Medicine today has the availability of advanced technologies and new devices for diagnosis. Telemedicine gives a new scenario that allows remote diagnosis, control and treatment of patients at home without physical contact with the doctor. Routine checkups can be outsourced to small care facilities or even to the patient's own home. In Europe the elderly are more than the young but the funds for the health system are decreasing. The medicine paradigm must be rethought. E-Health can be the solution to support for the delocalization of some medical services: new micro and nano electronic circuits, IOT for pervasive and efficient communication, Artificial Intelligence to solve problems where models are not easy to apply but a lot of data is available. The ability to combine the power of AI algorithms and data from different sensors and databases can greatly increase the reliability of the final choice of the right therapy. This is the new Medicine 4.0. The digitalization of the processes and the improvement of technology allow interfacing the human body with computers and Artificial Intelligence allows you to work with a large amount of data (big data) and identify unknown correlations between the parameters to allow a new diagnosis. Several new perspectives will be discussed in this presentation.

• Telemedicine: Difficult, diverse and vast geographical areas are important factors for poor access to healthcare systems. Even in wealthy countries people have to travel up to 100 km to reach a health facility. A smartphone, wearable devices, smart sensors can solve this problem without any transfer. Technology does not replace a doctor but is an answer for an objective need that the patient can directly request. The de-localization of the medical services is the answer to this problem.
• E-Health: correct and rapid measurement can be crucial in many cases. ECG showing heart disease (e.g. atrial fibrillation) can be a life-saving indication. Electronic Health Record (HER): the ability to store and share medical data between the Physician located, for example in New York, a specialist in Chicago and the patient on vacation in Mexico is a new reality. Great advantages but also great challenges to find what information to store in order not to have big unnecessary records.
• Artificial Intelligence: these algorithms can be used for advanced diagnostic systems. In a hospital intensive care unit, for example, a patient is connected to advanced medical equipment that can measure many parameters giving beeps and rings when they are outside the normal parameters. Too many parameters for a single therapist and too many patients for a team. All this information can be managed by AI systems that can keep the patient in optimal condition.
• Artificial Intelligence models: Mathematical models of human parameters are often not usable but data measured under disease conditions are often available. AI systems can use these data to predict other parameters without using models. We’ll see how ECG and PPG can give blood pressure with better precision than a sphygmomanometer using an Artificial Neural Network.

We will investigate both new technologies showing wearable devices that can be used both to monitor patients at home (this topic was very important with the Covid 19) and Artificial Intelligence applied to medical image processing to perform remote diagnoses (once again used to distinguish pneumonia from lung problems due to Covid 19).

After this difficult period Medicine 4.0 will change several aspects of the interface between doctors and patients by improving the performance of national health services and reducing unnecessary costs. The future will provide a new digital hospital and a comprehensive monitoring system that integrates the interface between patients and hospitals.

Keywords: Telemedicine, Artificial Intelligence, Artificial neural networks, Electronic Health, smart sensors

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Medicine today has the availability of advanced technologies and new devices for diagnosis. Telemedicine gives a new scenario that allows remote diagnosis, control and treatment of patients at home without physical contact with the doctor. Routine checkups can be outsourced to small care facilities or even to the patient's own home. In Europe the elderly are more than the young but the funds for the health system are decreasing. The medicine paradigm must be rethought. E-Health can be the solution to support for the delocalization of some medical services: new micro and nano electronic circuits, IOT for pervasive and efficient communication, Artificial Intelligence to solve problems where models are not easy to apply but a lot of data is available. The ability to combine the power of AI algorithms and data from different sensors and databases can greatly increase the reliability of the final choice of the right therapy. This is the new Medicine 4.0. The digitalization of the processes and the improvement of technology allow interfacing the human body with computers and Artificial Intelligence allows you to work with a large amount of data (big data) and identify unknown correlations between the parameters to allow a new diagnosis. Several new perspectives will be discussed in this presentation.

• Telemedicine: Difficult, diverse and vast geographical areas are important factors for poor access to healthcare systems. Even in wealthy countries people have to travel up to 100 km to reach a health facility. A smartphone, wearable devices, smart sensors can solve this problem without any transfer. Technology does not replace a doctor but is an answer for an objective need that the patient can directly request. The de-localization of the medical services is the answer to this problem.
• E-Health: correct and rapid measurement can be crucial in many cases. ECG showing heart disease (e.g. atrial fibrillation) can be a life-saving indication.
• Electronic Health Record (HER): the ability to store and share medical data between the Physician located, for example in New York, a specialist in Chicago and the patient on vacation in Mexico is a new reality. Great advantages but also great challenges to find what information to store in order not to have big unnecessary records.
• Artificial Intelligence: these algorithms can be used for advanced diagnostic systems. In a hospital intensive care unit, for example, a patient is connected to advanced medical equipment that can measure many parameters giving beeps and rings when they are outside the normal parameters. Too many parameters for a single therapist and too many patients for a team. All this information can be managed by AI systems that can keep the patient in optimal condition.
• Artificial Intelligence models: Mathematical models of human parameters are often not usable but data measured under disease conditions are often available. AI systems can use these data to predict other parameters without using models. We’ll see how ECG and PPG can give blood pressure with better precision than a sphygmomanometer using an Artificial Neural Network.

We will investigate both new technologies showing wearable devices that can be used both to monitor patients at home (this topic was very important with the Covid 19) and Artificial Intelligence applied to medical image processing to perform remote diagnoses (once again used to distinguish pneumonia from lung problems due to Covid 19).

After this difficult period Medicine 4.0 will change several aspects of the interface between doctors and patients by improving the performance of national health services and reducing unnecessary costs. The future will provide a new digital hospital and a comprehensive monitoring system that integrates the interface between patients and hospitals.

• A basic introduction to the sense-plan-act challenges of autonomous vehicles
• Introduction to the most common state-of-the-art sensors used in autonomous driving (radar, camera, lidar, GPS, odometry, vehicle-2-x) in terms of benefits and disadvantages along with mathematical models of these sensors

Autonomous driving is seen as one of the pivotal technologies that considerably will shape our society and will influence future transportation modes and quality of life, altering the face of mobility as we experience it by today. Many benefits are expected ranging from reduced accidents, optimized traffic, improved comfort, social inclusion, lower emissions, and better road utilization due to efficient integration of private and public transport. Autonomous driving is a highly complex sensing and control problem. State-of-the-art vehicles include many different compositions of sensors including radar, cameras, and lidar. Each sensor provides specific information about the environment at varying levels and has an inherent uncertainty and accuracy measure. Sensors are the key to the perception of the outside world in an autonomous driving system and whose cooperation performance directly determines the safety of such vehicles. The ability of one isolated sensor to provide accurate reliable data of its environment is extremely limited as the environment is usually not very well defined. Beyond the sensors needed for perception, the control system needs some basic measure of its position in space and its surrounding reality. Real-time capable sensor processing techniques used to integrate this information have to manage the propagation of their inaccuracies, fuse information to reduce the uncertainties and, ultimately, offer levels of confidence in the produced representations that can be then used for safe navigation decisions and actions.

• Overview of different sensor data fusion taxonomies as well as different ways to model the environment (dynamic object tracking vs. occupancy grid) in the Bayesian framework including uncertainty quantification
• Exploiting potential problems of sensor data fusion, e.g. data association, outlier treatment, anomalies, bias, correlation, or out-of-sequence measurements
• Propagation of uncertainties from object recognition to decision making based on selected examples, e.g. the real-time vehicle pose estimation based on uncertain measurements of different sources (GPS, odometry, lidar) including the discussion of fault detection and localization (sensor drift, breakdown, outliers etc.)

Sensor fusion overcomes the drawbacks of current sensor technology by combining information from many independent sources of limited accuracy and reliability. This makes the system less vulnerable to random and systematic failures of a single component. Multi-source information fusion avoids the perceptual limitations and uncertainties of a single sensor and forms a more comprehensive perception and recognition of the environment including static and dynamic objects. Through sensor fusion we combine readings from different sensors, remove inconsistencies and combine the information into one coherent structure. This kind of processing is a fundamental feature of all animal and human navigation, where multiple information sources such as vision, hearing and balance are combined to determine position and plan a path to a destination. In addition, several readings from the same sensor are combined, making the system less sensitive to noise and anomalous observations. In general, multi-sensor data fusion can achieve an increased classification accuracy of objects, improved state estimation accuracy, improved robustness for instance in adverse weather conditions, an increased availability, and an enlarged field of view. Emerging applications such as autonomous driving systems that are in direct contact and interact with the real world, require reliable and accurate information about their environment in real-time.&

The applications of microwave technology in the biomedical realm have undergone a remarkable evolution, revolutionizing various aspects of diagnosis and treatment. From its humble beginnings to cutting-edge applications, microwave engineering has continually pushed the boundaries of medical innovation, offering new solutions for improved health services and patient care.

This talk will review the chronology of the application of microwave-based instrumentation and measurement systems for the advancement of medicine and healthcare. During our travel through this thrilling timeline, we will discuss:

• The early stages of microwave technology adoption in medicine, focusing on its use for therapeutic purposes.
• The immediate recognition by the scientific community of the potential of microwaves for non-invasive measurements in different domains, such as non-invasive medical imaging.
• The following evolution of microwave technology in medicine led by the seminal development of microwave ablation systems.
• The modern exploration of new frontiers in the application of microwaves for biomedical purposes, such as microwave-induced hyperthermia for targeted drug delivery.
• The current intriguing research for microwave instruments and sensors in outpatient healthcare scenarios, envisioning sophisticated wearable and implantable devices for remote health monitoring and treatment.

Throughout this journey we will also highlight that the evolution of microwave technology in the biomedical realm has not been without challenges. We will thereby analyze some of the main concerns and their potential solutions, such as the risk for tissue heating and damage, especially in sensitive areas of the body, or the need to optimize the performance and reliability of microwave-based imaging systems for clinical use.

This talk will show that the future of microwave technology in biomedicine holds immense promise. With ongoing research and technological advancements, microwave-based techniques are expected to play an increasingly prominent role in personalized medicine, medical imaging, precision therapeutics, and remote, non-invasive patient monitoring. From early detection and diagnosis to targeted treatment and long-term management, microwave technology continues to drive innovation in healthcare, ultimately improving outcomes and quality of life for patients worldwide.

During the last years we have witnessed unprecedented advancements of electronic technology in many fields of application, and particularly in the biomedical realm. Among the countless possibilities, the technologies based upon the propagation of electromagnetic fields, such as microwaves or millimeter waves, raise as potential instruments as for non-invasive measurement of certain biomarkers.

During this lecture, we will acquaint ourselves with the fundamental principles of operation of these technologies, with a particular focus on the measurement of blood glucose concentration, the quintessential marker for diabetes management. Indeed, self-measuring the blood glucose level (BGL) is part and parcel of diabetes daily routines. Currently, most of the measuring methods are invasive and uncomfortable, often leading to a reduced, intermittent number of measurements. The development of a reliable non-invasive method able to provide the user with their BGL in a comfortable way, with capabilities of continuous BGL measurement, seems therefore highly desirable.

In this sense, we will see how the scientific community is endeavoring to develop a suitable technological solution for the craved non-invasive measurement of glucose concentration, leveraging the benefits of electromagnetic technologies. During our review of the battle against this technical challenge, we will focus on:

• The scientific foundations of remote measurement by electromagnetic means, underlining the interesting properties of microwaves and millimeter waves for the particular requirements of biomedical contexts.
• The main sensing approaches, with the resonator as the overarching element for these measurement systems.
• The initial works demonstrating the measurement of glucose concentrations in aqueous and biological solutions.
• The current challenges, including path-breaking human trials, sensitivity boosting and selectivity analysis.
• The required instrumentation and driving electronics for such devices in out-of-the-lab applications.
• Other potential applications of these instrumentation and measurement systems for biomarkers detection.
• The zestful future prospects and expectations in the burgeoning pursue of reliable non-invasive BGL measurement, especially considering the arrival of modern artificial intelligence techniques.

All in all, we will show that the desired non-invasive, continuous BGL measurement might no longer be a figment of our imagination in the near future. With intriguing ongoing research and technological advancements, we will highlight the potential of current electromagnetic technologies for instrumentation and measurement purposes in the biomedical domain. This talk will allow us to gain insights on the basic working principles that inspired the past pioneering works, made possible the current thrilling advancements, and will facilitate the unthinkable future applications.

The Data Deluge, in which data is generated faster than it can be efficiently managed, analyzed and used to make informed decisions, represents a commonly used mantra of the ICT world. From an engineering and science perspective, Big Analog Data, has been previously coined by the National Instruments company as a suitable term to characterize high sample rate, digitized, measurements from sensors which can eventually produce high fidelity and (almost) infinitely complex digital twin representations of the physical world.

In practice, we consider that distributed measurement systems generate large quantities of online and streaming datasets that need to be processed in real-time for decision support and/or control purposes. In many situations the resulting data cannot be used directly by intelligent algorithms and suitable data preprocessing pipelines need to be defined and implemented. Furthermore, the heterogeneous reporting rates, embedded measurement models and spatial scales at which the measurements are collected need to be aligned in a robust manner for many tasks. In particular, for (Industrial) Internet of Things systems the dynamic trade-off between high spatial and time resolution measurements and data quality from large numbers of low-cost distributed sensors has to be accounted for, in conjunction with the application (metrological) requirements. These inherent compromises can be mitigated, albeit to a limited extent, through advanced data processing methods that lead to an improved reconstruction of the original signal.

The focus of the talk is thus how to best exploit increasingly available and quality data sources within a rigorous and robust instrumentation and measurement context while leveraging cross-domain interactions with the computing and control technical communities?

The talk will also introduce well-established programming and scientific computing libraries and frameworks that can be used to extract information and lead to accurate characterization of the underlying dynamic processes, with replicable and computationally efficient results. Relevant case studies will focus on smart meter data in real-world scenarios, where effective labelling and classification of microscale features can lead to improved energy management and an environmentally friendly and resilient electrical grid of the future. We focus on methods and techniques to first detect and label such features as anomalies in a data processing and learning pipeline. Subsequently, the labeled datasets are used in a forecasting framework as an early-warning system for potential imbalances in the local energy network. One key novelty is the combination of extracted features using time series data mining methods, such as the matrix profile, with state-of-the-art machine learning algorithms, including deep learning to optimize classification metrics in real time, across various model/algorithm structures and hyper-parametrization options.  

Microwave sensing systems and methodologies have been demonstrated to address critical challenges in industrial measurements and process monitoring. The utility of microwave measurement techniques for multiphase mixture characterization and liquid flow metering date back to early 1970s and 1980s. Over the past 50 years, many multiphase metering technologies and liquid flow sensors based on microwave measurements alone or combined with other methods were developed and commercialized. Microwave techniques are particularly promising for multi-phase component fraction measurements. In fact, they have been proposed and utilized most prominently to realize water-cut monitors (“microwave analyzers”) since late 1980s. Realizing the critical need for efficient methods to detect contaminants and measure their concentrations in natural gas flows, a real-time and highly sensitive microwave-based measurement system was recently developed and piloted in the Middle East and North America. This device has been methodically advanced through the Technology Readiness Levels (TRL) from concept to field piloting. The presentation highlights the design, functionality, certification, application, and qualification of this intrinsically safe system. While the developed technology was demonstrated in certain applications, it finds utility in a wide range of sectors. These applications will be also overviewed in the presentation along with prospects of the technology. The talk will be structured as follows:

• Microwaves in Flow Monitoring (succinct overview and recent advancements)
• Key Principles (Dielectric interactions, time- and frequency-domain techniques)
• Applications (Level Measurement, Fluid Characterization, Flow Measurement, and Multiphase Flow Analysis)
• Operational envelope, Advantages and Limitations
• Case Study: detection of contaminants in gas flows