation methods have been proposed resulting in different performance score for the same candidate edge map. However, an effective measure is the one that can be automated and which correlates with human judgement perceived quality of the edge map. Distance-based edge map measures are widely used for assessment of edge map quality. These methods consider distance and statistical properties of edge pixels to estimate a performance score. The existing methods can be automated; however, they lack perceptual features. This paper presents edge map quality measure based on Just-Noticeable-Difference (JND) feature of human visual system, to compensate the shortcomings of distance-based edge measures. For this purpose, we have designed constant stimulus experiment to measure the JND value for two spatial alternative. Experimental results show that JND based distance calculation outperforms existing distance-based measures according to subjective evaluation.

cs.CV↗

arXiv2022

A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models

In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a sequence (as opposed to a single value used in the existing methods) generated by a chaotic map. We have used logistic maps for their low computational requirements. In addition, to compensate for any inefficiency because of the logistic maps, we use a second key to shuffle the sequence. We have compared the proposed method in terms of encryption efficiency and classification accuracy of the DL models on them. We have validated the proposed method with CIFAR datasets. The analysis shows that when classification is performed on the cipher images, the model preserves accuracy of the existing methods while provides better security.

cs.CR↗

arXiv2014

Robust Modulation Technique for Diffusion-based Molecular Communication in Nanonetworks

Diffusion-based molecular communication over nanonetworks is an emerging communication paradigm that enables nanomachines to communicate by using molecules as the information carrier. For such a communication paradigm, Concentration Shift Keying (CSK) has been considered as one of the most promising techniques for modulating information symbols, owing to its inherent simplicity and practicality. CSK modulated subsequent information symbols, however, may interfere with each other due to the random amount of time that molecules of each modulated symbols take to reach the receiver nanomachine. To alleviate Inter Symbol Interference (ISI) problem associated with CSK, we propose a new modulation technique called Zebra-CSK. The proposed Zebra-CSK adds inhibitor molecules in CSK-modulated molecular signal to selectively suppress ISI causing molecules. Numerical results from our newly developed probabilistic analytical model show that Zebra-CSK not only enhances capacity of the molecular channel but also reduces symbol error probability observed at the receiver nanomachine.

cs.IT↗

arXiv2013

Initial Ranging for Prioritized Network Entry in IEEE 802.16 Network

Prioritized network entry is desirable for establishing preferential network connectivity for the higher priority users when different priority users exist over a given network. In line with such desirability, we propose a simple but efficient priority differentiated initial ranging mechanism considering an Orthogonal Frequency Division Multiple Access (OFDMA) based IEEE 802.16 network. In the proposed mechanism, we introduce an approach that integrates an explicit CDMA-ranging code reservation scheme with a Ranging Slot Selection Window (RSSW) differentiation scheme. Simulation results are provided to characterize the performance of the proposed mechanism.

cs.NI↗

arXiv2013

Optimum Tag Reading Efficiency of Multi-Packet Reception Capable RFID Readers

Maximizing the tag reading rate of a reader is one of the most important design objectives in RFID systems as the tag reading rate is inversely proportional to the time required to completely read all the tags within the reader's radio field. To this end, numerous techniques have been independently suggested so far and they can be broadly categorized into pure advancements in the link-layer tag anti-collision protocols and pure advancements in the physical-layer RF transmission/reception model. In this paper, we show by rigorous mathematical analysis and Monte Carlo simulations that how such two independent approaches can be coupled to attain the optimum tag reading efficiency in a RFID system considering a Dynamic Frame Slotted Aloha based link layer anti-collision protocol at tags and a Multi-Packet Reception capable RF reception model at the reader.

cs.NI↗