Alternative implementations of a fractional order control algorithm on FPGAs

Traditionally, microprocessor and digital signal processors have been used extensively in controlling simple processes, such as direct current motors. The Field Programmable Gate Arrays (FPGA) are currently emerging as an alternative to the previously used devices in controlling all sorts of processes. The fractional order proportional-integrative control algorithm has the advantage of enhancing the closed loop performance as compared to traditional proportional-integrative controllers, but the implementation requires a higher number of computations. Implementations of control algorithms on FPGAs are nowadays much faster than implementations on microprocessors. This allows for a more accurate digital realization of the fractional order controller. The paper presents nine alternative implementations of such control algorithm on two different FPGA targets. The experimental results, considering DC motor speed control, show that double, fixed-point and integer data representation may be used efficiently for control purposes

Development and Analysis of Low-Cost IoT Sensors for Urban Environmental Monitoring

The accelerated pace of urbanization is having a major impact over the world’s environment. Although urban dwellers have higher living standards and can access better public services as compared to their rural counterparts, they are usually exposed to poor environmental conditions such as air pollution and noise. In order for municipalities and citizens to mitigate the negative effects of pollution, the monitoring of certain parameters, such as air quality and ambient sound levels, both in indoor and outdoor locations, has to be performed. The current paper presents a complete solution that allows the monitoring of ambient parameters such as Volatile Organic Compounds, temperature, relative humidity, pressure, and sound intensity levels both in indoor and outdoor spaces. The presented solution comprises of low-cost, easy to deploy, wireless sensors and a cloud application for their management and for storing and visualizing the recorded data

Energy Harvesting Techniques for Internet of Things (IoT)

The rapid growth of the Internet of Things (IoT) has accelerated strong interests in the development of low-power wireless sensors. Today, wireless sensors are integrated within IoT systems to gather information in a reliable and practical manner to monitor processes and control activities in areas such as transportation, energy, civil infrastructure, smart buildings, environment monitoring, healthcare, defense, manufacturing, and production. The long-term and self-sustainable operation of these IoT devices must be considered early on when they are designed and implemented. Traditionally, wireless sensors have often been powered by batteries, which, despite allowing low overall system costs, can negatively impact the lifespan and the performance of the entire network they are used in. Energy Harvesting (EH) technology is a promising environment-friendly solution that extends the lifetime of these sensors, and, in some cases completely replaces the use of battery power. In addition, energy harvesting offers economic and practical advantages through the optimal use of energy, and the provisioning of lower network maintenance costs. We review recent advances in energy harvesting techniques for IoT. We demonstrate two energy harvesting techniques using case studies. Finally, we discuss some future research challenges that must be addressed to enable the large-scale deployment of energy harvesting solutions for IoT environments

Implementation of an extended prediction self-adaptive controller using LabVIEW (TM)

The implementation of the Extended Prediction Self-Adaptive Controller is presented in this paper. It employs LabVIEWTM graphical programming of industrial equipment and it is suitable for controlling fast processes. Three different systems are used for implementing the control algorithm. The research regarding the controller design using graphical programming demonstrates that a single advanced control application can run on Windows, real time operating systems and FPGA targets without requiring significant program modifications. The most appropriate device may be selected according to the required processing time of the control signal and of the application. A relevant case study is used to exemplify the procedure

A Portable Implementation on Industrial Devices of a Predictive Controller Using Graphical Programming

This paper presents an approach for developing an Extended Prediction Self-Adaptive Controller employing graphical programming of industrial standard devices, for controlling fast processes. For comparison purposes, the algorithm has been implemented on three different FPGA (Field Programmable Gate Arrays) chips. The paper presents research aspects regarding graphical programming controller design, showing that a single advanced control application can run on different targets without requiring significant program modifications. Based on the time needed for processing the control signal and on the application, one can efficiently and easily select the most appropriate device. To exemplify the procedure, a conclusive case study is presented

Core Competencies to Promote Consistency and Standardization of Best Practices for Digital Peer Support: Focus Group Study

Background: As digital peer support is quickly expanding across the globe in the wake of the COVID-19 pandemic, standardization in the training and delivery of digital peer support can advance the professionalism of this field. While telehealth competencies exist for other fields of mental health practice, such as social work, psychiatry, and psychology, limited research has been done to develop and promote digital peer support competencies. Objective: The goal of this study is to introduce the coproduction of core competencies that can guide digital peer support. Methods: Peer support specialists were recruited through an international listserv and participated in a 1-hour virtual focus group. A total of four focus groups were conducted with 59 peer support specialists from 11 US states and three countries. Results: Analysis was conducted using the rigorous and accelerated data reduction (RADaR) technique, and 10 themes were identified: (1) protecting the rights of service users, (2) technical knowledge and skills in the practice of digital peer support, (3) available technologies, (4) equity of access, (5) digital communication skills, (6) performance-based training, (7) self-care, (8) monitoring digital peer support and addressing digital crisis, (9) peer support competencies, and (10) health literacy (emerging). The authors present recommendations based on these themes. Conclusions: The introduction of digital peer support core competencies is an initial first step to promote the standardization of best practices in digital peer support. The established competencies can potentially act as a guide for training and skill development to be integrated into US state peer support specialist competencies and to enhance competencies endorsed by the Substance Abuse and Mental Health Services Administration (SAMHSA).publishedVersio

Enhancing standards and principles in digital mental health with recovery-focused guidelines for mobile, online, and remote monitoring technologies

No abstract available

Core Competencies to Promote Consistency and Standardization of Best Practices for Digital Peer Support: Focus Group Study

Background: As digital peer support is quickly expanding across the globe in the wake of the COVID-19 pandemic, standardization in the training and delivery of digital peer support can advance the professionalism of this field. While telehealth competencies exist for other fields of mental health practice, such as social work, psychiatry, and psychology, limited research has been done to develop and promote digital peer support competencies. Objective: The goal of this study is to introduce the coproduction of core competencies that can guide digital peer support. Methods: Peer support specialists were recruited through an international listserv and participated in a 1-hour virtual focus group. A total of four focus groups were conducted with 59 peer support specialists from 11 US states and three countries. Results: Analysis was conducted using the rigorous and accelerated data reduction (RADaR) technique, and 10 themes were identified: (1) protecting the rights of service users, (2) technical knowledge and skills in the practice of digital peer support, (3) available technologies, (4) equity of access, (5) digital communication skills, (6) performance-based training, (7) self-care, (8) monitoring digital peer support and addressing digital crisis, (9) peer support competencies, and (10) health literacy (emerging). The authors present recommendations based on these themes. Conclusions: The introduction of digital peer support core competencies is an initial first step to promote the standardization of best practices in digital peer support. The established competencies can potentially act as a guide for training and skill development to be integrated into US state peer support specialist competencies and to enhance competencies endorsed by the Substance Abuse and Mental Health Services Administration (SAMHSA)