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Deep Reinforcement Learning for Sim-to-Real Transfer in a Humanoid Robot Barista

Deep Reinforcement Learning for Sim-to-Real Transfer in a Hu...

引用

ieee International conference on Robotics and Biomimetics

作者： Ziyuan Wang Yefan Lin Leyu Zhao Jiahang Zhang Xiaojun Hei Hubei Key Laboratory of Smart Internet Technology School of Electronic Information & Communications Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In recent years, an increasing amount of research and development efforts have been invested in home-service humanoid robots. Nevertheless, the complexity of home environ-ments poses challenges in the robustness of robot manipulation with high accuracy and swift promptness. In this paper, we study the coffee-making application as a demonstrative example to enable humanoid robots in the edge-cloud cooperative intelligence architecture. We propose a reinforcement learning robot manipulation method with visual perception for filling-up the sim-to-real gap. We construct a high-fidelity coffee making digital twin simulation environment. In addition, we extract the key points of robot hands using computer vision algorithms to achieve consistency between the simulation and the real scenes. The domain randomization has been applied to enhance the policy tolerant to the positional deviation of the coffee machine and coffee cups. We deploy the trained policy to the real robot in a zero-shot transfer. Our experimental results have demonstrated that the proposed method achieved over 90% success rate in end-to-end coffee making tasks on both simulation and real robots. The proposed method significantly outperforms other image-based or point cloud-based methods during the sim-to-real process.

关键词： Hands Dimensionality reduction Humanoid robots Deep reinforcement learning Manipulators Robustness Digital twins Robots Research and development visual perception

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Earth-Based Radar Height Dimension Imaging for Astronomical Object

Earth-Based Radar Height Dimension Imaging for Astronomical ...

引用

Signal, Information and Data processing (ICSIDP), ieee International conference on

作者： Kaiwen Zhu Peiyao Liu Ziyi Zhou Zhen Wang Zehua Dong Zegang Ding School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Observation for astronomical object, including the moon, asteroids, and terrestrial planets, is of significance in deep space observation. Earth-based radar is an important approach for astronomical object observation. Earth-based radar has the ability of two-dimensional (2-D) imaging for astronomical objects. However, earth-based radar height dimension imaging for astronomocal object has not been reported yet. In this manuscript, the analysis of earth-based radar height dimension imaging for astronomical object is discussed. The relative motion between earth-based radar and astronomical object is analyzed, and the sparse height sampling of radar is discussed. Thereafter the problem of sidelobe and grating lobe in height dimension imaging is discussed, and a data domain-image domain joint windowing method is proposed for sidelobe and grating lobe suppression. Computer simulation experiment and real data equivalent experiment with electromagnetic anechoic chamber are conducted to validate the analysis and the proposed method.

关键词： Computer simulation Two-dimensional displays Moon Imaging Radar Radar imaging Gratings Electromagnetics Synthetic aperture radar Deep-space communications

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imageFlowNet: Forecasting Multiscale image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical images

ImageFlowNet: Forecasting Multiscale Image-Level Trajectorie...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chen Liu Ke Xu Liangbo L. Shen Guillaume Huguet Zilong Wang Alexander Tong Danilo Bzdok Jay Stewart Jay C. Wang Lucian V. Del Priore Smita Krishnaswamy Yale University UCSF Mila Univ. de Montréal Mila McGill Yale NorCal Retina Vitreous Associates

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same patients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, and data sparsity. To address these issues, we propose imageFlowNet, a novel model designed to forecast disease trajectories from initial images while preserving spatial details. imageFlowNet first learns multiscale joint representation spaces across patients and time points, then optimizes deterministic or stochastic flow fields within these spaces using a position-parameterized neural ODE/SDE framework. The model leverages a UNet architecture to create robust multiscale representations and mitigates data scarcity by combining knowledge from all patients. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We validate imageFlowNet on three longitudinal medical image datasets depicting progression in geographic atrophy, multiple sclerosis, and glioblastoma, demonstrating its ability to effectively forecast disease progression and outperform existing methods. Our contributions include the development of imageFlowNet, its theoretical underpinnings, and empirical validation on real-world datasets.

关键词： visualization Multiple sclerosis Stochastic processes Organizations Predictive models Signal processing Data models Trajectory Speech processing Biomedical imaging

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ViEdge: An Edge-based Platform for Video Analytics Applications in Smart Estates

ViEdge: An Edge-based Platform for Video Analytics Applicati...

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International conference on Computer communications and Networks (ICCCN)

作者： Vishal Choudhary Rahul Aggarwal Hock Beng Lim Binbin Chen Center for Smart Systems (CS2) Singapore University of Technology and Design Singapore Information Systems Technology and Design Pillar (ISTD) Singapore University of Technology and Design Singapore

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

Video analytics plays a crucial role in the development of smart estates and cities. Applications such as garbage dumping detection, lift monitoring, safety surveillance, etc. rely on video analytics, and require fast response time. Traditional cloud-based systems are ill-suited for these applications due to their limitations in handling large volume of video data with low latency. In contrast, the IoT-Edge-Cloud paradigm is better suited for such applications, but it presents challenges such as system heterogeneity, and resource allocation and orchestration. There is a need for an efficient platform for distributed video stream processing where resource orchestration and scalability aspects are tailored to smart estate *** this paper, we present ViEdge, an edge-based platform for video analytics applications in smart estates. It is highly adaptable and scalable, making it ideal for various deployments in such environments. Our implementation of ViEdge utilizes Kubernetes (K8s) for resource management and orchestration, and Apache Storm for distributed video stream processing. To study ViEdge’s customization capabilities, we evaluated its performance on a heterogeneous edge testbed. We observed increased latency in Apache Storm when integrated with Kubernetes, affecting overall application performance. However, by developing a heuristic-based scheduler, we demonstrate that ViEdge effectively reduces end-to-end latency and enhances frame processing rates.

关键词： Storms visual analytics Surveillance image edge detection Urban areas Streaming media Safety

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Deep-Learning Based Quantification of Bovine Oocyte Quality From Microscopy images*

Deep-Learning Based Quantification of Bovine Oocyte Quality ...

引用

Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Matilde Costa Anoek Strumane Annelies Raes Ann Van Soom Danilo Babin Jan Aelterman Department of Chemical Engineering Faculty of Engineering University of Porto Porto Portugal Department of Telecommunications and Information Processing - Image Processing and Interpretation Ghent University-Imec Ghent Belgium Department of Internal Medicine Reproduction and Population Medicine Faculty of Veterinary Medicine Ghent University Merelbeke Belgium

The success rate of bovine in vitro embryo reproduction is low and highly dependent on the oocyte quality. The selection of the oocyte to be fertilized is done by the embryologists’ visual examination of oocytes. It is time-consuming, subjective, and inconsistent between specialists in the area. In this paper, a semi-automatic solution is proposed to score the quality of an immature oocyte. It consists of a deep learning model to classify oocyte competence. The model was trained and tested with real data, composed of images of immature oocytes and their label of whether they developed into blastocysts after fertilization. To the best of our knowledge, automated bovine oocyte classification was not attempted before, but experimental results show that our proposed solution is more robust and objective than specialists’ visual assessment and comparable with other works on human *** relevance— This establishes a semi-automatic real-time method to score bovine immature oocytes, based on stereo-microscopy images. Our method will significantly reduce the time of in vitro embryo production and its success.

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Evaluation of Convolutional Neural Network for Automatic Caries Detection in Digital Radiograph Panoramic on Small Dataset

Evaluation of Convolutional Neural Network for Automatic Car...

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2022 International conference on Data and Software Engineering, ICoDSE 2022

作者： Fariza, Arna Asmara, Rengga Rojaby, Muhammad Oktavian Fajar Astuti, Eha Renwi Putra, Ramadhan Hardani Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Airlangga Department of Dentistry Surabaya Indonesia

ISBN: (数字)9798350397055

ISBN: (纸本)9798350397055

Dental caries or tooth decay is damage to the hard tissues of the teeth that can occur in the enamel, dentin, and cementum areas. Panoramic radiography is a screening tool for tactile or visual examination of the oral cavity which is useful for further diagnosis and treatment. The process of segmentation of panoramic radiographs is a difficult process because there is no homogeneity between panoramic images with one another. Noise levels, vertebral column images, and low contrast are the main challenges in image processing. This study evaluates CNN to detect caries automatically on panoramic radiographs on a small dataset. The dataset consisted of manually cropped maxillary and mandibular premolars and molars. An augmentation strategy consisting of horizontal flip, vertical flip, and affine transformation is used to produce a wider variety of images. This study compares the architecture of non-pretrained and pretrained models consisting of 3-layer CNN, 3-layer CNN with batch normalization, ResNet18, and ResNeXt50 32×4d. Evaluation was carried out on 400 training data and 76 testing data. Combination of augmentation strategies and pre-trained ResNet18 and ResNeXt50 32×4d achieves high accuracy compared to other models. © 2022 ieee.

关键词： image segmentation

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Smart Sorting: Enhancing Almond Quality with DenseNet-201-Based image Classification

Smart Sorting: Enhancing Almond Quality with DenseNet-201-Ba...

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2024 International conference on Information Science and communications Technologies, ICISCT 2024

作者： Kaur, Arpanpreet Mishra, Amit Kumar Kumar, Bura Vijay Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527921

Considered as two varieties: damaged and undamaged, almonds are a rather consumed agricultural product. Maintaining consumer satisfaction and industry standards depends on good detection of damaged almonds., which could be influenced by mechanical damage., mold, or pests. On the other hand, slow, labor-intensive hand inspection methods prone to mistake demand automated classification systems. This work meets that demand by automating the categorization process using DenseNet-121, a convolutional neural network (CNN), therefore lowering labor expenses and increasing accuracy. Particularly considering the tiny dataset of 1,000 photos split equally between damaged and undamaged almonds, DenseNet-121's architecture-with its dense connections and capacity to reuse features-is well-suited for the almond classification challenge. Pre-trained on imageNet and polished for this particular purpose, the model-trained across 60 epochs using a 32-batch size-achieved an accuracy of 95.27%. Random horizontal flips, resizing., and center cropping among other data augmentation techniques improved the model's generalizing capability. This work demonstrates the model's ability to substitute human methods in actual environments by showing how well it employs little visual information to classify almonds. Automating almond sorting speeds and provides higher consistency in quality control as well as efficiency of sorting. This work opens the road for further research of machine learning techniques in agricultural processing with possible applications in food quality assurance. © 2024 ieee.

关键词： Damage detection

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Localizing Base Stations With Measured RSSI: A Deep Learning Approach

Localizing Base Stations With Measured RSSI: A Deep Learning...

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ieee International conference on communications in China (ICCC)

作者： Ziyuan Liu Feng Jiang Yang Chen Mengyu Li Hong Shen Shen Wang Wei Xu Ritao Cheng National Mobile Communications Research Laboratory Southeast University Nanjing China China Mobile Group Design Institute Co. Ltd. Beijing China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

The positioning of the radio transmitter of base stations (BSs) is a vital task in planning the communication network and ensuring the user equipment (UE) localization. In particular, for the widely used centralized radio access network (CRAN), the positions of the distributed remote radio units (RRUs) cannot be readily obtained in practice due to their simplified implementations. In this work, we devise a deep network based solution for the BS localization by using the measured received signal strength indication (RSSI) data from China Mobile. Specifically, we first develop a sliding window approach to generate RSSI-based images. Accordingly, we transform the original coordinate regression problem with unequal-length RSSI input into an image processing problem with equal-sized input, such that the network training can be facilitated. Based on the above input transformation, we propose an enhanced UNet named BSLocNet by introducing residual modules. Finally, a two-step method via heatmap is adopted to improve the network interpretability and an argmax-N method is proposed to yield accurate location estimates. Experimental results validate the superiority of the proposed BSLocNet over existing methods. In particular, the localization error can be lower than the side length of the sampling grid with a probability of 97.23%.

关键词： Location awareness Heating systems Deep learning Training Base stations Atmospheric measurements Radio transmitters

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Structure-Preserving and Redundancy-Free Features Refinement for Generalized Zero-Shot Learning

Structure-Preserving and Redundancy-Free Features Refinement...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jian Ni Yong Liao School of Cyber Science and Technology University of Science and Technology of China

Generalized zero-shot learning (GZSL) aims to recognize images from seen and unseen classes. Most models achieve competitive performance but still suffer from two problems: (1) Topological structure neglection; (2) Redundant information interference. In this paper, we propose a Structure-preserving and Redundancy-free Features Refinement model (referred to as SP-RFFR) to address these problems correspondingly in two modules: (1) Structure-preserving, to explicitly incorporate the topological structure into the learning of the latent space and the generator; (2) Redundancy-free features refinement, to remove the redundant information from the visual features and learn class- and semantically-relevant representations. To the best of our knowledge, this is the first work that incorporates topological structure preserving and redundancy-free features refinement into a unified framework for GZSL. Extensive experiments show that SP-RFFR outperforms the state-of-the-art methods on four benchmarks.

关键词： visualization image recognition Interference Benchmark testing Signal processing Generators Acoustics

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An Effective Clustering Using Moth Flame Optimization for Content-Based image Retrieval

An Effective Clustering Using Moth Flame Optimization for Co...

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Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： Srinivas Aluvala Zainab Abed Almoussawi Muntather Almusawi Zamen Latef Naser S. Meenakshi Sundaram School of Computer Science & Artificial Intelligence SR University Warangal Telangana India College of Islamic Science Ahl Al Bayt University karbala Iraq The Islamic university Najaf Iraq National University of Science and Technology Dhi Qar Iraq Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

Content-based image retrieval plays an important role in many domains and the volume of the image database increasing tremendously. It is very complex to comparing the query image feature with all images in the dataset during the retrieval phase. Due to computational complexity increase which degrades the performance of recognition accuracy. The proposed model Clustering using Moth Flame Optimizer are pre-processing, feature extraction, feature fusion, clustering and classification. The corel lk dataset is utilized in this research for effective classification of CBIR method. Filter using First Average Peer Group (F APG) is used to remove the noise in the pre-processing stage. Feature extracted using Local Binary Pattern (LBP) and Hue Saturation Value (HSV) based feature extraction techniques. All these two features are fused into single feature using average and weighted technique. Next, proposed model clustering using Moth Flame Optimization algorithm, it enhance the flames and flame value is obtained in ***, the relevant image is retrieved using SAR classifier which utilized Deep Learning (DL) for effective classification. The obtained result shows the proposed clustering using MFO model achieve better accuracy of 99.87% on Corel lk dataset which insure accurate classification compared to other existing methods like Bi-Iayer method and Texture from semantic pyramid.

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