Abstract

The video is addressing the general topic of measurements in emerging power systems. Firstly, disruptive changes in electric power systems are analyzed in order to understand the impact on the requirements for control and instrumentation in smart grids; then  modern measurement chains are presented together with their potential use in coping with limited knowledge on the grid infrastructure, new power quality issues generated by distributed generation or wide area measurement and control in low inertial systems. Ways of merging the information delivered by existing (SCADA, intelligent electronic devices ) and emerging (Phasor measurement units -PMUs and microPMUs) measurement systems are presented, as part of applications like the power system state estimation; The tutorial highlights the importance of assessment the measurement channel quality together with the silently adopted models for energy transfer, and issues like voltage and frequency variability; rate of change of frequency; the steady-state signal and rapid voltage changes; measurement data aggregation; filtering properties; time- aggregation algorithms in the PQ framework. The presentation ends with new applications enabled by smart metering with high reporting rate (1s) and highlights some of the challenges for measurement systems in smart grids.

Keywords: smart grids; active distribution grids; smart metering; high reporting rate measurements; unbundled smart meter

View Video Tutorial Here

Abstract

Autonomous systems are nowadays having an undisputed pervasiveness in the modern society. Autonomous driving cars as well as applications of service robots (e.g. cleaning robots, companion robots, intelligent healthcare solutions, tour guided systems) are becoming more and more popular and a general acceptance is now developing around such systems in the modern societies. Nonetheless, one of the major problems in building such applications relies on the capability of autonomous systems to understand their surroundings and then plan proper counteractions. The most popular solutions, which are gaining more and more attention, rely on artificial intelligence and deep learning as a means to understand the structured and complex natural environment.  Nonetheless, besides the importance of such complex tools, classical concept of metrology, such as uncertainty and precision, are still unavoidable to a clear and effective application of modern autonomous systems applications.

In this tutorial, some measurement concepts will be revised in light of the autonomous systems domain. In particular, we will cover the main concepts of the statistical approach to measurements that will then be applied to:

• Uncertainty analysis and synthesis for autonomous systems localisation
• Precision-based feedback for social robotics

Keywords: electrical, capacitance, tomography, ieee, ims, wuqiang yang, tutorials, education, applications

View Video Tutorial Here

Abstract

The increasing need for wearable systems capable of assessing cardiorespiratory functions across diverse domains, including clinical settings and sports science, is driven by the critical importance of cardiac and respiratory parameters in detecting various health conditions and stressors. However, achieving noninvasive data collection while ensuring comfort and accuracy remains a considerable challenge. Recent advances in flexible systems and materials offer promise in addressing these challenges by introducing a new generation of wearable devices that are both more effective and comfortable.

This tutorial provides an overview of next-generation wearables tailored for monitoring cardiac and respiratory activity, particularly focusing on those based on strain sensing. It then outlines the essential steps for developing flexible wearable strain sensors capable of detecting respiratory rate and heart rate through chest wall deformation.

These steps include:

1. The use of a finite element analysis to optimize the structural design of the sensor to enhance its performance in strain sensing.
2. A description of the main fabrication phases necessary for developing the modeled flexible sensor.
3. A description of experimental setups and protocols required to characterize the metrological properties of the fabricated sensor.
4. An exploration of the key data analysis techniques used to estimate cardiorespiratory parameters from the raw signal recorded by the developed flexible wearable sensor.

Keywords

Wearables; cardiorespiratory monitoring; design optimization; data analysis techniques; hear rate monitoring; respiratory rate monitoring.

View the Full Video Tutorial

Abstract

Near infrared spectroscopy (NIRS) is an optical technique that allows investigating tissue hemodynamics in-vivo and non-invasively by measuring optical absorption Near infrared spectroscopy (NIRS) is an optical technique that allows investigating tissue hemodynamics in-vivo and non-invasively by measuring optical absorption properties of oxy- and deoxy-hemoglobin using near infrared light (650-1000 nm). Since its introduction more than forty years ago, NIRS has seen a tremendous research growth due to its unique combination of performance, portability and reduced cost in comparison to other imaging techniques such as functional magnetic resonance imaging (fMRI) or positron emission tomography (PET). Importantly, NIRS has also been adopted in the clinical setting as a reliable technique for monitoring cerebral oxygenation in critical care, and many other scientific and clinical applications are rapidly developing. This tutorial introduces the basic principles of NIRS and briefly describes some of the most relevant applications in the field. 

Keywords: 

View Video Tutorial Here

Abstract

Heartrate monitors are becoming ubiquitous and are being used by both athletes and the general public to keep track of their health. Heartrate monitors are just an example of the wearables currently available to the public; other examples include oxygen saturation monitors, activity monitors, and muscle activity monitors. Wearables are typically not used in a controlled environment; therefore, the quality of the collected signals might be questionable. Even in a controlled environment such as a hospital, deterioration in the quality of the collected signals can lead to false alarm and reduction in the quality of patient care. As the signals are used to inform users about their health, it is imperative that the signals are of acceptable quality. Signal Quality is the field of identifying and improving the quality of collected signals. Signal Quality can be divided into four categories: 1) detection; 2) identification; 3) quantification; and 4) mitigation. Detection is the acknowledgement of the presence of noise in the signal. Identification is the determination of the type of noise. Quantification is the estimation of the level of the noise. Mitigation is the reduction of the noise through noise removal techniques. This tutorial will provide a high-level overview of the different techniques in each of the Signal Quality categories.

Keywords: wearables, hospital, mohamed abdelazez, ieee, ims, signal quality, tutorials, education

View Video Tutorial Here

Abstract

Low-noise current and charge sensing circuits are pivotal in a large variety of biomedical instruments, spanning from electrochemical biosensors to photodetectors for visible and gamma radiation. In this tutorial, the common electronic design challenges and guidelines will be discussed, for circuits detecting charge and current, with special focus on front-end CMOS ASICs. Key aspects of the signal readout chain will be discussed spanning from the front-end to the back-end processing for charge, current and impedance sensing. Applicative examples will focus on diagnostics, both from the (apparently opposite) perspectives of electrochemical nano-biosensors, leveraging molecular affinity (miniaturized and integrated with lab-on-chip microfluidics), monitoring single cells, as well as up to hospital-based scanners for multi-modal medical imaging.

Keywords: Impedance, low-noise, analog electronics, charge detection, lab-on-chip, bio-sensors.

View the Full Video Tutorial

Abstract

The accurate design of experimental tests is pivotal to guarantee the quality of the output data as well as for the optimization of the resources needed to carry out an experimental campaign. Indeed, if it is true that an objective can be achieved following different paths, it is beyond doubt that it is often necessary to save time and money while guaranteeing the accuracy of the tests performed and the quality of the data gathered. Hence, starting from the fundamentals of measurements, it is important to follow basic and straightforward guidelines to make the right choices when dealing with the design of an experimental setup.

This tutorial is intended to provide a comprehensive overview of a rigorous approach to be adopted to optimize a measurement setup, going through the essential aspects of the topic and showing how each step can be optimized.

These steps include the choice and the definition of multiple aspects, as follows:

1. The acquisition system, dealing with sampling and quantification procedures.
2. The test population, considering statistical power and significance level.
3. The operating conditions, taking into account possible interfering factors.
4. Signal processing, including filtering techniques and comparative analyses.

Finally, an insight on practical applications is provided.

Keywords: Measurement setup; optimization; measurement systems; signal digitization; test population; signal processing; uncertainty analysis; wearable sensors.

View Video Tutorial Here