https://scholar.dgist.ac.kr/handle/20.500.11750/372 2026-04-08T18:39:25Z 2026-04-08T18:39:25Z Woojin Jeong https://scholar.dgist.ac.kr/handle/20.500.11750/16253 2025-07-25T04:25:03Z 2021-12-31T15:00:00Z

Title: Adaptive Controller Area Network Intrusion Detection System Considering Temperature Variation Author(s): Woojin Jeong Description: Controller area network (CAN), Intrusion detection system (IDS), Transmitter identification, Temperature, Physical layer security, 침임 탐지 시스템, 송신기 식별, 차량의 작동온도, 물리계층 보안

2021-12-31T15:00:00Z Torati, Ramulu Reddy, Venu Yoon, Seok Soo Kim, CheolGi https://scholar.dgist.ac.kr/handle/20.500.11750/685 2025-07-24T07:23:10Z 2015-12-31T15:00:00Z

Title: Electrochemical biosensor for Mycobacterium tuberculosis DNA detection based on gold nanotubes array electrode platform Author(s): Torati, Ramulu; Reddy, Venu; Yoon, Seok Soo; Kim, CheolGi Abstract: The template assisted electrochemical deposition technique was used for the synthesis of gold nanotubes array (AuNTsA). The morphological structure of the synthesized AuNTsA was observed by scanning electron microscopy and found that the individual nanotubes are around 1.5 mu m in length with a diameter of 200 nm. Nanotubes are vertically aligned to the Au thick film, which is formed during the synthesis process of nanotubes. The electrochemical performance of the AuNTsA was compared with the bare Au electrode and found that AuNTsA has better electron transfer surface than bare Au electrode which is due to the high surface area. Hence, the AuNTsA was used as an electrode for the fabrication of DNA hybridization biosensor for detection of Mycobacterium Tuberculosis DNA. The DNA hybridization biosensor constructed by AuNTsA electrode was characterized by cyclic voltammetry technique with Fe (CN)(6)(3-/4-) as an electrochemical redox indicator. The selectivity of the fabricated biosensor was illustrated by hybridization with complementary DNA and non-complementary DNA with probe DNA immobilized AuNTsA electrode using methylene blue as a hybridization indicator. The developed electrochemical DNA biosensor shows good linear range of complementary DNA concentration from 0.01 ng/mu L to 100 ng/mu L with high detection limit. (C) 2015 Elsevier B.V. All rights reserved. Description: Aromatic compounds; Biosensors; Cyclic voltammetry; DNA; Electrodeposition; Electrodes; Gold; Nanotubes; Reduction; Scanning electron microscopy; Synthesis (chemical); Thick films; Yarn; DNA biosensors; Electrochemical biosensor; Electrochemical deposition; Electrochemical DNA biosensors; Electrochemical performance; Hybridization; Methylene Blue; Mycobacterium tuberculosis; Electrochemical electrodes; bacterial DNA; complementary DNA; gold nanotube; nanotube; unclassified drug; Article; biosensor; cyclic potentiometry; DNA determination; DNA hybridization; DNA probe; electrochemical analysis; electron transport; gold nanotube array; limit of detection; nanoarray; nanofabrication; nonhuman

2015-12-31T15:00:00Z Abibullaev, B[Abibullaev, Berdakh] An, J[An, Jinung] https://scholar.dgist.ac.kr/handle/20.500.11750/1615 2025-07-24T07:24:02Z 2012-11-30T15:00:00Z

Title: Classification of frontal cortex haemodynamic responses during cognitive tasks using wavelet transforms and machine learning algorithms Author(s): Abibullaev, B[Abibullaev, Berdakh]; An, J[An, Jinung] Abstract: Recent advances in neuroimaging demonstrate the potential of functional near-infrared spectroscopy (fNIRS) for use in brain-computer interfaces (BCIs). fNIRS uses light in the near-infrared range to measure brain surface haemoglobin concentrations and thus determine human neural activity. Our primary goal in this study is to analyse brain haemodynamic responses for application in a BCI. Specifically, we develop an efficient signal processing algorithm to extract important mental-task-relevant neural features and obtain the best possible classification performance. We recorded brain haemodynamic responses due to frontal cortex brain activity from nine subjects using a 19-channel fNIRS system. Our algorithm is based on continuous wavelet transforms (CWTs) for multi-scale decomposition and a soft thresholding algorithm for de-noising. We adopted three machine learning algorithms and compared their performance. Good performance can be achieved by using the de-noised wavelet coefficients as input features for the classifier. Moreover, the classifier performance varied depending on the type of mother wavelet used for wavelet decomposition. Our quantitative results showed that CWTs can be used efficiently to extract important brain haemodynamic features at multiple frequencies if an appropriate mother wavelet function is chosen. The best classification results were obtained by a specific combination of input feature type and classifier. © 2012 IPEM.

2012-11-30T15:00:00Z Abibullaev, Berdakh An, Jinung https://scholar.dgist.ac.kr/handle/20.500.11750/1618 2025-07-24T07:24:02Z 2012-07-31T15:00:00Z

Title: Decision Support Algorithm for Diagnosis of ADHD Using Electroencephalograms Author(s): Abibullaev, Berdakh; An, Jinung Abstract: Attention deficit hyperactivity disorder is a complex brain disorder which is usually difficult to diagnose. As a result many literature reports about the increasing rate of misdiagnosis of ADHD disorder with other types of brain disorder. There is also a risk of normal children to be associated with ADHD if practical diagnostic criteria are not supported. To this end we propose a decision support system in diagnosing of ADHD disorder through brain electroencephalographic signals. Subjects of 10 children participated in this study, 7 of them were diagnosed with ADHD disorder and remaining 3 children are normal group. Our main goal of this sthudy is to present a supporting diagnostic tool that uses signal processing for feature selection and machine learning algorithms for diagnosis.Particularly, for a feature selection we propose information theoretic which is based on entropy and mutual information measure. We propose a maximal discrepancy criterion for selecting distinct (most distinguishing) features of two groups as well as a semi-supervised formulation for efficiently updating the training set. Further, support vector machine classifier trained and tested for identification of robust marker of EEG patterns for accurate diagnosis of ADHD group. We demonstrate that the applicability of the proposed approach provides higher accuracy in diagnostic process of ADHD disorder than the few currently available methods. © 2011 Springer Science+Business Media, LLC.

2012-07-31T15:00:00Z