Seismological data acquisition and signal processing using wavelets

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed

3D multiple description coding for error resilience over wireless networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Mobile communications has gained a growing interest from both customers and service providers alike in the last 1-2 decades. Visual information is used in many application domains such as remote health care, video –on demand, broadcasting, video surveillance etc. In order to enhance the visual effects of digital video content, the depth perception needs to be provided with the actual visual content. 3D video has earned a significant interest from the research community in recent years, due to the tremendous impact it leaves on viewers and its enhancement of the user’s quality of experience (QoE). In the near future, 3D video is likely to be used in most video applications, as it offers a greater sense of immersion and perceptual experience. When 3D video is compressed and transmitted over error prone channels, the associated packet loss leads to visual quality degradation. When a picture is lost or corrupted so severely that the concealment result is not acceptable, the receiver typically pauses video playback and waits for the next INTRA picture to resume decoding. Error propagation caused by employing predictive coding may degrade the video quality severely. There are several ways used to mitigate the effects of such transmission errors. One widely used technique in International Video Coding Standards is error resilience. The motivation behind this research work is that, existing schemes for 2D colour video compression such as MPEG, JPEG and H.263 cannot be applied to 3D video content. 3D video signals contain depth as well as colour information and are bandwidth demanding, as they require the transmission of multiple high-bandwidth 3D video streams. On the other hand, the capacity of wireless channels is limited and wireless links are prone to various types of errors caused by noise, interference, fading, handoff, error burst and network congestion. Given the maximum bit rate budget to represent the 3D scene, optimal bit-rate allocation between texture and depth information rendering distortion/losses should be minimised. To mitigate the effect of these errors on the perceptual 3D video quality, error resilience video coding needs to be investigated further to offer better quality of experience (QoE) to end users. This research work aims at enhancing the error resilience capability of compressed 3D video, when transmitted over mobile channels, using Multiple Description Coding (MDC) in order to improve better user’s quality of experience (QoE). Furthermore, this thesis examines the sensitivity of the human visual system (HVS) when employed to view 3D video scenes. The approach used in this study is to use subjective testing in order to rate people’s perception of 3D video under error free and error prone conditions through the use of a carefully designed bespoke questionnaire.Petroleum Technology Development Fund (PTDF

Development of a Heart Rate Variability Measurement System using Embedded Electronics

Recent advances in embedded electronics have a remarkable influence on the health care system. One of the most important applications is to monitor the health care of the patients at anytime and anyplace. In the last two decades, many researchers have focused mainly on heart rate variability (HRV) measurements. Patient\u27s heart rate variability should be continuously monitored to help them in case of emergency. Under these circumstances, patients are required to have a HRV measuring kit for a constant observation. The proposed project focuses on the development of a heart rate variability measurement system with the use of embedded electronics. This project consists of two systems: transmitter and a receiver side system. The transmitter section composed of sensor, amplifier, processing unit, and display unit, and transmitter module. The sensors, which are pasted on the body, are used to sense the electrical activity of the heart. These electric signals are given to an amplification unit. This amplification unit is designed with IC ADS1293 to amplify and filter the signals, and also reduce the noise. The output of the amplifier is given to the processing unit. Here, the microcontroller is programmed to process the input signal, and calculate the heart rate. The output of the microcontroller is transmitted to the display unit. The display unit shows the current value of the heart rate. The continuous measurement of heart rate variability is done in the transmitter side system. In case of abnormalities, a GSM module is used to transmit the heart rate alert, which has been processed by the control unit, to the user\u27s mobile phone and GSM receiver modem. In the receiver system, GSM receiver modem receives the data and processed with Visual Basic program to display, and, in the mobile phone, data is received and displayed as a text message. This kind of health monitoring system can offer flexibility and cost saving options to both health care professionals and patients

JTIT

kwartalni

Secure Control and Operation of Energy Cyber-Physical Systems Through Intelligent Agents

The operation of the smart grid is expected to be heavily reliant on microprocessor-based control. Thus, there is a strong need for interoperability standards to address the heterogeneous nature of the data in the smart grid. In this research, we analyzed in detail the security threats of the Generic Object Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV) protocol mappings of the IEC 61850 data modeling standard, which is the most widely industry-accepted standard for power system automation and control. We found that there is a strong need for security solutions that are capable of defending the grid against cyber-attacks, minimizing the damage in case a cyber-incident occurs, and restoring services within minimal time. To address these risks, we focused on correlating cyber security algorithms with physical characteristics of the power system by developing intelligent agents that use this knowledge as an important second line of defense in detecting malicious activity. This will complement the cyber security methods, including encryption and authentication. Firstly, we developed a physical-model-checking algorithm, which uses artificial neural networks to identify switching-related attacks on power systems based on load flow characteristics. Secondly, the feasibility of using neural network forecasters to detect spoofed sampled values was investigated. We showed that although such forecasters have high spoofed-data-detection accuracy, they are prone to the accumulation of forecasting error. In this research, we proposed an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed algorithms was experimentally verified on the Smart Grid testbed at FIU. The test results showed that the proposed techniques have a minimal detection latency, in the range of microseconds. Also, in this research we developed a network-in-the-loop co-simulation platform that seamlessly integrates the components of the smart grid together, especially since they are governed by different regulations and owned by different entities. Power system simulation software, microcontrollers, and a real communication infrastructure were combined together to provide a cohesive smart grid platform. A data-centric communication scheme was selected to provide an interoperability layer between multi-vendor devices, software packages, and to bridge different protocols together

Performance analysis and application development of hybrid WiMAX-WiFi IP video surveillance systems

Traditional Closed Circuit Television (CCTV) analogue cameras installed in buildings and other areas of security interest necessitates the use of cable lines. However, analogue systems are limited by distance; and storing analogue data requires huge space or bandwidth. Wired systems are also prone to vandalism, they cannot be installed in a hostile terrain and in heritage sites, where cabling would distort original design. Currently, there is a paradigm shift towards wireless solutions (WiMAX, Wi-Fi, 3G, 4G) to complement and in some cases replace the wired system. A wireless solution of the Fourth-Generation Surveillance System (4GSS) has been proposed in this thesis. It is a hybrid WiMAX-WiFi video surveillance system. The performance analysis of the hybrid WiMAX-WiFi is compared with the conventional WiMAX surveillance models. The video surveillance models and the algorithm that exploit the advantages of both WiMAX and Wi-Fi for scenarios of fixed and mobile wireless cameras have been proposed, simulated and compared with the mathematical/analytical models. The hybrid WiMAX-WiFi video surveillance model has been extended to include a Wireless Mesh configuration on the Wi-Fi part, to improve the scalability and reliability. A performance analysis for hybrid WiMAX-WiFi system with an appropriate Mobility model has been considered for the case of mobile cameras. A security software application for mobile smartphones that sends surveillance images to either local or remote servers has been developed. The developed software has been tested, evaluated and deployed in low bandwidth Wi-Fi wireless network environments. WiMAX is a wireless metropolitan access network technology that provides broadband services to the connected customers. Major modules and units of WiMAX include the Customer Provided Equipment (CPE), the Access Service Network (ASN) which consist one or more Base Stations (BS) and the Connectivity Service Network (CSN). Various interfaces exist between each unit and module. WiMAX is based on the IEEE 802.16 family of standards. Wi-Fi, on the other hand, is a wireless access network operating in the local area network; and it is based on the IEEE 802.11 standards

Seismological data acquisition and signal processing using wavelets

This work deals with two main fields: a) The design, built, installation, test, evaluation, deployment and maintenance of Seismological Network of Crete (SNC) of the Laboratory of Geophysics and Seismology (LGS) at Technological Educational Institute (TEI) at Chania. b) The use of Wavelet Transform (WT) in several applications during the operation of the aforementioned network. SNC began its operation in 2003. It is designed and built in order to provide denser network coverage, real time data transmission to CRC, real time telemetry, use of wired ADSL lines and dedicated private satellite links, real time data processing and estimation of source parameters as well as rapid dissemination of results. All the above are implemented using commercial hardware and software which is modified and where is necessary, author designs and deploy additional software modules. Up to now (July 2008) SNC has recorded 5500 identified events (around 970 more than those reported by national bulletin the same period) and its seismic catalogue is complete for magnitudes over 3.2, instead national catalogue which was complete for magnitudes over 3.7 before the operation of SNC. During its operation, several applications at SNC used WT as a signal processing tool. These applications benefited from the adaptation of WT to non-stationary signals such as the seismic signals. These applications are: HVSR method. WT used to reveal undetectable non-stationarities in order to eliminate errors in site’s fundamental frequency estimation. Denoising. Several wavelet denoising schemes compared with the widely used in seismology band-pass filtering in order to prove the superiority of wavelet denoising and to choose the most appropriate scheme for different signal to noise ratios of seismograms. EEWS. WT used for producing magnitude prediction equations and epicentral estimations from the first 5 secs of P wave arrival. As an alternative analysis tool for detection of significant indicators in temporal patterns of seismicity. Multiresolution wavelet analysis of seismicity used to estimate (in a several years time period) the time where the maximum emitted earthquake energy was observed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Building appliances energy performance assessment

Trabalho de Projeto de Mestrado, Informática, 2021, Universidade de Lisboa, Faculdade de CiênciasO consumo de energia tem vindo a crescer na União Europeia todos os anos, sendo de prever que, a curto prazo, se torne insustentável. No sentido de prevenir este cenário, a Comissão Europeia decidiu definir uma Estratégia Energética para a União Europeia, destacando dois objetivos: aumentar a eficiência energética e promover a descarbonização. Atualmente, cerca de 72% dos edifícios existentes na União Europeia não são energeticamente eficientes. Este problema motivou-nos à pesquisa e criação de soluções que permitam uma melhor avaliação do consumo energético por dispositivos elétricos em edifícios residenciais. Neste contexto, o trabalho desenvolvido nesta tese consiste no desenho de uma solução de monitorização remota que recolhe informações de consumo energético recorrendo a técnicas de intrusive load monitoring, onde cada dispositivo elétrico individual é continuamente monitorizado quanto ao seu consumo energético. Esta abordagem permite compreender o consumo de energia, em tempo real e no dia-a-dia. Este conhecimento oferece-nos a capacidade de avaliar as diferenças existentes entre as medições laboratoriais (abordagem utilizada no sistema de rotulagem de equipamentos elétricos de acordo com a sua eficiência energética) e os consumos domésticos estimados. Para tal, nesta tese exploram-se abordagens de machine learning que pretendem descrever padrões de consumo, bem como reconhecer marcas, modelos e que funções os dispositivos elétricos estarão a executar. O principal objetivo deste trabalho é desenhar e implementar um protótipo de uma solução de IoT flexível e de baixo custo para avaliar equipamentos elétricos. Será utilizado um conjunto de sensores que recolherá dados relacionados com o consumo de energia e os entrega à plataforma SATO para serem posteriormente processados. O sistema será usado para monitorar aparelhos comumente encontrados em residências. Além disso, o sistema terá a capacidade de monitorizar o consumo de água de aparelhos que necessitem de abastecimento de água, como máquinas de lavar e de lavar louça. Os dados recolhidos serão usados para classificação dos aparelhos e modos de operação dos mesmos, em tempo real, permitindo fornecer relatórios sobre o consumo energético e modo de uso dos aparelhos, com grande grau de detalhe. Os relatórios podem incluir o uso de energia por vários ciclos de operação. Por exemplo, um aparelho pode executar vários ciclos de operação, como uma máquina de lavar que consume diferentes quantidades de energia elétrica e água consoante o modo de operação escolhido pelo utilizador. Toda a informação recolhida pode ser posteriormente utilizada em novos serviços de recomendação que ajudaram os utilizadores a definir melhor as configurações adequadas a um determinado dispositivo, minimizando o consumo energético e melhorando a sua eficiência. Adicionalmente toda esta informação pode ser utilizada para o diagnóstico de avarias e/ou manutenção preventiva. Em termos de proposta, o trabalho desenvolvido nesta tese tem as seguintes contribuições: Sistema de monitorização remota: o sistema de monitorização desenhado e implementado nesta tese avança o estado da arte dos sistemas de monitorização propostos pela literatura devido ao facto de incluir uma lista aprimorada de sensores que podem fornecer mais informações sobre os aparelhos, como o consumo de água da máquina de lavar. Além disso, é altamente flexível e pode ser implementado sem esforço em dispositivos novos ou antigos para monitorização de consumo de recursos. Conjunto de dados de consumo de energia de eletrodomésticos: Os dados recolhidos podem ser usados para futura investigação científica sobre o consumo de consumo de energia, padrões de uso de energia pelos eletrodomésticos e classificação dos mesmos. Abordagem de computação na borda (Edge Computing): O sistema de monitorização proposto explora o paradigma de computação na borda, onde parte da computação de preparação de dados é executada na borda, libertando recursos da nuvem para cálculos essenciais e que necessitem de mais poder computacional. Classificação precisa de dispositivos em tempo real: Coma proposta desenhada nesta tese, podemos classificar os dispositivos com alta precisão, usando os dados recolhidos pelo sistema de monitorização desenvolvido na tese. A abordagem proposta consegue classificar os dispositivos, que são monitorizados, com baixas taxas de falsos positivos. Para fácil compreensão do trabalho desenvolvido nesta tese, de seguida descreve-se a organização do documento. O Capítulo 1 apresenta o problema do consumo de energia na União Europeia e discute o aumento do consumo da mesma. O capítulo apresenta também os principais objetivos e contribuições do trabalho. No Capítulo 2 revê-se o trabalho relacionado em termos de sistema de monitorização remota, que inclui sensores, microcontroladores, processamento e filtragem de sinal. Por fim, este capítulo revê os trabalhos existentes na literatura relacionados com o problema de classificação de dispositivos usando abordagens de machine learning. No Capítulo 3 discutem-se os requisitos do sistema e o projeto de arquitetura conceitual do sistema. Neste capítulo é proposta uma solução de hardware, bem como, o software e firmware necessários à sua operação. Os algoritmos de machine learning necessários à classificação são também discutidos, em termos de configurações necessárias e adequadas ao problema que queremos resolver nesta tese. O Capítulo 4 representa a implementação de um protótipo que servirá de prova de conceito dos mecanismos discutidos no Capítulo 3. Neste capítulo discute-se também a forma de integração do protótipo na plataforma SATO. Com base na implementação feita, no Capítulo 5 especificam-se um conjunto de testes funcionais que permitem avaliar o desempenho da solução proposta e discutem-se os resultados obtidos a partir desses testes. Por fim, o Capítulo 6 apresenta as conclusões e o trabalho futuro que poderá ser desenvolvido partindo da solução atual.Energy consumption is daily growing in European Union (EU). One day it will become hardly sustainable. For this not to happen European Commission decided to implement a European Union Strategy, emphasizing two objectives: increasing energy efficiency and decarbonization. About 72% of all buildings in the EU are not adapted to be energy efficient. This problem encourages us to create solutions that would help assess the energy consumption of appliances at residential houses. In this thesis, we proposed a system that collects data using an intrusive load monitoring approach, where each appliance will have a dedicated monitoring rig to collect the energy consumption data. The proposed solution will help us understand the real-life consumption of each device being monitored and compare the laboratory measurements observed versus domestic consumption estimated by the energy consumption based on the EU energy efficiency labelling system. The system proposed detects device consumption patterns and recognize its brand, model and what actions that appliance is executing, e.g., program of washing in a washing machine. To achieve our goal, we designed a hardware solution capable of collecting sensor data, filtering and send it to a cloud platform (the SATO platform). Additionally, in the cloud, we have a Machine Learning solution that deals with the data and recognizes the appliance and its operation modes. This recognition allows drawing a device/settings profile, which can detect faults and create a recommendation service that helps users define the better settings for a specific appliance, minimizing energy consumption and improving efficiency. Finally, we examine our prototype approach of the system implemented for targeted objectives in this project report. The document describes the experiments that we did and the final results. Our results show that we can identify the appliance and some of its operation modes. The proposed approach must be improved to make the identification of all operation modes. However, the current version of the system shows exciting results. It can be used to support the design of a new labelling system where daily operation measures can be used to support the new classification system. This way, we have an approach that allows improving the energy consumption, making builds more efficient

Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams

Video streaming traffic has been surging in the last few years, which has resulted in an increase of its Internet traffic share on a daily basis. The importance of video streaming management has been emphasized with the advent of High Definition: HD) video streaming, as it requires by its nature more network resources. In this dissertation, we provide a better support for managing HD video traffic over both wireless and wired networks through several contributions. We present a simple, general and accurate video source model: Simplified Seasonal ARIMA Model: SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded with MPEG-4 Part2, MPEG-4 Part10, and Scalable Video Codec: SVC) standards, using various encoding settings. We also provide a large and publicly-available collection of HD video traces along with their analyses results. These analyses include a full statistical analysis of HD videos, in addition to modeling, factor and cluster analyses. These results show that by using SAM, we can achieve up to 50% improvement in video traffic prediction accuracy. In addition, we developed several video tools, including an HD video traffic generator based on our model. Finally, to improve HD video streaming resource management, we present a SAM-based delay-guaranteed dynamic resource allocation: DRA) scheme that can provide up to 32.4% improvement in bandwidth utilization