About

Ulisses Braga-Neto is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University. He holds a Ph.D. in Electrical & Computer Engineering from The Johns Hopkins University, obtained in 2002, along with multiple master's degrees in Electrical & Computer Engineering and Mathematical Sciences from the same institution, and a bachelor's degree in Electrical Engineering from the Federal University of Pernambuco. His research interests include machine learning, signal processing, and scientific computation. Braga-Neto has contributed to the fields through various publications, including a recent textbook titled 'Fundamentals of Pattern Recognition and Machine Learning.' His work encompasses the development of probabilistic solutions for nonlinear partial differential equations, physics-informed neural networks, Bayesian control of Markov decision processes, and adaptive filtering for Boolean dynamical systems, reflecting a focus on advancing computational methods and data-driven approaches in engineering.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Physics
• Mathematics
• Agronomy
• Biology
• Cognitive science
• Algorithm
• Statistical physics
• Remote sensing
• Statistics
• Geography
• Quantum mechanics
• Theoretical physics
• Psychology

Selected publications

• Detecting volunteer cotton plants in a corn field with deep learning on UAV remote-sensing imagery

  Computers and Electronics in Agriculture · 2022 · 47 citations

  • Artificial Intelligence
  • Computer Science
  • Artificial Intelligence

  Volunteer cotton (VC) plants growing in the fields of inter-seasonal and rotated crops, like corn, can serve as hosts to boll weevil pests once they reach pin-head square stage (5–6 leaf stage). The VC plants therefore need to be detected, located, and destroyed or sprayed. In this paper, we present a study on using deep learning (DL) to detect VC plants in a corn field using RGB images collected with an unmanned aerial vehicle (UAV). The objectives were (i) to determine whether the YOLOv3 DL algorithm could be used for VC detection in a corn field based on UAV-derived RGB images, and (ii) to investigate the behavior of YOLOv3 on images at three different pixel scales (320 × 320, S1; 416 × 416, S2; and 512 × 512, S3). The metrics used to evaluate the results were average precision (AP), mean average precision (mAP) and F1-score at 95 % confidence level. It was found that YOLOv3 was able to detect VC plants in corn field at an average detection accuracy of more than 80 %, F1-score of 78.5 % and mAP of 80.38 %. With respect to images size, no significant differences existed for mAP among the three scales, but a significant difference was found for AP between S1 and S3 (p = 0.04) and between S2 and S3 (p = 0.02). A significant difference was also found for F1-score between S2 and S3 (p = 0.02). The overall goal of this study was to minimize boll weevil pest infestation by maximizing the true positive detection of VC plants in a corn field which is represented by the mAP values. The lack of significant differences of these at all three scales indicated that the trained YOLOv3 model can be used for VC detection irrespective of the three input image sizes. The capability of YOLOv3 to detect VC plants demonstrates the potential of DL algorithms for real-time detection and mitigation using computer vision and a spot-spray capable UAV.

  PublisherDOI

• Self-adaptive physics-informed neural networks

  Journal of Computational Physics · 2022 · 435 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Statistical physics
  DOI

• Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages

  Artificial Intelligence in Agriculture · 2022 · 22 citations

  • Algorithm
  • Mathematics
  • Agronomy

  The feral or volunteer cotton (VC) plants when reach the pinhead squaring phase (5–6 leaf stage) can act as hosts for the boll weevil (Anthonomus grandis L.) pests. The Texas Boll Weevil Eradication Program (TBWEP) employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected. In this paper, we demonstrate the application of computer vision (CV) algorithm based on You Only Look Once version 5 (YOLOv5) for detecting VC plants growing in the middle of corn fields at three different growth stages (V3, V6 and VT) using unmanned aircraft systems (UAS) remote sensing imagery. All the four variants of YOLOv5 (s, m, l, and x) were used and their performances were compared based on classification accuracy, mean average precision (mAP) and F1-score. It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98% and mAP of 96.3% at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85% and YOLOv5m and YOLOv5l had the least mAP of 86.5% at VT stage on images of size 416 × 416 pixels. The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.

  PublisherDOI

• Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism

  arXiv (Cornell University) · 2020 · 125 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Cognitive science

  Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, it has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of "stiff" PDEs. In this paper, we propose a fundamentally new way to train PINNs adaptively, where the adaptation weights are fully trainable and applied to each training point individually, so the neural network learns autonomously which regions of the solution are difficult and is forced to focus on them. The self-adaptation weights specify a soft multiplicative soft attention mask, which is reminiscent of similar mechanisms used in computer vision. The basic idea behind these SA-PINNs is to make the weights increase as the corresponding losses increase, which is accomplished by training the network to simultaneously minimize the losses and maximize the weights. In addition, we show how to build a continuous map of self-adaptive weights using Gaussian Process regression, which allows the use of stochastic gradient descent in problems where conventional gradient descent is not enough to produce accurate solutions. Finally, we derive the Neural Tangent Kernel matrix for SA-PINNs and use it to obtain a heuristic understanding of the effect of the self-adaptive weights on the dynamics of training in the limiting case of infinitely-wide PINNs, which suggests that SA-PINNs work by producing a smooth equalization of the eigenvalues of the NTK matrix corresponding to the different loss terms. In numerical experiments with several linear and nonlinear benchmark problems, the SA-PINN outperformed other state-of-the-art PINN algorithm in L2 error, while using a smaller number of training epochs.

  DOI

• Fundamentals of Pattern Recognition and Machine Learning

  Springer eBooks · 2020 · 54 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence
  DOI

Frequent coauthors

• Edward R. Dougherty

  Salve Regina University

  95 shared

• Mahdi Imani

  Northeastern University

  34 shared

• Ernesto T. A. Marques

  30 shared

• Pappu Kumar Yadav

  17 shared

• Sorin Popescu

  Texas A&M University

  17 shared

• Robert G. Hardin

  17 shared

• Stephen W. Searcy

  Texas A&M University

  17 shared

• Levi D. McClenny

  DEVCOM Army Research Laboratory

  16 shared

Education

• Ph.D., Electrical Engineering

  University of California, Los Angeles

  1995

• M.S., Electrical Engineering

  University of California, Los Angeles

  1991

• B.S., Electrical Engineering

  University of São Paulo

  1987

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