• Title/Summary/Keyword: Cortex transform

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Effect of EEG Wave Type of Visual Cortex on Conjugate Movement of Eyeball according to Movement of Visual Target (시 표적의 이동에 따른 안구의 동향운동이 대뇌 시피질의 뇌파에 미치는 영향)

  • Kim, Douk Hoon
    • Journal of Korean Ophthalmic Optics Society
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    • v.7 no.1
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    • pp.51-55
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    • 2002
  • This study was to investigate the effect of EEG wave type of visual cortex on conjugate movement of eyeball according to movement of visual target. Visual evoked potential(VEP) system used the Bio-Pag(production in USA) and recorded to 586 computer. The illumination of test room was 50lux and the visual target was red light dot of 3cm size. The results of dextroversion and levoversion as follows : The visual stimulation waves on the visual cortex have about 71% of delta wave, about 12% of beta wave, about 9% theta wave and about 6% of alpha wave respectively. The dextroversion and levoversion state was similar results on the histogram amplitude of EEG wave, frequency of EEG wave type, EEG wave style and phase diagram of amplitude. Expecially the histogram amplitude of EEG wave appeared almost the Gaussian shape and the phase analysis of amplitude of EEG wave was nearly linear shape. On the fast fourier transform of the amplitude and Hz, the frequency was almost low frequency under 20 Hz, and the dextroversion and levoversion shape was similar results.

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Study on the Implementation of Primitive Visual Cortex Model in Retina Using Gabor Wavelet (가버 웨이블릿을 이용한 원시 시각 피질 모델 구현에 관한 연구)

  • Lee, Youngseok
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.13 no.6
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    • pp.477-482
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    • 2020
  • The human visual cortex has the characteristic that reacts sensitively to stimuli with special directional or temporal frequency changes while it is insensitive to selective stimuli of spatial phases. In this paper we implemented the model of complex cell using an image estimation iterative algorithm by Gabor wavelet transform. The performance of implemented model evaluated the consistency between the physiological experimental results in related papers. The implemented model is limited in the complete model of the receptive field in the retina where simple cells and complex cells are distributed together. But the implemented model express the reaction of the complex cells from the point of view of the detection of corners and edges.

An Image Quality Assessment Scheme based on HVS using Gabor Function

  • Eom Minyoung;Choe Yoonsik
    • Proceedings of the IEEK Conference
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    • summer
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    • pp.128-132
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    • 2004
  • In this paper, we propose a new image quality assessment scheme considering the human visual perception characteristics. A subjective quality assessment is obtained by the response of the receptive field in the primary visual cortex and a human's eye can't focus on all of the visual range in a moment. Take advantage of two facts above, we apply Gabor wavelet transform, which is well fit the receptive field in the cortex, to divided constant sized subblocks. Then a local distortion of the subblocks and a global distortion for the entire image are calculated in order. The proposed method has been evaluated using video test sequences provided by the Video Quality Experts Group (VQEG). The experimental results show that good correlation with human perception is obtained using the proposed metric, which is what we called GPSNR.

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A Study on Performance Improvement of Non-Profiling Based Power Analysis Attack against CRYSTALS-Dilithium (CRYSTALS-Dilithium 대상 비프로파일링 기반 전력 분석 공격 성능 개선 연구)

  • Sechang Jang;Minjong Lee;Hyoju Kang;Jaecheol Ha
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.33 no.1
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    • pp.33-43
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    • 2023
  • The National Institute of Standards and Technology (NIST), which is working on the Post-Quantum Cryptography (PQC) standardization project, announced four algorithms that have been finalized for standardization. In this paper, we demonstrate through experiments that private keys can be exposed by Correlation Power Analysis (CPA) and Differential Deep Learning Analysis (DDLA) attacks on polynomial coefficient-wise multiplication algorithms that operate in the process of generating signatures using CRYSTALS-Dilithium algorithm. As a result of the experiment on ARM-Cortex-M4, we succeeded in recovering the private key coefficient using CPA or DDLA attacks. In particular, when StandardScaler preprocessing and continuous wavelet transform applied power traces were used in the DDLA attack, the minimum number of power traces required for attacks is reduced and the Normalized Maximum Margines (NMM) value increased by about 3 times. Conseqently, the proposed methods significantly improves the attack performance.

An Objective Quality Assessment Based on Gabor Wavelet (Gabor 웨이블릿 기반 객관적 화질 평가)

  • 엄민영;최윤식;장석각;조봉관
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.6
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    • pp.81-88
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    • 2004
  • In this paper, we propose a new objective quality assessment method considering the human visual perception characteristics. A subjective quality assessment is obtained by the response of the receptive field in the primary visual cortex and a human's eye can't focus on all of the visual range in a moment. Take advantage of two facts above, we apply Gabor wavelet transform which is well fit the receptive field in the cortex, to divided constant sized subblocks. Then a local distortion of the subblocks and a global distortion for the entire image are calculated in order. The proposed method has been evaluated using video test sequences provided by the Video Quality Experts Group (VQEG). The experimental results show that good correlation with human perception is obtained using the proposed metric, which is what we called GPSNR.

Instrumental Analysis of the Human Hair Damaged by Bleaching Treatments - Focused on ATR FT-IRM -

  • Ha, Byung-Jo
    • Journal of Fashion Business
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    • v.12 no.6
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    • pp.23-33
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    • 2008
  • The physico-chemical characteristics by bleaching treatments were assessed by several instrumental analyses such as surface morphology, chemical structural change, color change as well as tensile strength. The change of morphological characteristic was observed through scanning electron microscope(SEM). The observation of the fine structure on hair surface by SEM showed the bleached hair had much damaged to hair cuticle, and some of cuticle surface were worn away. To investigate the chemical structural changes in hair keratin, the cross-sections of hair samples were directly analysed using Fourier transform infrared microspectroscopy(FT-IRM). The results showed the cysteic acid S=O band intensity was distinctively increased by performing the bleaching treatment. The cleavage of cystine was appeared to proceed primarily through the sulfur-sulfur (-S-S-) fission whereby cysteic acid was formed as a principal oxidation products. The distribution of amide I band in hair keratin was determined by attenuated total reflectance(ATR) FT-IR mapping image. The results showed that the outer side of hair cortex was more damaged than the inner side of the hair cortex. Also, during chemical bleaching of the hair with alkaline peroxide, the hair was turned to reddish yellow due to the oxidative degradation of eumelanin. This means the eumelanin is more unstable than pheomelanin in chemical oxidation. With bleaching, the tensile strength was also reduced as a results of the chemical oxidation.

An Improved EEG Signal Classification Using Neural Network with the Consequence of ICA and STFT

  • Sivasankari, K.;Thanushkodi, K.
    • Journal of Electrical Engineering and Technology
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    • v.9 no.3
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    • pp.1060-1071
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    • 2014
  • Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

Visible Distortion Predictors Based on Visual Attention in Color Images

  • Cho, Sang-Gyu;Hwang, Jae-Jeong;Kwak, Nae-Joung
    • Journal of information and communication convergence engineering
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    • v.10 no.3
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    • pp.300-306
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    • 2012
  • An image attention model and its application to image quality assessment are discussed in this paper. The attention model is based on rarity quantification, which is related to self-information to attract the attention in an image. It is relatively simpler than the others but results in taking more consideration of global contrasts between a pixel and the whole image. The visual attention model is used to develop a local distortion predictor, named color visual differences predictor (CVDP), in color images in order to effectively detect luminance and color distortions.

Functional Neuroanatomy of Memory (기억의 기능적 신경 해부학)

  • Lee, Sung-Hoon
    • Sleep Medicine and Psychophysiology
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    • v.4 no.1
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    • pp.15-28
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    • 1997
  • Longterm memory is encoded in the neuronal connectivities of the brain. The most successful models of human memory in their operations are models of distributed and self-organized associative memory, which are founded in the principle of simulaneous convergence in network formation. Memory is not perceived as the qualities inherent in physical objects or events, but as a set of relations previously established in a neural net by simultaneousy occuring experiences. When it is easy to find correlations with existing neural networks through analysis of network structures, memory is automatically encoded in cerebral cortex. However, in the emergence of informations which are complicated to classify and correlated with existing networks, and conflictual with other networks, those informations are sent to the subcortex including hippocampus. Memory is stored in the form of templates distributed across several different cortical regions. The hippocampus provides detailed maps for the conjoint binding and calling up of widely distributed informations. Knowledge about the distribution of correlated networks can transform the existing networks into new one. Then, hippocampus consolidats new formed network. Amygdala may enable the emotions to influence the information processing and memory as well as providing the visceral informations to them. Cortico-striatal-pallido-thalamo-cortical loop also play an important role in memory function with analysis of language and concept. In case of difficulty in processing in spite of parallel process of informations, frontal lobe organizes theses complicated informations of network analysis through temporal processing. With understanding of brain mechanism of memory and information processing, the brain mechanism of mental phenomena including psychopathology can be better explained in terms of neurobiology and meuropsychology.

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Design and Verification of Pipelined Face Detection Hardware (파이프라인 구조의 얼굴 검출 하드웨어 설계 및 검증)

  • Kim, Shin-Ho;Jeong, Yong-Jin
    • Journal of Korea Multimedia Society
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    • v.15 no.10
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    • pp.1247-1256
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    • 2012
  • There are many filter based image processing algorithms and they usually require a huge amount of computations and memory accesses making it hard to attain a real-time performance, expecially in embedded applications. In this paper, we propose a pipelined hardware structure of the filter based face detection algorithm to show that the real time performance can be achieved by hardware design. In our design, the whole computation is divided into three pipeline stages: resizing the image (Resize), Transforming the image (ICT), and finding candidate area (Find Candidate). Each stage is optimized by considering the parallelism of the computation to reduce the number of cycles and utilizing the line memory to minimize the memory accesses. The resulting hardware uses 507 KB internal SRAM and occupies 9,039 LUTs when synthesized and configured on Xilinx Virtex5LX330 FPGA. It can operate at maximum 165MHz clock, giving the performance of 108 frame/sec, while detecting up to 20 faces.