The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Theory of Equations. By J. V. Uspensky. Pp. vii, 353. 27s. 1948. (McGraw-Hill)

Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges

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...

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

Technology has become ubiquitous, it is all around us and is becoming part of us. Togetherwith the rise of the Internet of Things (IoT) paradigm and enabling technologies (e.g., Augmented Reality (AR), Cyber-Physical Systems, Artificial Intelligence (AI), blockchain or edge computing), smart wearables and IoT-based garments can potentially have a lot of influence by harmonizing functionality and the delight created by fashion. Thus, smart clothes look for a balance among fashion, engineering, interaction, user experience, cybersecurity, design and science to reinvent technologies that can anticipate needs and desires. Nowadays, the rapid convergence of textile and electronics is enabling the seamless and massive integration of sensors into textiles and the development of conductive yarn. The potential of smart fabrics, which can communicate with smartphones to process biometric information such as heart rate, temperature, breathing, stress, movement, acceleration, or even hormone levels, promises a new era for retail. This article reviews the main requirements for developing smart IoT-enabled garments and shows smart clothing potential impact on business models in the medium-term. Specifically, a global IoT architecture is proposed, the main types and components of smart IoT wearables and garments are presented, their main requirements are analyzed and some of the most recent smart clothing applications are studied. In this way, this article reviews the past and present of smart garments in order to provide guidelines for the future developers of a network where garments will be connected like other IoT objects: the Internet of Smart Clothing.

https://www.mdpi.com/2079-9292/7/12/405/pdf

Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles

This work proposes the use of an indoor acoustic positioning system for the accurate positioning of unmanned aerial vehicles. This system is based on a Time-Code Division Multiple Access scheme, where the sequential emission of five BPSK modulated Kasami codes is performed to estimate the vehicle position through a multilateration procedure. The effect of the vehicle rotors on the detection of these broadband signals is experimentally analyzed for different relative powers. Preliminary results confirm the feasibility of the proposed system to provide accurate position estimates even under very low signal-to-noise ratios.

High accuracy APS for unmanned aerial vehicles

Underwater Wireless Sensor Network applications require the design of efficient communications links due to the rough environment. Acoustic signals make feasible the data transmission based on Direct Sequence Code Division Multiple Access (DS-CDMA) techniques, because compensates the multipath effect and allow receivers to simultaneously distinguish among multiple independent emitters. For that purpose, cross-correlation techniques based on matched filtering are often used, by detecting at what e instant arrives maximum correlation value is provided. Inter-Symbol Interference (ISI) caused by the Auto-Correlation (AC) function of the used sequences, and the Cross-Correlation (CC) between them, is not null around maximum matching value. This implies that it is sometimes difficult to detect the accurate position of correlation maxima. In order to improve the detection process without applying common communications algorithms for interference compensation, that usually involve a high computational load, the use of Generalized Cross-Correlation (GCC) is here analyzed.

Code detection based on generalized Cross-Correlation in DS-CDMA underwater aplications

In this paper the estimation of a channel impulse response using complementary sets of sequences is considered. A direct (non-iterative) technique is presented and applied to an asynchronous system where the signals are recovered by matched filtering. The robustness of this technique is quantified in terms of the signal-to-noise ratio (SNR) obtained at the process output and the relative root mean square error (RRMSE) committed in the estimation of the channel coefficients

Uusing complementary sets of sequences for direct channel estimation in asynchronous systems

Population ageing is becoming a key social issue in recent decades, particularly in Western countries, where this fact, together with the increase of life expectancy, has posed a significant strain on public finances and health services. In this context, many technological developments are often proposed to promote and support the independent living of elderly at their own homes, thus avoiding or postponing possible entries into social residences. Among them, smart meters provide a nonintrusive way to monitor and estimate the tenants’ daily activities, by only using a single-point measurement in the mains at the entrance of the household. This work describes a regression approach to estimate the energy consumption of a house by means of a LSTM neural network. For that purpose, a pilot has been run on a house during six months in order to collect the electrical data, which will be used later to train the neural network. After that training, the network tries to estimate the energy consumption every 15 minutes, so any deviation between the predicted sample and the measured one might be used to detect anomalies in the daily routine of the tenant.

Estimating Energy Consumption in Households for Non-Intrusive Elderly Monitoring

The use of Ultra -WideBand (UWB) techniques is considered an attractive solution for indoor Local Positioning Systems (LPS). In this case, the positioning is usually performed by hyperbolic trilateration techniques measuring the time difference of arrival (TDOA) between the received signals coming from the beacons placed in the environment. The detection of the true arrival instant for the used encoded signals is important in the accuracy of the system. The most widely used technique consist of performing the correlation between the received signal and the sequence to be detected. For that, matched filters are used and then, the instant at which appears the maximum of the correlation function is detected. In this paper the use of Generalized Cross Correlation (GCC) is presented. This allows the components around the carrier frequency to be enhanced, improving the detection of the maximum correlation peak and hence the accuracy of measurement of difference distances.

https://ieeexplore.ieee.org/iel7/6857932/6868455/06868564.pdf

J.M. Villadangos

Papers

High accuracy APS for unmanned aerial vehicles

Code detection based on generalized Cross-Correlation in DS-CDMA underwater aplications

Uusing complementary sets of sequences for direct channel estimation in asynchronous systems

Estimating Energy Consumption in Households for Non-Intrusive Elderly Monitoring

Detección de señales codificadas con correlación generalizada cruzada en UWB-LPS