• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.1-10
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• 2021

The Internet of things (IoT) is the main advancement in data processing and communication technologies. In IoT, intelligent devices play an exciting role in wireless communication. Although, sensor nodes are low-cost devices for communication and data gathering. However, sensor nodes are more vulnerable to different security threats because these nodes have continuous access to the internet. Therefore, the multiparty security credential-based key generation mechanism provides effective security against several attacks. The key generation-based methods are implemented at sensor nodes, edge nodes, and also at server nodes for secure communication. The main challenging issue in a collaborative key generation scheme is the extensive multiplication. When the number of parties increased the multiplications are more complex. Thus, the computational cost of batch key and multiparty key-based schemes is high. This paper presents a Secure Multipart Key Distribution scheme (SMKD) that provides secure communication among the nodes by generating a multiparty secure key for communication. In this paper, we provide node authentication and session key generation mechanism among mobile nodes, head nodes, and trusted servers. We analyzed the achievements of the SMKD scheme against SPPDA, PPDAS, and PFDA schemes. Thus, the simulation environment is established by employing an NS 2. Simulation results prove that the performance of SMKD is better in terms of communication cost, computational cost, and energy consumption.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.487-499
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• 2022

Machine learning such as deep learning have been widely used in recent years. Recently deep learning is performed in a trusted execution environment such as ARM TrustZone to improve security in edge devices and embedded devices with low computing resource. To mitigate this problem, we propose TPMP that efficiently uses the limited memory of TEE through DNN model partitioning. TPMP achieves high confidentiality of DNN by performing DNN models that could not be run with existing memory scheduling methods in TEE through optimized memory scheduling. TPMP required a similar amount of computational resources to previous methodologies.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.31-48
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• 2023

The popularization of smart devices and subsequent optimization of their sensing capacity has resulted in a novel mobile crowdsensing (MCS) pattern, which employs smart devices as sensing nodes by recruiting users to develop a sensing network for multiple-task performance. This technique has garnered much scholarly interest in terms of sensing range, cost, and integration. The MCS is prevalent in various fields, including environmental monitoring, noise monitoring, and road monitoring. A complete MCS life cycle entails task allocation, data collection, and data aggregation. Regardless, specific drawbacks remain unresolved in this study despite extensive research on this life cycle. This article mainly summarizes single-task, multi-task allocation, and space-time multi-task allocation at the task allocation stage. Meanwhile, the quality, safety, and efficiency of data collection are discussed at the data collection stage. Edge computing, which provides a novel development idea to derive data from the MCS system, is also highlighted. Furthermore, data aggregation security and quality are summarized at the data aggregation stage. The novel development of multi-modal data aggregation is also outlined following the diversity of data obtained from MCS. Overall, this article summarizes the three aspects of the MCS life cycle, analyzes the issues underlying this study, and offers developmental directions for future scholars' reference.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.240-257
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• 2023

The industrial Internet of Things (IIoT) is characterized by intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. The heterogeneous IIoT devices require a high data rate, high reliability, high coverage, and low delay, thus posing a significant challenge to information security. High-performance edge and cloud servers are a good backup solution for IIoT devices with limited capabilities. However, privacy leakage and network attack cases may occur in heterogeneous IIoT environments. Cloud-based multi-party computing is a reliable privacy-protecting technology that encourages multiparty participation in joint computing without privacy disclosure. However, the default cloud selection method does not meet the heterogeneous IIoT requirements. The server can be dishonest, significantly increasing the probability of multi-party computation failure or inefficiency. This paper proposes a blockchain and smart contract-based optimized cloud node selection framework. Different participants choose the best server that meets their performance demands, considering the communication delay. Smart contracts provide a progressive request mechanism to increase participation. The simulation results show that our framework improves overall multi-party computing efficiency by up to 44.73%.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.5
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• pp.1-7
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• 2002

Deep sub-micron bulk CMOS circuits require gate electrode materials such as metal silicide and titanium silicide for gate oxides. Many authors have conducted research to improve the quality of the sub-micron gate oxide. However, few have reported on the electrical quality and reliability of an ultra-thin gate. In this paper, we will recommend a novel shallow trench isolation structure and a two-step TiS $i_2$ formation process to improve the corner metal oxide semiconductor field-effect transistor (MOSFET) for sub-0.1${\mu}{\textrm}{m}$ VLSI devices. Differently from using normal LOCOS technology, deep sub-micron CMOS devices using the novel shallow trench isolation (STI) technology have unique "inverse narrow-channel effects" when the channel width of the device is scaled down. The titanium silicide process has problems because fluorine contamination caused by the gate sidewall etching inhibits the silicide reaction and accelerates agglomeration. To resolve these Problems, we developed a novel two-step deposited silicide process. The key point of this process is the deposition and subsequent removal of titanium before the titanium silicide process. It was found by using focused ion beam transmission electron microscopy that the STI structure improved the narrow channel effect and reduced the junction leakage current and threshold voltage at the edge of the channel. In terms of transistor characteristics, we also obtained a low gate voltage variation and a low trap density, saturation current, some more to be large transconductance at the channel for sub-0.1${\mu}{\textrm}{m}$ VLSI devices.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.659-665
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• 2017

Power semiconductor devices required the low on-resistance and high breakdown voltage. Wide band-gap materials opened a new technology of the power devices which promised a thin drift layer at an identical breakdown voltage. The diamond had the wide band-gap of 5.5 eV which induced the low power loss, high breakdown capability, low intrinsic carrier generation, and high operation temperature. We investigated the p-type pseudo-vertical diamond Schottky barrier diodes using a numerical simulation. The impact ionization rate was material to calculating the breakdown voltage. We revised the impact ionization rate of the diamond for adjusting the parallel-plane breakdown field at 10 MV/cm. Effects of the field plate on the breakdown voltage was also analyzed. A conventional diamond Schottky barrier diode without field plate exhibited the high forward current of 0.52 A/mm and low on-resistance of $1.71{\Omega}-mm$ at the forward voltage of 2 V. The simulated breakdown field of the conventional device was 13.3 MV/cm. The breakdown voltage of the conventional device and proposed devices with the $SiO_2$ passivation layer, anode field plate (AFP), and cathode field plate (CFP) was 680, 810, 810, and 1020 V, respectively. The AFP cannot alleviate the concentration of the electric field at the cathode edge. The CFP increased the breakdown voltage with evidences of the electric field and potential. However, we should consider the dielectric breakdown because the ideal breakdown field of the diamond is higher than that of the $SiO_2$, which is widely used as the passivation layer. The real breakdown voltage of the device with CFP decreased from 1020 to 565 V due to the dielectric breakdown.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.1
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• pp.8-22
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• 2014

In the past few decades, CMOS logic technologies and devices have been successfully developed with the steady miniaturization of the feature size. At the sub-30-nm CMOS technology nodes, one of the main hurdles for continuously and successfully scaling down CMOS devices is the parametric failure caused by random variations such as line edge roughness (LER), random dopant fluctuation (RDF), and work-function variation (WFV). The characteristics of each random variation source and its effect on advanced device structures such as multigate and ultra-thin-body devices (vs. conventional planar bulk MOSFET) are discussed in detail. Further, suggested are suppression methods for the LER-, RDF-, and WFV-induced threshold voltage (VTH) variations in advanced CMOS logic technologies including the double-patterning and double-etching (2P2E) technique and in advanced device structures including the fully depleted silicon-on-insulator (FD-SOI) MOSFET and FinFET/tri-gate MOSFET at the sub-30-nm nodes. The segmented-channel MOSFET (SegFET) and junctionless transistor (JLT) that can suppress the random variations and the SegFET-/JLT-based static random access memory (SRAM) cell that enhance the read and write margins at a time, though generally with a trade-off between the read and the write margins, are introduced.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.451-461
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• 2015

In megavoltage (MV) radiotherapy, delivering the dose to the target volume is important while protecting the surrounding normal tissue. The purpose of this study was to evaluate the modulation transfer function (MTF), the noise power spectrum (NPS), and the detective quantum efficiency (DQE) using an edge block in megavoltage X-ray imaging (MVI). We used an edge block, which consists of tungsten with dimensions of 19 (thickness) ${\times}$ 10 (length) ${\times}$ 1 (width) $cm^3$ and measured the pre-sampling MTF at 6 MV energy. Various radiation therapy (RT) devices such as TrueBeam$^{TM}$ (Varian), BEAMVIEW$^{PLUS}$ (Siemens), iViewGT (Elekta) and Clinac$^{(R)}$iX (Varian) were used. As for MTF results, TrueBeam$^{TM}$(Varian) flattening filter free(FFF) showed the highest values of $0.46mm^{-1}$ and $1.40mm^{-1}$ for MTF 0.5 and 0.1. In NPS, iViewGT (Elekta) showed the lowest noise distribution. In DQE, iViewGT (Elekta) showed the best efficiency at a peak DQE and $1mm^{-1}DQE$ of 0.0026 and 0.00014, respectively. This study could be used not only for traditional QA imaging but also for quantitative MTF, NPS, and DQE measurement for development of an electronic portal imaging device (EPID).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.523-535
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• 2017

Local intelligent services aim at controlling local services such as cooling or lightening services in a certain local area, using Internet-of-Things (IoT) sensor data in the area. As the IoT paradigm has evolved, local intelligent services have gained increasing attention. However, most of the local intelligent service mechanism proposed so far do not directly take the users' presence and service preference information into account for controlling local services. This study proposes an individual presence-and-preference-based local service system (IPP-LISS). We present a intelligent service control algorithm and implement a prototype system of IPP-LISS. Typically, the intelligence part of IPP-LISS including the prediction models, is generated on remote server in the cloud because of their compute-intense aspect. However, this can cause huge data traffic between IoT devices and servers in the cloud. The emerging mobile edge computing technology will be a promising solution of this challenge of IPP-LISS. In this paper, we implement IPP-LISS in the cloud, and then, based on the implementation result, we discuss applying the mobile edge computing technology to the IPP-LISS application.