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Distinguished Lecturer Program

The I&M Society Distinguished Lecturer Program (DLP) is one of the most exciting programs offered to our chapters, I&M members, and IEEE members. It provides I&M chapters around the world with talks by experts on topics of interest and importance to the I&M community. It, along with our conferences and publications, is the way we use to disseminate knowledge in the I&M field. Our lecturers are among the most qualified experts in their own field, and we offer our members a first-hand chance to interact with these experts during their lectures. The I&M Society aids chapters financially so that they might use this program.

All distinguished lecturers are outstanding in their fields of specialty. Collectively, the Distinguished Lecturers possess a broad range of expertise within the area of I&M. Thus, the chapters are encouraged to use this program as a means to make their local I&M community aware of the most recent scientific and technological trends and to enhance their member benefits. Although lectures are mainly organized to benefit existing members and Chapters, they can also be effective in generating membership and encouraging new chapter formation. Interested parties are encouraged to contact the I&M DLP Chair regarding this type of activity.

Looking for a DL topic for an upcoming event that isn’t covered by our current DL’s? Contact the DL Chair, Kristen Donnell, to suggest a topic or find a DL who may be able to adapt his or her topic for your event.

Please review the DL reports and take a peek at the pictures by sending a request to the DLP Chair.

DLP Chair:

Distinguished Lecturers

Distinguished Lecturer (2019 to 2022)

Talk Title: Optical sensing in bioinstrumentation

Optical sensors and techniques are used widely in many areas of instrumentation and measurement. Optical sensors are often, conveniently, ‘non-contact’ and thus impose negligible disturbance of the parameter undergoing measurement. Valuable information can be represented and recorded in space, time and optical wavelength. They can provide exceptionally high spatial and/or temporal-resolution, high bandwidth, and range. Moreover, optical sensors can be inexpensive and relatively simple to use.

At the Bioinstrumentation Lab at the Auckland Bioengineering Institute, we are particularly interested in developing techniques for measuring parameters from and inside and outside the body. Such measurements help us to quantify physiological performance, detect and treat disease, and develop novel medical and scientific instruments. In making such measurements we often draw upon and develop our own optical sensing and measurement methods – from interferometry, fluorimetry and diffuse light imaging, through to area-based and volume-based optical imaging and processing techniques.

In this talk, I will overview some of the new interesting optically-based methods that we have recently developed for use in bioengineering applications. These include 1) diffuse optical imaging methods for monitoring the depth of a drug as it is rapidly injected through the skin, without requiring a needle; 2) stretchy soft optical sensors for measuring strains of up to several 100 % during movement; 3) multi-camera image registration techniques for measuring the 3D shape and strain of soft tissues; 4) optical coherence tomography techniques for detecting the 3D shape of deforming muscle tissues, and 5) polarization sensitive imaging techniques for classifying the optical and mechanical properties of biological membranes.

While these techniques sensors and techniques have been motivated by applications in bioengineering, the underlying principles have broad applicability to other areas of instrumentation and measurement.

Distinguished Lecturer (2019 to 2021)

Talk Title: A dynamometer for the heart

The heart is a complex organic engine that converts chemical energy into work. Each heartbeat begins with an electrically-released pulse of calcium, which triggers force development and cell shortening, at the cost of energy and oxygen, and the dissipation of heat. My group have developed new instrumentation systems to measure all of these processes simultaneously, while subjecting isolated samples of heart tissue to realistic contraction patterns that mimic the pressure-volume-time loops experienced by the heart with each beat. These devices are effectively 'dynamometers' for the heart, that allow us to measure the performance of the heart and its tissues, much in the same way that you might test the performance of your motor vehicle on a 'dyno.'

This demanding undertaking has required us to develop our own actuators, force transducers, heat sensors, and optical measurement systems. Our instruments make use of several different measurement modalities which are integrated in a robotic hardware-based real-time acquisition and control environment and interpreted with the aid of a computational model. In this way, we can now resolve (to within a few nanoWatts) the heat released by living cardiac muscle fibers as they perform work at 37 °C.

Muscle force and length are controlled and measured to microNewton and nanometer precision by a laser interferometer, while the muscle is scanned in the view of an optical microscope equipped with a fluorescent calcium imaging system. Concurrently, the changing muscle geometry is monitored in 4D by a custom-built optical coherence tomograph, and the spacing of muscle-proteins is imaged in real-time by transmission-microscopy and laser diffraction systems. Oxygen consumption is measured using fluorescence-quenching techniques.

Equipped with these unique capabilities, we have probed the mechano-energetics of failing  hearts from rats with diabetes. We have found that the peak stress and peak mechanical efficiency of tissues from these hearts was normal, despite prolonged twitch duration. We have thus shown that the compromised mechanical performance of the diabetic heart arises from a reduced period of diastolic filling and does not reflect either diminished mechanical performance or diminished efficiency of its tissues. In another program of research, we have demonstrated that despite claims to the contrary, dietary supplementation by fish-oils has no effect on heart muscle efficiency. Neither of these insights were fully revealed until the development of this instrument.

Distinguished Lecturer (2018 to 2021)

Talk Title: Evolution of Microwave and Millimeter Wave Imaging for NDE Applications

Abstract - Microwave and millimeter-wave signals span the frequency range of ~300 MHz to 300 GHz, corresponding to a wavelength range of 1000 mm to 1 mm. Signals at these frequencies can easily penetrate inside dielectric materials and composites and interact with their inner structures. The relatively small wavelengths and wide bandwidths associated with these signals enable the production of high spatial-resolution images of materials and structures. Incorporating imaging techniques such as lens-focused and near-field techniques, synthetic aperture focusing, holographical methods based on robust back-propagation algorithms with more advanced and unique millimeter wave imaging systems have brought upon a flurry of activities in this area and in particular for nondestructive evaluation (NDE) applications.  These imaging systems and techniques have been successfully applied for a wide range of critical NDE-related applications.

Although, near-field techniques have also been prominently used for these applications in the past, undesired issues related to changing standoff distance and slowness of image production process have resulted in several innovative and automatic standoff distance variation removal techniques. Ultimately, imaging techniques must produce high-resolution 3D images, become real-time, and be implemented using portable systems.  To this end and to expedite the imaging process while providing a high-resolution images, the design and demonstration of a 6” by 6” one-shot, rapid and portable imaging system (Microwave Camera), consisting of 576 resonant slot elements, was demonstrated a few years ago.  Subsequently, efforts were expended to design and implement several different variations of this imaging system to accommodate one-sided and mono-static imaging, while enabling 3D image production using non-uniform rapid scanning of an object, as well as increasing the operating frequency into higher millimeter wave frequencies. These efforts have led to the development of a real-time, portable, high-resolution and 3D imaging microwave camera operating in the 20-30 GHz frequency range which was recently completed. This presentation provides an overview of these techniques, along with illustration of several typical examples where these imaging techniques have effectively provided viable solutions to many critical NDE problems.  

Distinguished Lecturer (2018 to 2021)

Talk Title: Accurate linearity testing for high performance data converters using significantly reduced measurement time and relaxed instrumentation

Semiconductor chip manufacturing cost consists of die cost, package cost, and test cost. The trends of increasing design complexity, increasing quality needs, and new process nodes and defect models are pushing test cost to the forefront. This is especially true for high-resolution data convertors, whose accurate testing requires expensive instruments and is extremely time-consuming. As a result, linearity test of data convertors often dominates the overall test cost of SoCs. This talk will present several recently developed techniques for reducing linearity test cost by dramatically reducing measurement time and dramatically relaxing instrumentation requirements.

The IEEE standard for ADC linearity test requires the stimulus signal to be at least 10 times more accurate than the ADC under test. To relax this stringent requirement, the SEIR (stimulus error identification and removal) algorithm is developed to accurately test high resolution ADCs using nonlinear stimuli. It has been demonstrated by industries that more than 16 bits of ADC test accuracy were achieved using 7-bit linear ramps instead of 20-bit linear ramps as required by IEEE, a relaxation of well over 1000 times on the instrumentation accuracy requirement.

The biggest contributor to test cost is the long measurement time. The recently developed uSMILE (ultrafast Segmented Model Identification for Linearity Errors) algorithm can dramatically reduce the measurement time needed for ADC linearity test. With a system identification approach using a segmented model for the integral nonlinearity, the algorithm can reduce the test time by a factor of over 100 and still achieve test accuracies superior to the standard histogram test method. This method has been extensively validated by industry and has been adopted for production test for multiple product families.

By combining the salient features of both SEIR and uSMILE, the ultrafast stimulus error removal and segmented model identification of linearity errors (USER- SMILE) algorithm is developed. The USER-SMILE algorithm uses two nonlinear signals as input to the ADC under test. One signal is shifted by a constant voltage with respect to the other nonlinear signal. By subtracting the two sets of output codes, input signal is canceled and the nonlinearity of ADC, modeled by a segmented non-parametric INL model, will be identified with the least square method.

A completely on-chip ADC BIST circuit is developed based on the USER-SMILE algorithm and demonstrated on a 28nm CMOS automotive microcontroller. The ADC test subsystem includes a nonlinear DAC as signal generator, a built-in voltage shift generator, a BIST computation engine, and dedicated memory cells. The silicon measurement results show accurate test results. The INL test results are further used to correct ADC linearity errors, thus providing a method for reliably calibrating the ADC. Measurement results demonstrated that the BIST-based calibration method achieved >10dB THD/SFDR improvements over the existing calibration method used by industry.

Jacob Scharcanski Headshot Photo
Distinguished Lecturer (2018 to 2021)

Talk Title: Computer Vision in Medical Imaging Measurements: Making Sense of Visual Data

In this talk, we discuss how computer vision can facilitate the interpretation of medical imaging data,  or help  making  inferences  based  on models  of such  data.  In order  to illustrate this presentation, several applications of medical imaging measurements and modeling are discussed, focusing in areas such as the correction of imaging artifacts that may occlude visual information, tumor detection, modeling and measurement in different imaging modalities.

 

When interpreting medical imaging data with computer vision, usually we are trying to describe anatomic structures (or medical phenomena) using one or more images, and reconstruct some of its properties based on imaging data (like shape, texture or color). Actually, this is an ill-posed problem that humans can learn to solve effortlessly, but computer algorithms often are prone to errors. Nevertheless, in some cases computers can surpass humans and interpret medical images more accurately, given the proper choice of models, as we will show in this talk.

 

Reconstructing interesting properties of real world objects or phenomena from captured imaging data involves solving an inverse problem, in which we seek to recover some unknowns given insufficient information to specify a unique solution. Therefore, we disambiguate   between   possible   solutions   relying   on   models   based   on   physics, mathematics or statistics. Modeling the real world in all its complexity still is an open problem. However, if we know the phenomenon or object of interest, we can construct detailed models using specialized techniques and domain specific representations, that are efficient at describing reliably the measurements (or obtaining measurements in some cases). In this talk, we briefly overview some challenging problems in computer vision for medical imaging and measurements, with illustrations and insights about model selection and model-based prediction. Some of the applications discussed in this talk are: modeling  tumor  shape and  size,  and  making  inferences  about  its  future  growth  or shrinkage; modeling relevant details in the background of medical images to discriminate them from useless background noise; and modeling shading artifacts to minimize their influence when detecting and measuring skin lesions in standard camera images.

 

Medical  images  contain  a wealth  of information,  which  makes  modeling of medical images a challenging task. Therefore, medical images often are segmented into multiple elementary parts, simplifying their representation and changing the image model into something that is more meaningful, or easier to analyze and measure (e.g. by describing the objects boundaries by lines or curves, or the image segments by their textures, colors, etc.). Nevertheless, these simpler image elements may be easy to perceive visually but difficult  to  describe.  For  example,  the  texture  of  a skin lesion may  not  have  an identifiable texture element or a model known a priori, and regardless of that skin lesion detection   must  be  accurate   and  precise.   Segmentation   of  medical imaging  data segmentation and analysis still is an open question, and some current directions are discussed in this talk.

 

Computer vision and modeling are interrelated. Modeling imaging measurements often involves errors, and estimating the expected error of a model can be important in applications  (e.g. estimating a  tumor  size  and  its  potential  growth,  or  shrinkage,  in response to a treatment). This issue can be approached by adapting machine learning and pattern recognition techniques to solve problems in medical imaging measurements. Typically, a model has tuning parameters, and these tuning parameters may change the model complexity. We wish to minimize modeling errors and the model complexity, in other words, to get the ‘big picture’ we often sacrifice some of the small details. For example, estimating tumor growth (or shrinkage) in response to treatment requires modeling the tumor shape and size, which can be challenging for real tumors, and simplified models may be justifiable if the predictions obtained are informative (e.g. to evaluate the treatment effectiveness). To conclude this talk, we outline the current trends in computer vision in medical imaging measurements, and discuss some open problems.

Distinguished Lecturer (2017 to 2020)

Talk Title: Unobtrusive Smart Sensing and Pervasive Computing for Healthcare

Abstract:  The world’s population is ageing fast. According to the United Nations the median age for all world countries will rise from 28 now to 38 by 2050. Also, is estimated that by 2050, the population over 60 years will increase worldwide from 11% to 22%, a higher percentage (33%) of elderly population will be in developed countries. In this context, governments and private investors, in addition to work for increase efficiency and quality of healthcare, are searching for sustainable solutions to prevent increase expenditure on healthcare related with higher care demands of elderly people. As such, instrumented environments, pervasive computing and deployment of a seemingly invisible infrastructure of various wired and/or wireless communication networks, intelligent, real-time interactions between different players such as health professionals, informal caregiver and assessed people, are created and developed in various research institutions and healthcare system.

This presentation reviews the recent advances in the development of sensing solutions for vital signals and daily activity monitoring. Will be highlighted:

- Vital signals acquisition and processing by embedded devices in clothes and/or accessories (e.g. smart wrist worn) or in walking aids and transportation equipment such as walker or manual wheelchair. The strength and drawbacks regarding cardiac and respiratory assessment capabilities, the studies on cardiac sensing accuracy estimation and artefacts influence on cardiac function sensing through capacitive coupled electrocardiography, electromechanical film sensor and microwave Doppler radar ballistocardiography, reflective photoplethismography will be discussed. Blood pressure, heart rate variability and autonomous nervous system activity estimation based on virtual sensors included in wearable or object embedded devices will also be presented.

- Daily activity signals acquisition and processing through microwave motion sensor, MEMS inertial measurement units, infrared multi-point and Laser motion sensors. Acquisition and conditioning of signals for motion assessment and theragames based on motion sensing and recognition will be presented. Using a set of metrics that are calculated using the information delivered by the unobtrusive sensors for motion capture, objective evaluation of rehabilitation session effectiveness can be performed. Several methods for diagnosis and therapy monitoring, as time frequency analysis, principal component analysis and pattern recognition of motion signals with application to gait rehabilitation evaluation will described. The work under project Electronic Health Record for Physiotherapy promoted by Fundação para Ciência e Tecnologia, Portugal, for developing serious games for physiotherapy based on Kinect technology will be presented.

Concerning the embedded processing, communication and interoperability requirements for smart sensing devices a critical analysis of the existent solutions and a proposed innovatory solutions are discussed. Special attention is granted to wireless sensor network, M2M and IoT as so as to ubiquitous computing particularly smartphone apps applications for healthcare. A fast prototyping vital signs and motor activity monitor as so as the usage of IEEE1451.X smart sensor standards for biomedical applications are included in the presentation.

The creation of novel smart environments including remote vital signs and motor activity monitoring devices for health monitoring and physiotherapy interventions promote preventive, personalized and participative medicine, as in-home rehabilitation that can provide more comfort to the patients, better efficiency of treatments, and lower recovery periods and healthcare costs. The use of unobtrusive smart sensing and pervasive computing for health monitoring and physiotherapy interventions allow better assessment and communication between health professionals and clients, and increase likelihood of development and adoption of best practice based on adopting recognized research-based techniques and technologies, and sharing knowledge and expertise.

Talk Title: Smart Tailored Environments for Neuro-Motor Rehabilitation Monitoring in IoT Era

The convergence of healthcare, instrumentation and measurement technologies will transform healthcare as we know it, improving quality of healthcare services, reducing inefficiencies, curbing costs and improving quality of life. Smart sensors, wearable devices, Internet of Things (IoT) platforms, and big data offer new and exciting possibilities for more robust, reliable, flexible and low-cost healthcare systems and patient care strategies. These may provide value-added information and functionalities for patients, particularly for those with neuro-motor impairments.

In this talk the focus will be on: hardware and software infrastructure for neuro-motor rehabilitation; distributed instrumentation and communication standards; motor rehabilitation based on virtual reality and serious game; use of cloud computing for healthcare monitoring; use of mobile technologies for data storage data communication related to patients’ care; wearable sensor network integration with unobtrusive sensing technologies; Internet of Things technologies; data processing, data presentation that may assist healthcare professionals in objective, accurate assessment of patients’ motor activity and health status during daily activities;  systems that support personalization of healthcare; systems that  promote independent living and empower individuals and their families for self-care and healthcare management. 

Technologies for unobtrusive measurement of patient posture and balance, patient’s muscles activation, movements’ characterization during neuro-motor rehabilitation will be presented and discussed during the talk. As part of these interactive environments, 3D image sensors for natural user interaction with rehabilitation scenarios and remote sensing of user movement, represented by Leap Motion Controller and Kinect, as well as thermographic camera for muscle activity evaluation will be presented. Instrumented daily used equipment for rehabilitation, such as smart walkers and crutches, force platform and wearable motor activity monitors based on smart sensors embedded in clothes and accessories for muscular activity monitoring by electromyography (EMG), force and acceleration measurement capabilities will be presented and discussed. Sensing technologies as part of smart tailored environments, such as piezo-resistive force sensors, e-textile EMG, microwave Doppler radar, MEMS inertial devices for motion measurement and optical fiber sensors will be presented in the context of IoT technologies, where RFID is used for smart object identification and localization in the augmented reality scenarios for therapy. Challenges related to simple and secure connectivity, signal processing, data storage, risk on data loss, data representation, data analysis including the development of specific metrics that can be used to evaluate the progress of the patients during the rehabilitation process will be discussed. Additional remote sensing technologies including thermography for training effectiveness evaluation will be also considered.

A network of physical things/objects, as part of smart environment, which is based on sensors and embedded platforms with Internet connectivity will collect and exchange data on monitored subjects under physical rehabilitation that may involve also the usage of serious games based on virtual and augmented reality. Training using these technologies may improve patients rehabilitation outcomes, may allow objective evaluation of the rehabilitation progress, early communication between health professionals, health professionals and their patients but also may support the research based on analysis of big data.

Sergio Saponara Photo
Distinguished Lecturer (2017 to 2020)

Talk Title: Measurement Performance of Sensor Systems towards Autonomous Vehicles

The tutorial will focus on sensor and measurement systems for new generations of vehicles with driver-assisted/autonomous capability. This is the main trend that is revolutionizing vehicles and mobility of people and goods, and is also making smart our cities. The economic and social impacts of this application field are huge. Worldwide every year 90 millions of vehicles are sold, but 1.25 millions of people are killed due to lack of safety. In US 3.1 billions of gallons of fuel are wasted due to traffic congestion. Assisted driving and autonomous driving aim at increasing safety, at improving fuel efficiency and our lifestyle by avoiding traffic congestion, at ensuring mobility for elderly and disabled people (inclusivity). The interest in this research subject is demonstrated by the huge investments of companies like Google, Intel, Tesla, Uber, Ford, GM, to name just a few, and by technology alliances, e.g. between BMW and Intel, planning autonomous cars for 2021. A convergence between automotive and ICT/Electronics industry is foreseen in the near future. An example of this convergence is the 5G Automotive Association http://www.5gaa.org/, which includes all main cars’ manufacturers, telecom service providers, electronic industries, measurement system providers (Keysight, Rohde&Schwarz).

The key enabling technologies for this scenario are the sensing and measurement systems, needed for the accurate vehicle positioning and navigation, for vehicle context-awareness, obstacle detection and collision avoidance, for driver-assistance (enhanced vision, driver’s attention and fatigue detection).

The lecture will be divided in multiple sections.

First, in the Introduction, innovation and market trends in the field of sensor and measurement technologies applied to vehicles and smart mobility systems will be discussed, focusing on next generation of driver-assisted/autonomous vehicles.

Then, new Radar and Lidar systems, appearing on-board vehicles beside array of imaging cameras, will be discussed for measurement of obstacle positions, distance and relative speed. A trade-off has to be found between power and size of active sensing systems like Radar and Lidar and their maximum measurement range. Moreover, in continuous wave Radars the limited frequency sweep range and the limited number of TX/RX channels lead to limits for the resolution in distance, direction of arrival, and speed measurements. Examples of X-band mobility surveillance Radar and mm-wave automotive Radar will be provided.

On the other hand, MOEMS (micro opto electro mechanical systems)-based scanned systems, used to reduce size and cost of Lidars are causing distortions that are worsening the accuracy of light-based measurements. Distortions due to fish-eye lenses, used to enlarge the field-of-view, are decreasing measurement performance of imaging sensors. Techniques to mitigate such artefacts will be discussed.

Practical examples of traffic sign recognition systems, road signs recognition, image mosaicking for all around view will be discussed. In addition, Lidar and imaging cameras suffer of decreased measurement performance in case of harsh operating conditions (e.g. bad weather or light conditions).

New biometric sensing and measurement systems will be also reviewed, such as Radar-based contactless heart/breath-rate measurement, smart steering-wheel for skin temperature/galvanic-response measurements or heart-rate detection, with the final aim of detecting the driver’s attention or health status.

Concerning on-board sensors for positioning and navigation, recent advances in MEMS accelerometers and gyroscope will be discussed. A careful analysis will be carried out about the measurement errors they cause on position and navigation, due to their bias and random walk output noise.

Finally, the lecture will analyze the trend in computing platforms, where parallel architectures and machine learning/AI (artificial intelligence) techniques, will be exploited to manage in real-time many and heterogeneous sources of measurements and to take autonomous decisions.

Suggestions for future directions of interest for the I&M society, and references to recent publications on IMS journals and conferences, in the field of automated and connected vehicles, will be provided as a conclusion.

Mugnaini photo
Distinguished Lecturer (2017 to 2020)

Talk Title: Advanced Reliability, Availability and Safety Design Tools for Industrial Applications

Scientific and industrial worlds have started recently to look again with interest to the basic rules to perform reliability, availability and safety analysis and design on complex electro-mechanical systems. The main failure modes on electronic devices and sensors as well as the main techniques for failure mode investigation are of interest in modern system design. Statistical characterization of the main probability density functions and degradation models of innovation is mandatory to build lasting and safe products. The main reliability design techniques such as: fault tree analysis, cut set method, minimal path approach, critical block analysis for reliability are requested by companies worldwide as well as the knowledge of the main failure modes and reliability databases and handbooks as MIL-HDBK217, OREDA, BELLCORE, etc… Maintenance policies with special attention to corrective and preventive ones are also affected by reliability design in terms of advantages and disadvantages when applied to electro-mechanical systems. The main safety standards as IEC61508, IEC 61511 and EN50129, EN50128, EN50126 are usually considered in industrial design. The aim of this talk is to enable companies to develop inner confidence on advanced modelling techniques involving reliability, availability and safe design. Under this spotlight in addition to traditional and well known statistical models, innovative modelling techniques based on statistical data representation will be introduced and tailored to some specific case studies in the fields of bio instruments, transportations and oil & gas contexts.     

Mihaela Albu Headshot Photo
Distinguished Lecturer (2016 to 2022)

Talk Title: High Reporting Rate Measurements for Smart[er] Grids

Abstract: Modern control algorithms in the emerging power systems process information delivered mainly by distributed, synchronized measurement systems, and available in data streams with different reporting rates. Multiple measurement approaches are used: on one side, the existing time-aggregation of measurements are offered by currently deployed IEDs (SCADA framework), including smart meters and other emerging units; on the other side, the high-resolution waveform-based monitoring devices like phasor measurement units (PMUs) use high reporting rates (50 frames per second or higher) and can include fault-recorder functionality.

There are several applications where synchronized data received with high reporting rate has to be used together with aggregated data from measurement equipment having a lower reporting rate (complying with power quality data aggregation standards) and the accompanying question is how adequate are the energy transfer models in such cases. For example, state estimators need both types of measurements: the so-called “classical” one, adapted for a de facto steady-state paradigm of relevant quantities and the “modern” one, i.e. with fewer embedded assumptions on the variability of same quantities. Another example is given by emerging active distribution grids operation, which assumes higher variability of the energy transfer and consequently a new model approximation for its characteristic quantities (voltages, currents) is needed. Such a model is required not only in order to be able to correctly design future measurement systems but also for better assessing the quality of existing “classical” measurements, still in use for power quality improvement, voltage control, frequency control, network parameters’ estimation etc.

The main constraint so far is put by the existing standards where several aggregation algorithms are recommended, with specific focus on the information compression. The further processing of rms values (already the output of a filtering algorithm) results in significant signal distortion.

Presently there is a gap between (i) the level of approximation used for modeling the current and voltage waveforms which is implicitly assumed by most of the measurement devices deployed in power systems and (ii) the capabilities and functionalities exhibited by the high fidelity, high accuracy and high number of potential reporting rates of the newly deployed synchronized measurement units.

The talk will address:

o The measurement paradigm in power systems;

  • System inertia, real time and steady-state
  • Instrument transformers; limited knowledge on the infrastructure
  • PQ, SCADA and PMUs
  • Power system state estimation; WAMCS
  • IEDs, PMUs, microPMUs
  • Time-stamped versus synchronized measurements

o Measurement channel quality and models for energy transfer

  • Voltage and frequency variability; rate of change of frequency
  • The steady-state signal and rapid voltage changes (RVC); rms-values reported with 100 frames/s;
  • Measurement data aggregation; filtering properties
  • Time- aggregation algorithms in the PQ framework
  • Statistical approaches;

o Applications and challenges

  • Communication channel requirements; delay assessment in WAMCS
  • Smart metering with high reporting rate (1s)

The presentation provides an overview of these techniques, with examples from worldwide measurement solutions for smart grids deployment.

Olfa Kanoun Photo
Distinguished Lecturer (2016 to 2019)

Talk Title: Impedance Spectroscopy for Measurement and Sensor Solutions

Impedance Spectroscopy is a measurement method used in many fields of science and technology including chemistry, medicine and material sciences. The possibility to measure the complex impedance over a wide frequency range involves interesting opportunities for separating different physical effects, accurate measurements and measurements of non-accessible quantities. Especially by sensors a multifunctional measurement can be realized, so that more than one quantity can be measured at the same time and the measurement accuracy and reliability can be significantly improved. 

In order to realize impedance spectroscopy based solutions, several aspects should be carefully addressed such as, measurement procedures, modelling and signal processing, parameter extraction. Development of suitable impedance models and extraction of target information by optimization techniques is one of the most used approaches for calculation of target quantities. 

Different presentations can be provided to specific topics to show the chances of application of this method in the fields of battery diagnosis, bioimpedance, sensors and material sciences. The aim is to attract scientist to be able to apply impedance spectroscopy in different fields of instrumentation and measurement in an adequate way.