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IoT-Based Strawberry Disease Detection With Wall-Mounted Monitoring Cameras

IEEE Internet of Things Journal 2024年第1期11卷 1439-1451页

作者： Lin, Yi-Bing Liu, Chun-You Chen, Wen-Liang Chang, Chia-Hui Ng, Fung-Ling Yang, Krista Hsung, Jerry Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan China Medical University College of Humanities and Sciences Taichung404 Taiwan National Cheng Kung University MiinWu School of Computing Tainan701 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei115 Taiwan National Yang Ming Chiao Tung University College of Biological Science and Technology Hsinchu30010 Taiwan National Yang Ming Chiao Tung University Industrial Development Graduate Program College of Biological Science and Technology Hsinchu30010 Taiwan Proto Solution System Company Ltd. Hsinchu302052 Taiwan

This article proposes StrawberryTalk, an Internet of Things (IoT) platform for image-based strawberry disease detection. StrawberryTalk reuses the wall-mounted monitoring cameras without extra hardware cost. The contributions of StrawberryTalk are the utilization of IoT for automatic photo shoot and the wind detection mechanism to eliminate the obscure photos due to the wind effects. Also, the data preprocessing and the infection detection models are manipulated as IoT devices to simplify the implementation of the multicascade artificial intelligence (AI) models. We derive the relationship between the camera zoom factor and the distance between the camera and the strawberries for optimal disease detection. Accuracy of detection may be affected by obscure photos. In terms of eliminating obscure photos due to wind effect, we analytically derive the relationship between the wind alert delay and the number of obscure photos that must be retaken. For the greenhouses in the Bao Mountain, we only need to retake one photo. Based on the experiments, the mean average precision (mAP) of StrawberryTalk (to detect exact spots in a leaf) can be up to 92.37%, which is better than the previous approaches. To detect if a pot has infected leaves, the accuracy of StrawberryTalk can be up to 97.92% at the zoom factor 30×. In commercial operation, it is important to detect all infected strawberry pots. StrawberryTalk is able to detect all infected pots (i.e., recall is 100%) with a camera of zoom factor of 12× . The accuracy is 96.88%. © 2014 IEEE.

关键词： Cameras

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Open Tool-kit for AI-based Sleep Apnea Scoring

Open Tool-kit for AI-based Sleep Apnea Scoring

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Emerging Technology (INCET), International Conference for

作者： Karthika Ratheesh Krishna Rajeev Jyothika S Athira B Menon Rahul Krishnan Pathinarupothi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

Sleep is the most essential and fundamental to an individual’s well-being and vitality because it provides for the restoration and re-energizing of both the body and mind. Sleep apnea is a common sleep problem that affects millions of individuals worldwide. It is distinguished by breathing pauses or shallow breathing during sleeping, which can result in a variety of health problems such as heart disease, stroke, and diabetes [1]. Sleep apnea diagnosis and management can be difficult because the cost and time-consuming polysomnography (PSG) testing requires specialized equipment and trained personnel. To address these concerns, we created and validated an artificial intelligence (AI)-based sleep apnea scoring system that analyses electrocardiogram (ECG) signals to predict the severity of sleep apnea. The system analyses ECG signals using 1D-CNN to predict the Apnea-Hypopnea Index (AHI), a measure of the severity of sleep apnea. The tool is organized into three sections: data exploration, data visualization, and prediction, and it is aimed at accurately forecasting patients’ risk of sleep apnea. Our research demonstrates the potential of AI-based approaches for the diagnosis and management of sleep apnea, and we believe that our system can help improve patient outcomes and quality of life.

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Architecture and Implementation of a patient criticality aware Edge-Cloud offloading technique

Architecture and Implementation of a patient criticality awa...

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International Conference for Convergence of Technology (I2CT)

作者： Athira B Menon Krishna Rajeev Jyothika S Karthika Ratheesh Rahul Krishnan Pathinarupothi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India

The Internet of Things (IoT)-based remote health monitoring is one of the most promising technological interventions that is emerging to address the unique challenges of affordability, accessibility, and availability in global health, which denote equitable access to healthcare, particularly in remote-rural-developing regions. IoT devices can monitor multiple vital signs, which are then used for AI-assisted decision-making systems, which in turn assist physicians in forecasting remotely. However, there are many difficulties, like bandwidth issues, data loss, and overburdened doctors due to the massive amount of data. Shifting data from cloud to edge improves performance, cost efficiency, privacy, reduces communication between distant servers and the edge, resulting in less processing delay. We developed a clinical response requirement based cloud-to-edge offloading technique. As a use case, we developed an edge AI application for acute hypotensive episode prediction and compared its performance both in the cloud as well as on the edge.

关键词： Performance evaluation Cloud computing Decision making Medical services Delays Internet of Things Servers

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ORA3D: Overlap Region Aware Multi-view 3D Object Detection 33

ORA3D: Overlap Region Aware Multi-view 3D Object Detection

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Roh, Wonseok Chang, Gyusam Moon, Seokha Nam, Giljoo Kim, Chanyoung Kim, Younghyun Kim, Sangpil Kim, Jinkyu Department of Artificial Intelligence Korea University Seoul Korea Republic of School of Computing KAIST Daejeon Korea Republic of Department of Computer Science and Engineering Korea University Seoul Korea Republic of Autonomous Driving Center Hyundai Motor Company R&D Division Seoul Korea Republic of

Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly, and the networks' understanding of the scene is often limited to that of a monocular detection network. Moreover, objects in the overlap region are often largely occluded or suffer from deformation due to camera distortion, causing a domain shift. To mitigate this issue, we propose using the following two main modules: (1) Stereo Disparity Estimation for Weak Depth Supervision and (2) Adversarial Overlap Region Discriminator. The former utilizes the traditional stereo disparity estimation method to obtain reliable disparity information from the overlap region. Given the disparity estimates as supervision, we propose regularizing the network to fully utilize the geometric potential of binocular images and improve the overall detection accuracy accordingly. Further, the latter module minimizes the representational gap between non-overlap and overlapping regions. We demonstrate the effectiveness of the proposed method with the nuScenes large-scale multi-view 3D object detection data. Our experiments show that our proposed method outperforms current state-of-the-art models, i.e., DETR3D and BEVDet. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Object detection

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Multimodal segmentation neural network to determine the cause of damage to grasslands 29

Multimodal segmentation neural network to determine the caus...

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29th International Conference on Software, Telecommunications and computer Networks, SoftCOM 2021

作者： Johenneken, Maximilian Drak, Ahmad Herpers, Rainer Asteroth, Alexander Department of Computer Science Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Institute of Visual Computing Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Faculty of Computer Science University of New Brunswick Fredericton Canada Lassonde School of Engineering York University Toronto Canada Computer Science Department University College London London United Kingdom

ISBN: (纸本)9789532901092

Agricultural fields suffer from damage due to changing climate conditions and wildlife foraging. Damage incurred by wild boar is classified as the main contributing factor to grasslands damage. Such damage results in losses for farmers due to reduced yield potential and repair costs. The extent of wild boar damage is typically estimated manually by using ground based approaches (e.g. GPS measurements), which is a time consuming task with questionable accuracy. Building upon our previous work, we present an autonomous approach to detect and classify the cause of damage to grasslands (wild boar, mole etc.). The approach entails utilizing convolutional neural networks for semantic segmentation of grasslands. An RGB baseline was established, in addition to evaluating multimodal architectures that incorporate different surface model feature representations, leading to a joint representation of spectral and elevation information. Testing and experimentation was performed in real-world grasslands around Bonn, Germany. The results show that incorporating elevation features with late fusion enhances the overall performance of the network over the RGB baselines. © SoftCOM 2021. All rights reserved.

关键词： Semantic Segmentation

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Multimodal Knowledge Graph Vision-Language Models for Precision Retrieval and Inference in Embedded Systems

Multimodal Knowledge Graph Vision-Language Models for Precis...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Li, Mingyi Li, Jiayin Liu, Hui Han, Lijin Liao, Dengting Zhang, Zekun Gao, Zong Liu, Baoshuai Jin, Zhongfeng School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China School of Computing and Data Science Xiamen University Malaysia Selangor 43900 Malaysia Shanghai Zhangjiang Institute of Mathematicas Zhangjiang Institute of Shanghai Shanghai201203 China China North Vehicle Research Institute General Technology Department 100072 China Coordination Center of China GNational Computer Network Emergency Response Technical Team 100083 China

ISBN: (纸本)9781665481090

In light of the challenges associated with enhancing multimodal knowledge graph retrieval and improving embodied intelligence in dynamic environments, we propose the Multimodal Knowledge Graph Vision-Language System (MKGVL). This system is designed to enhance the reasoning and feedback capabilities of embodied intelligent systems by continuously updating the knowledge graph through real-time feedback from the Vision-Language Model (VLM). By integrating visual encoders, language models, and knowledge graph networks, MKGVL constructs a unified multimodal representation that enables adaptive decision-making and responsiveness to environmental changes. Experimental results show that MKGVL outperforms existing models in fine-grained retrieval tasks, achieving an 11.5% improvement in Rank1 accuracy and a mean Average Precision (mAP) of 97.49%. Further evaluations conducted on datasets such as ARKitScenes, MultiScan, and 3RScan highlight the model's robustness and adaptability. Additionally, MKGVL's deployment on embedded platforms like Jetson Orin demonstrates its efficiency in real-time multimodal tasks, particularly in resource-constrained environments. These findings underscore MKGVL's ability to deliver accurate and efficient multimodal processing, making it a strong solution for adaptive knowledge-based systems in complex settings. © 2024 IEEE.

关键词： Knowledge graph

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DRSM: Efficient Neural 4D Decomposition for Dynamic Reconstruction in Stationary Monocular Cameras

DRSM: Efficient Neural 4D Decomposition for Dynamic Reconstr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weixing Xie Xiao Dong Yong Yang Qiqin Lin Jingze Chen Junfeng Yao Xiaohu Guo Center for Digital Media Computing School of Film School of Informatics Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Department of Computer Science BNU-HKBU United International College Department of Computer Science The University of Texas at Dallas

With the popularity of monocular videos generated by video sharing and live broadcasting applications, reconstructing and editing dynamic scenes in stationary monocular cameras has become a special but anticipated technology. In contrast to scene reconstructions that exploit multi-view observations, the problem of modeling a dynamic scene from a single view is significantly more under-constrained and ill-posed. Inspired by recent progress in neural rendering, we present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras. Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting. Inadequate 3D clues from a single-view and occlusion are also particular challenges in scene reconstruction. To overcome these difficulties, we propose deep supervised optimization and ray casting strategies. With experiments on various videos, our method generates higher-fidelity results than existing methods for single-view dynamic scene representation.

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Real-Time Sperm Detection Using Lightweight YOLOv5 8

Real-Time Sperm Detection Using Lightweight YOLOv5

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Zhang, Zebin Qi, Bolin Ou, Shimin Shi, Chenjian Shenyang Institute of Computing Technology Chinese Academy of Sciences Shengyang110106 China University of Chinese Academy of Sciences Beijing100049 China Xiamen Information Center Department of Pubulic Credit Information Technology Xiamen361001 China Linköping University Department of Computer and Information Science Sweden

ISBN: (纸本)9781665450515

Malformed sperm is an important cause of male infertility, and sperm morphology analysis (SMA) is an effective means to diagnose sperm morphology. Deep learning assists to enhance performance on precise SMA;however, existing deep learning based SMA methods mostly focus on single cell scale, which presents a challenge for obtaining single-sperm-image datasets (one sperm cell per image). It is also challenging to integrate current object detection models on low-performance devices. This paper presents a lightweight model for sperm detection. By removing 50% of convolutional kernels cutting the large-object-detecting head from YOLOv5s, our model got a similar precision to the original YOLOv3 (mAP.5 of 0.957 and 0.947, respectively), but with a model size of only 2.8 MB (123.6 MB of YOLOv3). There is a slight loss in precision compared to YOLOv5s (mAP.5:.95 of 0.604 with 14.4MB model size);however, our model still shows a significant advantage in reducing the number of parameters. Experimental results also indicated that MS COCO pre-training is helpful in sperm detection tasks, and the mosaic augmentation strongly enhances the precision for all YOLO models. © 2022 IEEE.

关键词： Object detection

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Preparing for HPC on RISC-V: Examining Vectorization and Distributed Performance of an Astrophysics Application with HPX and Kokkos

Preparing for HPC on RISC-V: Examining Vectorization and Dis...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Diehl, Patrick Syskakis, Panagiotis Dais, Gregor Brandt, Steven R. Kheirkhahan, Alireza Singanaboina, Srinivas Yadav Marcello, Dominic Taylor, Chris Leidel, John Kaiser, Hartmut Los AlamosNM87545 United States Louisiana State University Department of Physics and Astronomy Baton RougeLA70803 United States Louisiana State University LSU Center for Computation & Technology Baton RougeLA70803 United States University of Stuttgart IPVS Stuttgart70174 Germany Tactical Computing Labs LindsayTX776250 United States Louisiana State University Department of Computer Science Baton RougeLA70803 United States

ISBN: (纸本)9798350355543

In recent years, interest in RISC-V computing architectures has moved from academic to mainstream, especially in the field of High Performance computing where energy limitations are increasingly a concern. As of this year, the first single board RISC-V CPUs implementing the finalized ratified vector specification are being released. The RISC-V vector specification follows in the tradition of vector processors found in the CDC STAR-100, the Cray-1, the Convex C-Series, and the NEC SX machines and accelerators. The family of vector processors offers support for variable-length array processing as opposed to the fixed-length processing functionality offered by SIMD. Vector processors offer opportunities to perform vector-chaining which allows temporary results to be used without the need to resolve memory *** this work, we use the Octo-Tiger multi-physics, multi-scale, 3D adaptive mesh refinement astrophysics application to study these early RISC-V chips with vector machine support. We report on our experience in porting this modern C++ code (which is built upon several open-source libraries such as HPX and Kokkos) to RISC-V. In addition, we show the impact of the RISC-V Vector extension on a RISC-V single board computer by implementing the std :: experimental:simd interface and integrating it with our code. We also compare the application's performance, scalability, and power consumption on desktop-grade RISC-V computer to an A64FX *** results presented in this paper are part of a longer-term evaluation of RISC-V's viability for HPC applications. © 2024 IEEE.

关键词： asynchronous many-task system HPX Kokkos RISC-V scalable vector extensions task-based run time system vectorization

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AgileWatts: An Energy-Efficient CPU Core Idle-State Architecture for Latency-Sensitive Server Applications 22

AgileWatts: An Energy-Efficient CPU Core Idle-State Architec...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Jawad Haj Yahya Haris Volos Davide B. Bartolini Georgia Antoniou Jeremie S. Kim Zhe Wang Kleovoulos Kalaitzidis Tom Rollet Zhirui Chen Ye Geng Onur Mutlu Yiannakis Sazeides Rivos Inc Mountain View California United States Computer Architecture Laboratory Department of Computer Science University of Cyprus Computing Systems Lab Huawei Zurich Research Center Department of Information Technology and Electrical Engineering ETH Zurich Switzerland

ISBN: (纸本)9781665462723

User-facing applications running in modern datacenters exhibit irregular request patterns and are implemented using a multitude of services with tight latency requirements (30--250μs). These characteristics render existing energy-conserving techniques ineffective when processors are idle due to the long transition time (order of 100μs) from a deep CPU core idle power state (C-state). While prior works propose management techniques to mitigate this inefficiency, we tackle it at its root with AgileWatts (AW): a new deep CPU core C-state architecture optimized for datacenter server processors targeting latency-sensitive *** drastically reduces the transition latency from deep CPU core idle power states while retaining most of their power savings based on three key ideas. First, AW eliminates the latency (several microseconds) of saving/restoring the core context when powering-off/-on the core in a deep idle state by i) implementing medium-grained power-gates, carefully distributed across the CPU core, and ii) retaining context in the power-ungated domain. Second, AW eliminates the flush latency (several tens of microseconds) of the L1/L2 caches when entering a deep idle state by keeping L1/L2 content power-ungated. A small control logic also remains ungated to serve cache coherence traffic. AW implements cache sleep-mode and leakage reduction for the power-ungated domain by lowering a core's voltage to the minimum operational level. Third, using a state-of-the-art power efficient all-digital phase-locked loop (ADPLL) clock generator, AW keeps the PLL active and locked during the idle state, cutting microseconds of wake-up latency at negligible power *** evaluation with an accurate industrial-grade simulator calibrated against an Intel Skylake server shows that AW reduces the energy consumption of Memcached by up to 71% (35% on average) with <1% end-to-end performance degradation. We observe similar trends for other evaluated services (MySQL and Kafka).

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