A Computational Method based on Radio Frequency Technologies for the Analysis of Accessibility of Disabled People in Sustainable Cities

The sustainability strategy in urban spaces arises from reflecting on how to achieve a more habitable city and is materialized in a series of sustainable transformations aimed at humanizing different environments so that they can be used and enjoyed by everyone without exception and regardless of their ability. Modern communication technologies allow new opportunities to analyze efficiency in the use of urban spaces from several points of view: adequacy of facilities, usability, and social integration capabilities. The research presented in this paper proposes a method to perform an analysis of movement accessibility in sustainable cities based on radio frequency technologies and the ubiquitous computing possibilities of the new Internet of Things paradigm. The proposal can be deployed in both indoor and outdoor environments to check specific locations of a city. Finally, a case study in a controlled context has been simulated to validate the proposal as a pre-deployment step in urban environments

The multi-agent flood algorithm as an autonomous system for search and rescue applications

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Applications of pattern classification to time-domain signals

Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%

Exploring the potential of using radio frequency identification technology in retail supply chains - A Packaging Logistics perspective

In recent years RFID technology has attracted interest from the retail industry where it is being presented as a possible key to creating more efficient and effective retail supply chains. If RFID technology is to be implemented in packaging throughout retail supply chains, there is a need to develop an understanding of how and why the technology affects activities and processes within retail supply chains. Accordingly, the overall purpose of this licentiate thesis is to explore how the application of RFID technology to packaging could affect packaging logistics activities in retail supply chains. The packaging logistics activities discussed in this licentiate thesis are those related to ambient fast-moving consumer goods, from the product-filling point at the manufacturer’s, where the product is merged with the primary packaging, to the point of sale at retail outlets, where the products are sold to the end consumer. This thesis is based on multiple research strategies; a case study and a modelling and simulation study. The case study was conducted to describe and gain an in-depth understanding of and insight into existing packaging logistics activities in retail supply chains. A Dutch retail supply chain was chosen as a single-case study. The single-case study was both data-triangulated and investigator-triangulated with three Swedish case studies to further broaden the understanding of packaging logistics activities in retail supply chains. The case study resulted in a framework of packaging logistics activities in retail supply chains. The modelling and simulation study was conducted to describe what, how and why packaging logistics activities are affected when RFID technology is applied to packaging. A conceptual model and a simulation model were developed in the modelling and simulation study. The conceptual model describes and analyses “could-be” processes and activities in retail supply chains, whereas the simulation model primarily describes and anal

Improving Urban Accessibility: A Methodology for Urban Dynamics Analysis in Smart, Sustainable and Inclusive Cities

Despite the improvisations of current urban accessibility regulations and their application in urban systems, it is a fact that our cities are not accessible. Both, the assessment of the effectiveness of urban accessibility and its maintenance over time are issues that require a more consistent approach. In order to address these aspects, it is necessary to have an accurate awareness of the existing condition of urban accessibility. Therefore, the way this information is transformed into specific data, which must be collected, stored and assessed, is one of the main challenges that smart cities face. This research helps implement an integrated system for urban accessibility analysis, combining the latest advances in the Information and Communication Technologies, such as RF & GPS positioning, smart sensing and cloud computing. The main goal of this research is to develop a reliable and effective method to assess public space accessibility with special focus on people with disabilities, by eliciting from users personal experiences. Consequently, the data obtained will enable a better design for improving pedestrian mobility. As a result, a computational architecture for urban dynamics analysis has been designed. Finally, technology and data processing have been validated as an effective system for data collection, and, as a first approach to users’ real experience, it has been proposed to have a testing scenario at the University of Alicante

Sensor-based ICT Systems for Smart Societies

L'abstract è presente nell'allegato / the abstract is in the attachmen

Smart system and mobile interface for healthcare: stress and diabetes

In this thesis, a system with multi-channel measurement capabilities was designed and implemented, associated with the monitoring of stress levels, through a proposed algorithm that correlates heart rate, respiratory rate, and galvanic skin response. Experimental validation tests were carried out, as well as experiments with patients suffering from diabetes. To this end, measurements were made not only of stress-related parameters, but also of parameters such as blood glucose levels and blood pressure levels, seeking to extract correlations between stress and diabetes status. In addition, body temperature was another parameter acquired, in order to assess its importance and relation to stress and diabetes. The proposed multichannel system also features RFID technology for authentication purposes, as well as Wi-Fi access for internet connection and storage of the acquired data in a database structured for that purpose, thus enabling remote access. To allow the assessment of stress levels and diabetes progress, a mobile application was also developed, which also allows the visualisation of the analysed data.In this thesis, a system with multi-channel measurement capabilities was designed and implemented, associated with the monitoring of stress levels, through a proposed algorithm that correlates heart rate, respiratory rate, and galvanic skin response. Experimental validation tests were carried out, as well as experiments with patients suffering from diabetes. To this end, measurements were made not only of stress-related parameters, but also of parameters such as blood glucose levels and blood pressure levels, seeking to extract correlations between stress and diabetes status. In addition, body temperature was another parameter acquired, in order to assess its importance and relation to stress and diabetes. The proposed multichannel system also features RFID technology for authentication purposes, as well as Wi-Fi access for internet connection and storage of the acquired data in a database structured for that purpose, thus enabling remote access. To allow the assessment of stress levels and diabetes progress, a mobile application was also developed, which also allows the visualisation of the analysed data

Ultra high frequency (UHF) radio-frequency identification (RFID) for robot perception and mobile manipulation

Personal robots with autonomy, mobility, and manipulation capabilities have the potential to dramatically improve quality of life for various user populations, such as older adults and individuals with motor impairments. Unfortunately, unstructured environments present many challenges that hinder robot deployment in ordinary homes. This thesis seeks to address some of these challenges through a new robotic sensing modality that leverages a small amount of environmental augmentation in the form of Ultra High Frequency (UHF) Radio-Frequency Identification (RFID) tags. Previous research has demonstrated the utility of infrastructure tags (affixed to walls) for robot localization; in this thesis, we specifically focus on tagging objects. Owing to their low-cost and passive (battery-free) operation, users can apply UHF RFID tags to hundreds of objects throughout their homes. The tags provide two valuable properties for robots: a unique identifier and receive signal strength indicator (RSSI, the strength of a tag's response). This thesis explores robot behaviors and radio frequency perception techniques using robot-mounted UHF RFID readers that enable a robot to efficiently discover, locate, and interact with UHF RFID tags applied to objects and people of interest. The behaviors and algorithms explicitly rely on the robot's mobility and manipulation capabilities to provide multiple opportunistic views of the complex electromagnetic landscape inside a home environment. The electromagnetic properties of RFID tags change when applied to common household objects. Objects can have varied material properties, can be placed in diverse orientations, and be relocated to completely new environments. We present a new class of optimization-based techniques for RFID sensing that are robust to the variation in tag performance caused by these complexities. We discuss a hybrid global-local search algorithm where a robot employing long-range directional antennas searches for tagged objects by maximizing expected RSSI measurements; that is, the robot attempts to position itself (1) near a desired tagged object and (2) oriented towards it. The robot first performs a sparse, global RFID search to locate a pose in the neighborhood of the tagged object, followed by a series of local search behaviors (bearing estimation and RFID servoing) to refine the robot's state within the local basin of attraction. We report on RFID search experiments performed in Georgia Tech's Aware Home (a real home). Our optimization-based approach yields superior performance compared to state of the art tag localization algorithms, does not require RF sensor models, is easy to implement, and generalizes to other short-range RFID sensor systems embedded in a robot's end effector. We demonstrate proof of concept applications, such as medication delivery and multi-sensor fusion, using these techniques. Through our experimental results, we show that UHF RFID is a complementary sensing modality that can assist robots in unstructured human environments.PhDCommittee Chair: Kemp, Charles C.; Committee Member: Abowd, Gregory; Committee Member: Howard, Ayanna; Committee Member: Ingram, Mary Ann; Committee Member: Reynolds, Matt; Committee Member: Tentzeris, Emmanoui