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Journal of Engineering science and technology Review 2025年第1期18卷 110-119页

作者： Mali, Amey Kharche, Shubhangi Nevase, Anamika Nair, Parvathy Vaity, Chinmay Department of Electronics & Computer Science SIES Graduate School of Technology Nerul India

Globally, vision impairment poses significant challenges, especially for those who are blind, leading to their daily struggles. Our proposed system proposes an innovative system that combines computer vision and Internet of Things technologies to empower visually impaired people during navigation, with the goal of increasing their mobility and independence. Conventional camera-based solutions frequently fail to accurately recognize objects and obstacles, as well as achieve precise distance measurements. In response, our proposed work introduces a smart blind stick assistance system that aims to address these limitations. Using the COCO dataset supplemented with 80 standard objects and an additional 15, our system employs YOLOv5 for real-time object detection, allowing accurate identification of obstacles in the environment. Most importantly, real-time scene descriptions are delivered audibly with multilingual support, allowing users to understand spatial concepts more effectively. The implementation includes a seamless integration of the Raspberry Pi and Chainer library for efficient neural network inference, as well as text-to-speech synthesis to provide spoken feedback. Additionally, we have integrated an ultrasonic sensor for precise distance measurement, enhancing the system's ability to detect obstacles and provide accurate navigation assistance. During our tests, the system achieved 95% object detection accuracy (92-98% confidence interval, n=10) using an augmented COCO dataset and provides real-time, multilingual scene descriptions (average processing time 50ms ± 10ms;ultrasonic sensor latency 2ms ± 0.5ms). This novel approach represents a significant advancement in object recognition and accessibility for visually impaired people, promising to improve their quality of life and promote greater independence. © (2025), (International Hellenic University school of science and technology). All rights reserved.

关键词： Ultrasonic sensors

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Programmable directional color dynamics using plasmonics

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Microsystems & Nanoengineering 2024年第1期10卷 325-334页

作者： Gyurin Kim Doeun Kim Soeun Ko Jang-Hwan Han Juhwan Kim Joo Hwan Ko Young Min Song Hyeon-Ho Jeong School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Department of Semiconductor Engineering Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea Artificial Intelligence(AI)Graduate School Gwangju Institute of Science and TechnologyGwangju 61005Republic of Korea

Adaptive multicolor filters have emerged as key components for ensuring color accuracy and resolution in outdoor visual ***,the current state of this technology is still in its infancy and largely reliant on liquid crystal devices that require high voltage and bulky structural ***,we present a multicolor nanofilter consisting of multilayered‘active’plasmonic nanocomposites,wherein metallic nanoparticles are embedded within a conductive polymer *** nanocomposites are fabricated with a total thickness below 100 nm using a‘lithography-free’method at the wafer level,and they inherently exhibit three prominent optical modes,accompanying scattering phenomena that produce distinct dichroic reflection and transmission ***,a pivotal achievement is that all these colors are electrically manipulated with an applied external voltage of less than 1 V with 3.5 s of switching speed,encompassing the entire visible ***,this electrically programmable multicolor function enables the effective and dynamic modulation of the color temperature of white light across the warm-to-cool spectrum(3250 K-6250 K).This transformative capability is exceptionally valuable for enhancing the performance of outdoor optical devices that are independent of factors such as the sun’s elevation and prevailing weather conditions.

关键词： directional prevailing exceptional

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Score Calculation with Local and Global Features for Visual Place Recognition

Score Calculation with Local and Global Features for Visual ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Ikeda, Iori Adachi, Miho Miyamoto, Ryusuke Graduate School of Science and Technology Department of Computer Science Japan School of Science and Technology Meiji University Department of Computer Science Kawasaki Japan

ISBN: (纸本)9798350350821

Visual place recognition (VPR) seems effective for the localization process in visual navigation of a robot because a robot can recognize locations and poses using only visual information if the accuracy of VPR is sufficient. However, to apply VPR for actual scenarios where multiple locations to be distinguished are in a captured image, the classification accuracy should be improved. To solve this problem, we propose a novel method for likelihood computation in location matching composed of two kinds of components corresponding to local and global features. Experimental results using a novel dataset composed of actual images taken around the course of the Tsukuba Challenge showed that the proposed method improved accuracy by 1-6 % compared to conventional reranking methods using only local features. © 2024 IEEE.

关键词： Machine vision

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Understanding Brain Network Stimulation for Emotion Analyzing Connectivity Feature Map from Electroencephalography

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International Journal of Advanced computer science and Applications 2025年第4期16卷 542-551页

作者： Maria, Mahfuza Akter Akhand, M.A.H. Kamal, Abdus Samad Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Graduate School of Science and Technology Gunma University Kiryu376-8515 Japan

In understanding brain functioning by Electroencephalography (EEG), it is essential to be able to not only identify more active brain areas but also understand connectivity among different areas. The functional and efficient connectivity networks of the brain have been examined in this study by constructing a connectivity feature map (CFM) with four widely used connectivity methods from the Database for Emotion Analysis Using Physiological Signals (DEAP) emotional EEG data to research how this connectivity’s patterns are influenced by emotion. According to the investigation results, emotions are mainly related to the parietal, central, and frontal regions. The parietal region is more responsible for emotion alteration among these three regions. Positive emotions are associated with more direct correlations and dependencies than negative ones. When experiencing negative emotions, the regions of the brain function more synchronously as well as there are less flow of information. Whether direct or inverse, there is less correlation between brain regions in the higher frequency band than in the lower frequency band. Higher frequencies are associated with increased dependence and directed information transfer between brain regions. Generally, the electrodes in the same lobe show stronger connectivity than those in different lobes. At a glance, the present study is a comprehensive analysis to understand brain network stimulation for emotion from EEG, and it significantly differs from the existing emotion recognition studies typically focused on recognition proficiency. © (2025), (science and Information Organization). All rights reserved.

关键词： Emotion Recognition

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Spatiotemporal Pooling on Appropriate Topological Maps Represented as Two-Dimensional Images for EEG Classification 17th

Spatiotemporal Pooling on Appropriate Topological Maps Repr...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Fukushima, Takuto Miyamoto, Ryusuke Department of Computer Science Graduate School of Science and Technology Meiji University Kawasaki Japan Department of Computer Science School of Science and Technology Meiji University Kawasaki Japan

ISBN: (纸本)9789819609000

Motor imagery classification based on electroencephalography (EEG) signals is one of the most important brain-computer interface applications, although it need further improvement. Several methods have attempted to obtain useful information from EEG signals by using recent deep learning techniques such as transformers. To improve the classification accuracy, this study proposes a novel EEG-based motor imagery classification method with three key features: generation of a topological map represented as a two-dimensional image from EEG signals with coordinate transformation based on t-SNE, use of the InternImage to extract spatial features, and use of spatiotemporal pooling inspired by PoolFormer to exploit spatiotemporal information concealed in a sequence of EEG images. Experimental results using the PhysioNet EEG Motor Movement/Imagery dataset showed that the proposed method achieved the best classification accuracy of 88.57%, 80.69%, and 70.20% on two-, three-, and four-class motor imagery tasks in cross-individual validation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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DiffMat:Latent diffusion models for image-guided material generation

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Visual Informatics 2024年第1期8卷 6-14页

作者： Liang Yuan Dingkun Yan Suguru Saito Issei Fujishiro Graduate School of Science and Technology Keio UniversityKanagawaJapan School of Computing Tokyo Institute of TechnologyTokyoJapan Department of Information and Computer Science Keio UniversityKanagawaJapan

Creating realistic materials is essential in the construction of immersive virtual *** existing techniques for material capture and conditional generation rely on flash-lit photos,they often produce artifacts when the illumination mismatches the training *** this study,we introduce DiffMat,a novel diffusion model that integrates the CLIP image encoder and a multi-layer,crossattention denoising backbone to generate latent materials from images under various *** a pre-trained StyleGAN-based material generator,our method converts these latent materials into high-resolution SVBRDF textures,a process that enables a seamless fit into the standard physically based rendering pipeline,reducing the requirements for vast computational resources and expansive *** surpasses existing generative methods in terms of material quality and variety,and shows adaptability to a broader spectrum of lighting conditions in reference images.

关键词： SVBRDF Diffusion model Generative model Appearance modeling

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3D Structure Estimation of Room Environment Using Semantic Segmentation and Model Fitting

3D Structure Estimation of Room Environment Using Semantic S...

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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Morioka, Junya Miyamoto, Ryusuke Graduate School of Science and Technology Meiji University Department of Computer Science Kawasaki Japan School of Science and Technology Meiji University Department of Computer Science Kawasaki Japan

ISBN: (纸本)9798350385014

Recent advances in VR devices and technology have increased the demand for technologies that reflect real space in virtual space. In particular, technologies that reflect the indoor environment in a virtual space are used in various fields such as architecture, interior design, and robotics. To reflect a real space in a virtual space, estimating the 3D structure of the real space is significant. Yet, this requires expensive equipment and poses challenges in computational complexity and ease of measurement. In this study, we propose a method of estimating 3D structure of indoor environments using a single image. Existing methods generally use depth estimation, but in this study, we introduce a new approach using semantic segmentation and rectangular fitting. Experimental results with 3D CG show that the proposed method outperforms existing methods in accuracy and competitive results. © 2024 IEEE.

关键词： Semantics

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Personal Identification Based on Joint Motions with Normalization against Size Changes of Targets

Personal Identification Based on Joint Motions with Normaliz...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Yuda, Yuki Adachi, Miho Kamiya, Sho Aoyagi, Kosuke Miyamoto, Ryusuke Department of Computer Science Graduate School of Science and Technology Meiji University Kawasaki Japan Department of Computer Science School of Science and Technology Meiji University Kawasaki Japan

ISBN: (纸本)9798350389210

Gait analysis is useful for personal identification in public spaces. Recent, advancements in deep learning technology have enabled highly accurate estimation of human joint positions in images, making practical applications of gait analysis possible using only visible light cameras. We propose a method to improve the performance of personal identification methods based on joint movements captured by cameras, aiming to make them more practical for use in real-world scenarios. Specifically, we propose a novel normalization method for feature vectors to address variations in the size of target humans during walking sequences. Such variations have a significant impact on identification accuracy in gait analysis. The proposed method normalizes feature vectors based on the distance between the hip and neck joints, reducing the impact of differences in target human size in the images. To evaluate the effectiveness of this method, we created two datasets by intentionally varying the magnification of existing datasets. Experimental results showed that the proposed method improved identification accuracy by approximately 12%. Furthermore, even when multiple datasets were combined, the accuracy increased from 75.5% to 84.7%, demonstrating that the proposed method is highly robust under various conditions. © 2024 IEEE.

关键词： Deep learning

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Personal Identification Based on Joint Motions with Error Correction Using Particle Filter

Personal Identification Based on Joint Motions with Error Co...

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2024 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Aoyagi, Kosuke Yuda, Yuki Ohkoba, Masakazu Adachi, Miho Miyamoto, Ryusuke Department of Computer Science School of Science and Technology Meiji University Kawasaki Japan Department of Computer Science Graduate School of Science and Technology Meiji University Kawasaki Japan

ISBN: (纸本)9798350389210

Gait analysis is useful for personal identification in public spaces. Since the emergence of deep learning that can accurately estimate the joint locations of a human in an image, gait analysis has become feasible in practical applications that have only a visible light camera. This paper proposes a novel method for personal identification based on joint motions that can improve utility of gait analysis. Specifically, the proposed method adopts a particle filter to correct the misclassification of left and right legs using the time-series input of their locations. Experimental results using datasets comprising real-world images showed that the proposed error correction improved the accuracy of personal classification from 84.19% to 85.94% when joint locations were estimated using ViTPose. © 2024 IEEE.

关键词： Error correction

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