E. Diaz

Bio: E. Diaz is an academic researcher from University of Alcalá. The author has contributed to research in topics: MPSoC & Synchronization (computer science). The author has an hindex of 4, co-authored 11 publications receiving 49 citations.

Ultrasonic indoor positioning for smart environments: A mobile application

Abstract: In this paper we present a mobile application and solution for accurate smart indoor positioning. Smart society applications do normally require user location, specifically, in indoor environments high accuracy can enrich augmented or virtual reality, gaming, in-building guidance or support for ambient assisted living. We use encoded ultrasonic signals and TDMA protocol to obtain fine-grained distance measurements. Signals are emitted from a set of low cost ultrasonic local positioning systems, operating around 41kHz. An acquisition module, based on a MEMs microphone and a microcontroller, digitizes at 100kHz the incoming signals and send them over an USB protocol to the mobile device for their processing. We have implemented an Android Application that computes the Time Difference of Arrival (TDOA) to estimate the current position and display it in the mobile screen. Several users can compute their positions autonomously and user privacy is protected. The application can be configured for different encoding techniques and modulation schemes according to the environment requirements. Absolute error less than 5 cm is achieved in a 5×6m complex environment in 85% of the cases for an average position refresh period of 200ms.

Android application for indoor positioning of mobile devices using ultrasonic signals

Abstract: In this paper we present an Android application, called LOCATE-US, that allows accurate indoor positioning of mobile devices by processing the ultrasonic signals coming from a local positioning system (LPS). The LPS operates around 41KHz, and an external hardware based on an ultrasonic microphone digitizes the incoming signals and send them to the mobile device, where they are processed by means of the proposed application. This allows the use of CDMA techniques and overcomes most of the problems to be faced at the 20–22kHz range available in current smartphones, which include audible artifacts and poor resolution. The proposed software allows each mobile device in the environment to compute its own position by means of hyperbolic trilateration and represents the trajectory in the device screen, for future Location Based Services applications. Experimental results show that the proposed application achieves centimeter accuracy and can execute all the data processing for the position estimation in a time less than 0.5s.

Study of cooperative position estimations of mobile robots

Abstract: This work presents a study to determine the improvement of the simultaneous position estimations of several Mobile Robots (MRs) adding the relative distances between them, regarding independent position estimations for each MR. To evaluate the performance of the proposal, it is supposed that the localization scenario is composed of an Ultrasonic Local Positioning System (ULPS) and two MRs with odometry information that describe different trajectories. The independent position estimations of the MRs are obtained using an Extended Kalman Filter (EKF) for each MR that fuses the ULPS data and the odometry information. To evaluate the improvement in the estimation of the MR positions when the information of the relative distance between them is available, another EKF that estimates at the same time the positions of all MRs is implemented. The proposal has been validated by simulations, demonstrating that the simultaneous position estimations of both MRs using one EKF with the relative distance information presents an improvement in comparison with the independent estimations. Furthermore, the value of this improvement has been quantified considering the quality of the distance measurements between the MRs, supposing different noise levels based on a Gaussian distribution. Finally, a complexity evaluation of the independent and cooperative estimations is shown in terms of number of operations (products and additions) for each iteration of the EKF implementations.

Sensory system for the sleep disorders detection in the geriatric population

Abstract: This paper introduces the proposal of a remote sensory system for the detection of sleep disorders in geriatric outpatients. Although the most accurate solution would be an in-depth study in a sleep clinic, it is not a realistic environment for the elderly. The objective is that the patient stays at home, and without changing their daily routines, the clinicians get objective information in order to make a correct diagnosis of the sleep disorders. As a first step towards achieving a home remote monitory system, this work introduces a Body Sensor Network (BSN) to monitor various vital signals as Electrocardiogram (ECG) and Electromyogram (EMG) in order to collect enough information for sleep disorder diagnosis, focusing on the detection of obstructive sleep apnea. This work proposes an algorithm to infer obstructive sleep apnea (OSA) based on power spectral analysis of ECG signals from a single-lead electrocardiogram, demonstrating the feasibility of BSN to detect OSA with around 85% sensitivity.

A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective

Abstract: In combination with current sociological trends, the maturing development of Internet of Things devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data, that is, orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, Healthcare Internet of Things data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this article, we survey the existing and emerging technologies that can enable this vision for the future of healthcare, particularly, in the clinical practice of healthcare. Three main technology areas underlie the development of this field: 1) sensing, where there is an increased drive for miniaturization and power efficiency; 2) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure; and 3) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout this article, we use a case study to concretely illustrate the impact of these trends. We conclude this article with a discussion of the emerging directions, open issues, and challenges.

Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging

Abstract: Visual inspection has traditionally been used for structural health monitoring. However, assessments conducted by trained inspectors or using contact sensors on structures for monitoring a...

A Survey on Indoor Positioning Systems for IoT-Based Applications

Abstract: The Internet of Things (IoT), as a pervasive paradigm, is becoming an integral part of the tech industry and academic research in recent years. It forms a ubiquitous heterogeneous network connecting humans and things. The basic premise is acquiring data from the environment with sensors and remote intelligent management via actuators. For IoT service providers, time and place are functional parameters. Whereas most IoT scenarios are in indoor spaces and GPS cannot fully cover them, applying an indoor positioning system (IPS) is necessary. Besides, indoor enabling technologies can leverage the capability of IoT in context-aware services. In this article, we aim to provide a panoramic view of IPSs and localization services with the centrality of IoT. First, we explain the main concepts and review the latest positioning methods, techniques, and technologies with IoT remarks. Then, we discuss technical implementation challenges and open issues with feasible solutions. Finally, we mentioned location-based services (LBSs), real IoT applications, and active vendors in the realm of positioning services. This article provides a real insight into LBSs in IoT for future research.

3D Indoor Positioning of UAVs with Spread Spectrum Ultrasound and Time-of-Flight Cameras.

Abstract: This work proposes the use of a hybrid acoustic and optical indoor positioning system for the accurate 3D positioning of Unmanned Aerial Vehicles (UAVs). The acoustic module of this system is based on a Time-Code Division Multiple Access (T-CDMA) scheme, where the sequential emission of five spread spectrum ultrasonic codes is performed to compute the horizontal vehicle position following a 2D multilateration procedure. The optical module is based on a Time-Of-Flight (TOF) camera that provides an initial estimation for the vehicle height. A recursive algorithm programmed on an external computer is then proposed to refine the estimated position. Experimental results show that the proposed system can increase the accuracy of a solely acoustic system by 70–80% in terms of positioning mean square error.

Acoustic Local Positioning With Encoded Emission Beacons

Abstract: Acoustic local positioning systems (ALPSs) are an interesting alternative for indoor positioning due to certain advantages over other approaches, including their relatively high accuracy, low cost, and room-level signal propagation. Centimeter-level or fine-grained indoor positioning can be an asset for robot navigation, guiding a person to, for instance, a particular piece in a museum or to a specific product in a shop, targeted advertising, or augmented reality. In airborne system applications, acoustic positioning can be based on using opportunistic signals or sounds produced by the person or object to be located (e.g., noise from appliances or the speech from a speaker) or from encoded emission beacons (or anchors) specifically designed for this purpose. This work presents a review of the different challenges that designers of systems based on encoded emission beacons must address in order to achieve suitable performance. At low-level processing, the waveform design (coding and modulation) and the processing of the received signal are key factors to address such drawbacks as multipath propagation, multiple-access interference, near–far effect, or Doppler shifting. With regards to high-level system design, the issues to be addressed are related to the distribution of beacons, ease of deployment, and calibration and positioning algorithms, including the possible fusion of information obtained from maps and onboard sensors. Apart from theoretical discussions, this work also includes the description of an ALPS that was implemented, installed in a large area and tested for mobile robot navigation. In addition to practical interest for real applications, airborne ALPSs can also be used as an excellent platform to test complex algorithms (taking advantage of the low sampling frequency required), which can be subsequently adapted for other positioning systems, such as underwater acoustic systems or ultrawideband radio-frequency (UWB RF) systems.