Preface

Perseverance, humility and courage are necessary qualities in a good scientist, more than wisdom, curiosity, and intelligence. Generosity is probably the highest virtue of a great person.

Someone, somewhere, sometime

In today's society, there is an increasing need to sense multitude of variables of different types. Data collection from the environment, or, in general, from a certain system, is a fundamental requirement in order to gain insight on the state of such system, and thus take appropriate decisions and actions, either through human intervention or autonomously, when necessary. The subject of analysis of the system can be as diverse as large-scale (macro) systems, medium-sized systems, or microsystems. Examples include the space, the atmosphere, a forest, a city, a crop field, a civil infrastructure, a factory, industrial machinery, the home, a specific indoor/outdoor area, daily objects, persons, animals, food, a biological sample, etc. In certain cases, retrieving information of the system is the main, and sometimes unique, objective, without any further active action that modifies the system, or represents any kind of control over it. For example, weather forecasting is based on the measurement of ambient variables such as temperature, atmospheric pressure, relative humidity, wind velocity, etc., and the information provided by weather forecasters (inferred from such environmental data using complex meteorological models) is very useful for citizens and administrations for obvious reasons (in particular, when extreme meteorological conditions are predicted). However, obtaining these environmental variables has an informative intention (i.e. the weather forecast), exclusively. There are other macro systems, for instance, a region in the Earth susceptible to seismic action, where the sensed data (seismic variables, in this particular case) are not used to generate preventive actions (earthquakes cannot be avoided) but to make useful predictions (that avoid major catastrophes or, at least, protect the population).

However, in most systems, sensing is necessary as a first step to generate actions that modify, or perturb, it according to certain requirements. For example, any motion control system, present in many industrial scenarios (e.g. elevators, conveyor belts, and servomotors), is equipped with sensors that measure motion variables, such as position, velocity, etc., and such variables are used by system actuators to generate correcting actions, if needed. Another clear example is the autonomous and intelligent vehicle, where a set of sensors of different types continuously collects data, which are used not only to assist the driver (if it is present) but also to autonomously take decisions (the so-called unmanned vehicle is another paradigmatic example). In healthcare, smart systems able to monitor vital constants, to measure variables of medical interest, e.g. glucose content in blood, or to detect unexpected body movements (e.g. in disabled or elderly people), indicative of potential dangerous events (such as lipothymia or ictus), are of the highest interest. Naturally, such systems need sensors to collect these data, but the main relevant and distinctive feature of smart healthcare systems is their ability (not always present) to generate specific actions from the retrieved data. For example, in the event of a sudden increase of glucose in blood above a certain threshold, detected by a dedicated real-time glucose-monitoring sensor, an alert indicative of hyperglycemia should be activated, in order to prevent the patient from dramatic irreversible effects. Alternatively, in hospital environment, a smart system should be able to automatically inoculate insulin to the patient, in a controlled way, in order to compensate for the excess of sugar in blood.

The three examples reported in the previous paragraph (motion control, the autonomous vehicle, and smart systems for healthcare) are representative of three sectors where system intelligence is penetrating significantly and are thereby experiencing a considerable (digital) transformation, namely, the Smart Industry (intimately related to the fourth industrial revolution, also called Industry 4.0), the Intelligent Car, and the Smart Healthcare. Nevertheless, there are many other sectors, which are nowadays in the process of transformation toward the digital world (or Smart World), including agriculture, packaging, food industry, city management and sustainability, and civil engineering (e.g. structural health monitoring), to cite some of them. Thus, terms such as Smart Agriculture, Smart Packaging, Smart Cities, or Ambient Intelligence, among others, are becoming progressively more familiar within our society.

To make the Smart World concept a reality, or, at least, to achieve further levels of intelligence within the above-cited fields, efforts at different levels are needed. At system level, enabling technologies for the so-called Internet of Things (IoT), e.g. radiofrequency identification (RFID), near-field communications (NFC), wireless sensor networks (WSN), energy harvesting, cloud computing, big data analysis, communication protocols, and embedded systems, is the subject of an intensive research activity, and the progress in such technologies is fundamental to envisage a future interconnected and intelligent world. Nevertheless, one key factor for the deployment of IoT and related applications is the recent implantation of the fifth generation of mobile networks (5G), with higher capacity, connectivity, and broader bandwidths (among other advantages) as compared to 4G. At device level, the key components in smart sensing systems are sensors. The research activity in the sensors domain has experienced a very significant growth in recent years. There are many types of sensors, exploiting different technologies, e.g. optical sensors, acoustic sensors, and magnetic sensors, but the sensors that are expected to play a key role in future smart systems are microwave sensors, and particularly planar sensors, the subject of this book. Their low cost, small size, and low profile, as well as the possibility of sensor implementation in flexible and organic substrates (by either subtractive or additive processes) are important attributes of planar microwave sensors. Other important aspects of microwave technologies are low-cost generation and detection systems, microwave interaction with the materials at different scales (i.e. through the near field or the far field), wave propagation (and penetration) in many different types of materials, and system functionality in hostile and harsh environments, e.g. with pollution and dirtiness (encountered in many industrial scenarios), or under adverse meteorological conditions. Planar microwave sensors can be implemented in combination with other technologies, such as microfluidics, micromachining, lab-on-a-chip, textiles, etc., and, inherently, exhibit a potentially wireless connectivity. Moreover, the sensor substrate can integrate the associated sensor electronics, needed for signal generation, post-processing, and (eventually) communication purposes, representing a reduction in system complexity and cost. Versatility is another relevant characteristic of planar microwave sensors. Despite the fact that such devices are (canonically) permittivity sensors, able to detect changes in the dielectric properties of the immediate environment, it is possible to use these sensors to determine material composition or to detect defects or anomalies in samples or targets, in both cases related to permittivity changes. Nevertheless, it is also possible to use planar microwave sensors to measure many other parameters, including physical and chemical variables (e.g. temperature, humidity, motion, concentration of certain substances in samples, and gas detection) or to perform biological analysis (e.g. bacterial growth and presence of certain analytes in biosamples). In some cases, smart materials (i.e. functional materials with dielectric properties highly sensitive to the measurand), reagents (i.e. chemical agents able to activate a chemical reaction), or bioreceptors (i.e. biological elements that bind to a specific analyte) are needed in order to boost up sensor sensitivity, a key performance parameter. Finally, let us mention that by using biodegradable substrates and organic inks, planar microwave sensors are potential candidates for the implementation of "green" sensing systems (for instance, RFID sensors with battery-free and chipless sensing tags). Nevertheless, there are many challenging aspects, mainly related to performance degradation inherent to the use of eco-friendly materials, that should be addressed to make "green sensing" a reality.

The previous advantageous characteristics of planar microwave sensors explain the huge efforts dedicated to their research and optimization in recent years and justify the publication of the present book. Though a planar microwave sensor in a real scenario is composed of three main blocks (the electromagnetic module, including the sensitive element, the electronics module, responsible for signal generation and post-processing, and the communication module), and, eventually, of a mechanical part, this book is essentially focused on the electromagnetic, or microwave, block. Nevertheless, in some of the reported sensor implementations (proof-of-concept demonstrators), part of the electronics is also included (especially, when this is necessary to retrieve and visualize the sensing data). The book tries to emphasize the underlying physics behind the considered sensing mechanisms and...