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  • 1. Chilo, Josè
    et al.
    Jabor, Abbas
    KTH, School of Engineering Sciences (SCI), Physics.
    Liszka, L.
    Eide, Å.J.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Persson, L.
    Infrasonic and Seismic Signals from Earthquake and Explosions in Arequipa, Perú2006In: Western Pacific Geophysics Meeting. 24-27 July 2006, Beijing, China, 2006Conference paper (Refereed)
  • 2.
    Chilo, José
    KTH, School of Engineering Sciences (SCI), Physics.
    Feature Extraction for Low-Frequency Signal Classification2008Doctoral thesis, comprehensive summary (Other scientific)
    Abstract [en]

    The development of the International Monitoring System (IMS) for the verification of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) has led to a rapid revival of interest in infrasound. Furthermore, the installation of low-frequency sensors at seismic sites has increased in recent years, providing researchers with large and heterogeneous data-sets in near real-time. New techniques are needed to better process all of this data and to extract meaningful information quickly for various applications. In particular, there is a need to find distinct features in the infrasonic signals that allow one to distinguish low level nuclear tests from seismic events.

    In this thesis three methods for feature extraction from infrasound and other types of low frequency signal data are discussed: (1) discrete wavelets transforms (DWTs); (2) time scale spectra (TSSs) using continuous wavelet transforms (CWTs); and (3) empirical mode decomposition (EMD). The dimensionality of the feature space can range from a few to thousands. For processing highdimensional data we use multi-modal data space to find low-dimensional structures. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space.

    The overall goal of our research is a system for automatic identification and classification of lowfrequency signals in real-time that is easy to implement in hardware. In this thesis we present our design and implementation of the discrete wavelet transform (DWT) on FPGAs for processing a continuous flow of data to obtain optimal extraction of feature information. FPGA simulation and implementation has been realized by using the polyphase structure, the filter reuse method and techniques such as pipelining and basic operations on the QUARTUS II platform. VHDL has been used to describe the functionality of the discrete wavelet transform and ModelSim has been used for the functional verification.

    Advancements in electronics provide a vital new option for implementation of low-frequency smart sensors that can perform signal processing close to the sensors and transmit the data wirelessly. These smart sensors can improve the efficiency of an automatic classification system and reduce the cost of actual infrasound microphones. The design of a digital wireless data acquisition system using a QF4512 programmable signal converter from Quickfilter Technologies, a MSP430 microcontroller from Texas Instruments and a F2M03GLA Bluetooth module from Free2move for infrasonic records is also presented in this thesis. The digital wireless data acquisition system has passed extensive laboratory and field tests (e.g. with man-made explosions).

    A study of using a miniature 3-axis fluxgate magnetometer to get fingerprints from ferrous objects is also presented in this thesis. In this experiment, distinguishing features of iron samples of four different shapes were determined using wavelet methods. Systematic differences were observed between the signatures of the four shaped iron samples.

  • 3.
    Chilo, José
    KTH, School of Engineering Sciences (SCI), Physics.
    Filtering extracting features from infrasound data2006Licentiate thesis, comprehensive summary (Other scientific)
    Abstract [en]

    The goal of the research presented in this thesis is to extract features, to filter and get fingerprints from signals detected by infrasound, seismic and magnetic sensors. If this can be achieved in a real time system, then signals from various events can be detected and identified in an otherwise torrent data.

    Several approaches have been analyzed. Wavelet transform methods are used together with ampligram and time scale spectrum to analyze infrasound, seismic and magnetic data. The energy distribution in the frequency domain may be seen in wavelet scalograms. A scalogram displays the wavelet coefficients as a function of the time scale and of the elapsed time. The ampligram is a useful method of presentation of the physical properties of the time series. The ampligram demonstrate the amplitude and phase of components of the signal corresponding to different spectral densities. The ampligram may be considered as an analogy to signal decomposition into Fourier components. In that case different components correspond to different frequencies. In the present case different components correspond to different wavelet coefficient magnitudes, being equivalent to spectral densities. The time scale spectrum is a forward wavelet transform of each row (wavelet coefficient magnitude) in the ampligram. The time scale spectrum reveals individual signal components and indicates the statistical properties of each component: deterministic or stochastic.

    Next step is to distinguish between different sources of infrasound on-line. This will require signal classification after detection is made. The implementation of wavelet – neural network in hardware may be a first choice. In this work the Independent Component Analysis is presented to improve the quality of the infrasonic signals by removing background noise before the hardware classification. The implementation of the discrete wavelet transform in a Field Programmable Gate Array (FPGA) is also included in this thesis using Xilinx System Generator and Simulink software.

    A study of using infrasound recordings together with a miniature 3-axis fluxgate magnetometer to find meteorites as soon as possible after hitting the earth is also presented in this work.

  • 4.
    Chilo, José
    et al.
    University of Gävle.
    Jabor, Abbas
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    et al.,
    Filtering and extracting features from infrasound data2005In: 2005 14TH IEEE-NPSS Real Time Conference: Stockholm; 4 June 2005 through 10 June 2005, 2005, p. 451-455Conference paper (Refereed)
    Abstract [en]

    There are many reasons for using infrasound, i.e. low frequency sound, to monitor various events. Inherent features like its long-distance propagation and the use of simple, ground based equipment in very flexible system are some. The disadvantage is that it is a slow system due to the speed of sound. In this papr we try to show that there are several other advantages if one can extract all the features of the signal. In this way it is hoped that we can get a fingerprint of the event that caused the infrasound. Rayleigh waves and sound from epicentre may be obtained for earthquakes, pressure pulses and electro jets from aurora, core radius and funnel shape from tornados, etc. All these possibilities are suggestive for further R&D of the infrasound detection systems.

  • 5.
    Chilo, José
    et al.
    University of Gävle.
    Jabor, Abbas
    KTH, School of Engineering Sciences (SCI), Physics.
    Lizska, Ludwik
    Swedish Institute of Space Physics in Umeå.
    Eide, Åge J.
    Ostfold University College.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Obtaining "images" from iron objects using a 3-axis fluxgate magnetometer2007In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, ISSN 0168-9002, E-ISSN 1872-9576, Vol. 580, no 2, p. 1105-1109Article in journal (Refereed)
    Abstract [en]

    Magnetic objects can cause local variations in the Earth's magnetic field that can be measured with a magnetometer. Here we used triaxial magnetometer measurements and an analysis method employing wavelet techniques to determine the "signature" or "fingerprint" of different iron objects. Clear distinctions among the iron samples were observed. The time-dependent changes in the frequency powers were extracted by use of the Morlet wavelet corresponding to frequency bands from 0.1 to 100 Hz. (c) 2007 Elsevier B.V. All rights reserved.

  • 6.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Kinser, Jason M.
    George Mason University.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Discrimination of nuclear explosions sites by seismic signals using intrinsic mode functions and multi-modal data space2008In: 2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings; Boston, MA; 6 July 2008 through 11 July 2008, 2008, p. II895-II898Conference paper (Refereed)
    Abstract [en]

    Signal processing and feature extraction are investigated using the Empirical Mode Decomposition (EMD). It is believed that this approach is well suited for non-linear and non-stationary data. With EMD any complicated set of data can be decomposed into a finite, and usually small number, of functions called Intrinsic Mode Functions (IMFs). A new discriminating system is presented here that is capable of discriminating between different seismic signals from nuclear testing sites based on the IMFs and the multi-modal data space. The advantage of this space is that multiple metrics of similarity are converted into one single Euclidean space. This space is capable of extracting similarities among several signals through a combination of multiple metrics. This is a new way of associating data. After illustrating the technique with an investigation of an audio data example (piano), we examine the characteristics of seismic signals from nuclear testing (explosions). The results presented in this paper indicate that a relatively simple discriminating system can successfully cluster and classify seismic events.

  • 7.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    A low cost digital data acquisition system for infrasonic records2007In: IDAACS 2007: Proceedings Of The 4th IEEE Workshop On Intelligent Data Acquisition And Advanced Computing Systems: Technology And Applications, 2007, p. 35-37Conference paper (Refereed)
    Abstract [en]

    This paper describes a new digital data acquisition system that can be used to record signals from infrasound events. The system includes a QF4A512 programmable signal converter from Quickfilter Technologies and a MSP430 microcontroller from Texas Instruments. The signal output of the infrasound sensors is converted to digital via a 16-bits Analog to Digital Converter (ADC). To prevent errors in the conversion process, Anti-Aliasing Filters are employed prior to the ADC Digital filtering is performed after the ADC using a Digital Signal Processor, which is implemented on the QF4A512.

  • 8.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Data acquisition and digital filtering for infrasonic records on active volcanoes2007In: Sensors & Transducers Journal, ISSN 2306-8515, E-ISSN 1726-5479, ISSN 1726-5479, Vol. 77, no 3, p. 1058-1064Article in journal (Refereed)
    Abstract [en]

    This paper presents the design of a digital data acquisition system for volcanic infrasound records. The system includes four electret condenser element microphones, a QF4A512 programmable signal converter from Quickfilter Technologies and a MSP430 microcontroller from Texas Instruments. The signal output of every microphone is converted to digital via a 16-bit Analog to Digital Converter (ADC). To prevent errors in the conversion process, Anti-Aliasing Filters are employed prior to the ADC. Digital filtering is performed after the ADC using a Digital Signal Processor, which is implemented on the QF4A512. The four digital signals are summed to get only one signal. Data storing and digital wireless data transmission will be described in a future paper.

  • 9.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Hardware implementation of 1D wavelet transform on an FPGA for infrasound signal classification2008In: IEEE Transactions on Nuclear Science, ISSN 0018-9499, E-ISSN 1558-1578, Vol. 55, no 1, p. 9-13Article in journal (Refereed)
    Abstract [en]

    Infrasound is a low frequency acoustic phenomenon that typically ranges from 0.01 to 20 Hz. The data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, i.e., the Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important for real-time signal classification. Performing wavelet decomposition on the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of discrete wavelet transforms (DWT) for real-time infrasound data processing; our approach uses only two FIR filters, a high-pass and a low-pass filter. A compact implementation was realized with pipelining techniques and multiple use of generalized building blocks. The design was described in VHDL and the FPGA implementation and simulation were performed on the QUARTUS II platform.

  • 10.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Real-time signal processing of infrasound data using 1D wavelet transform on FPGA device2007In: 2007 15th IEEE-NPSS Real-Time Conference, Vols 1 And 2, 2007, p. 170-174Conference paper (Refereed)
    Abstract [en]

    Infrasound is a low frequency acoustic phenomenon typically in the frequency range 0.01 to 20 Hz. Data collected from infrasound microphones are presented online by the infrasound monitoring system operating in Northern Europe, Swedish-Finnish Infrasound Network (SFIN). Processing the continuous flow of data to extract optimal feature information is important. Using wavelet decomposition as a tool for removing noise from the real-time signals is an alternative. The purpose of this paper is to present the design and FPGA implementation of Discrete Wavelet Transform (DWT) for real-time infrasound data processing, in which only two FIR filters, a high-pass and a low-pass filter, are used. With the filter reuse method and techniques such as pipeline, basic operations, by the VHDL on the platform QUARTUS II, FPGA simulation and implementation are fulfilled. This implementation takes advantage from the low sampling rate used by the infrasound monitoring system that is only 18 Hz.

  • 11.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Olsson, Roland
    Ostfold University College, Faculty of Computer Science.
    Hansen, Stig-Erland
    Ostfold University College, Faculty of Computer Science.
    Comparison of three feature extraction techniques to distinguish between different infrasound signals2007In: Progress in Pattern Recognition / [ed] Singh S; Singh M, 2007, p. 75-82Conference paper (Refereed)
    Abstract [en]

    The main aim of this paper is to compare three feature extraction techniques, Discrete Wavelet Transform, Time Scale Spectrum using Continuous Wavelet Transforms, and Cepstral Coefficients and their derivatives, for the purposes of classifying time series type ;signal data. The features are classified by two types of neural networks. The paper draws a number of important conclusions on the suitability of these features for analysis, and provides a good comparative evaluation on four different data sets.

  • 12.
    Chilo, José
    et al.
    KTH, School of Engineering Sciences (SCI), Physics.
    Olsson, Roland
    Ostfold University College.
    Hansen, Stig-Erland
    Ostfold University College.
    Lindblad, Thomas
    KTH, School of Engineering Sciences (SCI), Physics.
    Classification of infrasound events with various machine learning techniques2007In: CITSA 2007 - Int. Conference on Cybernetics and Information Technologies, Systems and Applications and CCCT 2007 - Int. Conference on Computing, Communications and Control Technologies, Proceedings: Vol II / [ed] Savoie M; Aguilar J; Chu HW; Zinn CD; AroshaS SMN, International Institute of Informatics and Systemics, 2007, p. 191-195Conference paper (Refereed)
    Abstract [en]

    This paper presents classification results for infrasonic events using practically all well-known machine learning algorithms together with wavelet transforms for preprocessing. We show that there are great differences between different groups of classification algorithms and that nearest neighbor classifiers are superior to all others for accurate classification of infrasonic events.

1 - 12 of 12
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