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International Conference on Cyberworlds

作者： Da-Young Yu Hyung-Tak Lee Youngsam Kim Jong-Hyuk Roh Soohyung Kim Jeonghun Ku Han-Jeong Hwang Dept. Electronics and Information Engineering Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Republic of Korea Dept. Electronics and Information Engineering Korea University Sejong Republic of Korea Electronics and Telecommunications Research Institute (ETRI) Daejeon Republic of Korea Department of Biomedical Engineering College of Medicine Keimyung University Daegu Republic of Korea

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Biometrics based on electromyogram (EMG) and inertial measurement unit (IMU) signals has received significant attention due to its resistance to forgery and tampering, as well as its relatively higher convenience and security compared to other biometric modalities. However, most EMG and IMU signal-based authentication methods require explicit authentication using unnatural gestures, which is not userfriendly. To address this, we collected EMG and IMU data during virtual reality (VR) rhythm game play, which does not require explicit gestures, and performed implicit authentication. Using a linear discriminant analysis (LDA) classifier, we achieved classification accuracies of 91.71% for human identification and 87.91% for human authentication. The result demonstrated the feasibility of a seamless and implicit authentication in VR environments.

关键词： Biometrics Solid modeling Measurement units Accuracy Authentication Inertial navigation Games Virtual reality Rhythm Electromyography

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A Study on the Computer Aided Diagnosis System for Early Gastric Cancer Lesion Based on EfficientNetV2-L through Data Filtering

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1259-1265页

作者： Lee, Han-Sung Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

Gastric cancer is a common cancer worldwide, especially in Korea. Early diagnosis is very important to increase the full recovery rate. However, early gastric cancer has no special symptoms and is a disease that even experts find difficult to diagnose in gastroscopy. Therefore, in this paper proposed a computer-aided diagnosis(CADx) for early gastric cancer diagnosis using EfficientNetV2-L. Due to the nature of medical data, it is difficult to collect a large amount of data. The data used for training was augmented using Cifar10 policy of the Google's AutoAugment. Additionally, the augmented image was used as an input to the model trained with the original dataset and filtered according to the classification threshold. EfficientNetV2 is a classification network designed Training-NAS that can learn the feature of lesions with a small number of parameters. As a result, EfficientNetV2 set to the threshold value of 0.9 achieved the performance of accuracy 0.943 for early gastric cancer and abnormal image classification. The AUC value also increases from 0.972 to 0.991, showing that the data filtering method of this study was effective for improvement of classification performance. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Computer aided diagnosis

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Swoon Monitoring System based on YOLOv4-CSP Object Detection Algorithm

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Transactions of the Korean Institute of Electrical Engineers 2022年第1期71卷 239-245页

作者： Chae, Jung-Woo Kim, Tae-Kyeong Cho, Hyun-Chong Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering Kangwon National University Korea Republic of Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

When swoon occurs, it is important to detect it immediately. If immediate and quick detection is not possible, additional serious accidents may occur. Therefore, this study proposed swoon monitoring system that can detect swoon from real-time images of CCTV based on the increasing number and installation of CCTV. For the detection of swoon, deep learning based object detection algorithm applied with the Cross Stage Partial connections network model was used. Through this, swoon detection is performed within a single frame, and the amount of computation required for swoon detection is reduced and can have a fast processing speed. In addition, it is possible to have high detection performance through various BoF and BoS techniques with CSP network model. The result of swoon detection and classification performance through object detection algorithm showed 94.2% F1-Score, 92.6% mAP and 95.4% Accuracy. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Object detection

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A Study of the Ecological Dynamics of Batumi, on the Black Sea Coast, Analyzing Environmental Factors and Their Impact

A Study of the Ecological Dynamics of Batumi, on the Black S...

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Lily Petriashvili Irine Khomeriki Mzia Kiknadze Tamar Lominadze Nona Otkhozoria Dept. of Program Engineering Georgian Technical University Tbilisi Georgia Dept. of Interdisciplinary Informatics Georgian Technical University Tbilisi Georgia Dept. of Computer Engineering Georgian Technical University Tbilisi Georgia Dept. of Information Systems Georgian Technical University Tbilisi Georgia Dept. of Microprocessor and Measurement Systems Georgian Technical University Tbilisi Georgia

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

This paper studies the complex ecological dynamics of Batumi on the Black Sea coast, through adetailed analysis of environmental factors and their impact. We use Business Intelligence (BI) methods to explore the complex interactions between natural and human impacts on the local ecosystems. By using data-focused approaches, including statistical analysis and predictive modeling, we aim to distinguish patterns and trends in ecological dynamics. This study aspires to make clear the complex relationships between environmental factors such as climate change, pollution levels, habitat degradation, and biodiversity loss, and their impacts on the coastal ecosystem of Batumi. Through this study, we aim to provide valuable information on sustainable management practices and conservation measures to protect the ecological integrity of this important coastal region.

关键词： Sea coast Pollution Statistical analysis Ecosystems Habitats Sea measurements Predictive models

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Noise-robust Apple Disease Classification with Image Augmentation Methods

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1302-1307页

作者： Kim, Jang-Yeon Kim, Tae-Kyeong Cho, Hyun-Chong Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

When the apple disease occurs, accurate and rapid control must be carried out. If appropriate measures are not taken, the spread of the disease and secondary damage such as soil contamination caused by pesticides may occur. In this paper, the apple disease classification system that can classify the type of disease as well as normal from image is proposed. The apple disease classes consists of Marssonina blotch, Fire Blight, Valsa cacker, Alernaria blotch, and Bitter rot. Xception network was used to extract and learn features from image. Google's AutoAugment CIFAR-10 policy is used to increase apple disease data to increase network's classification performance. Then, in order to increase the reliability of data, the augmented data was selected by model trained only with original data. Gaussian, Salt-and-pepper, Speckle and Poisson noise were added to the test data to show good performance for noisy input data. We compared the performance of the model trained with original data and augmented data selected by threshold value 0.9. As a result, the proposed study showed a performance improvement of up to 6% in F1-Score. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Deep learning

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Effect of Dynamic Binaural Beats on Concentration Enhancement

Effect of Dynamic Binaural Beats on Concentration Enhancemen...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Jun-Seok Lee Yun-Sung Lee Han-Jeong Hwang Dept. of Electronics and Information Engineering Korea University Sejong Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong Korea

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

The objective of this study was to develop an auditory stimulation method that can improve concentration and to investigate its neurophysiological effects using electrocardiography (ECG). The auditory stimulus was developed by mixing the sound of white noise with dynamic binaural beats (DBB) that featured time-varying carrier frequencies. The experimental paradigm involved conducting an association memory (AM) task and a mental arithmetic (MA) task to assess cognitive performance. In the behavioral results, no significant differences were confirmed between the two experimental conditions (sound vs. no sound) for both cognitive tasks. In the AM task, no significant differences in HRV change between the baseline and task were observed between the two conditions. However, during the MA task, there was a significant decrease in HRV when participants were exposed to the auditory stimulation compared to when it was not.

关键词： Time-frequency analysis Rain Asia Information processing Electrocardiography White noise Heart rate variability Arithmetic

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Zero-Shot Vision-and-Language Navigation with Collision Mitigation in Continuous Environment

arXiv

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arXiv 2024年

作者： Jeong, Seongjun Kang, Gi-Cheon Kim, Joochan Zhang, Byoung-Tak Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Dept. of Computer Science and Engineering Seoul National University Korea Republic of Korea Republic of

We propose the zero-shot Vision-and-Language Navigation with Collision Mitigation (VLN-CM), which takes these considerations. VLN-CM is composed of four modules and predicts the direction and distance of the next movement at each step. We utilize large foundation models for each modules. To select the direction, we use the Attention Spot Predictor (ASP), View Selector (VS), and Progress Monitor (PM). The ASP employs a Large Language Model (e.g. ChatGPT) to split navigation instructions into attention spots, which are objects or scenes at the location to move to (e.g. a yellow door). The VS selects from panorama images provided at 30-degree intervals the one that includes the attention spot, using CLIP similarity. We then choose the angle of the selected image as the direction to move in. The PM uses a rule-based approach to decide which attention spot to focus on next, among multiple spots derived from the instructions. If the similarity between the current attention spot and the visual observations decreases consecutively at each step, the PM determines that the agent has passed the current spot and moves on to the next one. For selecting the distance to move, we employed the Open Map Predictor (OMP). The OMP uses panorama dept. information to predict an occupancy mask. We then selected a collision-free distance in the predicted direction based on the occupancy mask. We evaluated our method using the validation data of VLN-CE. Our approach showed better performance than several baseline methods, and the OPM was effective in mitigating collisions for the agent. © 2024, CC BY-NC-ND.

关键词： Visual languages

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Balanced Spherical Grid for Egocentric View Synthesis

Balanced Spherical Grid for Egocentric View Synthesis

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Changwoon Choi Sang Min Kim Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Korea Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We present EgoNeRF, a practical solution to reconstruct large-scale real-world environments for VR assets. Given a few seconds of casually captured 360 video, EgoNeRF can efficiently build neural radiance fields. Motivated by the recent acceleration of NeRF using feature grids, we adopt spherical coordinate instead of conventional Cartesian coordinate. Cartesian feature grid is inefficient to represent large-scale unbounded scenes because it has a spatially uniform resolution, regardless of distance from viewers. The spherical parameterization better aligns with the rays of egocentric images, and yet enables factorization for performance enhancement. However, the naive spherical grid suffers from singularities at two poles, and also cannot represent unbounded scenes. To avoid singularities near poles, we combine two balanced grids, which results in a quasi-uniform angular grid. We also partition the radial grid exponentially and place an environment map at infinity to represent unbounded scenes. Furthermore, with our resampling technique for grid-based methods, we can increase the number of valid samples to train NeRF volume. We extensively evaluate our method in our newly introduced synthetic and real-world egocentric 360 video datasets, and it consistently achieves state-of-the-art performance.

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DDD: A Perceptually Superior Low-Response-Time DNN-Based Declipper

DDD: A Perceptually Superior Low-Response-Time DNN-Based Dec...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jayeon Yi Junghyun Koo Kyogu Lee Dept. of Electrical Engineering and Computer Science University of Michigan Dept. of Intelligence and Information Seoul National University Interdisciplinary Program in Artificial Intelligence Seoul National University Artificial Intelligence Institute Seoul National University

Clipping is a common nonlinear distortion that occurs whenever the input or output of an audio system exceeds the supported range. This phenomenon undermines not only the perception of speech quality but also downstream processes utilizing the disrupted signal. Therefore, a real-time-capable, robust, and low-response-time method for speech declipping (SD) is desired. In this work, we introduce DDD (Demucs-Discriminator-Declipper), a real-time-capable speech-declipping deep neural network (DNN) that requires less response time by design. We first observe that a previously untested real-time-capable DNN model, Demucs, exhibits a reasonable declipping performance. Then we utilize adversarial learning objectives to increase the perceptual quality of output speech without additional inference overhead. Subjective evaluations on harshly clipped speech shows that DDD outperforms the baselines by a wide margin in terms of speech quality. We perform detailed waveform and spectral analyses to gain an insight into the output behavior of DDD in comparison to the baselines. Finally, our streaming simulations also show that DDD is capable of sub-decisecond mean response times, outperforming the state-of-the-art DNN approach by a factor of six.

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A 4-Channel 0.23$mm^{2}$ Voltage-to-Time Converter AFE with $3.7\mu V_{rms}$ Noise and $480nW$ Galvanic Impulse Uplink

A 4-Channel 0.23$mm^{2}$ Voltage-to-Time Converter AFE with ...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Mehdi Bandali Morgan Riley Benjamin C. Johnson Dept. of Electrical and Computer Engineering Boise State University Boise ID USA Morgan Riley is with Biomedical Engineering Doctoral Program Boise State Universitvy Boise ID USA

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

We present a 4-channel 15 kS/s Voltage-to-Time Converter (VTC) analog front-end (AFE) with a $0.49\mu W$ impulse-based galvanic uplink for a peripheral nerve interface. Multiple, low-noise, high-data-rate channels are needed to sense compound action potentials and measure their conduction velocity as they propagate down a peripheral nerve. To achieve high energy efficiency for these constraints, the AFE encodes and transmits data with time-domain charge-balanced impulses through an implantable galvanic link. Each channel consists of an integrator with charge-based sampling and amplification for rapid multiplexing. A shared VTC encodes the amplitude-domain outputs of each integrator into differential time-domain impulses. Since the timing can be synchronized with stimulation, this AFE achieves instant artifact recovery after rail-to-rail stimulation events. We designed this AFE in a 180nm CMOS process, and the simulation results show an SNDR of 60dB and noise of $3.7\mu Vrms$ . Thanks to the new galvanic uplink protocol, this front-end only consumes $11.28\mu W$ including wireless data transmission for four channels.

关键词： Wireless communication Wireless sensor networks Protocols Simulation Noise Data communication Velocity measurement

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