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Soybeannet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (Uav) Images

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SSRN

SSRN 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute & Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein, and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source at https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images and the labeled soybean dataset at https://***/datasets/jiajiali/uav-based-soybean-pod-images, offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, The Authors. All rights reserved.

关键词： Statistical tests

Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

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Application of Deep Artificial Neural Network to Model Chara...

IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

Combining rough set-based relevance and redundancy for the ranking and selection of nominal features 24

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Combining rough set-based relevance and redundancy for the r...

24th KES International Conference on Knowledge-Based and Intelligent Information and engineering systems, KES 2020

作者： Froelich, Wojciech Hajek, Petr Institute of Computer Science University of Silesia ul. Bedzinska 39 Sosnowiec Poland Institute of System Engineering and Informatics Faculty of Economics and Administration University of Pardubice Studentska 84 Pardubice532 10 Czech Republic

In this paper, we propose a new method for features ranking and selection. Our approach is based on ranking nominal features in terms of their relevance to the assigned class and mutual redundancy with the other features. To calculate the relevance and redundancy, we propose to use a rough-set based approach. After performing the ranking, features filtering is carried out in a supervised way enabling the user to decide on the number of the retained features. The experiments revealed that thanks to our method, it is possible to filter out numerous features describing data while still maintaining satisfactory classification accuracy achieved by the classifier trained using the reduced dataset. The comparative experiments performed with the use of publicly available datasets proved the high efficiency and competitiveness of our approach. © 2020 The Authors. Published by Elsevier B.V.

关键词： Redundancy

Video-based Tancho Koi Fish Tracking system Using CSK, DFT, and LOT 3

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Video-based Tancho Koi Fish Tracking System Using CSK, DFT, ...

3rd International Conference on Science and Technology 2019, ICST 2019

作者： Rahmat, Basuki Waluyo, Minto Rachmanto, Tuhu Agung Afandi, Mohamad Irwan Widyantara, Helmy Harianto Universitas Pembangunan Nasional Veteran Jawa Timur Surabaya Indonesia Electrical Engineering Department Institut Teknologi Telkom Surabaya Indonesia Computer System Department Institut Bisnis Dan Informatika STIKOM Surabaya Surabaya Indonesia

In this paper, a comparison of Circulant Structure of Tracking-by-detection with Kernels (CSK), Distribution Fields for Tracking (DFT) and Locally Orderless Tracking (LOT) methods is proposed for the process of video-based object tracking. The object in question is the tancho koi fish and its variants, in the form of video recordings. All three methods are applied to trace the red roundabout in the head of the koi fish. The purpose of this study is to provide the best tracking process recommendations for this case. From the experimental results, it is found that the average sample accuracy of the DFT tracking method is more recommended than the other two methods, even though it has an average frame rate that is no better than the CSK method. © Published under licence by IOP Publishing Ltd.

关键词： Fish

Semi-supervised learning for COVID-19 image classification via ResNet

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arXiv 2021年

作者： Nwosu, Lucy Li, Xiangfang Qian, Lijun Kim, Seungchan Dong, Xishuang Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Excellence in Research and Education for Big Military Data Intelligence CREDIT Center Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States Center of Computational Systems Biology Department of Electrical and Computer Engineering Prairie View A&M University Texas A&M University System Prairie ViewTX77446 United States

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic in over 200 countries and territories, which has resulted in a great public health concern across the international community. Analysis of X-ray imaging data can play a critical role in timely and accurate screening and fighting against COVID-19. Supervised deep learning has been successfully applied to recognize COVID-19 pathology from X-ray imaging datasets. However, it requires a substantial amount of annotated X-ray images to train models, which is often not applicable to data analysis for emerging events such as COVID-19 outbreak, especially in the early stage of the outbreak. To address this challenge, this paper proposes a two-path semi-supervised deep learning model, ssResNet, based on Residual Neural Network (ResNet) for COVID-19 image classification, where two paths refer to a supervised path and an unsupervised path, respectively. Moreover, we design a weighted supervised loss that assigns higher weight for the minority classes in the training process to resolve the data imbalance. Experimental results on a large-scale of X-ray image dataset COVIDx demonstrate that the proposed model can achieve promising performance even when trained on very few labeled training images. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

Combining Kernelized Autoencoding and Centroid Prediction for Dynamic Multi-objective Optimization

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arXiv 2023年

作者： Hou, Zhanglu Zou, Juan Ruan, Gan Liu, Yuan Xia, Yizhang Hunan Engineering Research Center of Intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China CERCIA School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

Evolutionary algorithms face significant challenges when dealing with dynamic multi-objective optimization because Pareto optimal solutions and/or Pareto optimal fronts change. This paper proposes a unified paradigm, which combines the kernelized autoncoding evolutionary search and the centriod-based prediction (denoted by KAEP), for solving dynamic multi-objective optimization problems (DMOPs). Specifically, whenever a change is detected, KAEP reacts effectively to it by generating two subpopulations. The first subpoulation is generated by a simple centriod-based prediction strategy. For the second initial subpopulation, the kernel autoencoder is derived to predict the moving of the Pareto-optimal solutions based on the historical elite solutions. In this way, an initial population is predicted by the proposed combination strategies with good convergence and diversity, which can be effective for solving DMOPs. The performance of our proposed method is compared with five state-of-the-art algorithms on a number of complex benchmark problems. Empirical results fully demonstrate the superiority of our proposed method on most test instances. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Intelligent Reflecting Surface for MIMO VLC: Joint Design of Surface Configuration and Transceiver Signal Processing

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arXiv 2022年

作者： Sun, Shiyuan Yang, Fang Song, Jian Zhang, Rui Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen518057 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

With the capability of reconfiguring the wireless electromagnetic environment, intelligent reflecting surface (IRS) is a new paradigm for designing future wireless communication systems. In this paper, we consider optical IRS for improving the performance of visible light communication (VLC) under a multiple-input and multiple-output (MIMO) setting. Specifically, we focus on the downlink communication of an indoor MIMO VLC system and aim to minimize the mean square error (MSE) of demodulated signals at the receiver. To this end, the MIMO channel gain of the IRS-aided VLC is first derived under the point source assumption, based on which the MSE minimization problem is then formulated subject to the emission power constraints. Next, we propose an alternating optimization algorithm, which decomposes the original problem into three subproblems, to iteratively optimize the IRS configuration, the precoding and detection matrices for minimizing the MSE. Moreover, theoretical analysis on the performance of the proposed algorithm in high and low signal-to-noise ratio (SNR) regimes is provided, revealing that the joint optimization process can be simplified in such special cases, and the algorithm’s convergence property and computational complexity are also discussed. Finally, numerical results show that IRS-aided schemes significantly reduce the MSE as compared to their counterparts without IRS, and the proposed algorithm outperforms other baseline schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

SoybeanNet: Transformer-Based Convolutional Neural Network for Soybean Pod Counting from Unmanned Aerial Vehicle (UAV) Images

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arXiv 2023年

作者： Li, Jiajia Magar, Raju Thada Chen, Dong Lin, Feng Wang, Dechun Yin, Xiang Zhuang, Weichao Li, Zhaojian Department of Electrical and Computer Engineering Michigan State University East LansingMI United States Department of Plant Soil and Microbial Sciences Michigan State University East LansingMI United States Environmental Institute Link Lab University of Virginia CharlottesvilleVA United States Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Mechanical Engineering Southeast University Nanjing China Department of Mechanical Engineering Michigan State University East LansingMI48824 United States

Soybean is a critical source of food, protein and oil, and thus has received extensive research aimed at enhancing their yield, refining cultivation practices, and advancing soybean breeding techniques. Within this context, soybean pod counting plays an essential role in understanding and optimizing production. Despite recent advancements, the development of a robust pod-counting algorithm capable of performing effectively in real-field conditions remains a significant challenge This paper presents a pioneering work of accurate soybean pod counting utilizing unmanned aerial vehicle (UAV) images captured from actual soybean fields in Michigan, USA. Specifically, this paper presents SoybeanNet, a novel point-based counting network that harnesses powerful transformer backbones for simultaneous soybean pod counting and localization with high accuracy. In addition, a new dataset of UAV-acquired images for soybean pod counting was created and open-sourced, consisting of 113 drone images with more than 260k manually annotated soybean pods captured under natural lighting conditions. Through comprehensive evaluations, SoybeanNet demonstrates superior performance over five state-of-the-art approaches when tested on the collected images. Remarkably, SoybeanNet achieves a counting accuracy of 84.51% when tested on the testing dataset, attesting to its efficacy in real-world scenarios. The publication also provides both the source code (https://***/JiajiaLi04/Soybean-Pod-Counting-from-UAV-Images) and the labeled soybean dataset (https://***/datasets/jiajiali/uav-based-soybean-pod-images), offering a valuable resource for future research endeavors in soybean pod counting and related fields. © 2023, CC BY-NC-SA.

关键词： Statistical tests

Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach

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arXiv 2021年

作者： Wang, Xiong Ye, Jiancheng Lui, John C.S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Network Technology Lab Hong Kong Research Center Huawei Technologies Co. Ltd. Hong Kong Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short to handle task placement involving many coexisting users in a dynamic and uncertain environment. In this paper, we develop a multi-user offloading framework considering unknown yet stochastic system-side information to enable a decentralized user-initiated service placement. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We show that DEBO can deduce the optimal user-server assignment, thereby achieving a close-to-optimal service performance and tight O(log T) offloading regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require heterogeneous computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems