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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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Watermarking technique for document images using discrete curvelet transform and discrete cosine transform

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Multimedia Tools and Applications 2024年第40期83卷 87647-87671页

作者： Singh, Balkar Sharma, M.K. Post Graduate Department of Computer Science Gujranwala Guru Nanak Khalsa College Punjab Ludhiana India School of Mathematics Thapar Institute of Engineering and Technology Punjab Patiala India

In this paper, a watermarking technique based on discrete curvelet transform and discrete cosine trans- form is proposed to protect the color document images. The six layers of the document image are created using the Discrete Curvelet Transform (DCuT). The sixth layer is chosen for the embedding process, while the remaining layers are discarded. Discrete Cosine Transform (DCT) is applied on the 8 × 8 blocks of the real part of sixth layer. DCT coefficients from low to medium are selected for the embedding process, excluding the first one. To choose the Direct Current (DC) coefficients of DCT to embed the watermark bits, a zigzag function is used. The same process is applied on the receiver side watermark image is extracted. Since this is a non-blind method, we require both a cover image and a watermarked image. DCT of watermarked image and the cover image are compared to extract the watermark image. The resistivity of the watermarked image against image processing attacks is measured using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Square Error (MSE), Normalization Correlation (NC), Bit Error Rate (BER) and Universal Quality Index (UQI). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Mean square error

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Integration of data science with the intelligent IoT(IIoT): Current challenges and future perspectives

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Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon University University of Electronic Science and Technology of China College of Information Science and Engineering Hohai University Department of Computer Science University of Salerno Graduate School of Informatics and Engineering The University of Electro-Communications

The Intelligent Internet of Things(IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence(AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most state-of-the-art research focuses independently on either data science or IIoT, rather than exploring their integration. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions.

关键词： Data science Internet of things (IoT) Big data Communication systems Networks Security Data science analytics

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Autonomous face mask detection using single shot multibox detector, and ResNet-50 with identity retrieval through face matching using deep siamese neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11195-11205页

作者： Vignesh Baalaji, S. Sandhya, S. Sajidha, S.A. Nisha, V.M. Vimalapriya, M.D. Tyagi, Amit Kumar School of Compter Science and Engineering Vellore Institute of Technology Chennai India Post Graduate Department of Computer Science and Technology Women’s Christian College Chennai India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

The COVID-19 pandemic poses a global health challenge. The World Health Organization states that face masks are proven to be effective, especially in public areas. Real-time monitoring of face masks is challenging and exhaustive for humans. To reduce human effort and to provide an enforcement mechanism, an autonomous system has been proposed to detect non-masked people and retrieve their identity using computer vision. The proposed method introduces a novel and efficient method that involves fine-tuning the pre-trained ResNet-50 model with a new head layer for classification between masked and non-masked people. The classifier is trained using adaptive momentum optimization algorithm with decaying learning rate and binary cross-entropy loss. Data augmentation and dropout regularization are employed to achieve best convergence. During real-time application of our classifier on videos, a Caffe face detector model based on Single Shot MultiBox Detector is used to extract the face regions of interest from each frame, on which the trained classifier is applied for detecting the non-masked people. The faces of these people are then captured, which is passed on to a deep siamese neural network, based on VGG-Face model for face matching. The captured faces are compared with the reference images from the database, by extracting the features and calculating cosine distance. If the faces match, the details of that person are retrieved from the database and displayed on the web application. The proposed method has secured best results where the trained classifier has achieved 99.74% accuracy, and the identity retrieval model achieved 98.24% accuracy. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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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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Improving free space optics performance using QC-LDPC codes and simplified bit flipping decoding in low visibility conditions

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Optical and Quantum Electronics 2025年第1期57卷 1-18页

作者： Marlina, Lina Pamukti, Brian Liaw, Shien-Kuei Lee, Pei-Jun Kishikawa, Hiroki Graduate Institute of Electro-Optical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Institute of Post-LED Photonics Tokushima University Tokushima Japan

Currently, free space optics (FSO) is the most promising technology for achieving high data transfer over short to medium or even long distances. Thus, FSO provides wireless line-of-sight (LOS) connectivity in the unlicensed spectrum and can be a high-bandwidth wireless alternative in contrast to fiber optic cabling. Moreover, the FSO offers rapid assembly times and significant cost savings. However, atmospheric weather (e.g., fog, rain, and snow) can degrade the FSO performance, especially in low visibility conditions. Therefore, this research aims to improve the FSO performance using quasi-cyclic low-density parity-check (QC-LDPC) codes in low visibility scenarios or thick turbulence conditions. In addition, a simplified bit-flipping (SBF) algorithm was proposed to decode the information by the receiver side to minimize complexity. Subsequently, the FSO system is simulated using a 1550 nm laser at different data rates of 1 to 20 Gbps data rate and 1-Watt transmitted power. This paper was analyzed using different empirical models of atmosphere attenuation, like the Kim, Kruse, and Naboulsi models. This simulation also uses modulation techniques such as on-off keying, phase shift keying, and quadrature amplitude modulation. Moreover, the looping iteration number of SBF could improve the system’s performance. We also consider using a low-cost PIN photodetector for practical implementation. Our simulation results show that QC-LDPC codes can achieve an average bit error rate of 10-9 in lower visibility than uncoded information. Our research has significant implications for enhancing fiber optic performance in adverse atmospheric phenomena. © The Author(s) 2025.

关键词： Quadrature amplitude modulation

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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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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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In-silico Evaluation of Structurally Homologous Drosophila p53 with Human p53 to Identify Functional Differences for Future Therapeutic Research

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BIOLOGY BULLETIN 2023年第SUPPL 3期50卷 S310-S316页

作者： Tudu, Nutan Sarkar, Aniket Mondal, Sayan Bandyopadhyay, Bidyut Swami Vivekananda University Barrackpore North 24 Parganas West Bengal India Post Graduate Department of Biotechnology Oriental Institute of Science and Technology Vidyasagar University 721102 Midnapore West Bengal India Post Graduate Department of Biochemistry Oriental Institute of Science and Technology Vidyasagar University 721102 Midnapore West Bengal India Post Graduate Department of Biotechnology and Biochemistry Molecular Informatics Laboratory Oriental Institute of Science and Technology Vidyasagar University 721102 Midnapore West Bengal India

p53 is a transcription factor, which is encoded by the TP53 gene in human. p53 regulates the cell cycle and functions as a tumour suppressor, thus, involved in preventing cancer. In vivo, analysis of p53 mechanisms in whole animal models are critical, alternatively Drosophila can be selected. Our present study worked on comparative and interactome profiling of p53 protein using PyMol and Haddock 2.4. The structural similarity resulted that p53 of Drosophila (Dmp53) is almost alike to that of human p53 (Hp53) protein. Hp53 interacting proteins were systematically docked and analyzed with Hp53 and Dmp53 both. Furthermore the associated complexes are analysed through Discovery Studio. The interactome study revealed better homologous interaction like human p53. Drosophila p53 exhibits characteristics that makes it a promising model for cancer research, bearing resemblance to human p53.

关键词： Drosophila p53 Human p53 RMSD interactome

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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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