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检索条件"机构=Department of Electrial and Computer Engineering Worcester Polytechnic Institute"

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Robot-Assisted Wide-Area Photoacoustic System

Robot-Assisted Wide-Area Photoacoustic System

引用

2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Gao, Shang Ma, Xihan Zhang, Haichong K. Worcester Polytechnic Institute Department of Robotics Engineering Worcester United States Worcester Polytechnic Institute Department of Biomedical Engineering Worcester United States Worcester Polytechnic Institute Department of Computer Science Worcester United States

ISBN: (纸本)9798350346459

Liver ischemia and reperfusion injury (IRI) is a vascular complication that can lead to hepatic functional impairment. IRI is relevant in liver transplantation and during liver surgery performed with intermittent vascular inflow occlusion. Detecting intraoperative liver oxygenation impairment and ischemia could help prevent IRI, which is challenging partially due to the multiple hepatic vascular inflows. We propose a robot-assisted wide-area photoacoustic (PA) imaging system to monitor the liver oxygenation level intraoperatively. PA imaging has been widely used for oxygenation detection in preclinical research. We used a robotic arm to actuate the 2D PA probe and scan across the liver surface. An accurate volumetric tomography can be reconstructed via registering 2D images to the recorded robot trajectory. An ex vivo porcine liver sample was scanned using the proposed system. The result suggested that the robot-assisted wide-area PA tomography has the potential to be incorporated into clinical practice to evaluate liver SO2 intraoperatively. © 2023 IEEE.

关键词： Photoacoustic effect

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DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Guided and Class-Balanced Losses 22

DeepSC-Edge: Scientific Corrosion Segmentation with Edge-Gui...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Yin, Biao Josselyn, Nicholas Considine, Thomas Kelley, John Rinderspacher, Berend Jensen, Robert Snyder, James Zhang, Ziming Rundensteiner, Elke Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Worcester Polytechnic Institute Department of Data Science WorcesterMA United States Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350345346

Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing 3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion-inhibiting materials on a large scale, accurate and efficient corrosion assessment is crucial. Yet it is hindered by a lack of automatic tools for expert-level corrosion segmentation of material science experimental images. Developing such tools is challenging due to limited domain-valid data, image artifacts visually similar to corrosion, various corrosion morphology, strong class imbalance, and millimeter-precision corrosion boundaries. To help the community address these challenges, we curate the first expert-level segmentation annotations for a real-world image dataset [1] for scientific corrosion segmentation. In addition, we design a deep learning based model, called DeepSC-Edge that achieves guidance of ground-truth edge learning by adopting a novel loss that avoids over-fitting to edges. It also is enriched by integrating a class-balanced loss that improves segmentation with small area but crucial edges of interest for scientific corrosion assessment. Our dataset and methods pave the way to advanced deep-learning models for corrosion assessment and generation - promoting new research to connect computer vision and material science discovery. Once the appropriate approvals have been cleared, we expect to release the code and data at: https://***/ © 2023 IEEE.

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An Attention-based Self-Supervised Approach to Monocular Depth Estimation from UAV captured Video Sequences 6

An Attention-based Self-Supervised Approach to Monocular Dep...

引用

6th IEEE International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Benkhoui, Yasmina El-Korchi, Tahar Ludwig, Reinhold Worcester Polytechnic Institute Electrical and Computer Engineering Department United States Worcester Polytechnic Institute Civil and Environmental Engineering Department United States

ISBN: (纸本)9798350325485

UAV-based inspections represent a promising solution to contactless civil infrastructure condition assessment, especially bridges. In this context, depth estimation is crucial as it helps predict the distance to objects, plan flight trajectories, and avoid obstacles. Recently, learning-based methods made significant progress in monocular depth estimation. However, supervised approaches require a large amount of annotated depth data which remains challenging to collect. As an alternative, researchers have been investigating self-supervised learning, where the depth estimation network is trained by projecting a view to nearby views and minimizing the re-projection photometric loss. However, current methods lack accuracy and robustness since they do not exploit structural information, leading to inaccurate spatial representations and surface discontinuities. In addition, monocular depth estimation suffers from scale ambiguity, and external or prior information is needed for scale recovery. This work sought to overcome these issues by adapting the standard joint network of estimating depth and pose based on a self-learned depth prediction network from monocular videos. The proposed method introduces a soft attention mechanism and a recurrent block. The attention mechanism guides the neural network's attention to the global structures and local attributes and recovers the spatial relationship of visual features, whereas the recurrent block leverages sequence frames to capture temporal information in addition to spatial information. Our experiments and ablation study on AeroBridge dataset demonstrate that these modifications efficiently capture spatio-Temporal information and generate competitive qualitative and quantitative results compared to state-of-The-Art methods confirming the practical applicability of our design in inspection tasks. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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PFAS Incineration: Known Unknowns, Reaction Simulation, and ML to Predict Product Properties

PFAS Incineration: Known Unknowns, Reaction Simulation, and ...

引用

2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Soiffer, Duncan Pyorre, Tuomas Kenney, David Teixeira, Andrew Worcester Polytechnic Institute Department of Computer Science and Mathematical Sciences WorcesterMA United States Worcester Polytechnic Institute Department of Robotics Engineering WorcesterMA United States Worcester Polytechnic Institute Department of Chemical Engineering WorcesterMA United States

ISBN: (纸本)9798350308600

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals used for their impressive stability, a property which has also led them to be found polluting virtually every sector of the environment-including our own bodies. Presently, our ability to destroy PFAS is limited largely to incineration, whose products and side effects are poorly understood. Research on PFAS incineration is constrained by high experimental costs and difficulty in accurately measuring reaction products. This presents an opportunity for computer simulation and machine learning to provide valuable assistance. In this study, three kinds of thermal PFAS decomposition are simulated. Further, to broaden physical understanding of PFAS incineration products, a recent in-house machine learning model for predicting the octanol-water partition coefficient (LogP) of chemicals is improved and applied to simulation results. A decrease in LogP is observed in all treatments, with the most dramatic effect (-2 average LogP) observed in oxidative pyrolysis. © 2023 IEEE.

关键词： Machine learning

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Outlier Detection and Removal Signal Processing for Wearable Transcutaneous Oxygen Sensor

Outlier Detection and Removal Signal Processing for Wearable...

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2023 IEEE MIT Undergraduate Research Technology Conference, URTC 2023

作者： Leonardi, Abigail Murphy, Ciara Hobson, Sydney Rohera, Vanshika Guler, Ulkuhan Worcester Polytechnic Institute Electrical and Computer Engineering Department WorcesterMA01609 United States

ISBN: (纸本)9798350308600

Respiratory diseases impact millions of people worldwide. In the smart and connected health era, there is a pressing need for reliable medical devices for remote monitoring of patients with respiratory disorders and patients with chronic diseases in general. For respiratory monitoring, there exist widely-used miniaturized photoplethysmography devices to measure peripheral blood oxygen saturation. More recently, transcutaneous oxygen monitors have been designed in a small form factor to be used as wearable devices to enable remote and continuous measurement of transcutaneous oxygen. In this study, we focus on processing the raw data collected from a noninvasive transcutaneous blood gas monitor that is in the development phase. Post-processing should be applied to remove the outliers in the measurements and smooth out the measurement noise. Various filters to remove outliers were tested in MATLAB, including the Hampel, rmoutliers, filloutliers, and moving average filters. The Hampel filter yielded the best results while maintaining the integrity of the data. The fillmissing function in MATLAB using the previous or linear interpolation methods is used to replace outlier data. The comparison of the raw data and the result of the data processing is presented. © 2023 IEEE.

关键词： Blood

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Additively Manufactured Receiver Design for Wirelessly-Powered Biomedical Applications

Additively Manufactured Receiver Design for Wirelessly-Power...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Marangalou, Amin Hazrati Arturo Gonzalez, Miguel Guler, Ulkuhan Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA01609 United States

ISBN: (纸本)9798350300260

This paper presents a miniaturized receiver prototype fabricated using an additive manufacturing electronics process. We aim to demonstrate the feasibility and advantages of using additive manufacturing for embedding the essential components of an inductive link in a compact form factor for miniaturized biomedical devices. We demonstrated various geometries of planar and helical coils, with average diameters of 20 mm. The wireless power transfer system comprises a class-E power amplifier operating at 6.78 MHz, a 10 μH transmitter coil, and a full-wave active rectifier fabricated in TSMC 180 nm CMOS process technology. The power delivered to a 500 Ω load at the output of the active rectifier varies from 2.7 mW to 42 mW for planar coils and 3 mW to 45 mW for helical coils, over a range of 1 cm to 4 cm. The transmitter's power consumption is kept constant at 1.25 W throughout the experiments. Furthermore, in the case of helical coils, a lateral center-to-center misalignment of 1 cm at a distance of 2 cm between the receiver and transmitter results in a 10 mW drop in the output power. © 2023 IEEE.

关键词： Additive manufacturing electronics Coils Implantables Inductive link Power transfer efficiency Wearables Wireless power transfer system

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A Behavioral Model for Transcutaneous Carbon Dioxide Measurement Using Time-Correlated Single Photon Counting Technique 46

A Behavioral Model for Transcutaneous Carbon Dioxide Measure...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Isiksalan, Isil Karli, Hakan B. Tufan, Tuna B. Matthews, John Unluturk, Bige D. Guler, Ulkuhan Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA01609 United States Michigan State University Michigan Department of Electrical and Computer Engineering MI48824 United States

ISBN: (纸本)9798350371499

Millimeter-size carbon dioxide (CO2)-sensitive luminescence films emerge as practical sensors in miniaturized biomedical devices for measuring transcutaneous CO2. The intensity and lifetime of the excited luminophores in the film carry critical information about the CO2 values in the surrounding environment. It has been proven that lifetime-based measurement is superior to intensity-based measurement because of its robustness to confounding factors. Addressing the challenge of accurately determining the rapid nanosecond-scale lifetime of CO2-sensitive luminophores, we introduce a behavioral model using a time-correlated single photon counting system equipped with silicon photomultipliers. We included nonidealities such as dark count rate, afterpulsing, and crosstalk to this model. To recover the lifetime from photon counts, we applied denoising and fitting techniques. With hyperparameter optimization to determine various post-processing parameters, we improved accuracy in recovery for lifetime values ranging from 3 ns to 7 ns. Our results demonstrate the applicability of the proposed model and techniques in real-world scenarios. © 2024 IEEE.

关键词： Photomultipliers

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Evaluating Vulnerability of Chiplet-Based Systems to Contactless Probing Techniques

Evaluating Vulnerability of Chiplet-Based Systems to Contact...

引用

2024 IEEE International Test Conference, ITC 2024

作者： Deric, Aleksa Mitard, Kyle Tajik, Shahin Holcomb, Daniel University of Massachusetts Amherst Department of Electrical and Computer Engineering AmherstMA01003 United States Worcester Polytechnic Institute Department of Electrical and Computer Engineering WorcesterMA01609 United States

ISBN: (纸本)9798331520137

Driven by a need for ever-increasing chip performance, a growing number of semiconductor companies are opting for all-inclusive System-on-Chip (SoC) architectures. Increasingly, the solution adopted to minimize the impact of silicon defects on manufacturing yield of larger dies has been to split a design into multiple smaller dies called chiplets, which are then brought together on a silicon interposer. Advanced 2.5D and 3D packaging techniques that enable this kind of integration also promise increased power efficiency and opportunities for heterogeneous ***, despite their advantages, chiplets are not without issues. Disaggregating a design into multiple separate dies introduces new security threats, including the possibility of tampering with and probing exposed data lines. In this paper we evaluate the exposure of chiplets to probing by applying laser contactless probing techniques to a chiplet-based AMD/Xilinx VU9P FPGA. First, we identify and map interposer wire drivers, and show that probing them is easier compared to probing internal nodes. Lastly, we demonstrate that delay-based sensors, which can be used to protect against physical probes, are insufficient to protect against laser probing. © 2024 IEEE.

关键词： System-on-chip

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Multiview Compounding for Linear Array-Based 3D Photoacoustic Imaging

Multiview Compounding for Linear Array-Based 3D Photoacousti...

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Photons Plus Ultrasound: Imaging and Sensing 2023

作者： Garje, Yash Tang, Yichuan Jaupi, Enxhi Zhang, Haichong K. Robotics Engineering Department Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Biomedical Engineering Department Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States

ISBN: (纸本)9781510658639

Two-dimensional and three-dimensional photoacoustic (PA) visualization is an emerging diagnostic procedure for investigation and research in vascular studies. Current manual/mechanized target scanning techniques involve sweeping the target along the elevation to generate 3D visuals. However, since the linear array probes exhibit poor resolution along the direction orthogonal to its focal plane, these techniques are prone to miss out on the organic structures parallel to the lateral direction. This could result in a misrepresentation of the target and is a critical shortfall of the method. We propose a multiview scanning and compounding technique to overcome the directionality bias and obtain more accurate and isotropic imaging performance. Using electromechanical translatory and rotary stages for multiview data acquisition, we generate a unified 3D visualization. A data processing pipeline illustrates an axial implementation of the Hilbert transform followed by spatial integration of the volumetric data to obtain the output. A 6-directional scanning approach improves the completeness of the structural details. We validated the technique using sub-millimeter-sized balls and wire phantoms. We first observed an enhanced resemblance of the outcome with the actual target in the ball phantom. Secondly, we observed imaging quality improvement with isotropic intensity distribution prominently in the wire phantom. A comparative analysis showed around a 50% reduction in the standard deviation of intensity distribution as compared to conventional unidirectional 3D PA imaging. © 2023 SPIE.

关键词： Phantoms

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On Contrastive Learning for Domain Generalization 23

On Contrastive Learning for Domain Generalization

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Nguyen, Thuan Brown, D. Richard East Tennessee State University Department of Engineering Engineering Technology and Surveying TN United States Worcester Polytechnic Institute Department of Electrical and-Computer Engineering WorcesterMA United States

ISBN: (纸本)9798350374889

Given the training data and labels from several seen domains, Domain Generalization (DG) aims to learn models that generalize well on unlabeled data from unseen domains. Due to the distribution of data and/or labels may vary between domains, current DG methods are mainly based on the theme of domain-invariant representation learning which decomposes the learning process into two steps: (1) finding a representation function from the input space to the representation/feature space to learn the so-called domain-invariant features, i.e., the features that are statistically stable between domains, and (2) designing a classifier on top of these domain-invariant features which is mutually optimal for all domains. Recent works on DG show that Contrastive Learning (CL) which was originally proposed to learn a representation function such that similar samples are pulled closer while dissimilar samples are pushed far away in the representation/feature space, appears as a promising solution for domain-invariant features learning. In this paper, we first establish the fundamental theory to justify why CL might be a potential approach for DG by showing that CL, under particular settings, allows a mechanism to minimize a loss function that enforces an optimal classifier for all training domains. Based on these foundations, we revisit the recent works that apply CL for DG to indicate their limitations and suggest a modification by combining the CL method with current DG methods for better out-of-domain generalization. Our numerical results point out that our proposed algorithm can achieve state-of-the-art performance on multiple DG benchmark datasets. © 2024 IEEE.

关键词： Contrastive Learning

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