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wavelet-based detector for underwater communication
Title
A wavelet-based detector for underwater communication.
Creator
Petljanski, Branko., Florida Atlantic University, Erdol, Nurgun, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The need for reliable underwater communication at Florida Atlantic University is critical in transmitting data to and from Autonomous Underwater Vehicles (AUV) and remote sensors. Since a received signal is corrupted with ambient ocean noise, the nature of such noise is investigated. Furthermore, we establish connection between ambient ocean noise and fractal noise. Since the matched filter is designed under the assumption that noise is white, performance degradation of the matched filter due...
Show moreThe need for reliable underwater communication at Florida Atlantic University is critical in transmitting data to and from Autonomous Underwater Vehicles (AUV) and remote sensors. Since a received signal is corrupted with ambient ocean noise, the nature of such noise is investigated. Furthermore, we establish connection between ambient ocean noise and fractal noise. Since the matched filter is designed under the assumption that noise is white, performance degradation of the matched filter due non-white noise is investigated. We show empirical results that the wavelet transform provides an approximate Karhunen-Loeve expansion for 1/f-type noise. Since whitening can improve only broadband signals, a new method for synchronization signal design in wavelet subspaces with increased energy-to-peak amplitude ratio is presented. The wavelet detector with whitening of fractal noise and detection in wavelet subspace is shown. Results show that the wavelet detector improves detectability, however this is below expectation due to differences between fractal noise and ambient ocean noise.
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Date Issued
2001
PURL
http://purl.flvc.org/fcla/dt/12778
Subject Headings
Wavelets (Mathematics), Underwater acoustics
Format
Document (PDF)
Subspace detection and scale evolutionary eigendecomposition
Title
Subspace detection and scale evolutionary eigendecomposition.
Creator
Kyperountas, Spyros C., Florida Atlantic University, Erdol, Nurgun, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
A measure of the potential of a receiver for detection is detectability. Detectability is a function of the signal and noise, and given any one of them the detectability is fixed. In addition, complete transforms of the signal and noise cannot change detectability. Throughout this work we show that "Subspace methods" as defined here can improve detectability in specific subspaces, resulting in improved Receiver Operating Curves (ROC) and thus better detection in arbitrary noise environments....
Show moreA measure of the potential of a receiver for detection is detectability. Detectability is a function of the signal and noise, and given any one of them the detectability is fixed. In addition, complete transforms of the signal and noise cannot change detectability. Throughout this work we show that "Subspace methods" as defined here can improve detectability in specific subspaces, resulting in improved Receiver Operating Curves (ROC) and thus better detection in arbitrary noise environments. Our method is tested and verified on various signals and noises, both simulated and real. The optimum detection of signals in noise requires the computation of noise eigenvalues and vectors (EVD). This process neither is a trivial one nor is it computationally cheap, especially for non-stationary noise and can result in numerical instabilities when the covariance matrix is large. This work addresses this problem and provides solutions that take advantage of the subspace structure through plane rotations to improve on existing algorithms for EVD by improving their convergence rate and reducing their computational expense for given thresholds.
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Date Issued
2001
PURL
http://purl.flvc.org/fcla/dt/11965
Subject Headings
Eigenvalues, Eigenvectors, Wavelets (Mathematics)
Format
Document (PDF)
Wavelet transform-based digital signal processing
Title
Wavelet transform-based digital signal processing.
Creator
Basbug, Filiz., Florida Atlantic University, Erdol, Nurgun, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
This study deals with applying the wavelet transform to mainly two different areas of signal processing: adaptive signal processing, and signal detection. It starts with background information on the theory of wavelets with an emphasis on the multiresolution representation of signals by the wavelet transform in Chapter 1. Chapter 2 begins with an overview of adaptive filtering in general and extends it to transform domain adaptive filtering. Later in the chapter, a novel adaptive filtering...
Show moreThis study deals with applying the wavelet transform to mainly two different areas of signal processing: adaptive signal processing, and signal detection. It starts with background information on the theory of wavelets with an emphasis on the multiresolution representation of signals by the wavelet transform in Chapter 1. Chapter 2 begins with an overview of adaptive filtering in general and extends it to transform domain adaptive filtering. Later in the chapter, a novel adaptive filtering architecture using the wavelet transform is introduced. The performance of this new structure is evaluated by using the LMS algorithm with variations in step size. As a result of this study, the wavelet transform based adaptive filter is shown to reduce the eigenvalue ratio, or condition number, of the input signal. As a result, the new structure is shown to have faster convergence, implying an improvement in the ability to track rapidly changing signals. Chapter 3 deals with signal detection with the help of the wavelet transform. One scheme studies signal detection by projecting the input signal onto different scales. The relationship between this approach and that of matched filtering is established. Then the effect of different factors on signal detection with the wavelet transform is examined. It is found that the method is robust in the presence of white noise. Also, the wavelets are analyzed as eigenfunctions of a certain random process, and how this gives way to optimal receiver design is shown. It is further demonstrated that the design of an optimum receiver leads to the wavelet transform based adaptive filter structure described in Chapter 2.
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Date Issued
1993
PURL
http://purl.flvc.org/fcla/dt/12354
Subject Headings
Wavelets (Mathematics), Signal processing--Digital techniques
Format
Document (PDF)
HVS-based wavelet color image coding
Title
HVS-based wavelet color image coding.
Creator
Guo, Linfeng., Florida Atlantic University, Glenn, William E., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
This work is an attempt of incorporating the latest advances in vision research and signal processing into the field of image coding. The scope of the dissertation is twofold. Firstly, it sets up a framework of the wavelet color image coder and makes optimizations of its performance. Secondly, it investigates the human vision models and implements human visual properties into the wavelet color image coder. A wavelet image coding framework consisting of image decomposition, coefficients...
Show moreThis work is an attempt of incorporating the latest advances in vision research and signal processing into the field of image coding. The scope of the dissertation is twofold. Firstly, it sets up a framework of the wavelet color image coder and makes optimizations of its performance. Secondly, it investigates the human vision models and implements human visual properties into the wavelet color image coder. A wavelet image coding framework consisting of image decomposition, coefficients quantization, data representation, and entropy coding is first set up, and then a couple of unsolved issues of wavelet image coding are studied and the consequent optimization schemes are presented and applied to the basic framework. These issues include the best wavelet bases selection, quantizer optimization, adaptive probability estimation in arithmetic coding, and the explicit transmission of significant map of wavelet data. Based on the established wavelet image coding framework, a human visual system (HVS) based adaptive color image coding scheme is proposed. Compared with the non-HVS-based coding methods, our method results in a superior performance without any cost of additional side information. As the rudiments of the proposed HVS-based coding scheme, the visual properties of the early stage of human vision are investigated first, especially the contrast sensitivity, the luminance adaptation, and the complicated simultaneous masking and crossed masking effects. To implement these visual properties into the wavelet image coding, the suitable estimation of local background luminance and contrast in the wavelet domain is also re-investigated. Based upon these prerequisite works, the effects of contrast sensitivity weighting and luminance adaptation are incorporated into our coding scheme. Furthermore, the mechanisms of all kinds of masking effects in color image, e.g., the self-masking, the neighbor masking, the crossbands masking, and the luminance-chrominance crossed-masking, are also studied and properly utilized into the coding scheme through an adaptive quantization scheme. Owing to elaborate arrangement and integration of the different parts of the perception based quantization scheme, the coefficient-dependent adaptive quantization step size can be losslessly restored during the decoding without any overhead of side information.
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Date Issued
2001
PURL
http://purl.flvc.org/fcla/dt/11941
Subject Headings
Wavelets (Mathematics), Image processing--Digital techniques
Format
Document (PDF)
Non-separable two dimensional wavelets and their filter banks in polar coordinates
Title
Non-separable two dimensional wavelets and their filter banks in polar coordinates.
Creator
Andric, Oleg., Florida Atlantic University, Erdol, Nurgun
Abstract/Description
The problems encountered in development and implementation of orthonormal two dimensional wavelet bases and their filter banks in polar coordinates are addressed. These wavelets and filter banks have possible applications in processing signals that are collected by sensors working in the polar coordinate system, such as biomedical and radar generated signals. The relationship between the space of measurable, square-integrable functions on the punctured polar coordinate system L^2(P) and space...
Show moreThe problems encountered in development and implementation of orthonormal two dimensional wavelet bases and their filter banks in polar coordinates are addressed. These wavelets and filter banks have possible applications in processing signals that are collected by sensors working in the polar coordinate system, such as biomedical and radar generated signals. The relationship between the space of measurable, square-integrable functions on the punctured polar coordinate system L^2(P) and space of measurable, square-integrable functions on the rectangular plane L^2(R^2) is developed. This allows us to develop complete wavelet bases in a more convenient and familiar surrounding of L^2(R^2) and to transport this theory to L^2(P). Corresponding filter banks are also developed. The implementation of wavelet analysis of punctured polar plane is discussed. An example of wavelet bases, filter banks, and implementation is provided.
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Date Issued
1995
PURL
http://purl.flvc.org/fcla/dt/15190
Subject Headings
Wavelets (Mathematics), Coordinates, Polar, Signal processing--Mathematical models
Format
Document (PDF)
Wavelet de-noising applied to vibrational envelope analysis methods
Title
Wavelet de-noising applied to vibrational envelope analysis methods.
Creator
Bertot, Edward Max, Khoshgoftaar, Taghi M., Beaujean, Pierre-Philippe, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
In the field of machine prognostics, vibration analysis is a proven method for detecting and diagnosing bearing faults in rotating machines. One popular method for interpreting vibration signals is envelope demodulation, which allows a technician to clearly identify an impulsive fault source and its severity. However incipient faults -faults in early stages - are masked by in-band noise, which can make the associated impulses difficult to detect and interpret. In this thesis, Wavelet De...
Show moreIn the field of machine prognostics, vibration analysis is a proven method for detecting and diagnosing bearing faults in rotating machines. One popular method for interpreting vibration signals is envelope demodulation, which allows a technician to clearly identify an impulsive fault source and its severity. However incipient faults -faults in early stages - are masked by in-band noise, which can make the associated impulses difficult to detect and interpret. In this thesis, Wavelet De-Noising (WDN) is implemented after envelope-demodulation to improve accuracy of bearing fault diagnostics. This contrasts the typical approach of de-noising as a preprocessing step. When manually measuring time-domain impulse amplitudes, the algorithm shows varying improvements in Signal-to-Noise Ratio (SNR) relative to background vibrational noise. A frequency-domain measure of SNR agrees with this result.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004080, http://purl.flvc.org/fau/fd/FA00004080
Subject Headings
Fluid dynamics, Signal processing, Structural dynamics, Wavelet (Mathematics)
Format
Document (PDF)
Application of wavelets to image and video coding
Title
Application of wavelets to image and video coding.
Creator
Zolghadr, Esfandiar, Florida Atlantic University, Furht, Borko, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
In this thesis we applied wavelet transforms to image and video coding. First, a survey of various wavelets and their features is presented, including continuous, discrete, and orthogonal wavelets. Theories and concepts underlying one and two-dimensional wavelet transforms are introduced and compared to Fourier transform and sub-band coding. The core of the thesis is the implementation of two-dimensional and three-dimensional codec architectures and their application to coding images and...
Show moreIn this thesis we applied wavelet transforms to image and video coding. First, a survey of various wavelets and their features is presented, including continuous, discrete, and orthogonal wavelets. Theories and concepts underlying one and two-dimensional wavelet transforms are introduced and compared to Fourier transform and sub-band coding. The core of the thesis is the implementation of two-dimensional and three-dimensional codec architectures and their application to coding images and videos, respectively. We studied performance of the wavelet codec by comparing it to DCT and JPEG coding techniques. We applied these techniques for compression of a variety of test images and videos. We also analyzed the adaptability and scalability of 2D and 3D codec. Experimental results, presented in the thesis, illustrate the superior performance of wavelets compared to other coding techniques.
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Date Issued
2003
PURL
http://purl.flvc.org/fcla/dt/13050
Subject Headings
Wavelets (Mathematics), Image compression, JPEG (Image coding standard)
Format
Document (PDF)
Implementation and representation of the discrete wavelet transform
Title
Implementation and representation of the discrete wavelet transform.
Creator
Efthymoglou, George P., Florida Atlantic University, Erdol, Nurgun
Abstract/Description
This thesis presents a comprehensive analysis of a relatively new transform for discrete time signals, called the Discrete Wavelet Transform (DWT). We find how this transform is connected with the already existing theory of perfect reconstruction filter banks and the recently introduced theory of multiresolution analysis. We use the conditions obtained from these two theories in order to understand the construction of wavelet filters, which also generate continuous functions that prove to...
Show moreThis thesis presents a comprehensive analysis of a relatively new transform for discrete time signals, called the Discrete Wavelet Transform (DWT). We find how this transform is connected with the already existing theory of perfect reconstruction filter banks and the recently introduced theory of multiresolution analysis. We use the conditions obtained from these two theories in order to understand the construction of wavelet filters, which also generate continuous functions that prove to constitute an orthonormal basis for the L$\sp2$ space. We also investigate the connection of this transform to the sampled wavelet series of nonorthogonal functions with good time-frequency localization properties. Finally, we see the way that the DWT maps a discrete signal in the phase plane and the applications that such representations incorporate.
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Date Issued
1993
PURL
http://purl.flvc.org/fcla/dt/14944
Subject Headings
Wavelets (Mathematics), Integrals, Singular, Signal processing--Digital techniques
Format
Document (PDF)
Multiresolution analysis of glottal pulses
Title
Multiresolution analysis of glottal pulses.
Creator
Miguel, Agnieszka C., Florida Atlantic University, Erdol, Nurgun, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Glottal pulse models provide vocal tract excitation signals which are used in producing high quality speech. Most of the currently used glottal pulse models are obtained by concatenating a small number of parametric functions over the pitch period. In this thesis, a new glottal pulse model is proposed. It is an alternative approach, which is based on the projection of glottal volume velocity over multiresolution subspaces spanned by wavelets and scaling functions. A detailed multiresolution...
Show moreGlottal pulse models provide vocal tract excitation signals which are used in producing high quality speech. Most of the currently used glottal pulse models are obtained by concatenating a small number of parametric functions over the pitch period. In this thesis, a new glottal pulse model is proposed. It is an alternative approach, which is based on the projection of glottal volume velocity over multiresolution subspaces spanned by wavelets and scaling functions. A detailed multiresolution analysis of the glottal models is performed using the compactly supported orthogonal Daubechies wavelets. The wavelet representation has been tested for optimality in terms of the reconstruction error and the energy compactness of the coefficients. It is demonstrated that by choosing proper parameters of the wavelet representation, high compression ratios and low rms error can be achieved.
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Date Issued
1996
PURL
http://purl.flvc.org/fcla/dt/15334
Subject Headings
Signal processing, Speech processing systems, Wavelets (Mathematics)--Data processing
Format
Document (PDF)
Voice activity detection over multiresolution subspaces
Title
Voice activity detection over multiresolution subspaces.
Creator
Schultz, Robert Carl., Florida Atlantic University, Erdol, Nurgun, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Society's increased demand for communications requires searching for techniques that preserve bandwidth. It has been observed that much of the time spent during telephone communications is actually idle time with no voice activity present. Detecting these idle periods and preventing transmission during these idle periods can aid in reducing bandwidth requirements during high traffic periods. While techniques exist to perform this detection, certain types of noise can prove difficult at best...
Show moreSociety's increased demand for communications requires searching for techniques that preserve bandwidth. It has been observed that much of the time spent during telephone communications is actually idle time with no voice activity present. Detecting these idle periods and preventing transmission during these idle periods can aid in reducing bandwidth requirements during high traffic periods. While techniques exist to perform this detection, certain types of noise can prove difficult at best for signal detection. The use of wavelets with multi-resolution subspaces can aid detection by providing noise whitening and signal matching. This thesis explores its use and proposes a technique for detection.
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Date Issued
1999
PURL
http://purl.flvc.org/fcla/dt/15740
Subject Headings
Speech processing systems, Signal processing--Digital techniques, Wavelets (Mathematics)
Format
Document (PDF)
maximum entropy bandwidth extrapolation technique using wavelet subspaces
Title
A maximum entropy bandwidth extrapolation technique using wavelet subspaces.
Creator
Vann, Laura Dominick., Florida Atlantic University, Helmken, Henry, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
This dissertation is concerned with the development of a bandwidth extrapolation technique that performs maximum entropy estimations over wavelet subspaces. Bandwidth extrapolation techniques have been used in radar applications to improve range and cross range resolution of radar cross section (RCS) images. Comparisons are made of the performance of conventional maximum entropy estimation to maximum entropy estimation over wavelet subspaces. A least squares prediction error measure is used...
Show moreThis dissertation is concerned with the development of a bandwidth extrapolation technique that performs maximum entropy estimations over wavelet subspaces. Bandwidth extrapolation techniques have been used in radar applications to improve range and cross range resolution of radar cross section (RCS) images. Comparisons are made of the performance of conventional maximum entropy estimation to maximum entropy estimation over wavelet subspaces. A least squares prediction error measure is used to compare original measured RCS data to extrapolated data. Then a relative error is defined as the ratio of prediction error using conventional maximum entropy to prediction error using maximum entropy over wavelet subspaces. Application of the bandwidth extrapolation technique is to measured RCS data of two objects. The first object consists of two 3/8" diameter conducting spheres placed 4" apart. Measurements used are for vertical polarization and 0 degree aspect angle covering a frequency range of 8.0 to 12.3827 GHz. The second object is a 1.6 meter aluminum cone. Measurements used are for vertical polarization and 0 degree aspect angle (nose on) covering a frequency range of 4.64 to 18.00 GHz. Results are shown for extrapolate measured data plus the original data with Gaussian white noise added to noise ratios of 25 dB, 20 dB, 15 dB, and 10 dB.
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Date Issued
1997
PURL
http://purl.flvc.org/fcla/dt/12531
Subject Headings
Maximum entropy method, Wavelets (Mathematics), Radar cross sections
Format
Document (PDF)
Characterizing the Magnetic Signature of Internal Waves
Title
Characterizing the Magnetic Signature of Internal Waves.
Creator
Nieves, Eric, Beaujean, Pierre-Philippe, Florida Atlantic University, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
Abstract/Description
This study is performed in tandem with numerous experiments performed by the U.S. Navy to characterize the ocean environment in the South Florida region. The research performed in this study includes signal processing steps for isolating ocean phenomena, such as internal waves, in the magnetic field. Raw magnetometer signals, one on shore and one underwater, are processed and removed of common distortions. They are then run through a series of filtering techniques, including frequency domain...
Show moreThis study is performed in tandem with numerous experiments performed by the U.S. Navy to characterize the ocean environment in the South Florida region. The research performed in this study includes signal processing steps for isolating ocean phenomena, such as internal waves, in the magnetic field. Raw magnetometer signals, one on shore and one underwater, are processed and removed of common distortions. They are then run through a series of filtering techniques, including frequency domain cancellation (FDC). The results of the filtered magnetic residual are compared to similarly processed Acoustic Doppler Current Profiler (ADCP) data to correlate whether a magnetic signature is caused by ocean phenomena.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00004917, http://purl.flvc.org/fau/fd/FA00004917
Subject Headings
Ocean currents--Measurement., Adaptive signal processing., Wave-motion, Theory of., Wavelets (Mathematics)
Format
Document (PDF)
Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning
Title
Enhancement in Low-Dose Computed Tomography through Image Denoising Techniques: Wavelets and Deep Learning.
Creator
Mohammadi Khoroushadi, Mohammad Sadegh, Leventouri, Theodora, Zhuang, Hanqi, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Reducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient...
Show moreReducing the amount of radiation in X-ray computed tomography has been an active area of research in the recent years. The reduction of radiation has the downside of degrading the quality of the CT scans by increasing the ratio of the noise. Therefore, some techniques must be utilized to enhance the quality of images. In this research, we approach the denoising problem using two class of algorithms and we reduce the noise in CT scans that have been acquired with 75% less dose to the patient compared to the normal dose scans. Initially, we implemented wavelet denoising to successfully reduce the noise in low-dose X-ray computed tomography (CT) images. The denoising was improved by finding the optimal threshold value instead of a non-optimal selected value. The mean structural similarity (MSSIM) index was used as the objective function for the optimization. The denoising performance of combinations of wavelet families, wavelet orders, decomposition levels, and thresholding methods were investigated. Results of this study have revealed the best combinations of wavelet orders and decomposition levels for low dose CT denoising. In addition, a new shrinkage function is proposed that provides better denoising results compared to the traditional ones without requiring a selected parameter. Alternatively, convolutional neural networks were employed using different architectures to resolve the same denoising problem. This new approach improved denoising even more in comparison to the wavelet denoising.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013115
Subject Headings
Tomography--Image quality, Wavelets (Mathematics), Deep learning, Tomography, X-Ray Computed
Format
Document (PDF)
Detection and classification of marine mammal sounds
Title
Detection and classification of marine mammal sounds.
Creator
Esfahanian, Mahdi, Zhuang, Hanqi, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Ocean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods...
Show moreOcean is home to a large population of marine mammals such as dolphins and whales and concerns over anthropogenic activities in the regions close to their habitants have been increased. Therefore the ability to detect the presence of these species in the field, to analyze and classify their vocalization patterns for signs of distress and distortion of their communication calls will prove to be invaluable in protecting these species. The objective of this research is to investigate methods that automatically detect and classify vocalization patterns of marine mammals. The first work performed is the classification of bottlenose dolphin calls by type. The extraction of salient and distinguishing features from recordings is a major part of this endeavor. To this end, two strategies are evaluated with real datasets provided by Woods Hole Oceanographic Institution: The first strategy is to use contour-based features such as Time-Frequency Parameters and Fourier Descriptors and the second is to employ texture-based features such as Local Binary Patterns (LBP) and Gabor Wavelets. Once dolphin whistle features are extracted for spectrograms, selection of classification procedures is crucial to the success of the process. For this purpose, the performances of classifiers such as K-Nearest Neighbor, Support Vector Machine, and Sparse Representation Classifier (SRC) are assessed thoroughly, together with those of the underlined feature extractors.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004282, http://purl.flvc.org/fau/fd/FA00004282
Subject Headings
Acoustic phenomena in nature, Marine mammals -- Effect of noise on, Marine mammals -- Vocalization, Signal processing -- Mathematics, Underwater acoustics, Wavelets (Mathematics)
Format
Document (PDF)
Generating space-time hypotheses in complex social-ecological systems
Title
Generating space-time hypotheses in complex social-ecological systems.
Creator
Forbes, Dolores J., Xie, Zhixiao, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
Abstract/Description
As ecosystems degrade globally, ecosystem services that support life are increasingly threatened. Indications of degradation are occurring in the Northern Indian River Lagoon (IRL) estuary in east central Florida. Factors associated with ecosystem degradation are complex, including climate and land use change. Ecosystem research needs identified by the Millennium Ecosystem Assessment (MA) include the need to: consider the social with the physical; account for dynamism and change; account for...
Show moreAs ecosystems degrade globally, ecosystem services that support life are increasingly threatened. Indications of degradation are occurring in the Northern Indian River Lagoon (IRL) estuary in east central Florida. Factors associated with ecosystem degradation are complex, including climate and land use change. Ecosystem research needs identified by the Millennium Ecosystem Assessment (MA) include the need to: consider the social with the physical; account for dynamism and change; account for complexity; address issues of scale; and focus on ecosystem structure and process. Ecosystems are complex, self-organizing, multi-equilibrial, non-linear, middle-number systems that exist in multiple stable states. Results found are relative to the observation and the frame of analysis, requiring multi-scaled analytical techniques. This study addresses the identified ecosystem research needs and the complexity of the associated factors given these additional constraints. Relativity is addressed through univariate analysis of dissolved oxygen as a measure of the general health of the Northern IRL. Multiple spatial levels are employed to associate social process scales with physical process scales as basin, sub-basins, and watersheds. Scan statistics return extreme value clusters in space-time. Wavelet transforms decompose time-scales of cyclical data using varying window sizes to locate change in process scales in space over time. Wavelet transform comparative methods cluster temporal process scales across space. Combined these methods describe the space-time structure of process scales in a complex ecosystem relative to the variable examined, where the highly localized results allow for connection to unexamined variables.
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Date Issued
2014
PURL
http://purl.flvc.org/fau/fd/FA00004284
Subject Headings
Environmental sciences -- Mathematical models, Indian River (Fla. : Lagoon) -- Environmental aspects, Marine ecosystem management -- Florida -- Indian River (Lagoon), Sustainable development, Wavelets (Mathematics)
Format
Document (PDF)