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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Sumathy, R. Naveen, S. Dhinesh, P. Nazar Ali, P. Muhammadhu Srithikan, S. Pio, A. Micheal Mathura Kalasalingam Academy of Research and Education Department of ECE Krishnankoil India Kalasalingam Academy of Research and Education Department of CSE Krishnankoil India

ISBN: (纸本)9798350353778

This paper presents a unique way of door accessing system which uses the ESP32 microcontroller in conjunction with facial recognition technologies. Because of its Wi-Fi functionality and low power consumption, it's a correct platform for local facial recognition algorithm execution, which guarantees real-time access control without depending on other servers. A pre-trained deep learning model processes facial image data with a camera to recognize faces. The ESP32 determines whether to give or refuse access depending on the comparison of facial features with an authorized database. Security is improved by anti-spoofing methods, and access control. Experiments show the system's viability and efficacy, underscoring its potential in commercial, industrial, and residential environments. This paper advances the field of IoT based security systems by presenting a safe and effective door access method. © 2024 IEEE.

关键词： deep learning

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A Portable Object Detection System for Visually Impaired Individuals in Outdoor Environments 1

A Portable Object Detection System for Visually Impaired Ind...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Srivastava, Prabhat Kr Harit, Vibhor Mall, Shashank Kumar Singh Roohani, BasuDeo Pandey, Tushar Goel, Yash Ims Engineering College Department of Computer Science and Engineering Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350367720

In recent years, progress in machine learning methods has greatly influenced the creation of assistive technologies designed to enhance the quality of life for individuals with visual impairments. This paper introduces a unique real-time image analysis method specifically developed for the visually impaired community. Leveraging the You Only Look Once (YOLO) dataset and machine learning algorithms implemented in Python, our The suggested system provides a seamless and efficient solution for identifying objects in the circumventing environment in genuine-time. In this study, we performed comprehensive experiments to assess the effectiveness of our system, taking into account aspects like detection accuracy, processing speed, and usability for users with visual impairments. The results demonstrate the efficacy and reliability of our approach in genuine-world scenarios, showcasing its potential to be accommodated as a valuable and implement for improving autonomy and mobility of visually impaired individuals. © 2024 IEEE.

关键词： Adversarial machine learning

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deep learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward real-time Laryngeal Cancer Detection

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LARYNGOSCOPE 2022年第9期132卷 1798-1806页

作者： Azam, Muhammad Adeel Sampieri, Claudio Ioppi, Alessandro Africano, Stefano Vallin, Alberto Mocellin, Davide Fragale, Marco Guastini, Luca Moccia, Sara Piazza, Cesare Mattos, Leonardo S. Peretti, Giorgio Ist Italiano Tecnol Dept Adv Robot Genoa Italy Univ Genoa Dept Informat Bioengn Robot & Syst Engn Genoa Italy IRCCS Osped Policlinico San Martino Unit Otorhinolaryngol Head & Neck Surg Genoa Italy Univ Genoa Dept Surg Sci & Integrated Diagnost DISC Largo Rosanna Benzi 10 I-16132 Genoa Italy Biorobot Inst Dept Excellence Robot & AI Scuola Superiore SantAnna Brescia Italy ASST Spedali Civili Brescia Unit Otorhinolaryngol Head & Neck Surg Brescia Italy Univ Brescia Dept Med & Surg Specialties Radiol Sci & Publ Hlt Brescia Italy

Objectives To assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) deep learning convolutional neural network (CNN). Study Design Experimental study with retrospective data. Methods Recorded videos of LSCC were retrospectively collected from in-office transnasal videoendoscopies and intraoperative rigid endoscopies. LSCC videoframes were extracted for training, validation, and testing of various YOLO models. Different techniques were used to enhance the image analysis: contrast limited adaptive histogram equalization, data augmentation techniques, and test time augmentation (TTA). The best-performing model was used to assess the automatic detection of LSCC in six videolaryngoscopies. Results Two hundred and nineteen patients were retrospectively enrolled. A total of 624 LSCC videoframes were extracted. The YOLO models were trained after random distribution of images into a training set (82.6%), validation set (8.2%), and testing set (9.2%). Among the various models, the ensemble algorithm (YOLOv5s with YOLOv5m-TTA) achieved the best LSCC detection results, with performance metrics in par with the results reported by other state-of-the-art detection models: 0.66 Precision (positive predicted value), 0.62 Recall (sensitivity), and 0.63 mean Average Precision at 0.5 intersection over union. Tests on the six videolaryngoscopies demonstrated an average computation time per videoframe of 0.026 seconds. Three demonstration videos are provided. Conclusion This study identified a suitable CNN model for LSCC detection in WL and NBI videolaryngoscopies. Detection performances are highly promising. The limited complexity and quick computational times for LSCC detection make this model ideal for real-time processing. Level of Evidence 3 Laryngoscope, 2021

关键词： Larynx cancer deep learning narrow band imaging computer-assisted image interpretation videolaryngoscopy

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Adjacent Feature Propagation Network (AFPNet) for real-time Semantic Segmentation

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第9期52卷 5877-5888页

作者： Hyun, Junhyuk Seong, Hongje Kim, Sangki Kim, Euntai Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea LG Elect Robot Vis TP Adv Robot Lab ICT Technol CtrCTO Div Seoul 06772 South Korea

With the development of deep learning, semantic segmentation has received considerable attention within the robotics community. For semantic segmentation to be applied to mobile robots or autonomous vehicles, real-time processing is essential. In this article, a new real-time semantic segmentation network, called the adjacent feature propagation network (AFPNet), is proposed to achieve high performance and fast inference. AFPNet executes in real time on a commercial embedded GPU. The network includes two new modules. The local memory module (LMM) is the first;it improves the upsampling accuracy by propagating the high-level features to the adjacent grids. The cascaded pyramid pooling module (CPPM) is the second;it reduces computational time by changing the structure of the pyramid pooling module. Using these two modules, the proposed AFPNet achieved 76.4% mean intersection-over-union on the Cityscapes test dataset, outperforming other real-time semantic segmentation networks. Furthermore, AFPNet was successfully deployed on an embedded board Jetson AGX Xavier and applied to the real-world navigation of a mobile robot, proving that AFPNet can be effectively used in a variety of real-time applications.

关键词： Semantics real-time systems image segmentation Feature extraction Decoding Object segmentation Correlation Memory network pyramid pooling module (PPM) real-time semantic segmentation upsampling

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A segmentation method for left ventricular magnetic resonance images based on temporal feature enhancement 9

A segmentation method for left ventricular magnetic resonanc...

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9th International Symposium on Computer and Information processing Technology, ISCIPT 2024

作者： Li, Yuanzhe Xu, Huahu Cao, Yongji Chen, Xingyuan Shanghai University College of Computer Engineering and Science Shanghai China Shanghai Forensic Service Platform Shanghai Key Laboratory of Forensic Medicine Shanghai China

ISBN: (纸本)9798350388404

Accurate segmentation of the left ventricle (LV) from dynamic cardiac magnetic resonance imaging (MRI) is a critical focus in computer-assisted cardiovascular diagnostics. Most current deep learning methods, which are primarily designed for static imaging, often overlook the temporal features inherent in the heart's continuous motion. This oversight can limit their effectiveness in clinical diagnostic applications. To address this, we introduce a novel segmentation model, ST TransUNet, which is specifically tailored to capture the dynamic MRI data's feature information along the time dimension. Our model integrates temporal and deformable global attention modules to enhance the accuracy of LV segmentation throughout the cardiac cycle. We conducted experimental validation using three publicly available cine-MRI datasets: ACDC, NDSB2, and SCD. Results demonstrate that ST TransUNet achieves the highest mean Dice Similarity Coefficient (DSC) across all frames within the cardiac cycle and particularly excels in segmenting the LV during the end-systolic phase, when morphological complexity is at its peak. The proposed model offers a promising approach for automatic LV segmentation in MRI imaging. © 2024 IEEE.

关键词： deep learning Dynamic image Segmentation Left Ventricular MRI segmentation Temporal-feature Enhancement

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Human-Robot Collaborative Minimum time Search Through Sub-Priors in Ant Colony Optimization

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IEEE ROBOTICS AND AUTOMATION LETTERS 2024年第11期9卷 10216-10223页

作者： Gil, Oscar Sanfeliu, Alberto UPC CSIC Inst Robot & Informat Ind Barcelona 08028 Spain

Human-Robot Collaboration (HRC) has evolved into a highly promising issue owing to the latest breakthroughs in Artificial Intelligence (AI) and Human-Robot Interaction (HRI), among other reasons. This emerging growth increases the need to design multi-agent algorithms that can manage also human preferences. This letter presents an extension of the Ant Colony Optimization (ACO) meta-heuristic to solve the Minimum time Search (MTS) task, in the case where humans and robots perform an object searching task together. The proposed model consists of two main blocks. The first one is a convolutional neural network (CNN) that provides the prior probabilities about where an object may be from a segmented image. The second one is the Sub-prior MTS-ACO algorithm (SP-MTS-ACO), which takes as inputs the prior probabilities and the particular search preferences of the agents in different sub-priors to generate search plans for all agents. The model has been tested in real experiments for the joint search of an object through a Vizanti web-based visualization in a tablet computer. The designed interface allows the communication between a human and our humanoid robot named IVO. The obtained results show an improvement in the search perception of the users without loss of efficiency.

关键词： Robots Collaboration Search problems Computational modeling Probabilistic logic Optimization Metaheuristics Human-robot interaction Robot sensing systems Planning Human-robot collaboration (HRC) path planning for multiple mobile robots or agents deep learning methods semantic scene understanding search and rescue robots

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CTIS-GAN: computed tomography imaging spectrometry based on a generative adversarial network

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APPLIED OPTICS 2023年第10期62卷 2422-2433页

作者： Wu, Luoxiang Cai, Weiwei Shanghai Jiao Tong Univ Sch Mech Engn Key Lab Educ Minist Power Machinery & Engn 800 Dongchuan Rd Shanghai 200240 Peoples R China

Computed tomography imaging spectrometry (CTIS) is a snapshot hyperspectral imaging technique that can obtain a three-dimensional (2D spatial + 1D spectral) data cube of the scene captured within a single exposure. The CTIS inversion problem is typically highly ill-posed and is usually solved by time-consuming iterative algorithms. This work aims to take the full advantage of the recent advances in deep-learning algorithms to dramatically reduce the computational cost. For this purpose, a generative adversarial network is developed and integrated with self-attention, which cleverly exploits the clearly utilizable features of zero-order diffraction of CTIS. The proposed network is able to reconstruct a CTIS data cube (containing 31 spectral bands) in milliseconds with a higher quality than traditional methods and the state-of-the-art (SOTA). Simulation studies based on real image data sets confirmed the robustness and efficiency of the method. In numerical experiments with 1000 samples, the average reconstruction time for a single data cube was similar to 16 ms. The robustness of the method against noise is also confirmed by numerical experiments with different levels of Gaussian noise. The CTIS generative adversarial network framework can be easily extended to solve CTIS problems with larger spatial and spectral dimensions, or migrated to other compressed spectral imaging modalities. (c) 2023 Optica Publishing Group

关键词： Computed tomography Diffractive optical elements Hyperspectral imaging Imaging spectroscopy Medical image processing Phase modulation

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AirGeoNet: A Map-Guided Visual Geo-Localization Approach for Aerial Vehicles

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Meng, Xiangze Guo, Wulong Zhou, Kai Sun, Ting Deng, Lei Yu, Shijie Feng, Yuhao Beijing Informat Sci & Technol Univ Lab Intelligent Microsyst Beijing 100192 Peoples R China Beihang Univ Sch Astronaut Beijing 100191 Peoples R China Tsinghua Univ Sch Integrated Circuits Beijing 100190 Peoples R China

Aerial vehicles (AVs) commonly operate in vast environments, presenting a persistent challenge in achieving high-precision localization. The contemporary popular global positioning methods have their inherent limitations. For instance, the precision of GPS is susceptible to decline or even complete failure when the signal is disrupted or absent. Furthermore, the precision of image retrieval techniques is inadequate. The construction of 3-D models is a time-consuming and storage-intensive endeavor. In addition, scene coordinate regression necessitates retraining to adapt to varying scenarios, which presents challenges when attempting to generalize across expansive environments. Addressing these challenges, we propose a network named AirGeoNet, which integrates satellite images and semantic maps to achieve high-precision efficient localization. In the first phase, we introduce the foundation model DINOV2 to extract features from satellite and aerial images, employ a vector of locally aggregated descriptor (VLAD) for image retrieval to get coarse position, and, finally, significantly enhance retrieval accuracy by combining sequential images with particle filters. Subsequently, AirGeoNet matches aerial images with semantic maps to determine the three degrees of freedom in pose, including position and orientation. The semantic maps utilized by AirGeoNet are sourced from OpenStreetMap and our self-produced QMap, and training is conducted in a supervised manner using real camera poses. Our AirGeoNet method is highly efficient, requiring only a 1546-D feature vector per image for image retrieval and 240k storage for a 0.9- km(2) semantic map while achieving state-of-the-art accuracy with single-frame localization errors of 2.854 m on semantically rich datasets and 11 m in complex scenarios. Our code is publicly available at https://***/mxz520mxz/***

关键词： Semantics Location awareness Accuracy Three-dimensional displays image retrieval Feature extraction Solid modeling deep learning semantic map visual localization

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image Reconstruction Leveraging time-Event Feature Fusion for Event Cameras 7

Image Reconstruction Leveraging Time-Event Feature Fusion fo...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Jian Wu, Sheng Liu, Wenwei Feng, Hui Hu, Bo School of Information Science and Technology Fudan University Shanghai200433 China Shanghai Institute of Intelligent Electronics & Systems Shanghai200433 China

ISBN: (纸本)9798350350890

The event cameras generate asynchronous event sequences, rendering most existing image-based algorithms in-applicable for direct use and processing. Therefore, the development of a simulator that utilizes event streams captured by an event camera as input and reconstructs image sequences as output is crucial for subsequent tasks such as high-speed moving object tracking, object recognition and detection, and SLAM. Despite the extensive research and attention garnered by deep learning-based image sequence generation, there remains a problem of local representation information loss during the reconstruction process from event streams to image sequences. Existing solutions, whether based on Stacking Based on time (SBT) or Stacking Based on Events (SBE), exhibit significant limitations when using a single stacking approach. To address the insufficiency in feature representation capability, this paper proposes a visual image conversion method based on multi-view feature fusion, effectively transforming event streams into traditional image sequences. Extensive experiments and dataset validations, utilizing multiple evaluation metrics including both global and local assessments, demonstrate the advantages of our proposed method. © 2024 IEEE.

关键词： image reconstruction

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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