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A Systems Approach to Developing an Outdoor IoT Network for Wildlife Image Capture

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A Systems Approach to Developing an Outdoor IoT Network for ...

IEEE International Symposium on Systems engineering (ISSE)

作者： Ethan Martin Serena Raso Yael Rogoszinski Robert Starr Shamsnaz V. Bhada Alexander Wyglinski Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA

This paper highlights the impact of Just in Time Systems engineering education on a senior design or capstone project in electrical and computer engineering. The goal of the senior design project was to design an Internet of Things (IoT) network using multiple wireless network protocols and devices for purposes of wildlife tracking and image capturing. A novel IoT network for motion capture was built utilizing low-power microcontrollers, passive-infrared sensor modules, and an IoT-based camera module. The network was studied in an outdoor environment using weather-proofed encasements and Google Drive connectivity. Systems engineering approaches were essential for validation and verification of the success criteria of this senior design project.

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Transferring Indoor Corrosion Image Assessment Models to Outdoor Images via Domain Adaptation

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Transferring Indoor Corrosion Image Assessment Models to Out...

International Conference on Machine Learning and Applications (ICMLA)

作者： Nicholas Josselyn Biao Yin Thomas Considine John Kelley Berend Rinderspacher Robert Jensen James Snyder Ziming Zhang Elke Rundensteiner Department of Data Science Worcester Polytechnic Institute Worcester MA ARD DEVCOM Army Research Laboratory Aberdeen Proving Ground MD USA ARD DEVCOM Army Research Laboratory Northeast Regional Extended Site Burlington MA USA ARD DEVCOM Army Research Laboratory Adelphi MD USA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA Department of Computer Science Worcester Polytechnic Institute Worcester MA

Corrosion of materials impacts critical economic sectors from infrastructure, transportation, defense, health, to the environment. The development of safe anti-corrosive materials is thus an important area of study in materials science. Corrosion science of preparing materials and then monitoring their corrosion under adverse conditions is labor intensive, time consuming, and extremely costly. While deep learning has become popular in automating various engineering tasks, the development of deep models for corrosion assessment is lacking. We are the first to study deep domain adaptation (DA) models for the automated assessment of the corrosion status of anti-corrosive materials. Corrosion data, i.e., photographic images of treated corroding materials, is abundant when produced in artificially controlled laboratory settings, while corrosion image data sets from rich natural outdoor environments are more challenging to produce and thus much smaller. We leverage the more readily available indoor corrosion data to train a classifier and then transfer it via deep domain adaptation to also perform well on the small yet more realistic outdoor corrosion image data set – without requiring target labels. We empirically compare 5 popular domain adaptation models on real-world corrosion image data sets. Our study finds that DA achieves 27% improvement in test accuracy compared to the performance of the no-DA baseline for classifying real-world outdoor corrosion data.

关键词： Economics Deep learning Adaptation models Materials science and technology Corrosion Biological system modeling Laboratories

Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability

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arXiv 2023年

作者： Varde, Aparna S. Liang, Jianyu Department of Computer Science Clean Energy & Sustainability Analytics Center Montclair State University NJ United States Mechanical and Materials Engineering Worcester Polytechnic Institute WorcesterMA United States

It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and expert systems. This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. It is a safe and responsible way to turn a specific waste stream to value-added products. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing. This includes harnessing artificial neural networks to study parameters affecting heat treatment of materials and impacts on their properties;deep learning via advances such as convolutional neural networks to explore grain size detection;and other classifiers such as Random Forests to analyze phrase fraction detection. Results with all these methods seem promising to embark on further work, e.g. ANN yields accuracy around 90% for predicting micro-structure development as per quench tempering, a heat treatment process. Future work entails several challenges: investigating various computer vision models (VGG, ResNet etc.) to find optimal accuracy, efficiency and robustness adequate for sustainable processes;creating domain-specific tools using machine learning for decision support in agile manufacturing;and assessing impacts on sustainability with metrics incorporating the appropriate use of recycled materials as well as the effectiveness of developed products. Our work makes impacts on green technology for smart manufacturing, and is motivated by related work in the highly interesting realm of AI for materials science. © 2023, CC BY.

关键词： Deep learning

Toward Perfusion Defect Preservation in Deep Learning Denoising for Reduced-Dose Cardiac SPECT

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Toward Perfusion Defect Preservation in Deep Learning Denois...

2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Liu, Junchi Yang, Yongyi Wernick, Miles N. Pretorius, P. Hendrik Lindsay, Clifford King, Michael A. Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616 United States University of Massachusetts Medical School Division of Nuclear Medicine Department of Radiology WorcesterMA01655 United States

ISBN: (纸本)9781665488723

In cardiac SPECT perfusion imaging deep learning (DL) denoising methods have been found to be effective for noise suppression in reduced-dose studies. However, DL training could lead to potential loss of perfusion defect signals (i.e. over-smoothing) due to high imbalance between perfusion defect and non-defect regions in training images. In this work, we investigate the feasibility of improving preservation of perfusion defects by including artificial defects in training a denoising network, in which the number and variability of perfusion defects were increased in an inexpensive fashion. In the experiments we demonstrated this approach with quarter-dose data from a set of 895 clinical acquisitions. The quantitative results indicate that the proposed approach can significantly improve perfusion defect detectability on the quarter-dose data. Compared to conventional DL training, the proposed method also achieved better perfusion defect preservation, yielding higher perfusion defect detection accuracy. © 2022 IEEE.

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Three-way deep neural network for radio frequency map generation and source localization

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arXiv 2021年

作者： Gill, Kuldeep S. Nguyen, Son Thein, Myo M. Wyglinski, Alexander M. Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA United States

In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain to construct a smooth radio frequency map (RFMap) and then perform localization using a deep neural network. Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in facilitating dynamic spectrum access (DSA) in beyond-5G and 6G communication technologies. Localization, wireless signal detection, and spectrum policy-making are several of the applications where distributed spectrum sensing will play a significant role. Detection and positioning of wireless emitters is a very challenging task in a large spectral and spatial area. In order to construct a smooth RFMap database, a large number of measurements are required which can be very expensive and time consuming. One approach to help realize these systems is to collect finite localized measurements across a given area and then interpolate the measurement values to construct the database. Current methods in the literature employ channel modeling to construct the radio frequency map, which lacks the granularity for accurate localization whereas our proposed approach reconstructs a new generalized RFMap. Localization results are presented and compared with conventional channel models. © 2021, CC BY.

关键词： Spectroscopy

TreeRNN: Topology-Preserving Deep Graph Embedding and Learning

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TreeRNN: Topology-Preserving Deep Graph Embedding and Learni...

International Conference on Pattern Recognition

作者： Yecheng Lyu Ming Li Xinming Huang Ulkuhan Guler Patrick Schaumont Ziming Zhang Electrical and Computer Engineering Worcester Polytechnic Institute

General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized graph kernels, but few of them are effective for the integration of such local patterns into global features. In contrast, in this paper we study the methods to transfer the graphs into trees so that explicit orders are learned to direct the feature integration from local to global. To this end, we apply the breadth first search (BFS) to construct trees from the graphs, which adds direction to the graph edges from the center node to the peripheral nodes. In addition, we proposed a novel projection scheme that transfer the trees to image representations, which is suitable for conventional convolution neural networks (CNNs) and recurrent neural networks (RNNs). To best learn the patterns from the graph-tree-images, we propose TreeRNN, a 2D RNN architecture that recurrently integrates the image pixels by rows and columns to help classify the graph categories. We evaluate the proposed method on several graph classification datasets, and manage to demonstrate comparable accuracy with the state-of-the-art on MUTAG, PTC-MR and NCI1 datasets.

关键词： Image segmentation Recurrent neural networks Three-dimensional displays Convolution Message passing Image edge detection Image representation

Abstraction-Free Control Synthesis to Satisfy Temporal Logic Constraints under Sensor Faults and Attacks

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Abstraction-Free Control Synthesis to Satisfy Temporal Logic...

IEEE Conference on Decision and Control

作者： Luyao Niu Zhouchi Li Andrew Clark Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA Department McKelvey School of Engineering Electrical and Systems Engineering Washington University in St. Louis St. Louis MO

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We study the problem of synthesizing a controller to satisfy a complex task in the presence of sensor faults and attacks. We model the task using Gaussian distribution temporal logic (GDTL), and propose a solution approach that does not rely on computing any finite abstraction to model the system. We decompose the GDTL specification into a sequence of reach-avoid sub-tasks. We develop a class of fault-tolerant finite time convergence control barrier functions (CBFs) to guarantee that a dynamical system reaches a set within finite time almost surely in the presence of malicious attacks. We use the fault-tolerant finite time convergence CBFs to guarantee the satisfaction of ‘reach’ property. We ensure ‘avoid’ part in each sub-task using fault-tolerant zeroing CBFs. These fault-tolerant CBFs formulate a set of linear constraints on the control input for each sub-task. We prove that if the error incurred by system state estimation is bounded by a certain threshold, then our synthesized controller fulfills each reach-avoid sub-task almost surely for any possible sensor fault and attack, and thus the GDTL specification is satisfied with probability one. We demonstrate our proposed approach using a numerical study on the coordination of two wheeled mobile robots.

关键词： Fault tolerance Sufficient conditions Computational modeling Fault tolerant systems Gaussian distribution Robot sensing systems Mobile robots

A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data

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arXiv 2024年

作者： Ziaei, Navid Nazari, Behzad Eden, Uri T. Widge, Alik Yousefi, Ali Department of Electrical & Computer Engineering Isfahan University of Technology Iran Department of Mathematics & Statistics Boston University United States Department of Psychiatry & Behavioral Sciences University of Minnesota United States Department of Computer Science Worcester Polytechnic University United States

Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different modalities. This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold. This model, named the latent discriminative generative decoder (LDGD), employs both the data and associated labels in the manifold discovery process. We derive a Bayesian solution to infer the latent variables, allowing LDGD to effectively capture inherent stochasticity in the data. We demonstrate applications of LDGD on both synthetic and benchmark datasets. Not only does LDGD infer the manifold accurately, but its accuracy in predicting data points' labels surpasses state-of-the-art approaches. In the development of LDGD, we have incorporated inducing points to reduce the computational complexity of Gaussian processes for large datasets, enabling batch training for enhanced efficient processing and scalability. Additionally, we show that LDGD can robustly infer manifold and precisely predict labels for scenarios in that data size is limited, demonstrating its capability to efficiently characterize high-dimensional data with limited samples. These collective attributes highlight the importance of developing non-parametric modeling approaches to analyze high-dimensional data. © 2024, CC BY.

关键词： Large datasets

CoFi: Coarse-to-fine ICP for LiDAR Localization in an efficient long-lasting point cloud map

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arXiv 2021年

作者： Lyu, Yecheng Huang, Xinming Zhang, Ziming Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

— LiDAR odometry and localization has attracted increasing research interest in recent years. In the existing works, iterative closest point (ICP) is widely used since it is precise and efficient. Due to its non-convexity and its local iterative strategy, however, ICP-based method easily falls into local optima, which in turn calls for a precise initialization. In this paper, we propose CoFi, a Coarse-to-Fine ICP algorithm for LiDAR localization. Specifically, the proposed algorithm down-samples the input point sets under multiple voxel resolution, and gradually refines the transformation from the coarse point sets to the fine-grained point sets. In addition, we propose a map based LiDAR localization algorithm that extracts semantic feature points from the LiDAR frames and apply CoFi to estimate the pose on an efficient point cloud map. With the help of the Cylinder3D algorithm for LiDAR scan semantic segmentation, the proposed CoFi localization algorithm demonstrates the state-of-the-art performance on the KITTI odometry benchmark, with significant improvement over the literature. Copyright © 2021, The Authors. All rights reserved.

关键词： Geometry