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A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

computers, Materials & Continua 2023年第12期77卷 3969-3992页

作者： Naeem Ullah Javed Ali Khan Sultan Almakdi Mohammed S.Alshehri Mimonah Al Qathrady Eman Abdullah Aldakheel Doaa Sami Khafaga Department of Software Engineering University of Engineering and TechnologyTaxila4400Pakistan Department of Computer Science Faculty of PhysicsEngineeringand Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Departments of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Tomato leaf diseases significantly impact crop production,necessitating early detection for sustainable *** Learning(DL)has recently shown excellent results in identifying and classifying tomato leaf ***,current DL methods often require substantial computational resources,hindering their application on resource-constrained *** propose the Deep Tomato Detection Network(DTomatoDNet),a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome *** Convn kernels used in the proposed(DTomatoDNet)framework is 1×1,which reduces the number of parameters and helps in more detailed and descriptive feature extraction for *** proposed DTomatoDNet model is trained from scratch to determine the classification success rate.10,000 tomato leaf images(1000 images per class)from the publicly accessible dataset,covering one healthy category and nine disease categories,are utilized in training the proposed DTomatoDNet *** specifically,we classified tomato leaf images into Target Spot(TS),Early Blight(EB),Late Blight(LB),Bacterial Spot(BS),Leaf Mold(LM),Tomato Yellow Leaf Curl Virus(YLCV),Septoria Leaf Spot(SLS),Spider Mites(SM),Tomato Mosaic Virus(MV),and Tomato Healthy(H).The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%,demonstrating excellent accuracy in differentiating between tomato *** model could be used on mobile platforms because it is lightweight and designed with fewer *** farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.

关键词： CNN deep learning DTomatoDNet tomato leaf disease classification smart agriculture

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A Prototype Preceding Vehicle Identification System Development and Field Evaluation 10

A Prototype Preceding Vehicle Identification System Developm...

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10th International Conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2024

作者： Mu, Zeyu Tu, Guancheng Shi, Austin Yang, Kun Sun, Yixin Shen, Cong Park, B. Brian Department of Systems & Information Engineering University of Virginia CharlottesvilleVA United States Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Departments of Computer Science and Electrical & Computer Engineering University of Virginia CharlottesvilleVA United States Departments of Civil & Environmental Engineering and Systems & Information Engineering University of Virginia CharlottesvilleVA United States

ISBN: (纸本)9789897587030

Preceding vehicle identification is crucial for establishing cooperative platooning. This paper presents the development of a prototype preceding vehicle identification system (PVIS) and its field evaluation for the assessment of commercial viability. We designed and assembled a prototype consisting of a processing unit (Jetson Nano board), a communication device (Wi-Fi dongle), a GPS unit, and a distance measurement sensor (Terabee sensor). The Jetson Nano integrates the SparkFun GPS-RTK-SMA unit, the Terabee time-of-flight sensor, and the Wi-Fi dongle. The PVIS prototype in the ego vehicle measures the distance to its preceding vehicle and receives the GPS data from potential preceding vehicles with the PVIS prototypes. With these, the PVIS in the ego vehicle determines the connectivity of the preceding vehicle. The field evaluation results showed that the prototype PVIS works as designed, and each successful identification takes about 5.3 seconds. However, it was found that the Terabee (time of flight) sensor, at times, did not properly measure distances, likely due to an angle issue caused by the roadway surface and vibration of the vehicle. We discussed how to overcome the challenges identified and enhance the prototype for successful commercialization. Copyright © 2024 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Vehicle to vehicle communications

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Using an Evolutionary Algorithm Such as Genetic Algorithm (Ga) to Optimize Linear Equations 5

Using an Evolutionary Algorithm Such as Genetic Algorithm (G...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Wdaa, Abdul Sttar Ismail Al-Barzinji, Shokhan M. Tarish, Hiba A. College of Education for Pure Science University of Anbar Ramadi Iraq College of Computer Science and Information Technology University of Anbar Department of Computer Networks Systems Ramadi Iraq University of Technology Civil Engineering Departments Baghdad Iraq

ISBN: (纸本)9798350363739

An Evolutionary algorithm is a subset of evolutionary computations. Cloning, mutation, recombination, and selection are some of the methods used by the evolutionary algorithm that were inspired by biological evolution. The problem that was addressed in this study is how to find the coefficient values of the variables in linear equations that contain a set of variables, it must equal the right side. After reviewing previous research looking for optimization, genetic algorithm, which will use in this paper, so how are going to use this strategy to solve our problems? Select the best chromosomes from this population. Perform random chromosomal hybridization. Then, within the genes of our chromosomes, produce random mutations. Will send these chromosomes for selection again. The best chromosomes among them are selected again. It crosses and turns again, and the process is repeated until a certain number of generations have passed or find the best option. After applying the genetic algorithm through the Matlab program to the linear equation, which contains six variables, for example, the results were very excellent, as the parameter values were found in a very short time and with high accuracy, completely matching the right side of the equation. © 2024 IEEE.

关键词： Genetic algorithms

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Invertibility of Discrete-Time Linear systems with Sparse Inputs 63

Invertibility of Discrete-Time Linear Systems with Sparse In...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Poe, Kyle Mallada, Enrique Vidal, Rene University of Pennsylvania Applied Mathematics and Computational Science Group PA19104 United States Johns Hopkins University Mallada Is with the Department of Electrical and Computer Engineering MD21218 United States University of Pennsylvania Radiology Computer and Information Science Statistics and Data Science Departments of Electrical and Systems Engineering PA19104 United States

ISBN: (纸本)9798350316339

One of the fundamental problems of interest for discrete-time linear systems is whether its input sequence may be recovered given its output sequence, a.k.a. the left inversion problem. Many conditions on the state space geometry, dynamics, and spectral structure of a system have been used to characterize the well-posedness of this problem, without assumptions on the inputs. However, certain structural assumptions, such as input sparsity, have been shown to translate to practical gains in the performance of inversion algorithms, surpassing classical guarantees. Establishing necessary and sufficient conditions for left invertibility of systems with sparse inputs is therefore a crucial step toward understanding the performance limits of system inversion under structured input assumptions. In this work, we provide the first necessary and sufficient characterizations of left invertibility for linear systems with sparse inputs, echoing classic characterizations for standard linear systems. The key insight in deriving these results is in establishing the existence of two novel geometric invariants unique to the sparseinput setting, the weakly unobservable and strongly reachable subspace arrangements. By means of a concrete example, we demonstrate the utility of these characterizations. We conclude by discussing extensions and applications of this framework to several related problems in sparse control. © 2024 IEEE.

关键词： State space methods

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Towards Real-Time Generation of Delay-Compensated Video Feeds for Outdoor Mobile Robot Teleoperation

arXiv

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arXiv 2024年

作者： Chakraborty, Neeloy Fang, Yixiao Schreiber, Andre Ji, Tianchen Huang, Zhe Mihigo, Aganze Wall, Cassidy Almana, Abdulrahman Driggs-Campbell, Katherine Departments of Electrical and Computer Engineering Computer Science Industrial and Enterprise Systems Engineering The University of Illinois Urbana-Champaign United States

Teleoperation is an important technology to enable supervisors to control agricultural robots remotely. However, environmental factors in dense crop rows and limitations in network infrastructure hinder the reliability of data streamed to teleoperators. These issues result in delayed and variable frame rate video feeds that often deviate significantly from the robot’s actual viewpoint. We propose a modular learning-based vision pipeline to generate delay-compensated images in real-time for supervisors. Our extensive offline evaluations demonstrate that our method generates more accurate images compared to state-of-the-art approaches in our setting. Additionally, ours is one of the few works to evaluate a delay-compensation method in outdoor field environments with complex terrain on data from a real robot in real-time. Resulting videos and code are provided at https://***/***/comp-teleop. © 2024, CC BY.

关键词： Agricultural robots

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Accelerating the Use of Classical Infrastructure to Implement Quantum Networks

Accelerating the Use of Classical Infrastructure to Implemen...

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Quantum 2.0, QUANTUM 2022

作者： Broberg, Robert Bassett, Lee Smith, Jonathan University of Pennsylvania Departments of Electrical & Systems Engineering Computer and Information Science 3401 Walnut Street PhiladelphiaPA United States

For emerging quantum networks to coexist with the classical internet, they must conform to existing specifications. We examine the architectural model of the internet, especially physical layer requirements, and we pr...

ISBN: (纸本)9781957171111

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Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima

arXiv

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arXiv 2023年

作者： Dixit, Rishabh Gürbüzbalaban, Mert Bajwa, Waheed U. Department of Electrical and Computer Engineering Departments of Electrical & Computer Engineering Management Science and Information Systems and Statistics United States Departments of Electrical & Computer Engineering and Statistics

This paper considers the problem of understanding the behavior of a general class of accelerated gradient methods on smooth nonconvex functions. Motivated by some recent works that have proposed effective algorithms, based on Polyak’s heavy ball method and the Nesterov accelerated gradient method, to achieve convergence to a local minimum of nonconvex functions, this work proposes a broad class of Nesterov-type accelerated methods and puts forth a rigorous study of these methods encompassing the escape from saddle-points and convergence to local minima through a both asymptotic and a nonasymptotic analysis. In the asymptotic regime, this paper answers an open question of whether Nesterov’s accelerated gradient method (NAG) with variable momentum parameter avoids strict saddle points almost surely. This work also develops two metrics of asymptotic rate of convergence and divergence, and evaluates these two metrics for several popular standard accelerated methods such as the NAG, and Nesterov’s accelerated gradient with constant momentum (NCM) near strict saddle points. In the local regime, this work provides an analysis that leads to the "linear" exit time estimates from strict saddle neighborhoods for trajectories of these accelerated methods as well the necessary conditions for the existence of such trajectories. Finally, this work studies a sub-class of accelerated methods that can converge in convex neighborhoods of nonconvex functions with a near optimal rate to a local minima and at the same time this sub-class offers superior saddle-escape behavior compared to that of NAG. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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An Analysis of Vehicular Telematics and Geo-Location Data to Maximize Road Safety

An Analysis of Vehicular Telematics and Geo-Location Data to...

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2024 systems and Information engineering Design Symposium, SIEDS 2024

作者： Nguyen, Hoang Minh V. Oh, Sunho C. Saha, Goirick Blunt, Terrence L. Ball, Matthew J. Duffy, Michael E. Abdelzaher, Ahmed F Brian Park, B. University of Virginia Systems Engineering Department CharlottesvilleVA22903 United States Virginia State University Computer Science Department PetersburgVA23806 United States University of Virginia Facilities Management CharlottesvilleVA22903 United States University of Virginia Civil Engineering and the Systems Engineering Departments CharlottesvilleVA22903 United States

ISBN: (纸本)9798350385144

In scores of vehicle fleets, telematic tracking systems provide fleet managers with information regarding energy consumption, compliance with safety regulations, and driver performance. For a University's Facilities Management (FM) Fleet to take the next step towards an elevated Sustainable Fleet accreditation and overall team performance, the management has recognized the importance of safety tracking methods combined with data analytics and a comprehensive systems analysis to aid the reinforcement training and maintenance of safe and sustainable driving practices by fleet drivers. This paper demonstrated an effective method of identifying safety hotspots by analyzing safety surrogate measures, such as harsh braking, harsh cornering and speeding from vehicular telematics data and combining them with their geo-location information to enhance the safety of the University of Virginia's facilities management vehicles and common drivers in the area. Instances of safety surrogate measure violations were first mapped onto a cluster map and were then normalized and combined to pinpoint which spots on campus were most prone to accidents. We then validated the cluster maps with crash data from both Facilities Management vehicles and the state of Virginia to identify safety hotspots. From the investigations, we considered a list of comprehensive safety countermeasures to address the safety infractions identified at each hot spot. These safety countermeasures were recommended based on their likelihood of reducing safety violations. © 2024 IEEE.

关键词： Telematics

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Invertibility of Discrete-Time Linear systems with Sparse Inputs

Invertibility of Discrete-Time Linear Systems with Sparse In...

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IEEE Conference on Decision and Control

作者： Kyle Poe Enrique Mallada René Vidal Applied Mathematics and Computational Science Group University of Pennsylvania PA Mallada is with the Department of Electrical and Computer Engineering Johns Hopkins University MD Departments of Electrical and Systems Engineering Radiology Computer and Information Science Statistics and Data Science University of Pennsylvania PA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

关键词： Linear systems Geometry Sufficient conditions Systematics Heuristic algorithms Standards

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