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Millions of people use public transport systems daily, hence their interest for the epidemiology of respiratory infectious diseases, both from a scientific and a health control point of view. This article presents a methodology for obtaining epidemiological information on these types of diseases in the context of a public road transport system. This epidemiological information is based on an estimation of interactions with risk of infection between users of the public transport system. The methodology is novel in its aim since, to the best of our knowledge, there is no previous study in the context of epidemiology and public transport systems that addresses this challenge. The information is obtained by mining the data generated from trips made by transport users who use contactless cards as a means of payment. Data mining therefore underpins the methodology. One achievement of the methodology is that it is a comprehensive approach, since, starting from a formalisation of the problem based on epidemiological concepts and the transport activity itself, all the necessary steps to obtain the required epidemiological knowledge are described and implemented. This includes the estimation of data that are generally unknown in the context of public transport systems, but that are required to generate the desired results. The outcome is useful epidemiological data based on a complete and reliable description of all estimated potentially infectious interactions between users of the transport system. The methodology can be implemented using a variety of initial specifications: epidemiological, temporal, geographic, inter alia. Another feature of the methodology is that with the information it provides, epidemiological studies can be carried out involving a large number of people, producing large samples of interactions obtained over long periods of time, thereby making it possible to carry out comparative studies. Moreover, a real use case is described, in which the methodology is applied to a road transport system that annually moves around 20 million passengers, in a period that predates the COVID-19 pandemic. The results have made it possible to identify the group of users most exposed to infection, although they are not the largest group. Finally, it is estimated that the application of a seat allocation strategy that minimises the risk of infection reduces the risk by 50%.

This paper expands upon a previous publication and is the natural continuation of an earlier study which presented an industrial validator of expiration codes printed on aluminium or tin cans, called MONICOD. MONICOD is distinguished by its high operating speed, running at 200 frames per second and validating up to 35 cans per second. This paper adds further detail to this description by describing the final stage of the MONICOD industrial validator: the process of effectively validating the characters. In this process we compare the acquired shapes, segmented during the prior stages, with expected character shapes. To do this, we use a template matching scheme (here called &ldquo, morphologies&rdquo, ) based on bitwise operations. Two learning algorithms for building the valid morphology databases are also presented. The results of the study presented here show that in the acquisition of 9885 frames containing 465 cans to be validated, there was only one false positive (0.21% of the total). Another notable feature is that it is at least 20% faster in validation time with error rates similar to those of classifiers such as support vector machines (SVM), radial base functions (RBF), multi-layer perceptron with backpropagation (MLP) and k-nearest neighbours (KNN).

Road-based mass transit systems are an effective means to combat the negative impact of transport that is based on private vehicles. Providing quality of service in this type of transit system is a priority for transport authorities. In these systems, travel time (TT) is a basic factor in quality of service. This paper presents a methodology, based on data mining, for analyzing TT in a mass transit system that is planned by timetable. The objective of the methodology is to understand the behavior patterns of TTs on the different routes of the transport network, as well as the factors that influence these patterns. To achieve this objective, the methodology uses clustering techniques to process the GPS data provided by the vehicles of the public transport fleet. The results that were obtained when implementing this methodology in a public transport company are presented as a use case, demonstrating its validity.

Bluetooth low energy (BLE) technology coupled with fingerprinting provides a simple way to position users with high accuracy in indoor environments. In this paper, we study the effect of BLE protocols and channels on indoor positioning using different distance and similarity measures in a controlled environment. With the aim of reproducing a real positioning system situation, we also study the effect of the user's orientation in the positioning phase and, consequently, provide accuracy and precision results for each orientation. In a 168-m2 testbed, 12 beacons configured to broadcast with the Eddystone and iBeacon protocols were deployed and 40 distance/similarity measures were considered. According to our results, in a specific orientation there is a group of distance metrics coupled with a protocol-channel combination that produces similar accuracy results. Therefore, choosing the right distance metric in that specific orientation is not as critical as choosing the right protocol and, especially, the right channel. There is a trend whereby the protocol-channel combination that provides the best accuracy is almost unique for each orientation. Depending on the orientation, the accuracies obtained for the above mentioned group of distances are within the range of 1.1-1.5 m and the precisions are 90% within the range of 1.5-2.5 m.

Indoor Positioning Systems (IPS) are an alternative to Global Positioning System (GPS) in those environments where its signal is attenuated. This is one of the main reasons why IPS has been the subject of much research over the last two decades and where different technologies and methods have been used. Among the technologies used in IPS are those that use radio frequency (RF) signals, such as Bluetooth Low Energy (BLE) or Wi-Fi. BLE is widely used in ubiquitous computing and in many applications of the Internet of Things (IoT) mainly due to its low power consumption and because it can provide advanced services to users. The aim of this paper is to provide an overview of the state of the art of BLE-based IPS including its main methods and algorithms.

Promentor is a solution that advises job seekers how to effectively improve their chances of getting a job in a certain area of interest by focusing on what, at least historically, seems to work best. To this end, Promentor first analyzes previous selection processes, trying to quantitatively evaluate the effective value of the characteristics that the candidates put into play in the selection. With this evaluation, Promentor can estimate the value of a profile of the job seeker who requests advice based on the characteristics that make it up. Promentor then makes a simulation by applying each of the suggestions on the job seeker’s profile exhaustively and evaluating the modified profile. In this way, Promentor identifies which suggestions offer the greatest increase in qualification, and are therefore more recommendable.

The current paradigm of intelligent transport systems (ITS) is based on the continuous observation of what is happening in the transport network and the continuous processing of data coming from these observations. This implies the handling and processing of a massive amount of data, and for this reason, data mining and big data are fields increasingly used in transportation engineering. A framework to facilitate the phases of data preparation and knowledge modeling in the context of data mining projects for road-based mass transit systems is presented in this paper. To illustrate the utility of the framework, its utilization in the analysis of travel time in a road-based mass transit system is presented as a use case.

In road-based mass transit systems, the travel time is a key factor affecting quality of service. For this reason, to know the behavior of this time is a relevant challenge. Clustering methods are interesting tools for knowledge modeling because these are unsupervised techniques, allowing hidden behavior patterns in large data sets to be found. In this contribution, a study on the utility of different clustering techniques to obtain behavior pattern of travel time is presented. The study analyzed three clustering techniques: K-medoid, Diana, and Hclust, studying how two key factors of these techniques (distance metric and clusters number) affect the results obtained. The study was conducted using transport activity data provided by a public transport operator.

Character recognizers have generally focused on printed text on paper with an emphasis on generality rather than speed. This makes the proposed algorithms not applicable in the context of the very high-speed industrial validation of expiration codes printed on the metal surface of a can. The extreme demands of speed and the adverse effects of lighting and movement, among other things, make it necessary to develop an original and specific strategy. The strategy presented in this paper first selects which of the segmented shapes of a printed can are the best candidates for comparison with expected characters. This is followed by a technique based on the comparison of templates (templates matching), which we call “morphologies”, and are represented as bitmaps to take advantage of the hardware capabilities of general-purpose processors. The use of templates has the advantage of avoiding the construction of a feature vector. In an acquisition test in a real industrial plant, we have been able to successfully treat 438 cans in 44 s, with only one validation error in one character, achieving a compromise between speed and quality that is sufficient for industrial validation in the conditions cited.

Indoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS), however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration param of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (&minus, 20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%&ndash, 50%, the error increase was only around 8%&ndash, 9.2%.

In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

Blue Low Energy technology is playing an important role nowadays in ubiquitous systems, being the beacons a key element. The configuration of parameters related to the beacons, such as their transmission power or their advertising interval should be studied in order to build fingerprinting indoor positioning systems based on this technology as accurate as possible. In this work, we study the impact and the interplay of those parameters in static indoor positioning as well as the orientation effect in the calibration phase. To reduce the time of data collection, a semi-automatic system is introduced.

The development of efficient mass transit systems that provide quality of service is a major challenge for modern societies. To meet this challenge, it is essential to understand user demand. This article proposes using new time-dependent attributes to represent demand, attributes that differ from those that have traditionally been used in the design and planning of this type of transit system. Data mining was used to obtain these new attributes; they were created using clustering techniques, and their quality evaluated with the Shannon entropy function and with neural networks. The methodology was implemented on an intercity public transport company and the results demonstrate that the attributes obtained offer a more precise understanding of demand and enable predictions to be made with acceptable precision.

A prototype tool for the detection, segmentation, classification and counting of Lego pieces based on the OpenCV artificial vision library is presented. This prototype arises before the need to automate the complex and tedious task of the inventoried one of Lego kits of the MindStorm serie.

Quality is an essential aspect of public transport. In the case of regular public passenger transport by road, punctuality and regularity are criteria used to assess quality of service. Calculating metrics related to these criteria continuously over time and comprehensively across the entire transport network requires the handling of large amounts of data. This article describes a system for continuously and comprehensively monitoring punctuality and regularity. The system uses location data acquired continuously in the vehicles and automatically transferred for analysis. These data are processed intelligently by elements that are commonly used by transport operators: GPS-based tracking system, onboard computer and wireless networks for mobile data communications. The system was tested on a transport company, for which we measured the punctuality of one of the routes that it operates; the results are presented in this article.

This study describes a system for the automatic recording of positioning data for public transport vehicles used on roads. With the data provided by this system, transportation-regulatory authorities can control, verify and improve the routes that vehicles use, while also providing new data to improve the representation of the transportation network and providing new services in the context of intelligent metropolitan areas. The system is executed autonomously in the vehicles, by recording their massive positioning data and transferring them to remote data banks for subsequent processing. To illustrate the utility of the system, we present a case of application that consists of identifying the points at which vehicles stop systematically, which may be points of scheduled stops or points at which traffic signals or road topology force the vehicle to stop. This identification is performed using pattern recognition techniques. The system has been applied under real operating conditions, providing the results discussed in the present study.

Robotics in education is considered as a powerful tool for motivating and training students. Thanks to its multidisciplinary character, it allows for the development of the contents of a multitude of materials, mainly those related to Science, Technology, Engineering and Mathematics (STEM). This has been demonstrated in many studies and experiences that have been developed around the world over recent years. However, despite the undoubted benefits and the various initiatives, many educational communities have serious difficulties implementing this tool in the development of their curriculums. The purpose of this article is to present Ciberlandia, a real learning experience that responds to these difficulties and helps to extensively promote careers in scientific and technological disciplines for youngsters. This initiative has celebrated 4 editions and has involved 2,480 participants from 53 primary and secondary schools in The Canary Islands.

The quality of the time travel prediction is a key factor in the transport of people and goods. This prediction is used in different facets related to management and planning of the transport activity, having special influence in the service quality in public transport. In this paper a methodology to analyse the factors which affect to travel time prediction in routes of road public transport is presented. This methodology uses vehicles GPS data to identify the causes of the travel time variability, georeferencing these causes. The infrastructure elements required, data used and the processing techniques are explained. The methodology was applied to analyse the travel time of a line of a public transport company, presenting the results of this test.

This paper presents a study of the impact of dynamic factors on indoor positioning. A positioning system is presented that provides advanced information services based on two subsystems: Wi-Fi and Bluetooth Low Energy (BLE). The first subsystem was intended to position users with not very high levels of accuracy and precision, but not too far from reality, and the second one was intended to position users with greater precision. It is designed for use in stations and terminals of public transportation systems in which the conditions are “hostile” or unfavourable. Experimental results demonstrate that, using different devices for both offline and online phase, RSS differences, Euclidean distance and comparing fingerprints with Weighted k-Nearest Neighbours (WKNN) algorithm, the system is able to position users with reasonable values of accuracy and precision: for Wi-Fi, with only 3 samples, depending on the orientation and compared with 3 neighbours, an average accuracy between 4.15 and 4.58 m and a precision in the range 4–7 m or less 90% of the time were obtained; for BLE, best accuracy results were obtained by comparison with 2 neighbours, giving a position error of 1.59 m and a CDF value of 2.83 m or less 90% of the time.

The quality of service has a main relevance in mass transit systems, being the reliability a key factor for this quality. A system model for the continuous transport data acquisition and computing of these data to improve the quality of service of road mass transit systems is presented in this contribution. This proposal has been conceived to provide services adapted to the needs of the travellers by a continuous monitoring of the transport activity. The data obtained by buses on boarded systems have a special relevance in the proposed model, specially the data provided by the on boarded sensors, such as GPS positioning system. The system model has been applied to analyse the reliability of the operation scheduling of a road mass transit operator, and the results of this test are presented in this paper.

In this work, we provide an analysis of BLE channel-separate fingerprinting using different distance and similarity measures. In a 168 m\(^{2}\) testbed, 12 beacons with Eddystone and iBeacon protocols set were deployed, taking into account the orientation of users and considering 10 distance/similarity measures. We have observed that there is an orientation that offers the best positioning performance with the combination of iBeacon protocol, channel 38 and Mahalanobis distance. Taking 8 samples in the online phase, accuracy values obtained are in the range 1.28 m–1.88 m, and precision values are within 1.90 m–3.76 m or less, 90% of the time and depending which orientation the observer is facing.

This paper presents an architecture model for the development of intelligent systems for public passenger transport by road. The main objective of our proposal is to provide a framework for the systematic development and deployment of telematics systems to improve various aspects of this type of transport, such as efficiency, accessibility and safety. The architecture model presented herein is based on international standards on intelligent transport system architectures, ubiquitous computing and service-oriented architecture for distributed systems. To illustrate the utility of the model, we also present a use case of a monitoring system for stops on a public passenger road transport network.

In this article we describe a low-cost, minimally-intrusive system for the efficient management of parking spaces on both public roads and controlled zones. This system is based on wireless networks of photoelectric sensors that are deployed on the access roads into and out of these areas. The sensors detect the passage of vehicles on these roads and communicate this information to a data centre, thus making it possible to know the number of vehicles in the controlled zone and the occupancy levels in real-time. This information may be communicated to drivers to facilitate their search for a parking space and to authorities so that they may take steps to control traffic when congestion is detected.

This paper describes how to provide suitable computing systems for information systems technologies learning using virtualization and hosting technologies. The main functionalities and components of an university learning lab based on these technologies are presented. All the software components used in its development are open source. Also, the use of this lab, providing the computing systems required for the learning activities of different matters related to the information systems technologies, is illustrated. The model of computing lab proposed is a more sustainable and scalable alternative than the traditional academic computing lab.

This paper outlines an information system for tourists using collective public transport based on mobile devices with limited computation and wireless connection capacities. In this system, the mobile device collaborates with the vehicle infrastructure in order to provide the user with multimedia (visual and audio) information about his/her trip. The information delivered, adapted to the user preferences, is synchronized with the passage of vehicles through points of interest along the route, for example: bus stops, tourist sights, public service centres, etc.

This paper presents OnRoute, a framework for developing and running ubiquitous software that provides information services to passengers of public transportation, including payment systems and on-route guidance services. To achieve a high level of interoperability, accessibility and context awareness, OnRoute uses the ubiquitous computing paradigm. To guarantee the quality of the software produced, the reliable software principles used in critical contexts, such as automotive systems, are also considered by the framework. The main components of its architecture (run-time, system services, software components and development discipline) and how they are deployed in the transportation network (stations and vehicles) are described in this paper. Finally, to illustrate the use of OnRoute, the development of a guidance service for travellers is explained.

Expiration date labels are ubiquitous in the food industry. With the passage of time, almost any food becomes unhealthy, even when well preserved. The expiration date is estimated based on the type and manufacture/packaging time of that particular food unit. This date is then printed on the container so it is available to the end user at the time of consumption. MONICOD (MONItoring of CODes); an industrial validator of expiration codes; allows the expiration code printed on a drink can to be read. This verification occurs immediately after printing. MONICOD faces difficulties due to the high printing rate (35 cans per second) and problematic lighting caused by the metallic surface on which the code is printed. This article describes a solution that allows MONICOD to extract shapes and presents quantitative results for the speed and quality.

Mobility is a basic need for people, therefore the authorities in advanced societies pay special attention to systems and initiatives that improve or facilitate it. In this work, a ubiquitous signaling system for visually impaired people, using a combination of WiFi and Bluetooth Low Energy (BLE) technology with Smart Mobile Devices, is proposed. The system is conceived as a solution to help and support these users in places of public road transportation with travellers massive transit, such as bus stations, interchangers or preferred stops. In addition, once the user is positioned in the desired location, the system would offer advanced services: information on lines schedules, tickets sale, etc.

Nowadays the public transport systems play a main role in the advanced societies. How to evaluate the quality of the public transport is a critic task of the transport regulatory agencies. One indicator used to measure this quality is the fulfilment of the scheduled operation by the transport operators, specially scheduled arrival times and frequencies at stops. In this paper, an automatic system to estimate arrival times in the context of road public transport is proposed. The system works autonomously, acquiring massive vehicle position readings, registering and processing them automatically in order to estimate arrival times. This autonomous behaviour is achieved using pattern recognition and statistical techniques. To illustrate the application of the system, the estimation of arrival times at a bus stop is presented.

The focus is set in presenting a system that could be a realistic solution for urban traffic reduction thanks to the installation of GUIATE, an intelligent management system for public parking in both on-street and off-street. Strategy and planning methods are complemented with the use of non-invasive wireless sensors networks to gather information on traffic and both in-site and in-cloud services to inform drivers and public authorities about parking availability or saturation in real time. A previous study of current models and strategies is also included.

The development of public transit systems that are accessible and safe for everyone, including people with special needs, is an objective that is justified from the civic and economic points of view. Unfortunately, public transit services are conceived for people who do not have reduced physical or cognitive abilities. In this paper, we present an intelligent public transit system by highway with the goal of facilitating access and improving the safety of public transit for persons with special needs. The system is deployed using components that are commonly available in transport infrastructure, e.g., sensors, mobile communications systems, and positioning systems. In addition, the system can operate in non-urban transport contexts, e.g., isolated rural areas, where the availability of basic infrastructure, such as electricity and communications infrastructures, is not always guaranteed. To construct the system, the principles and techniques of Ubiquitous Computing and Ambient Intelligence have been employed. To illustrate the utility of the system, two cases of services rendered by the system are described: the first case involves a surveillance system to guarantee accessibility at bus stops, the second case involves a route assistant for blind people.

In this paper we present a working vision system for estimating size, location and motion of an object by using a set of randomly distributed receptive fields on a retina. The approach used here differs from more conventional ones in which the receptive fields are arranged in a geometric pattern. From the input level, computations are performed in parallel in two different channels: one for purely spatial properties, the other for time-space analysis, and are then used at a subsequent level to yield estimates of the size and centre of gravity of an object and the speed and direction of motion. Movement analysis refining is implemented by a lateral interaction (spatial) and memory (temporal) schemes in which direction and speed are used to build a trajectory. The different parameters involved (receptive field size, memory weighting function, number of cells) are tested for different speeds and the results compared, yielding new insights on the functioning of the living retina and suggesting ideas for improving the artificial system.

In this work a system architecture model for the provision of advanced information services in the context of public transport of passengers by road is presented. The main aim of this model is to provide a frame for systematic development of ubiquitous services integrated in transport infrastructures. The proposed architecture is based on standard specification about ITS architectures and ubiquitous paradigm. To illustrate the utility of the proposed model, a case of use is presented, specifically an intelligent surveillance system for on route public transport vehicles that has been developed using the proposed model.

In the context of public passenger transport by road, the surveillance of the transport network is a responsibility of transport authorities and companies. In the context of intercity transport, there are isolated places where this task is difficult to carry out due to lack access to basic resources such as electricity and communications. In this paper we present an intelligent surveillance system proposal for such places, that is able to monitor selectively and autonomously, making use of the on-board equipment: tracking system, image sensors and communications system. This paper describes how these elements have been combined to implement a surveillance system with the ability to recognize the different physical contexts in the transport network.

In this paper we present a first prototype of a teleoperated telepresence system that uses a range of devices aimed at operating a mobile platform situated in a remote location while offering the user the sensation of being at that remote location. The objective of this first stage of the project is to achieve a sensation of telepresence by simply giving the user the freedom of movement of the platform and the freedom of movement of the camera system.

For this project we have used new technologies to create a new channel of communication between doctors and patients in the treatment of mental disorders. We have created a web application using an adaptable design accessible from any mobile device, which allows doctors to adapt their patients’ therapy to real-time knowledge of their current condition. In turn, patients can express their mood state with respect to the component elements of their therapy.

How to manage the parking space in urban areas is explained in this work, assuming the aims of Smart City, presenting a realistic solution to this important problem. The proposed solution consists of an intelligent management system for on-street public parking based on wireless sensor network and cloud computing technologies.

This paper presents a study of positioning system that provides advanced information services based on Wi-Fi and Bluetooth Low Energy (BLE) technologies. It uses Wi-Fi for rough positioning and BLE for fine positioning. It is designed for use in public transportation system stations and terminals where the conditions are “hostile” or unfavourable due to signal noise produced by the continuous movement of passengers and buses, data collection conducted in the constant presence thereof, multipath fading, non-line of sight (NLOS) conditions, the fact that part of the wireless communication infrastructure has already been deployed and positioned in a way that may not be optimal for positioning purposes, variable humidity conditions, etc. The ultimate goal is to provide a service that may be used to assist people with special needs. We present experimental results based on scene analysis; the main distance metric used was the Euclidean distance but the Mahalanobis distance was also used in one case. The algorithm employed to compare fingerprints was the weighted k-nearest neighbor one. For Wi-Fi, with only three visible access points, accuracy ranged from 3.94 to 4.82 m, and precision from 5.21 to 7.0 m 90% of the time. With respect to BLE, with a low beacon density (1 beacon per 45.7 m2), accuracy ranged from 1.47 to 2.15 m, and precision from 1.81 to 3.58 m 90% of the time. Taking into account the fact that this system is designed to work in real situations in a scenario with high environmental fluctuations, and comparing the results with others obtained in laboratory scenarios, our results are promising and demonstrate that the system would be able to position users with these reasonable values of accuracy and precision.

Georeferencing consists in establishing a relationship between information and geographic locations, usually through geospatial referencing (i.e., longitude and latitude coordinates). Super-Resolution is a technique that enhances the spatial resolution of an imaging system. In this paper, the integration of both Georeferencing and Super-Resolution techniques is presented for video navigation in the web landscape applied to underwater videos.

In this paper, we have focused on applying robotics as a social element and an intermediary in tourism. To that end, a system has been developed that will benefit the tourist experience. To do this, every room of the hotel would have a low-cost social robot, a Karotz. This robot interacts orally with the tourist to provide him or her with news about the hotel and the best tourism activities for the user. The components of the robot were used during the development to make communication flows natural. This has enabled us to create a social robot that covers a need faced by the tourist industry. The developed prototype shows that this is a plausible field of application for social robotics.

This paper presents the development of a pervasive system prototype for the Local Tow-Truck service. This system makes it possible to video, process, store and transfer each service carried out by the company with any of the tow-trucks in its fleet. The data are administered by the tow-truck operators by means of mobile devices and are temporarily stored in a system on board the tow-truck in question. On arrival at their base, these data are automatically transferred to a common repository, which can be accessed from the offices of the central municipal vehicle pound. The aim of this system is to enable the Local Tow-Truck Service to certify the quality and safety of each transfer and guarantee the state of the transported vehicle at all times, and thereby to respond to any potential claims.

In this paper we describe our proposal for humanoid robotic system to develop a football team that compete in the Robocup, describing hardware components used and software components developed.

This paper described the smart system that is executed on board the vehicles of the fleet of a public transport company whose mission is to help the regulating authorities to control, verify and enhance the public transport service. This system is autonomous and does not interfere in the operations carried out by the vehicle; it provides useful data obtained transparently from drivers and passengers, using different sensors installed in the vehicle. The system has been used in several vehicles of the public transport fleet’s in real operational conditions and some of the results obtained are presented here.

This paper covers a new approach on Cloud Computing for Scientific Data Storage and Processing using a hybrid system with both on-site and on-the-cloud systems. The system analyzes use cases which are not resolved by either one of the single systems themselves.

A traveler assistant for public transport is described in this paper. The main purpose of this system is to provide to the passenger real time information about the route, for example: estimated time to the next stop of the route or estimated time to the traveler destination, also if desired, tourist information about nearby interesting points. The application is designed for all kind of passengers, but it is especially useful for travelers with special needs or for those who are unfamiliar with a public transport network, for example tourists. Based on Bluetooth and Android technologies, the assistant has a clear and intuitive user interface permitting to the users a friendly interaction with the system and breaking down the barrier to entry for those users not accustomed to work with mobile software applications.