This review article is focused on the analysis of the state of the art of sensors for guided ultrasonic waves for the detection and localization of impacts for structural health monitoring (SHM). The recent developments in sensor technologies are then reported and discussed through the many references in recent scientific literature. The physical phenomena that are related to impact event and the related main physical quantities are then introduced to discuss their importance in the development of the hardware and software components for SHM systems. An important aspect of the article is the description of the different ultrasonic sensor technologies that are currently present in the literature and what advantages and disadvantages they could bring in relation to the various phenomena investigated. In this context, the analysis of the front-end electronics is deepened, the type of data transmission both in terms of wired and wireless technology and of online and offline signal processing. The integration aspects of sensors for the creation of networks with autonomous nodes with the possibility of powering through energy harvesting devices and the embedded processing capacity is also studied. Finally, the emerging sector of processing techniques using deep learning and artificial intelligence concludes the review by indicating the potential for the detection and autonomous characterization of the impacts.

A method is presented for isolating the overlapping hyperbolic arcs found when a radar scan is made over several adjacent buried objects. The reflected signal is first converted into a series of data pairs (yj, tj) giving, for a radar antennae position yj along the scan, the times-of-flight tj of the maxima or minima in the reflected radar amplitude. The generalized Hough transform method is extended to record in an associative store the sets of data pairs contributing to each bin in the Hough accumulator space. A cluster of high bins, defining a peak in this space, may then be broken down to reveal its contributing data pairs. This gives the important advantage that a conventional least-squares algorithm can be used to reveal the object position, depth, and radius or velocity. The method is demonstrated on real radar data from buried pipes. The radius of a 0.18-m radius concrete pipe at 1-m depth is estimated at 0.14 m.

Ultrawideband radar is commonly used in the frequency range of 50-500 MHz to detect buried pipes at a depth of about 1-2 m depending on the soil characteristics. The typical feature used to locate the pipes is the hyperbolic pattern of the time of flight generated by a linear scan of the antenna above the surface. When the pipes are close together, the hyperbolas overlap, and a straightforward least squares fit is not possible. The Hough transform provides one possible solution. This paper extends the Hough transform by introducing a weighting factor depending on the differentials of the unknown parameters with respect to the experimental errors, namely, the probe position error and the time-of-flight error. This enables optimally placed sets of data pairs to be given greater weight than "ill-conditioned" sets, as for example when all data pairs lie near one end of the arc. The result is a decrease in the background amplitude with respect to the maximum of the peaks in the Hough accumulator space. It is shown that this improvement persists even when many arcs are present. A mathematical analysis with analytical results is given for the case of four unknowns: pipe radius R, pipe center position (Y, Z), and soil propagation velocity V. The results are presented through simulations introducing controlled uncertainties in the probe position, the time of flight, and its bin size. The simulations demonstrate the correlations that occur between the radius, depth, and velocity for given experimental uncertainties.

Holographic subsurface radars (HSR) are not in common usage now; possibly because of the historical view amongst radar practitioners that high attenuation of electromagnetic waves in most media of interest will not allow sufficient depth of penetration. It is true that the fundamental physics of HSR prevent the possibility to change receiver amplification with time (i.e., depth) to adapt to lossy media (as is possible with impulse subsurface radar or ISR). However, use of HSR for surveying of shallow subsurface objects, defects, or inhomogeneities is an increasingly proven area of application. In this case, HSR can record images with higher resolution than is possible for ISR images. The RASCAN family of holographic radars is presented along with technical specifications and typical case histories. Among the applications considered are civil and historic building surveys, nondestructive testing of dielectric materials, security applications, and humanitarian demining. Each application area is illustrated by relevant data acquired in laboratory experiments or field tests. This paper presents experiments with RASCAN imaging in media with different degrees of attenuation, and illustrates the principle of HSR through an optical analogy.

Smart textiles have received a lot of attention of research in the last decade for their use in wearable technologies [1]. Soft sensors and actuators have been developed for interaction with environment, increase of operator's safety, entertainment and physiological parameters monitoring [2]. This paper reviews the fabrication processes for smart textiles with conductive fibers and analyses the applications of soft sensors (pressure, temperature, etc) as components of e-textiles, the fabrication processes of smart fabrics with different type of fibers and discusses the properties of resistance related to smart sensors fabrication with different metal and polymeric fibers. Finally it is reported an example of a smart textile piezoresistive sensor developed for a smart training shoe for step rate monitoring interfaced to a smart watch