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This paper presents the design and evaluation of an engagement-free and contactless vital signs and occupancy monitoring system called BedDot. While many existing works demonstrated contactless vital signs estimation, they do not address the practical challenge of environment noises, online bed occupancy detection and data quality assessment in the realworld environment. This work presents a robust signal quality assessment algorithm consisting of three parts: bed occupancy detection, movement detection, and heartbeat detection, to identify high-quality data. It also presents a series of innovative vital signs estimation algorithms that leverage the advanced signal processing and Bayesian theorem for contactless heart rate (HR), respiration rate (RR), and inter-beat interval (IBI) estimation. The experimental results demonstrate that BedDot achieves over 99% accuracy for bed occupancy detection, and MAE of 1.38 BPM, 1.54 BPM, and 24.84 ms for HR, RR, and IBI estimation, respectively, compared with an FDA-approved device. The BedDot system has been extensively tested with data collected from 75 subjects for more than 80 hours under different conditions, demonstrating its generalizability across different people and environments.

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Due to their wide range of clinical application possibilities, magnetic actuation technologies have grabbed the attention of researchers worldwide. The design, execution, and analysis of magnetic catheter systems have advanced significantly during the last decade. The review focuses on magnetic actuation for catheter steering and control of the device, which will be explored in detail in the following sections. There is a discussion of future work and the challenges of the review systems, and the conclusions are finally addressed.

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With the growing popularity of microgrids for alternative energy management, there is demand for tools that allow us to study the effect of microgrids in distributed power systems. Popular methods involve software simulation and prototype validation with physical hardware. Simulations often do not capture the complex interactions, and combinations of software simulations with hardware testbeds promise to give a more accurate picture. These testbeds, however, usually aim at the validation of hardware for industrial-scale use, which makes them expensive and not readily accessible. To fill the gap between full-scale hardware and software simulation, we propose a modular lab-scale grid model at a 1:100 power scale over residential single-phase networks with 12 V AC and 60 Hz grid voltage. We present different modules-power sources, inverters, demanders, grid monitors, and grid-to-grid bridges-that can be assembled into distributed grids of almost arbitrary complexity. The model voltage poses no electrical hazards, and microgrids can readily be assembled with an open power line model. Unlike a prior DC-based grid testbed, the proposed AC model allows us to examine additional aspects, such as frequency, phase, active and apparent power, and reactive loads. Grid metrics, including the discretely sampled voltage and current waveforms, can be collected and sent to higher-tier grid management systems. We integrated the modules with Beagle Bone micro-PCs, which in turn connect any such microgrid with an emulation platform built on CORE (Common Open Research Emulator) and the Gridlab-D power simulator, thereby allowing hybrid software/hardware simulations. Our grid modules were shown to fully operate in this environment. Through the CORE system, multitiered control and even remote grid management is possible. However, we also found that the AC waveform poses design challenges that require us to balance accurate emulation (most notably with respect to harmonic distortion) with per-module costs.

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Magnetically steerable catheters (MSCs) have caught the interest of researchers due to their various potential uses in clinical applications, for example, minimally invasive surgery. Many significant advances in the design, implementation and analysis of MSCs have been accomplished in the last decade. This review concentrates on the configurations of current MSCs with an in depth look at control of the device and the specific workspace. This review also evaluates MSCs and references possible future system designs and difficulties. The concept of magnetic manipulation is briefly presented. Then, by category, the MSC is introduced. Following that, a discussion of future works and challenges of the review systems is provided. The conclusions are finally addressed.

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Training medical students in surgical procedures and evaluating their performance are both necessary steps to ensure the safety and efficacy of surgeries. Traditionally, trainees practiced on live patients, cadavers or animals under the supervision of skilled physicians, but realistic anatomical phantom models have provided a low-cost alternative because of the advance of material technology that mimics multi-layer tissue structures. This setup provides safer and more efficient training. Many research prototypes of phantom models allow rapid in-house prototyping for specific geometries and tissue properties. The gel-based method and 3D printing-based method are two major methods for developing phantom prototypes. This study excluded virtual reality based technologies and focused on physical phantoms, total 189 works published between 2015 and 2020 on anatomical phantom prototypes made for interventional radiology were reviewed in terms of their functions and applications. The phantom prototypes were first categorized based on fabrication methods and then subcategorized based on the organ or body part they simulated; the paper is organized accordingly. Engineering specifications and applications were analyzed and summarized for each study. Finally, current challenges in the development of phantom models and directions for future work were discussed.

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Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI). Firstly, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we construct a high dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score based attack detection and binary matrix factorization based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.

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Background: During COVID-19, clinical and health care demands have been on the rapid rise. Major challenges that have arisen during the pandemic have included a lack of testing kits, shortages of ventilators to treat severe cases of COVID-19, and insufficient accessibility to personal protective equipment for both hospitals and the public. New technologies have been developed by scientists, researchers, and companies in response to these demands. Objective: The primary objective of this review is to compare different supporting technologies in the subjugation of the COVID-19 spread. Methods: In this paper, 150 news articles and scientific reports on COVID-19-related innovations during 2020-2021 were checked, screened, and shortlisted to yield a total of 23 articles for review. The keywords "COVID-19 technology," "COVID-19 invention," and "COVID-19 equipment" were used in a Google search to generate related news articles and scientific reports. The search was performed on February 1, 2021. These were then categorized into three sections, which are personal protective equipment (PPE), testing methods, and medical treatments. Each study was analyzed for its engineering characteristics and potential social impact on the COVID-19 pandemic. Results: A total of 9 articles were selected for review concerning PPE. In general, the design and fabrication of PPE were moving toward the direction of additive manufacturing and intelligent information feedback while being eco-friendly. Moreover, 8 articles were selected for reviewing testing methods within the two main categories of molecular and antigen tests. All the inventions endeavored to increase sensitivity while reducing the turnaround time. However, the inventions reported in this review paper were not sufficiently tested for their safety and efficiency. Most of the inventions are temporary solutions intended to be used only during shortages of medical resources. Finally, 6 articles were selected for the review of COVID-19 medical treatment. The major challenge identified was the uncertainty in applying novel ideas to speed up the production of ventilators. Conclusions: The technologies developed during the COVID-19 pandemic were considered for review. In order to better respond to future pandemics, national reserves of critical medical supplies should be increased to improve preparation. This pandemic has also highlighted the need for the automation and optimization of medical manufacturing.

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Catheters are used in various clinical applications, and the ability to direct the catheter to the desired location is critical for clinical outcomes. Steerable catheters assist clinicians to access targeted areas, notably the vascular bundles and major vessels, while causing no damage to the surrounding tissue. A novel catheter actuation technology for catheter steering is presented in this study. The technique is simple and relies on three magnetic couples interacting with one another to generate steering motions. A proof-of-concept catheter prototype demonstrated the capacity to remotely steer a catheter over 100 mm of distance and ±45° of angular positioning, showing the potential manoeuvrability for clinical applications. It is feasible to steer a catheter using this three-magnet pair approach with the great potential to be used for catheterisation procedures. The presented mechanism's kinematics and a near-form solution for catheter steering regardless of design factors will be studied in the future.

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Many countries have enacted a quick response to the unexpected coronavirus disease 2019 (COVID-19) pandemic by using existing technologies. For example, robotics, artificial intelligence, and digital technology have been deployed in hospitals and public areas for maintaining social distancing, reducing person-to-person contact, enabling rapid diagnosis, tracking virus spread, and providing sanitation. In this study, 163 news articles and scientific reports on COVID-19-related technology adoption were screened, shortlisted, categorized by application scenario, and reviewed for functionality. Technologies related to robots, artificial intelligence, and digital technology were selected from the pool of candidates, yielding a total of 50 applications for review. Each case was analyzed for its engineering characteristics and potential impact on the COVID-19 pandemic. Finally, challenges and future directions regarding the response to this pandemic and future pandemics were summarized and discussed.

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Rapid and accurate detection and localization of electronic disturbances simultaneously are important for preventing its potential damages and determining potential remedies. Existing anomaly detection methods are severely limited by the low accuracy, the expensive computational cost and the need for highly trained personnel. There is an urgent need for a scalable online algorithm for in-field analysis of large-scale power electronics networks. In this paper, we propose a fast and accurate algorithm for anomaly detection and localization of power electronics networks: stratified colored-node graph (CONGO2). This algorithm hierarchically models the change of correlated waveforms and then correlated sensors using the colored-node graph. By aggregating the change of each sensor with its neighbors' inputs, we can spontaneously identify and localize the anomaly that cannot be detected by data collected from a single sensor. As our proposed method only focuses on the changes within a short time frame, it is highly computational efficient and only needs small data storage. Thus, our method is ideal for online and reliable anomaly detection and localization of large-scale power electronic networks. Compared to existing anomaly detection methods, our method is entirely data-driven without training data, highly accurate and reliable for wide-spectrum anomalies detection, and more importantly, capable of both detection and localization. Thus, it is ideal for in-field deployment for large-scale power electronic networks. As illustrated by a distributed energy resources (DERs) power grid with 37-node, our method can effectively detect and localize various cyber and physical attacks.

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Image-guided therapies have been on the rise in recent years as they can achieve higher accuracy and are less invasive than traditional methods. By combining augmented reality technology with image-guided therapy, more organs, and tissues can be observed by surgeons to improve surgical accuracy. In this review, 233 publications (dated from 2015 to 2020) on the design and application of augmented reality-based systems for image-guided therapy, including both research prototypes and commercial products, were considered for review. Based on their functions and applications. Sixteen studies were selected. The engineering specifications and applications were analyzed and summarized for each study. Finally, future directions and existing challenges in the field were summarized and discussed.

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Locating underground microseismic events is important for monitoring subsurface activity and understanding the planetary subsurface evolution. Due to bandwidth limitations, especially in applications involving planetarily-distributed sensor networks, networks should be designed to perform the localization algorithm in-situ, so that only the source location information needs to be sent out, not the raw data. In this paper, we propose a decentralized Gaussian beam time-reverse imaging (GB-TRI) algorithm that can be incorporated to the distributed sensors to detect and locate underground microseismic events with reduced usage of computational resources and communication bandwidth of the network. After the in-situ distributed computation, the final real-time location result is generated and delivered. We used a real-time simulation platform to test the performance of the system. We also evaluated the stability and accuracy of our proposed GB-TRI localization algorithm using extensive experiments and tests.

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The recent advancement of motion tracking technology offers better treatment tools for conditions, such as movement disorders, as the outcome of the rehabilitation could be quantitatively defined. The accurate and fast angular information output of the inertial measurement unit tracking systems enables the collection of accurate kinematic data for clinical assessment. This article presents a study of a low-cost microelectromechanical system inertial measurement unit-based tracking system in comparison with the conventional optical tracking system. The system consists of seven microelectromechanical system inertial measurement units, which could be mounted on the lower limbs of the subjects. For the feasibility test, 10 human participants were instructed to perform three different motions: walking, running, and fencing lunges when wearing specially designed sleeves. The subjects' lower body movements were tracked using our inertial measurement unit-based system and compared with the gold standard-the NDI Polaris Vega optical tracking system. The results of the angular comparison between the inertial measurement unit and the NDI Polaris Vega optical tracking system were as follows: the average cross-correlation value was 0.85, the mean difference of joint angles was 2.00°, and the standard deviation of joint angles was ± 2.65°. The developed microelectromechanical system-based tracking system provides an alternative low-cost solution to track joint movement. Moreover, it is able to operate on an Android platform and could potentially be used to assist outdoor or home-based rehabilitation.

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This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.

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Internet of Things (IoT) enables extensive connections between cyber and physical "things". Nevertheless, the streaming data among IoT sensors bring "big data" issues, for example, large data volumes, data redundancy, lack of scalability and so on. Under "big data" circumstances, IoT system monitoring becomes a challenge. Furthermore, cyberattacks which threaten IoT security are hard to be detected. In this paper, we propose an online distributed IoT security monitoring algorithm (ODIS). An advanced influential point selection operation extracts important information from multidimensional time series data across distributed sensor nodes based on the spatial and temporal data dependence structure. Then, an accurate data structure model is constructed to capture the IoT system behaviors. Next, hypothesis testing is carried out to quantify the uncertainty of the monitoring tasks. Besides, the distributed system architecture solves the scalability issue. Using a real sensor network testbed, we commit cyberattacks to an IoT system with different patterns and strengths. The proposed ODIS algorithm demonstrates promising detection and monitoring performances.

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Seismic reflectivity inversion is widely applied to improve the seismic resolution to obtain detailed underground understandings. Based on the convolution model, seismic inversion removes the wavelet effect by solving an optimization problem. Taking advantage of the sparsity property, the ℓ 1 norm is commonly adopted in the regularization terms to overcome the noise/interference vulnerability observed in the ℓ p -losses minimization. However, no one has provided a deterministic conclusion that ℓ1 norm regularization is the best choice for seismic reflectivity inversion. Instead of using an unproved fixed regularization norm, we propose an optimal seismic reflectivity inversion approach. Our method adaptively adopts a ℓ p -loss-ℓ q -regularization (i.e. ℓ p,q regularization) for p = 2, 0 < q < 1 to estimate a more accurate and detailed reflectivity profile. In addition, we employ a K fold cross-validation based approach to obtain the optimal damping factor λ to further improve the seismic inversion results. The letter starts with the introduction of non-convex constraint for seismic inversion, and the necessity of the ℓ q norm regularization. Then the majorization-minimization and cross validation algorithms are briefly described. The performance of the proposed seismic inversion approach is evaluated through synthetic examples and a field example from the Bohai Bay Basin, China.

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A wireless seismic network can be effectively used as a tool for subsurface monitoring and imaging. By recording and analyzing ambient noise, a seismic network can image underground infrastructures and provide velocity variation information of the subsurface that can help to detect anomalies. By studying the variation in the noise cross-correlation function of the noise, it is possible to determine the subsurface seismic velocity and image underground infrastructures. Ambient noise imaging can be done in a decentralized fashion using Distributed Spatial Auto-Correlation (dSPAC). In dSPAC over sensor networks, the cross-correlation is the most intensive communication process since nodes need to communicate their data with neighbor nodes. In this paper, a new communication-reduced method for cross-correlation is presented to meet bandwidth and cost of communication constraints in networks while ambient noise imaging is performed using dSPAC method. By applying the proposed communication-reduced method, we show that energy and computational cost of the nodes is also preserved.

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In this article, the framework and architecture of a Subsurface Camera (SAMERA) are envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals and computes a 3D subsurface image in situ in real time. The basic mechanism is geophysical waves propagating/reflected/refracted through subsurface enter a network of geophysical sensors, where a 2D or 3D image is computed and recorded; control software may be connected to this network to allow view of the 2D/3D image and adjustment of settings such as resolution, filter, regularization, and other algorithm parameters. System prototypes based on seismic imaging have been designed. SAMERA technology is envisioned as a game changer to transform many subsurface survey and monitoring applications, including oil/gas exploration and production, subsurface infrastructures and homeland security, wastewater and CO2 sequestration, and earthquake and volcano hazard monitoring. System prototypes for seismic imaging have been built. Creating SAMERA requires interdisciplinary collaboration and the transformation of sensor networks, signal processing, distributed computing, and geophysical imaging.

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The biomedical signal classification accuracy on motor imagery is not always satisfactory, partially because not all the important features have been effectively extracted. This paper proposes an improved dynamic feature extraction approach based on a time-frequency representation and an optimal sequence similarity measurement. Since the wavelet packet decomposition (WPD) generates more detailed signal variation information and the dynamic time warping (DTW) helps optimally measure the sequence similarity, more important features are kept for classification. We apply the extracted features from our proposed method to Electroencephalogram (EEG) based motor imagery through the OpenBCI device and obtain higher classification accuracy. Compared with traditional feature extraction methods, there is a significant classification accuracy improvement from 83.53% to 90.89%. Our work demonstrates the importance of the advanced feature extraction in time series data analysis, e.g. biomedical signal.

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The rapid convergence rate, high fidelity learning outcome and low computational cost are key targets in solving the learning problem of the complex physical system. Guided by physical laws of wave propagation, in full waveform inversion (FWI), we learn the subsurface images through optimizing the media velocity model in a large scale non-linear problem. In this paper, we combine randomized subsampling techniques with a second-order optimization algorithm to propose the Sub-Sampled Newton (SSN) method for learning velocity model of FWI. By incorporating the curvature information, SSN preserves comparable convergence rate to Newtons method and significantly reduces the iteration cost by approximating the Hessian matrix through a non-uniform subsampling scheme. The numerical experiments demonstrate that the proposed SSN method has a faster convergence rate, and achieves a more accurate velocity model in terms of mean squared error than commonly used methods.