Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

Applications of MEMS Gyroscope for Human Gait Analysis

After decades of development, quantitative instruments for human gait analysis have become an important tool for revealing underlying pathologies manifested by gait abnormalities. However, the gold standard instruments (e.g., optical motion capture systems) are commonly expensive and complex while needing expert operation and maintenance and thereby be limited to a small number of specialized gait laboratories. Therefore, in current clinical settings, gait analysis still mainly relies on visual observation and assessment. Due to recent developments in microelectromechanical systems (MEMS) technology, the cost and size of gyroscopes are decreasing, while the accuracy is being improved, which provides an effective way for qualifying gait features. This chapter aims to give a close examination of human gait patterns (normal and abnormal) using gyroscope-based wearable technology. Both healthy subjects and hemiparesis patients participated in the experiment, and experimental results show that foot-mounted gyroscopes could assess gait abnormalities in both temporal and spatial domains. Gait analysis systems constructed of wearable gyroscopes can be more easily used in both clinical and home environments than their gold standard counterparts, which have few requirements for operation, maintenance, and working environment, thereby suggesting a promising future for gait analysis

csi and csi7 current source inverters for modular transformerless pv inverters

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Stacked Autoencoder and Meta-Learning based Heterogeneous Domain Adaptation for Human Activity Recognition

The field of human activity recognition (HAR) using machine learning approaches has gained a lot of interest in the research community due to its empowerment of automation and autonomous systems in industries and homes with respect to the given context and due to the increasing number of smart wearable devices. However, it is challenging to achieve a considerable accuracy for recognizing actions with diverse set of wearable devices due to their variance in feature spaces, sampling rate, units, sensor modalities and so forth. Furthermore, collecting annotated data has always been a serious issue in the machine learning community. Domain adaptation is a field that helps to cope with the issue by training on the source domain and labeling the samples in the target domain, however, due to the aforementioned variances (heterogeneity) in wearable sensor data, the action recognition accuracy remains on the lower side. Existing studies try to make the target domain feature space compliant with the source domain to improve the results, but it assumes that the system has a prior knowledge of the feature space of the target domain, which does not reflect real-world implication. In this regard, we propose stacked autoencoder and meta-learning based heterogeneous domain adaptation (SAM- HDD) network. The stacked autoencoder part is trained on the source domain feature space to extract the latent representation and train the employed classifiers, accordingly. The classification probabilities from the classifiers are trained with meta-learner to further improve the recognition performance. The data from tar- get domain undergoes the encoding layers of the trained stacked autoencoders to extract the latent representations, followed by the classification of label from the trained classifiers and meta- learner. The results show that the proposed approach is efficient in terms of accuracy score and achieves best results among the existing works, respectivel

The Boston University Photonics Center annual report 2015-2016

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2015-2016 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that this year the Center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.9M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and cooperated in supporting National Science Foundation sponsored Sites for Research Experiences for Undergraduates and for Research Experiences for Teachers. As a community, we emphasized the theme of “Frontiers in Plasmonics as Enabling Science in Photonics and Beyond” at our annual symposium, hosted by Bjoern Reinhard. We continued to support the National Photonics Initiative, and contributed as a cooperating site in the American Institute for Manufacturing Integrated Photonics (AIM Photonics) which began this year as a new photonics-themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Development of Less Toxic Treatment Strategies for Metastatic and Drug Resistant Breast Cancer Using Noninvasive Optical Monitoring led by Professor Darren Roblyer, continued support of our NIH-sponsored, Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and an exciting confluence of new grant awards in the area of Neurophotonics led by Professors Christopher Gabel, Timothy Gardner, Xue Han, Jerome Mertz, Siddharth Ramachandran, Jason Ritt, and John White. Neurophotonics is fast becoming a leading area of strength of the Photonics Center. The Industry/University Collaborative Research Center, which has become the centerpiece of our translational biophotonics program, continues to focus onadvancing the health care and medical device industries, and has entered its sixth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base

Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward

This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics. The initial phase of the discussion is elucidating the internal mechanics of these automobiles, encompassing the crucial involvement of sensors, artificial intelligence (AI) identification systems, control mechanisms, and their integration with cloud-based servers within the framework of the Internet of Things (IoT). It delves into practical implementations of autonomous cars, emphasizing their utilization in forecasting traffic patterns and transforming the dynamics of transportation. The text also explores the topic of Robotic Process Automation (RPA), illustrating the impact of autonomous cars on different businesses through the automation of tasks. The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles. A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats including ethical, environmental, legal, professional, and social dimensions, offering a comprehensive perspective on their societal implications. A strategic plan for addressing the challenges and proposing strategies for effectively traversing the complex terrain of autonomous car systems, cybersecurity, hazards, and other concerns are some resources for acquiring an understanding of the intricate realm of autonomous cars and their ramifications in contemporary society, supported by a comprehensive compilation of resources for additional investigation. Keywords: RPA, Cyber Security, AV, Risk, Smart Car

Proceedings of the 5th Baltic Mechatronics Symposium - Espoo April 17, 2020

The Baltic Mechatronics Symposium is annual symposium with the objective to provide a forum for young scientists from Baltic countries to exchange knowledge, experience, results and information in large variety of fields in mechatronics. The symposium was organized in co-operation with Taltech and Aalto University. Due to Coronavirus COVID-19 the symposium was organized as a virtual conference. The content of the proceedings1. Monitoring Cleanliness of Public Transportation with Computer Vision2. Device for Bending and Cutting Coaxial Wires for Cryostat in Quantum Computing3. Inertial Measurement Method and Application for Bowling Performance Metrics4. Mechatronics Escape Room5. Hardware-In-the-Loop Test Setup for Tuning Semi-Active Hydraulic Suspension Systems6. Newtonian Telescope Design for Stand-off Laser Induced Breakdown Spectroscopy7. Simulation and Testing of Temperature Behavior in Flat Type Linear Motor Carrier8. Powder Removal Device for Metal Additive Manufacturing9. Self-Leveling Spreader Beam for Adjusting the Orientation of an Overhead Crane Loa