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International Conference on Mixed Design of Integrated Circuits and Systems (MIXDES)

作者： Guido Di Patrizio Stanchieri Andrea De Marcellis Marco Faccio Elia Palange Ulkuhan Guler Department of Information Engineering Computer Science and Mathematics (DISIM) University of L’Aquila L’Aquila Italy Department of Electrical and Computer Engineering (ECE) Worcester Polytechnic Institute Worcester Massachusetts USA

This paper reports on a fully integrated optical wireless power transfer system designed for implantable biomedical devices. If compared with the solutions based on radiative and non-radiative methodologies, like the capacitive, inductive, magnetic couplings and radio frequency techniques, the optical systems reach the highest electromagnetic compatibility respect to the simultaneous presence of other electronic instrumentations. A further advantage common to all the radio frequencies, is the capability to transfer power to implantable and wearable biomedical devices also from long distances using environmental visible and near infrared light and/or LED-based lamps. The proposed solution has been designed at transistor level in TSMC 0.18 μm CMOS technology using inverter stages with the aim to maximize the output voltage value starting from low input voltages coming from a Si photodiode, also included in the overall layout of the system that requires a total Si area of about 0.466 mm 2 . By performing post-layout simulations, considering a minimum input voltage as low as 0.5 V, the achieved output voltage is higher than 2 V that increases up to 3.4 V for an input voltage of 0.7 V, with a best power conversion efficiency of about 70 %. The comparison of the main characteristics of the proposed solution with the State-of-Art demonstrates that, starting from the lower input voltages, the proposed system is capable to work with a reduced clock frequency achieving higher output voltages to supply external loads, satisfactory output currents and good power conversion efficiencies, requiring a reduced Si area.

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AMS-HD: Acute Mountain Sickness Detection with Hyperdimensional Computing

AMS-HD: Acute Mountain Sickness Detection with Hyperdimensio...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Abu Kaisar Mohammad Masum Reeti Pradhananga Jonas I. Schmidt Mehran Shoushtari Moghadam M. Hassan Najafi Bige Unluturk Ulkuhan Guler Sercan Aygun School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA USA Electrical Computer and Systems Engineering Department Case Western Reserve University Cleveland OH USA Electrical and Computer Engineering & Biomedical Engineering Michigan State University East Lansing MI USA Electrical & Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient alternative solution, making it well-suited for resource-constrained environments, such as wearable devices. Its lightweight architecture and efficient memory management make it ideal for embedded systems, enabling real-time AMS detection with accuracy comparable to traditional ML models. We introduce AMS-HD, a novel framework that leverages custom feature engineering and quasi-random hyper-vector encoding to further enhance the efficiency and accuracy of HDC for AMS detection. The proposed framework demonstrates the potential for seamless integration into wearable biomedical devices for on-the-go health monitoring.

关键词： Temperature distribution Accuracy Memory management Memory architecture Real-time systems Biomedical monitoring Bioinformatics Wearable devices Monitoring Medical diagnostic imaging

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A Near Sensor Edge Computing System for Point Cloud Semantic Segmentation

arXiv

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arXiv 2022年

作者： Bai, Lin Zhao, Yiming Huang, Xinming Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural networks, putting all the computing into vehicle Electronic Control Unit (ECU) is not efficient or practical. In this paper, we proposed a light weighted point cloud semantic segmentation network based on range view. Due to its simple pre-processing and standard convolution, it is efficient when running on deep learning accelerator like DPU. Furthermore, a near sensor computing system is built for autonomous vehicles. In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network. By leaving only the post-processing step to ECU, this solution heavily alleviate the computation burden of ECU and consequently shortens the decision making and vehicles reaction latency. Our semantic segmentation network achieved 10 frame per second (fps) on Xilinx DPU with computation efficiency 42.5 GOP/W. Copyright © 2022, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives

arXiv

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arXiv 2023年

作者： Li, Lening Rahmani, Hazhar Fu, Jie The Department of Robotics Worcester Polytechnic Institute WorcesterMA01609 United States The Department of Electrical and Computer Engineering University of Florida GainesvilleFL32605 United States

This paper studies temporal planning in probabilistic environments, modeled as labeled Markov decision processes (MDPs), with user preferences over multiple temporal goals. Existing works reflect such preferences as a prioritized list of goals. This paper introduces a new specification language, termed prioritized qualitative choice linear temporal logic on finite traces, which augments linear temporal logic on finite traces with prioritized conjunction and ordered disjunction from prioritized qualitative choice logic. This language allows for succinctly specifying temporal objectives with corresponding preferences accomplishing each temporal task. The finite traces that describe the system's behaviors are ranked based on their dissatisfaction scores with respect to the formula. We propose a systematic translation from the new language to a weighted deterministic finite automaton. Utilizing this computational model, we formulate and solve a problem of computing an optimal policy that minimizes the expected score of dissatisfaction given user preferences. We demonstrate the efficacy and applicability of the logic and the algorithm on several case studies with detailed analyses for each. Copyright © 2023, The Authors. All rights reserved.

关键词： Specification languages

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A Hybrid Model and Learning-Based Force Estimation Framework for Surgical Robots

A Hybrid Model and Learning-Based Force Estimation Framework...

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

作者： Hao Yang Haoying Zhou Gregory S. Fischer Jie Ying Wu Department of Computer Science Vanderbilt University TN USA Department of Robotics Engineering Worcester Polytechnic Institute MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Haptic feedback to the surgeon during robotic surgery would enable safer and more immersive surgeries but estimating tissue interaction forces at the tips of robotically controlled surgical instruments has proven challenging. Few existing surgical robots can measure interaction forces directly and the additional sensor may limit the life of instruments. We present a hybrid model and learning-based framework for force estimation for the Patient Side Manipulators (PSM) of a da Vinci Research Kit (dVRK). The model-based component identifies the dynamic parameters of the robot and estimates free-space joint torque, while the learning-based component compensates for environmental factors, such as the additional torque caused by trocar interaction between the PSM instrument and the patient’s body wall. We evaluate our method in an abdominal phantom and achieve an error in force estimation of under 10% normalized root-mean-squared error. We show that by using a model-based method to perform dynamics identification, we reduce reliance on the training data covering the entire workspace. Although originally developed for the dVRK, the proposed method is a generalizable framework for other compliant surgical robots. The code is available at https://***/vu-maple-lab/dvrk_force_estimation.

关键词： Learning systems Torque Medical robotics Instruments Force Dynamics Estimation Surgery Robot sensing systems Trajectory optimization

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Realistic Data Generation for 6D Pose Estimation of Surgical Instruments

arXiv

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arXiv 2024年

作者： Barragan, Juan Antonio Zhang, Jintan Zhou, Haoying Munawar, Adnan Kazanzides, Peter Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01608 United States

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks;yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59 mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Ishikawa, JESS, and Visual Analytics for engineering

Ishikawa, JESS, and Visual Analytics for Engineering

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Varde, Aparna S. Liang, Jianyu Yang, Zhaotong Sisson, Richard D. Montclair State University Department of Computer Science NJ United States Worcester Polytechnic Institute Mechanical and Materials Engineering MA United States

ISBN: (纸本)9781665480451

Domain-specific big data poses issues in knowledge discovery and decision support given its many Vs such as volume, variety, and visualization. In this work, we propose solutions via Ishikawa diagrams, JESS (Java Expert System Shell) with rule discovery, as well as visual analytics. We deploy data science and domain models, targeting engineering and scientific domains. Our results yield high accuracy, efficiency and cost-effectiveness. © 2022 IEEE.

关键词： Big data

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Optimizing Transcutaneous Oxygen Measurement Sites on Humans

Optimizing Transcutaneous Oxygen Measurement Sites on Humans

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Abigail Leonardi Ciara Murphy Sydney Hobson Vanshika Rohera Vladimir Vakhter Burak Kahraman Guixue Bu Foroohar Foroozan Lawrence Rhein Ulkuhan Guler Electrical and Computer Engineering Department Worcester Polytechnic Institute Worcester MA USA Analog Devices Inc Wilmington MA USA Department of Pediatrics UMass Chan Medical School University of Massachusetts Worcester MA USA

This study utilizes an optical method of transcutaneous oxygen sensing that has the potential to revolutionize at-home care. This technique is based on quenching the luminescence of a platinum porphyrin film. Since oxygen quenches luminescence, its lifetime is further measured to assess the partial pressure of transcutaneous oxygen diffusing through the skin. Unlike conventional transcutaneous oxygen monitors that use electrochemical sensors, the luminescence-based sensor allows the use of dry electrodes that do not require heating and reduce the risk of accidental skin irritations or burns. These properties not only improve patient safety but also allow the creation of miniature wearable transcutaneous oxygen sensors for continuous and accurate remote respiratory monitoring. To this end, it is critical to assess the efficiency of the wearable sensor by determining the optimal location for its placement on the body. Depending on the location on the body, physiological factors such as blood flow rate and skin thickness affect dermal perfusion of transcutaneous oxygen. In this work, four healthy volunteers participated in subject testing. We assessed each participant at the following locations: thumb, top of the wrist, forearm, thigh, and shin. All locations consistently reported accurate and reliable data. Among them, the thumb demonstrated shorter settling times and the most uniform luminescence lifetime values.

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Enhancing LoRaWAN RSSI Prediction Accuracy with Weather-Aware Machine Learning Models

Enhancing LoRaWAN RSSI Prediction Accuracy with Weather-Awar...

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IEEE ANDESCON

作者： Boris Ramos Edison del Rosario Nelson Tovar Edwin Valarezo Añazco Seyed Reza Zekavat Faculty of Electrical and Computer Engineering Escuela Superior Politécnica del Litoral (ESPOL) and Worcester Polytechnic Institute (WPI) Guayaquil Ecuador

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

This investigation compares the prediction accuracy of three baseline machine learning (ML) models and our proposed model combining Random Forest and Multi-Layer Perceptron (RF-MLP) to predict received signal strength indicator (RSSI) in a LoRaWAN network, with and without using climate data. The ML models are Random Forest (RF), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM). All models were tested in a maize crop setting, using weather variables such as humidity, temperature, solar radiation, and the terminal nodes' geographical coordinates and height as input features. The result shows that MLP performs better than RF and LSTM when predicting the RSSI values of a newly inserted node in the network. On the other hand, the RF model performed the best when forecasting future RSSI values of a single node. The proposed RF-MLP achieved the best performance overall. Also, ML models improved their performance when using climate data, compared to when it was excluded as input, with a maximum improvement level of 46.8%.

关键词： Radio frequency Accuracy LoRaWAN Predictive models Data models Received signal strength indicator Solar radiation Forecasting Long short term memory Random forests

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Human Context Recognition: A Controllable GAN Approach

Human Context Recognition: A Controllable GAN Approach

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2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021

作者： Deoliveira, Joshua Kim, Harrison Martin, Maryclare Gerych, Walter Rundensteiner, Elke Worcester Polytechnic Institute Department of Computer Science WorcesterMA United States Northeastern University Department of Electrical and Computer Engineering BostonMA United States College of the Holy Cross Department of Mathematics WorcesterMA United States

ISBN: (纸本)9781665405959

Human activity recognition (HAR), the use of sensor data to identify activities performed by an individual, has garnered substantial research attention. However, obtaining data for HAR is costly. As such, recent studies have used Generative Adversarial Networks (GANs) to create synthetic HAR data. Problematically, these solutions cannot generate personalized data that match the behavior of a user, limiting potential HAR applications as prior work has shown that personalized HAR models outperform generalized ones. To address this problem, we propose a novel controllable GAN where user and activity attributes can be explicitly requested for generation under one model. The GAN can learn these attributes independently and infer context pairs never seen during training: An impossibility for existing methodologies. We show that while our model is comparable to standard HAR GANs, its ability to consider user differences and generate novel context pairs outperforms these state-of-The-Art GANs. © 2021 IEEE.

关键词： Generative adversarial networks

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