• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.463-468
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• 2021

Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.313-330
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• 2021

In an autonomous driving system equipped with a camera and lidar, depth completion techniques enable dense depth estimation. In particular, using self-supervised learning it is possible to train the depth completion network even without ground truth. In actual autonomous driving, such depth completion should have very short latency as it is the input of other algorithms. So, rather than complicate the network structure to increase the accuracy like previous studies, this paper focuses on network latency. We design a U-Net type network with RegNet encoders optimized for GPU computation. Instead, this paper presents several techniques that can increase accuracy during the process of self-supervised learning. The proposed techniques increase the robustness to unreliable lidar inputs. Also, they improve the depth quality for edge and sky regions based on the semantic information extracted in advance. Our experiments confirm that our model is very lightweight (2.42 ms at 1280x480) but resistant to noise and has qualities close to the latest studies.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.69-75
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• 2018

Recently, various convergence technologies are attracting attention due to the block chain innovation technology in the M2M environment. Although the block-chain-based technology is known to be secure in its own right, there are various problems such as security and weight reduction in various M2M environments connected with this. In this paper, we propose a new lightweight method for the hash tree generation of block chains to solve the lightweight problem. It is designed considering extensibility without affecting the existing block chain. Performance analysis shows that the computation performance increases with decreasing the existing hash length.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4C
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• pp.326-332
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• 2002

To transfer the large size of data more quickly among cluster nodes, the lightweight messaging scheme has been developed. VIA(Virtual Interface Architecture) allows that user can directly communicate with network devices without any interference of kernel and has become a communication protocol for clusters. But one must spend a lot of time to be skillful with it because of difficulties of programming. Therefore, we propose an easier library called EVIL(Easy-to-use Virtual Interface Library) that developers can easily deal with. We evaluated the performance of EVIL, Native VIA, TCP/IP respectively.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.17-26
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• 2019

The Constrained Application Protocol (CoAP) is a specialized web transfer protocol proposed by the IETF for use in IoT environments. CoAP was designed as a lightweight machine-to-machine protocol for resource constrained environments. Due to the strength of low overhead, the number of CoAP devices is expected to rise rapidly. When CoAP runs over UDP for wireless sensor networks, CoAP needs to support congestion control mechanisms. Since the default CoAP defines a minimal mechanism for congestion control, several schemes to improve the mechanism have been proposed. To keep CoAP lightweight, the majority of the schemes have been focused mainly on how to measure RTT accurately and how to set RTO adaptively according to network conditions, but other approaches such as rate-based congestion control were proposed more recently. In this paper, we survey the literature on congestion control for CoAP and discuss the future research directions.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.239-252
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• 2005

Ubiquitous Sensor Network(USN) , the core technology for the Ubiquitous environments ,must be operated in the restrictive battery capacity and computing. From this cause, USN needs the lightweight design for low electric energy and the minimum computing. The previous mutual authentication. based on J$\emptyset$sang's trust model, in USN has a character that makes the lightweight mutual authentication possible in conformity with minimum computing. But, it has an imperfection at the components of representing the trust from a lightweight point of view. In this paper, we improve on the J$\emptyset$sang's trust model to apply a lightweight mutual authentication in USN. The proposed trust model in USN defines the trust information with the only degree of trust-entity(x)'s belief. The defined trust information has a superiority over the J$\emptyset$sang's trust model from a computing Point of view. because it computes information by Probability and logic operation(AND).

• Journal of Internet of Things and Convergence
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• v.7 no.1
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• pp.1-7
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• 2021

The IoT market continues to expand and grow, but the security threat to IoT devices is also increasing. However, it is difficult to apply the security technology applied to the existing system to IoT devices that have a problem of resource limitation. Therefore, in this paper, we present a service that can improve the security of IoT devices by presenting authentication and lightweight cryptographic algorithms that can reduce the overhead of applying security features, taking into account the nature of resource limitations of IoT devices. We want to apply these service to home network IoT equipment to provide security. The authentication and lightweight cryptographic algorithm application protocols presented in this paper have secured the safety of the service through the use of LEA encryption algorithms and secret key generation by users, IoT devices and server in the IoT environment. Although there is no difference in speed from randomly generating secret keys in experiments, we verify that the problem of resource limitation of IoT devices can be solved by additionally not applying logic for secret key sharing to IoT devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.23-29
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• 2022

Recently, due to the increase in the use of Internet of Things (IoT) devices, the amount of data transmitted and processed to cloud computing servers has increased rapidly. As a result, network problems (delay, server overload and security threats) are emerging. In particular, edge computing with lower computational capabilities than cloud computing requires a lightweight authentication algorithm that can easily authenticate numerous IoT devices.In this paper, we proposed a key-agreement protocol of a lightweight algorithm that guarantees anonymity and forward and backward secrecy between IoT and edge devices. and the proposed algorithm is stable in MITM and replay attacks for edge device and IoT. As a result of comparing and analyzing the proposed key-agreement protocol with previous studies, it was shown that a lightweight protocol that can be efficiently used in IoT and edge devices.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.17-25
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• 2023

In this paper, we propose a lightweight deep learning architecture tailored for efficient text detection in fashion design sketch images. Given the increasing prominence of Digital Transformation in the fashion industry, there is a growing emphasis on harnessing digital tools for creating fashion design sketches. As digitization becomes more pervasive in the fashion design process, the initial stages of text detection and recognition take on pivotal roles. In this study, a lightweight network was designed by building upon existing text detection deep learning models, taking into consideration the unique characteristics of apparel design drawings. Additionally, a separately collected dataset of apparel design drawings was added to train the deep learning model. Experimental results underscore the superior performance of our proposed deep learning model, outperforming existing text detection models by approximately 20% when applied to fashion design sketch images. As a result, this paper is expected to contribute to the Digital Transformation in the field of clothing design by means of research on optimizing deep learning models and detecting specialized text information.