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arXiv 2024年

作者： Sun, Yanguang Yang, Jian Luo, Lei The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Recently, deep learning-based salient object detection (SOD) in optical remote sensing images (ORSIs) have achieved significant breakthroughs. We observe that existing ORSIs-SOD methods consistently center around optimizing pixel features in the spatial domain, progressively distinguishing between backgrounds and objects. However, pixel information represents local attributes, which are often correlated with their surrounding context. Even with strategies expanding the local region, spatial features remain biased towards local characteristics, lacking the ability of global perception. To address this problem, we introduce the Fourier transform that generate global frequency features and achieve an image-size receptive field. To be specific, we propose a novel United Domain Cognition Network (UDCNet) to jointly explore the global-local information in the frequency and spatial domains. Technically, we first design a frequency-spatial domain transformer block that mutually amalgamates the complementary local spatial and global frequency features to strength the capability of initial input features. Furthermore, a dense semantic excavation module is constructed to capture higher-level semantic for guiding the positioning of remote sensing objects. Finally, we devise a dual-branch joint optimization decoder that applies the saliency and edge branches to generate high-quality representations for predicting salient objects. Experimental results demonstrate the superiority of the proposed UDCNet method over 24 state-of-the-art models, through extensive quantitative and qualitative comparisons in three widely-used ORSIs-SOD datasets. The source code is available at: https://***/CSYSI/UDCNet. © 2024, CC BY.

关键词： Optical remote sensing

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Neuro-Haptic BCI Spark for TensorFlow Flying Avatars

Neuro-Haptic BCI Spark for TensorFlow Flying Avatars

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IEEE International Conference on Electro-Information technology

作者： John Knudson Adriano Cavalcanti Maninder Singh Department of Computer Science and Information Technology Autonomous Systems Laboratory St. Cloud State University St. Cloud MN USA

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

The article explores the bleeding-edge intersection of neuro-haptics, brain-computer interface (BCI) technology, and machine learning to enable the creation and control of flying avatars. Our work integrates neuroscience and haptic feedback to develop human computer system interfaces that allow users to control cyber physical systems using their brain signals. This innovative approach uses advanced machine learning to classify signal patterns and respond to brain thoughts in real-time, providing an immersive flying bird’s eyes feeling. The article highlights the challenges and opportunities associated with Neuro-Haptics BCI for flying avatars, paving the way for a new era in human-computer interaction.

关键词： Human computer interaction Training Neuroscience Avatars Machine learning Brain-computer interfaces Real-time systems

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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Towards Hierarchical Categorical Named Entity Recognition via Injective Multi-task Learning Strategy 8

Towards Hierarchical Categorical Named Entity Recognition vi...

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8th International Conference on Big Data and Information Analytics, BigDIA 2022

作者： Xiaoguang, Yuan Chaofan, Dai Youchen, Wang Yaokai, Cheng Zengwenpro, Yu Tianfeng, Du National University of Defense Technology Changsha China Beijing Institute of Computer Technology and Application Beijing China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China School of Computer Science and Technology Xidian University Xi'an China School of Management and Engineering Captital University of Economics and Business Beijing China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

The Named Entity Recognition (NER) task aims to locate meaningful entities in a text and classify them into pre-defined categories. NER is a fundamental task in the field of natural language processing. In practice, entities often have multiple types at different levels of granularity, and it is essential to identify various levels of categories of entities simultaneously. A deep learning-based approach treats NER as a sequence annotation problem by giving each character a unique entity label, which significantly improves the accuracy of NER. Deep learning-based NER performs well on single-category and coarse-grained NER but does not perform well on hierarchical category NER problems. This paper proposes a multi-task learning-based hierarchical category named entity recognition model to solve this problem. The model uses a multi-task learning mechanism to simultaneously learn multiple levels of information of named entity recognition tasks. Named entity recognition results at higher levels are directly constructed from lower-level recognition structures. Experiments show that both systems can significantly improve the recognition performance at each level compared to the basic multi-tasking model. The model is also compared with each level's traditional named entity recognition method. The hierarchical category named entity recognition model can sig-nificantly improve the performance of named entity recognition tasks at each level. © 2022 IEEE.

关键词： Learning systems

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Cumulus cloud modeling from images based on VAE-GAN

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Virtual Reality & Intelligent Hardware 2021年第2期3卷 171-181页

作者： Zili ZHANG Yunchi CEN Fan ZHANG Xiaohui LIANG State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Department of Computer Science and Engineering Shijiazhuang UniversityShijiazhuang 050035China

Background Cumulus clouds are important elements in creating virtual outdoor *** cumulus clouds that have a specific shape is difficult owing to the fluid nature of the ***-based modeling is an efficient method to solve this *** of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development *** In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single *** method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial ***,a 3D cloud shape is mapped into a unique hidden space using the proposed ***,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered *** train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus *** cumulus clouds were rendered under different lighting *** The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing ***,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction *** The proposed autoencoder network learns the latent space of 3D cumulus cloud *** presented reconstruction architecture models a cloud from a single *** demonstrated the effectiveness of the two models.

关键词： 3D cloud model 3D autoencoder network Generative adversarial network

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Hybrid Scenario Generation Method for Stochastic Virtual Bidding in Electricity Market

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CSEE Journal of Power and Energy systems 2021年第6期7卷 1312-1321页

作者： Dongliang Xiao Wei Qiao the School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China the Power and Energy Systems Laboratory Department of Electrical and Computer EngineeringUniversity of Nebraska-LincolnLincolnNE 68588-0511 USA

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using *** paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical *** electricity price spikes are first identified by using an outlier detection ***,the historical data are decomposed into base and spike ***,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,***,the electricity price scenarios are obtained by combining the base and spike component *** studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed *** optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method,the SARIMA method,and a historical data-based method,*** study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and,therefore,can help the virtual bidder earn more profit in the electricity market.

关键词： Electricity market electricity price scenario generation stochastic optimization virtual bidding

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DEAMP: Dominant-Eye-Aware Foveated Rendering with Multi-Parameter optimization

DEAMP: Dominant-Eye-Aware Foveated Rendering with Multi-Para...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Zhimin Wang Xiangyuan Gu Feng Lu State Key Laboratory of VR Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

The increasing use of high-resolution displays and the demand for interactive frame rates presents a major challenge to widespread adoption of virtual reality. Foveated rendering address this issue by lowering pixel sampling rate at the periphery of the display. How-ever, existing techniques do not fully exploit the feature of human binocular vision, i.e., the dominant eye. In this paper, we propose a Dominant-Eye-Aware foveated rendering method optimized with Multi-Parameter foveation (DEAMP). Specifically, we control the level of foveation for both eyes with two distinct sets of foveation parameters. To achieve this, each eye’s visual field is divided into three nested layers based on eccentricity. Multiple parameters govern the level of foveation of each layer, respectively. We conduct user studies to evaluate our method. Experimental results demonstrate that DEAMP is superior in terms of rendering time and reduces the disparity between pixel sampling rate and the visual acuity fall-off model while maintaining the perceptual quality.

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Weed Detection and Treatment by RGB System - Case of the FPL Smart Farm 23

Weed Detection and Treatment by RGB System - Case of the FPL...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Laftouty, Zineb Bouraada, Khalid Bakrim, Ahmed Boudhir, Anouar Abdelhakim Essafi, Mariam Abdelmalek Essaadi University Tetouan Morocco Smart Systems and Emerging Technologies Research Team Computer Sciences and Smart Systems Laboratory Faculty of Science and Technology of Tangier Abdelmalek Essaadi University Tetouan Morocco Biology Department Dhar El Mehraz Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (纸本)9798400700194

Strawberry is one of the most important crops in the Loukkos area in Morocco;it exceeds 80% of the national production. This crop is very sensitive and requires well-controlled climatic conditions, specific inputs and meticulous monitoring to avoid any uncertainty in yield. Our work consists in treating the side related to the weeds, which can cause considerable losses of yield and disturb plant development. The danger of these weeds in this culture is the strong competition with the needs of the culture (water, nutrients, light...). The objective of our work is to make a state of the art by bibliographic systemic analysis and propose intelligent methods for detection and treatment of weeds based on RGB images. Most of the works are based on image acquisition mainly by smartphones or unmanned aerial vehicles (UAVs), and some by professional cameras. The image processing can be done by deep learning: through convolutional neural networks (CNN), support vector machine (SVM), or local binary pattern (LBP). Other methods mentionned allow detecting and controlling weeds using a robot that determines the undesirable plants by image processing (via deep learning), records their location and proceeds to a weeding or a localized treatment using herbicides. These methods allow decreasing labor dependency, a better treatment efficiency, and a lower herbicide use up to 90%. We thus propose a best suitable method to detect and treat weeds in an intelligent way in the "Smart Farm"of the Polydisciplinary Faculty in Larache. This method consists in using fluorescent paint at the stem base of the strawberry plant in order to differentiate the main crop from any undesirable plant. The robot that takes RGB images under UV rays considers as "weed"any plant that does not contain fluorescent paint. © 2023 ACM.

关键词： Weed control

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Fast Approximate LUT-based Vector Multiplication in DRAM

Fast Approximate LUT-based Vector Multiplication in DRAM

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Ziming Chen Quan Deng Yongwen Wang College of Computer National University of Defense Technology Changsha China Key Laboratory of Advanced Microprocessor Chips and Systems

Vector multiplication is widely used in real-world applications. To accelerate vector multiplication, processing-in-memory-based domain-specific architectures leverage lookup tables (LUTs) to decrease the computational complexity of multi-plication. However, the overhead of LUTs increases exponentially with the result space, which throttles the system *** decouple the LUT size and the performance improvement, we make a trade-off between accuracy and performance. We propose fast approximate LUT-based vector multiplication in DRAM, which builds the partial LUT of higher-bit locations and reuses the LUT to calculate the lower-bit data. We propose an operand reorganizing optimization to group more zero, which does not affect the results. To reduce the LUT and pre-calculation cost further, we propose a value encoding. Our experiment shows that the performance of the proposed design can be improved by 4x and 1.25x compared with LAcc in AlexNet and MobileNetV2 without any accuracy loss, respectively. Besides, the performance of the proposed design improves by up to 3.74x compared with pLUTo in multi-vector multiplication. The area and power of the proposed design are 54.8mm 2 and 5.35W, respectively.

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MAC Protocols for UAV-Aided Relaying Optical Ground-to-HAP Networks

MAC Protocols for UAV-Aided Relaying Optical Ground-to-HAP N...

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International Conference on Advanced Technologies for Communications, ATC

作者： Tien H. Do Huy Q. Nguyen Hoang D. Le Hien T. T. Pham Thang V. Nguyen Ngoc T. Dang Wireless Systems and Applications Lab. Posts and Telecommunications Institute of Technology Hanoi Vietnam Computer Communications Laboratory The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Air-ground integrated network (AGIN) incorporating high-altitude platforms (HAP), unmanned aerial vehicles (UAV), and ground base stations (BS) has been recently rec-ognized as a promising architecture for 6G wireless networks. In addition, free-space optical (FSO) technology has shown its potential for delivering exceptionally high-speed data connections over a long distance. While previous works on FSO-based AGIN mainly focused on point-to-point connectivity, this study instead addresses the context of multiple users. This, in turn, raises concerns about the effective multiple access control (MAC) protocol design. In this study, we analyze the performance of MAC protocols, which jointly employ the rate adaptation and bandwidth allocation, in FSO-based AGIN. Particularly, we exploit the performance of different rate adaptation/bandwidth allocation-based MAC protocols for UAV-aided relaying between multiple BSs and HAP via FSO links. Performance metrics are analytically obtained based on Markov models, including frame loss probability, system throughput and average delay. Numerical results offer insights for properly choosing MAC protocols for FSO-based AGIN towards 6G wireless networks.

关键词： Optical losses Measurement Protocols Free-space optical communication Wireless networks Media Access Control Throughput Autonomous aerial vehicles Delays Numerical models

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