VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals

A major trust of modal parameters identification (MPI) research in recent years has been based on using artificial and natural vibrations sources because vibration measurements can reflect the true dynamic behavior of a structure while analytical prediction methods, such as finite element models, are less accurate due to the numerous structural idealizations and uncertainties involved in the simulations. This paper presents a state-of-the-art review of the time-frequency techniques for modal parameters identification of civil structures from acquired dynamic signals as well as the factors that affect the estimation accuracy. Further, the latest signal processing techniques proposed since 2012 are also reviewed. These algorithms are worth being researched for MPI of large real-life structures because they provide good time-frequency resolution and noise-immunity

Structural health monitoring of bridges using wireless sensor networks

Structural Health Monitoring, damage detection and localization of bridges using Wireless Sensor Networks (WSN) are studied in this thesis. The continuous monitoring of bridges to detect damage is a very useful tools for preventing unnecessary costly and emergent maintenance. The optimal design aims to maximize the lifetime of the system, the accuracy of the sensed data, and the system reliability, and to minimize the system cost and complexity Finite Element Analysis (FEA) is carried out using LUSAS Bridge Plus software to determine sensor locations and measurement types and effectively minimize the number of sensors, data for transmission, and volume of data for processing. In order to verify the computer simulation outputs and evaluate the proposed optimal design and algorithms, a WSN system mounted on a simple reinforced concrete frame model is employed in the lab. A series of tests are carried out on the reinforced concrete frame mounted on the shaking table in order to simulate the existing extreme loading condition. Experimental methods which are based on modal analysis under ambient vibrational excitation are often employed to detect structural damages of mechanical systems, many of such frequency domain methods as first step use a Fast Fourier Transform estimate of the Power Spectral Density (PSD) associated with the response of the system. In this study it is also shown that higher order statistical estimators such as Spectral Kurtosis (SK) and Sample to Model Ratio (SMR) may be successfully employed to more reliably discriminate the response of the system against the ambient noise and better identify and separate contributions from closely spaced individual modes. Subsequently, the identified modal parameters are used for damage detection and Structural Health Monitoring. To evaluate the preliminary results of the project\u27s prototype and quantify the current bridge response as well as demonstrate the ability of the SHM system to successfully perform on a bridge, the deployment of Wireless Sensor Networks in an existing highway bridge in Qatar is implemented. The proposed technique will eventually be applied to the new stadium that State of Qatar will build in preparation for the 2022 World Cup. This monitoring system will help permanently record the vibration levels reached in all substructures during each event to evaluate the actual health state of the stadiums. This offers the opportunity to detect potentially dangerous situations before they become critical

Workshop on Smart Sensors - Instrumentation and Measurement: Program

On 18-19 February, the School of Engineering successfully ran a two-day workshop on Smart Sensors - Instrumentation and Measurement. Associate Professor Rainer Künnemeyer organised the event on behalf of the IEEE Instrumentation and Measurement Society, New Zealand Chapter. Over 60 delegates attended and appreciated the 34 presentations which covered a wide range of topics related to sensors, sensor networks and instrumentation. There was substantial interest and support from local industry and crown research institutes

NASA/ASEE Summer Faculty Fellowship Program, 1990, Volume 1

The 1990 Johnson Space Center (JSC) NASA/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston-University Park and JSC. A compilation of the final reports on the research projects are presented. The topics covered include: the Space Station; the Space Shuttle; exobiology; cell biology; culture techniques; control systems design; laser induced fluorescence; spacecraft reliability analysis; reduced gravity; biotechnology; microgravity applications; regenerative life support systems; imaging techniques; cardiovascular system; physiological effects; extravehicular mobility units; mathematical models; bioreactors; computerized simulation; microgravity simulation; and dynamic structural analysis

Continuity of object tracking

2022 Spring.Includes bibliographical references.The demand for object tracking (OT) applications has been increasing for the past few decades in many areas of interest: security, surveillance, intelligence gathering, and reconnaissance. Lately, newly-defined requirements for unmanned vehicles have enhanced the interest in OT. Advancements in machine learning, data analytics, and deep learning have facilitated the recognition and tracking of objects of interest; however, continuous tracking is currently a problem of interest to many research projects. This dissertation presents a system implementing a means to continuously track an object and predict its trajectory based on its previous pathway, even when the object is partially or fully concealed for a period of time. The system is divided into two phases: The first phase exploits a single fixed camera system and the second phase is composed of a mesh of multiple fixed cameras. The first phase system is composed of six main subsystems: Image Processing, Detection Algorithm, Image Subtractor, Image Tracking, Tracking Predictor, and the Feedback Analyzer. The second phase of the system adds two main subsystems: Coordination Manager and Camera Controller Manager. Combined, these systems allow for reasonable object continuity in the face of object concealment

Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior