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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

SPP-CNN: An Efficient Framework for Network Robustness Prediction

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： Complex networks

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

Optimal repairing schemes for Reed-Solomon codes with alphabet sizes linear in lengths under the rack-aware model

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arXiv 2019年

作者： Jin, Lingfei Luo, Gaojun Xing, Chaoping Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Engineering Research Center of Blockchain Shanghai Institute of Intelligent Electronics and Systems Shanghai Department of Mathematics Nanjing University of Aeronautics and Astronautics Nanjing211100 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Physical and Mathematical Sciences Nanyang Technological University Singapore Singapore

In modern practical data centers, storage nodes are usually organized into equally sized groups, which is called racks. The cost of cross-rack communication is much more expensive compared with the intra-rack communication cost. The codes for this system are called rack-aware regenerating codes. Similar to standard minimum storage regenerating (MSR) codes, it is a challenging task to construct minimum storage rack-aware regenerating (MSRR) codes achieving the cut-set bound. The known constructions of MSRR codes achieving the cut-set bound give codes with alphabet size q exponential in the code length n, more precisely, q = Ω(exp(nn)). The main contribution of this paper is to provide explicit construction of MSRR codes achieving the cut-set bound with the alphabet size linear in n. To achieve this goal, we first present a general framework to repair Reed-Solomon codes. It turns out that the known repairing schemes of Reed-Solomon codes can be realized under our general framework. Several techniques are used in this paper. In particular, we use the degree decent method to repair failure node. This technique allows us to get Reed-Solomon codes with the alphabet size linear in n. The other techniques include choice of good polynomials. Note that good polynomials are used for construction of locally repairable code in literature. To the best of our knowledge, it is the first time in this paper to make use of good polynomials for constructions of regenerating codes. Keywods: Rack-aware model, Reed-solomon codes, minimum storage regenerating codes. Copyright © 2019, The Authors. All rights reserved.

关键词： Reed-Solomon codes

Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Developments in the radio search for extraterrestrial intelligence

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Developments in the radio search for extraterrestrial intell...

General Assembly and Scientific Symposium, URSI

作者： A. P. V. Siemion D. Werthimer D. Anderson H. Chen J. Cobb J. Cordes T. Filiba G. Foster S. Gowda E. Korpela M. Lebofsky A. Little W. Mallard L. Spitler M. Wagner Department of Astronomy University of California Berkeley Berkeley CA USA University of California Berkeley Berkeley CA US Center for Astronomy Signal Processing and Electronics Research University of California Berkeley Berkeley CA USA Artment of Electrical Engineering University of California Los Angeles Los Angeles CA USA Space Sciences Laboratory University of California Berkeley Berkeley CA USA Department of Astronomy Cornell University Ithaca NY USA Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA USA Department of Physics Oxford University Oxford UK Department of Physics University of California Berkeley Berkeley CA USA

We present developments in the search for engineered radio emissions from advanced extraterrestrial life. Our group is currently engaging in both targeted and sky survey searches for extraterrestrial intelligence (SETI), covering a wide variety of narrow-band and pulsed signal types. We are also developing new SETI instrumentation, designed to be flexible, modular and to employ commodity components that lower cost and enhance upgradability. Here we will discuss the status of these observational and engineering projects, as well as prospects for future radio SETI endeavors.

关键词： Educational institutions Telescopes Bandwidth Planets Filter banks Physics

Neural Signatures of Motor Skill in the Resting Brain

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Neural Signatures of Motor Skill in the Resting Brain

IEEE International Conference on Systems, Man and Cybernetics

作者： Ozan Özdenizci Timm Meyer Felix Wichmann Jan Peters Bernhard Schölkopf Müjdat Çetin Moritz Grosse-Wentrup Department of Electrical and Computer Engineering Northeastern University Boston MA United States Max Planck Institute for Intelligent Systems Tübingen Germany Faculty of Science University of Tübingen and Bernstein Center for Computational Neuroscience Tübingen Tübingen Germany FG Intelligente Autonome Systeme Technische Universität Darmstadt Darmstadt Germany Signal Processing and Information Systems Laboratory Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task-as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation.

关键词： Electroencephalography End effectors Task analysis Integrated circuits Stroke (medical condition) Planning Visualization

LocalViT: Analyzing Locality in Vision Transformers

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LocalViT: Analyzing Locality in Vision Transformers

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yawei Li Kai Zhang Jiezhang Cao Radu Timofte Michele Magno Luca Benini Luc Van Goo Computer Vision Lab D-ITET ETH Zurich Switzerland Center for Artificial Intelligence and Data Science (CAIDAS) University of Wurzburg Germany Center for Project-Based Learning D-ITET ETH Zurich Switzerland Integrated Systems Laboratory D-ITET ETH Zurich Switzerland Department of Electrical Electronic and Information Engineering University of Bologna Italy Processing Speech and Images (PSI) KU Leuven Belgium

The aim of this paper is to study the influence of locality mechanisms in vision transformers. Transformers originated from machine translation and are particularly good at modelling long-range dependencies within a long sequence. Although the global interaction between the token embeddings could be well modelled by the self-attention mechanism of transformers, what is lacking is a locality mechanism for infor-mation exchange within a local region. In this paper, locality mechanism is systematically investigated by carefully designed controlled experiments. We add locality to vision transformers into the feed-forward network. This seemingly simple solution is inspired by the comparison between feed-forward networks and inverted residual blocks. The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to vision transformers with different architecture designs, which shows the generalization of the locality concept. For ImageNet2012 classification, the locality-enhanced transformers outperform the baselines Swin-T [1], DeiT-T [2] and PVT-T [3] by 1.0%, 2.6 % and 3.1 % with a negligible increase in the number of parameters and computational effort. Code is available at https://***/ofsoundof/LocalViT.

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Digitally Enhanced Analog Circuits: System Aspects

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Digitally Enhanced Analog Circuits: System Aspects

2008 IEEE International Symposium on Circuits and Systems (ISCAS 2008), vol.2

作者： Boris Murmann Christian Vogel Heinz Koeppl Department of Electrical Engineering University of Stanford Stanford CA USA Signal Processing and Speech Communication Laboratory Graz University of Technology Graz Austria School of Communication and Computer Sciences EPF Lausanne Lausanne Switzerland

An overview of digital enhancement techniques for analog circuits is presented. Recent research suggests that the high density and low energy of digital circuits can be leveraged to enable a new generation of interface electronics that is based on minimal precision, low complexity analog blocks. Today, examples of enhancement schemes can be found in diverse applications and include nonlinearity compensation of ADCs, predistortion of power amplifiers and mismatch calibration in radio receivers. Since it is often difficult to identify commonalities among these different, but conceptually related schemes, this tutorial paper aims to provide a unified and system-oriented perspective of the field.

关键词： Analog circuits Calibration Signal processing Microprocessors Circuit noise Digital circuits Linearity Power dissipation Signal resolution Digital signal processing