The impulse response of a piezoelectric transducer can be calculated using the electrical equivalent circuit model with the Manson method for a bandwidth in far field. Nevertheless, these approaches are not sufficiently precise because the importance of homogeneous structure medium where the transducer emit the signal in part determine the bandwidth in which it acts due to the interactions of this with the environment. This paper describes preliminary research results on piezoelectric impulse response measurement in small space, making use of the procedure presented by Angelo Farina for transducers emitting in reverberant spaces. Combination the Basics of Exponential Sine Sweep (ESS) method, techniques of arrival detection and signal processing it is possible to obtain the impulse response in piezoelectric transducer emitting in a homogeneous medium. The propose of this technique is the characterization of the signal produced by a hadron beam in cancer treatments or hadrontherapy, for its corresponding analysis in homogeneous mediums whose characteristics are similar to human tissue.

Mapping and exploration are important tasks of mobile robots for various applications such as search and rescue, inspection, and surveillance. Unmanned Aerial Vehicles (UAVs) are more suited for such tasks because they have a large field of view compared to ground robots. An autonomous operation of UAV is desirable for exploration in unknown environments. In such environments, the UAV must make a map of the environment and simultaneously localize itself in it which is commonly known as the SLAM (Simultaneous Localization and Mapping) problem. This is also required to safely navigate between open spaces, and make informed decisions about the exploration targets. UAVs have physical constraints of limited payload, and are generally equipped with low-spec embedded computational devices and sensors. Therefore, it is often challenging to achieve robust SLAM on UAVs which also affects exploration. In this paper, we present an autonomous exploration of UAV in completely unknown environments using low cost sensors such as LIDAR and RGBD camera. A sensor fusion method is proposed to build a dense 3D map of the environment. Multiple images from the scene are geometrically aligned as the UAV explores the environment, and then a frontier exploration technique is used to search for the next target in the mapped area to explore maximum possible area. The results show that the proposed algorithm can build precise maps even with low-cost sensors, and explore the environment efficiently.

This paper addresses the issue of robotic haptic exploration of 3D objects using an enhanced model of visual attention where the latter is applied to obtain a sequence of eye fixations on the surface of objects guiding the haptic exploratory procedure. According to psychological studies, somatosensory data resulted as a response to surface changes sensed by human skin is used in combination with kinesthetic cues from muscles and tendons to recognize object. Accordingly, a series of sequential tactile images are obtained for each object from various viewpoint during an exploration process. We take advantage of the contourlet transform to extract several features from each tactile image whose dimensionality is reduced to one using a Self Organizing Map. Through this process, a numerical sequence is obtained for each exploration. Similar sequences are then grouped in clusters whose labels are used as features for a classification algorithm. In addition to this somatosensory feature, other kinesthetic inputs including the probing locations and the angle of the sensor surface with respect to the object, in consecutive contacts are added as features and further used for the purpose of object recognition. The proposed framework is applied to a set of four virtual objects and a virtual force sensing resistor array (FSR) is used to capture tactile (haptic) imprints. Trained classifiers are finally tested to recognize data from new objects of same categories.

The data recording of underwater noise is a key aspect for the prevention and improvement of management systems of maritime spaces. Thus, due to the presence of activities potentially generating impulsive noise, the ports deserve special attention. This article describes and shows the results of the spatial monitoring of both the basal noise and the impulsive noise sources present in Port Facilities. A vessel has been equipped with a smart digital hydrophone with a working frequency range between 10 and 200 kHz and an RVR of, approximately, -170 dB @ 1V / µPa. Coupling the acoustic data together with the GPS coordinates of the vessel, a GIS map with the spatiotemporal distribution of the basal sound pressure levels has been made, identifying the sources of impulsive noise of interest and its temporal characteristics. This is a preliminary step for the development of future studies on underwater noise pollution and its potential impact on biodiversity in the environment of Port Facilities with the maximum guarantees possible.

The following paper presents a novel method for the exploitation of urban noise and sound measurement. The overarching objective of this work is to advantageously mine & exploit the information embedded into urban sounds. Effectively contributing to the development of services and products via the advancement of the State of The Art in technology of information and telecommunication.

Traditionally, the grand majority of city and urban noise/sound is measure, analysed and classified with the purpose of drawing appropriate government legislation and regulations aimed at contributing to a healthier environment for humans. Furthermore, noise measurements are often, if not always, measured and processed with dedicated high-end microphones and devices. Costly microphones and processing hardware, often based on limited data samples, limit the extension of the exploitation of the data campaigns. Effectively, the trading of sound-related information is almost often a simple absolute noise level measurement business between a sound expert and a council-like or government-like institution.

With these premises, we can safely say that most of the urban noise and sound business is carried out with the sole purpose of reporting or denouncing, to the appropriate authorities, a misconduct (e.g. noisy streets, bars, cities) or correct a misuse of council resources (e.g. bus re-routing, city planning). Other types of urban noise business activities clearly exist but its span is definitely limited.

We believe that urban sounds do carry more information than what it is extracted to date. The wide availability of powerful AI tools makes it even more true. We believe that urban sound data can be conveniently mined via modern sound algorithm methods. We also believe that in the traditional way of processing urban sound datasets, the employment of costly hardware does represent a limiting factor for the business that can be built around urban sound analysis.

We present a technologically novel method for the capturing, processing and trading of urban sound-based information. The presented method is developed around consumer-grade sound devices that, being relatively inexpensive, can be considered a commodity hardware. An example that we would like to present as such is Echo, the cloud-based commercial multi-microphone smart speaker product by Amazon. Being widely available and easily purchased for US$60 over the Internet, Echo represents a great example of a commodity hardware that can be purchased or rented for virtually no cost.

Unlike traditional urban sound processing systems, we discuss how to move the sound processing algorithms to a cloud-based server. In a way, similarly to what Amazon did with the cloud product Alexa, we propose to decentralize part of the software processing of sound/noise to a more agile cloud-based server. In the following way we will be able to enable the use of these algorithms to a third party via the use of multiple APIs. This will naturally free the consumer-grade sound sensors from the difficult task of processing, making it extremely inexpensive.

In this paper, beside primarily focusing on the scientific aspect of the method, we will like to discuss as well, perhaps in greater details than what it is normally done, the main business scenarios where the presented idea could enable novel and exciting commercial opportunities. Hence, we will show that such a method will open new business models in the area of urban sound analysis and in its exploitation.

The novelty aspects of the approach presented in this proposal is manifold:

1. Instead of using proprietary and costly devices, we propose to explore the use of a consumer-grade commodity hardware for sound capturing and measurement.

2. Develop and implement de-centralized urban sound analysis algorithms for the processing of urban sound samples that are cloud-based. As such, their use will be offered via an edge server and will not depend, or will only marginally depend, on the capabilities of the employed sound capturing device. Effectively making it simpler and cheaper.

3. Build an overall system that is heavily cloud-based (e.g. edge computing)so that the information collected by the inexpensive sound capturing devices can be processed by professional remote sound algorithms, developed by independent experts, via APIs that are accessed following a pay-per-use business scheme.

From the business development point of view, the proposed overall system design structure is the most innovative aspect of this paper. Furthermore, the fact that all processing algorithms are basically cloud-based services, allows us to envision a large number of business cases effectively being enabled by what we propose. A detailed description of such an important aspect is given in further sections.

Diabetes Mellitus, usually called only Diabetes, is a worldwide chronic metabolic disorder that is characterized by abnormal oscillations in blood sugar levels. Such levels should be monitored by diabetes patients, which traditionally have had to take blood samples by finger-pricking at least between twice and four times a day. Finger-pricking has a number of drawbacks that can be tackled by Continuous Glucose Monitors (CGMs), which are able to determine blood sugar levels throughout the day and not only at specific time instants. In this paper it is proposed the design of an IoT CGM-based system whose collected blood sugar sample values can be accessed remotely, thus being able to monitor patients, specifically dependent ones (e.g., children, elders, pregnant women) and warn them in case a dangerous situation is detected. In order to create such a system, a fog computing system based on distributed mobile smart phones has been devised to collect data from the CGMs. Moreover, it is proposed the use of a blockchain to receive, validate and store the collected data with the objective of avoiding untrusted sources and, thus, provide a transparent and trustworthy data source of a population, which can vary in age, ethnicity, psychology, education, self-care and/or geographic location, in a rapid, flexible, scalable and low-cost way. These crowdsourced data can enable novel mHealth applications for diagnosis, patient monitoring or even public health actions that help to advance in the control of the disease and raise global awareness on the increasing prevalence of diabetes.

Industry 4.0 has paved the way for a world where smart factories will automate and upgrade many processes through the use of some of the latest emerging technologies. One of such technologies is Unmanned Aerial Vehicles (UAVs), which have evolved a great deal in the last years in terms of technology (e.g., control units, sensors, UAV frames) and have reduced significantly their cost. UAVs can help industry in automatable and tedious tasks, like the ones performed on a regular basis for determining the inventory and for preserving the traceability of certain items. Moreover, in such tasks it is essential to determine whether the collected information is valid or true, especially when it comes from untrusted third-parties. In such a case, blockchain, another Industry 4.0 technology which has become very popular in other fields like finance, has the potential to provide a higher level of transparency, security, trust and efficiency in the supply chain and enable the use of smart contracts. Thus, in this paper it is presented the design and some preliminary results of an UAV-based system aimed at automating the inventory and keeping the traceability of industrial items attached to Radio-Frequency IDentification (RFID) tags. Such a system is able to make use of a blockchain, which receives the inventory data collected by the UAVs, validate them, ensure their trustworthiness and make them available to the interested parties.

The purpose of this project was to design and implement an autonomous system based on Arduino to monitor environmental parameters that intervene in the perception of human comforts such as temperature, humidity, and solar radiation, and use them to analyze factors related to climate control and energy efficiency in buildings.

The system was tested in laboratory conditions as well as by in situ measurements of building elements and living spaces. Some of the experiments carried out were contrasted with numerical simulations that allowed us to understand the implemented system.

The thermal and optical sensors were calibrated in the laboratory by comparison with standard probes for temperature and illuminance. The result was used to adjust the measurements and to control any possible errors due to the sensors.

Preliminary measurements were made in systems that simulate enclosures, with walls of different thermal conductivities, for comparison with computer simulations, and later measurements were performed outside the laboratory, which was contrasted with a thermodynamic simulation tool.

Additionally, it was assessed the use of the system to measure the surface evolution of thermal parameters in a space.

With the use of the system to measure real conditions and its calibration, it was possible to demonstrate its practicality and good operation. The result of this work can be the basis for an interesting alternative to systems for recording and monitoring thermodynamic variables in the field of architecture and energy efficiency for its versatility and economy.

Efficient task coordination is an important problem in multi-robot systems. Explicit programming of each robot to perform specific tasks (ex. cleaning) is too cumbersome and inefficient as the areas to serve in a map may vary with time. Moreover, the number of the robots available to serve may also vary, as some of the robots may be charging and not available. Improper task division can cause two or more robots to serve same areas of the map, which is a waste of computation and resources. Hence, there is a need for a simpler scheme for autonomous task coordination of multiple robots without the need of explicit programming. This paper presents a bio-inspired algorithm, which uses the repelling behavior of pheromones for autonomous task coordination. The proposed algorithm uses a node representation of the navigational paths, and integrates the pheromone signaling mechanism in robot localization which allows the robots to capture areas or sub-areas of the map so that there is efficient task coordination, and robots work without interruption from other robots. We show through experiments that the proposed scheme enables multiple service robots to perform cooperative tasks intelligently without any explicit programming.

Structural identification methods using sensor data have received increased attention in the civil engineering research community with the objective of identifying structural performance, and evaluating the remaining useful life of structures. While many researchers have successfully applied various approaches to numerical and/or small-scale laboratory models of structures, the literature lacks many successful applications to large‐scale civil structures under real loading environment. This study highlights the challenges of structural health monitoring methods for applications to large‐scale civil structures, especially when dealing with changing ambient and environmental conditions. A hierarchical Bayesian framework is presented for probabilistic model updating and damage identification to account for inherent as well as parameter estimation and measurement uncertainties. It is shown that the proposed hierarchical framework allows to explicitly account for pertinent sources of variability such as ambient temperature and/or excitation amplitude and therefore yields more accurate predictions. The study also highlights the value of using point cloud data in addition to vibration measurements for structural performance assessment. The point clouds are informative about identification of cracks at their early stages while the vibration data provide measure of stiffness at later stages of damage. Performance of the proposed approach is demonstrated through application to three large-scale reinforced concrete building structures.