Academic literature on the topic 'Fractal Detection'
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Journal articles on the topic "Fractal Detection"
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Zhang, Pei-Lin, Bing Li, Shuang-Shan Mi, Ying-Tang Zhang, and Dong-Sheng Liu. "Bearing Fault Detection Using Multi-Scale Fractal Dimensions Based on Morphological Covers." Shock and Vibration 19, no. 6 (2012): 1373–83. http://dx.doi.org/10.1155/2012/438789.
Full textAbstract:
Vibration signals acquired from bearing have been found to demonstrate complicated nonlinear characteristics in literature. Fractal geometry theory has provided effective tools such as fractal dimension for characterizing the vibration signals in bearing faults detection. However, most of the natural signals are not critical self-similar fractals; the assumption of a constant fractal dimension at all scales may not be true. Motivated by this fact, this work explores the application of the multi-scale fractal dimensions (MFDs) based on morphological cover (MC) technique for bearing fault diagnosis. Vibration signals from bearing with seven different states under four operations conditions are collected to validate the presented MFDs based on MC technique. Experimental results reveal that the vibration signals acquired from bearing are not critical self-similar fractals. The MFDs can provide more discriminative information about the signals than the single global fractal dimension. Furthermore, three classifiers are employed to evaluate and compare the classification performance of the MFDs with other feature extraction methods. Experimental results demonstrate the MFDs to be a desirable approach to improve the performance of bearing fault diagnosis.
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Potapov, Alexander A. "Fractal applications in radio electronics as fractal engineering." Radioelectronics. Nanosystems. Information Technologies. 14, no. 3 (2022): 215–32. http://dx.doi.org/10.17725/rensit.2022.14.215.
Full textAbstract:
The use of the fractal paradigm is presented - the main directions for introducing textures, fractals, fractional operators, dynamic chaos and methods of nonlinear dynamics for the design and creation of real technical projects in radio electronics - fractal radio systems, taking into account the hereditarity, non-Gaussianity and scaling of physical signals and fields. The substantiation of the use of fractal-scaling and texture methods for the synthesis of fundamentally new topological texture-fractal methods for detecting signals in the space-time channel of scattering waves (a new type of radar) is discussed. It is shown that the use of fractal systems, sensors and nodes is a fundamentally new solution that significantly changes the principles of constructing intelligent radio engineering systems and devices. It is shown that the use of computational dielectric metasurfaces brings to a new level all the functional characteristics of a multifunctional system of topological texture-fractal processing of signals and fields in solving classical problems of detection, measurement, recognition and classification by intelligent radio engineering systems and devices. The concept of "fractal engineering" is introduced, the methodology of its use is discussed.
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Polattimur, Rukiye, Mehmet Süleyman Yıldırım, and Emre Dandıl. "Fractal-Based Architectures with Skip Connections and Attention Mechanism for Improved Segmentation of MS Lesions in Cervical Spinal Cord." Diagnostics 15, no. 8 (2025): 1041. https://doi.org/10.3390/diagnostics15081041.
Full textAbstract:
Background/Objectives: Multiple sclerosis (MS) is an autoimmune disease that damages the myelin sheath of the central nervous system, which includes the brain and spinal cord. Although MS lesions in the brain are more frequently investigated, MS lesions in the cervical spinal cord (CSC) can be much more specific for the diagnosis of the disease. Furthermore, as lesion burden in the CSC is directly related to disease progression, the presence of lesions in the CSC may help to differentiate MS from other neurological diseases. Methods: In this study, two novel deep learning models based on fractal architectures are proposed for the automatic detection and segmentation of MS lesions in the CSC by improving the convolutional and connection structures used in the layers of the U-Net architecture. In our previous study, we introduced the FractalSpiNet architecture by incorporating fractal convolutional block structures into the U-Net framework to develop a deeper network for segmenting MS lesions in the CPC. In this study, to improve the detection of smaller structures and finer details in the images, an attention mechanism is integrated into the FractalSpiNet architecture, resulting in the Att-FractalSpiNet model. In addition, in the second hybrid model, a fractal convolutional block is incorporated into the skip connection structure of the U-Net architecture, resulting in the development of the Con-FractalU-Net model. Results: Experimental studies were conducted using U-Net, FractalSpiNet, Con-FractalU-Net, and Att-FractalSpiNet architectures to detect the CSC region and the MS lesions within its boundaries. In segmenting the CSC region, the proposed Con-FractalU-Net architecture achieved the highest Dice Similarity Coefficient (DSC) score of 98.89%. Similarly, in detecting MS lesions within the CSC region, the Con-FractalU-Net model again achieved the best performance with a DSC score of 91.48%. Conclusions: For segmentation of the CSC region and detection of MS lesions, the proposed fractal-based Con-FractalU-Net and Att-FractalSpiNet architectures achieved higher scores than the baseline U-Net architecture, particularly in segmenting small and complex structures.
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Sheng, Li Lian. "Fractal-Based Outlier Detection Algorithm over RFID Data Streams." International Journal of Online Engineering (iJOE) 12, no. 1 (2016): 35. http://dx.doi.org/10.3991/ijoe.v12i1.5171.
Full textAbstract:
Nowadays, Radio frequency identification (RFID) has been extensively deployed to retailing, supply chain management, object recognition, object monitoring and tracking and many other fields. Detecting outliers in RFID data streams can help us find abnormal activities and thus avoid disasters. In order to detect outliers in RFID data streams efficiently and effectively, we proposed a fractal based outlier detection algorithm. Firstly, we built a monotone searching space based on the self-similarity of fractal. Then, we proposed two piecewise fractal models for RFID data streams, and presented an outlier detection algorithm based on the piecewise fractal model. Finally, we validated the efficiency and effectiveness of the proposed algorithm by massive experiments.
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P.Tharaniya. "Border Detection of Skin Cancer Cells over Fractal Dimension Analysis and Image Processing Techniques." Communications on Applied Nonlinear Analysis 32, no. 3s (2024): 45–59. https://doi.org/10.52783/cana.v32.2533.
Full textAbstract:
Fractal dimension analysis is a novel technique that uses the self-similarity qualities of fractals to identify irregular forms, such as those prevalent in diseased tissues, in order to detect the borders of skin cancer cells. Acquire detailed pictures of skin tissue samples that have cancer cells in them. A variety of imaging methods, including microscopy and medical imaging tools like MRIs and CT scans, can be used for this. Determine the image's fractal dimension by applying suitable methods, like the fractal signature method or box-counting. A geometric shape's complexity is measured by its fractal dimension, and because malignant cells have uneven edges, they typically show higher complexity. To increase the border recognition process' accuracy, clean the photos to get rid of noise and boost contrast. Here, methods such as morphological procedures, histogram equalization, and median filtering can be used. To increase the border detection system's accuracy and resilience, fine-tune the parameters and algorithms in light of the validation results. A reliable approach for identifying the borders of skin cancer cells can be created by fusing fractal dimension analysis with image processing methods. This will help with early detection and therapy planning. Based on the fractal dimension, choose an appropriate threshold value to divide the image into zones of interest. This stage aids in the malignant cells' separation from the surrounding tissue.
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UTHAYAKUMAR, R., and G. JAYALALITHA. "BORDER DETECTION OF SKIN CANCER CELLS WITH FRACTAL DIMENSION." Fractals 17, no. 02 (2009): 171–80. http://dx.doi.org/10.1142/s0218348x09004417.
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In this paper we study a model of skin cancer (MM) in vitro, using geometry of fractals as the method of analysis. The fractal dimensions of moles (skin cancer cells) growth pattern have been measured by using the methods of Box-counting method (DB) and Sausage method (DS). The cell growth of this cancer can be modeled by Hidden Markov model (HMM) and percolation model which are depending upon the time complexity. From these models we can find the shape of the irregularity border by using the probability distribution of the cells. The variation in the irregular border of the skin cancer has been found out using ANOVA test and cell's compactness. The fractal approach led to very promising results which improved the determination and examination of the stage of skin cancer.
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MCGINLEY, PATTON, ROBIN G. SMITH, and JEROME C. LANDRY. "FRACTAL DIMENSIONS OF MYCOSIS FUNGOIDES." Fractals 02, no. 04 (1994): 493–501. http://dx.doi.org/10.1142/s0218348x94000715.
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Prior to an investigation of early diagnosis of mycosis fungoides (MF) using fractal geometry, we set out to see if MF lesions are fractal in nature. We analyzed three aspects of MF lesions: the dermoepidermal profile of photomicrographs of patch stage lesions and normal skin, the perimeter of patch and plaque stage lesions, and the size distribution of patch and plaque lesions on the skin surface. The perimeter of plaque lesions was measured on close-up photographs by the divider walk method using various step sizes. Based on the perimeter values, the fractal dimension was determined. The dermoepidermal profile of MF patch lesions was analyzed by the divider walk method for self-affine fractals. The size distribution of MF patch and plaque lesions was determined by counting the number of patch and plaque lesions with an area greater than or equal to a specific size A on scaled photographs of a 19.6 cm × 19.6 cm affected region. A plot of number of lesions with area greater than or equal to A vs. lesion area on log-log paper allows the detection of a power-law distribution, indicative of one type of self-similar fractals. The dermoepidermal profile of patch stage lesions and normal skin was found to be self-affine fractals. Global measurements of normal thin skin and of patch stage lesions were distinct. All observed patch and plaque lesion area distributions were a fractal set. The perimeter of non-confluent plaque lesions was not fractal. This work revealed fractal dimensions in two aspects of MF lesions. Further investigation of application of fractal geometry to the diagnosis and staging of MF is planned.
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Li, Chang Hai, Yong Sheng Liang, Kong Qiang Zhu, and Yan Chun Liu. "GPR Target Detection Based on Fractal Dimension." Applied Mechanics and Materials 321-324 (June 2013): 785–89. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.785.
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To achieve rapid target detection by GPR (Ground Penetrating Radar),the article introduces fractal dimension as the characteristic to describe the complexity of the one-dimensional discrete signal and puts forward an algorithm for rapid target detection by GPR. First, calculate the fractal dimension of signals at different points in the direction of the survey line and depth direction after wavelet transform processing to obtain the curve of fractal dimension on two directions. Finally, determine the suspicious region in the curve of fractal dimension based on the judgment criterion to fulfill target detection. The measured data testify that the method can achieve the rapid detection on a specific target in a certain context.
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HU, JING, JIANBO GAO, FRED L. POSNER, YI ZHENG, and WEN-WEN TUNG. "TARGET DETECTION WITHIN SEA CLUTTER: A COMPARATIVE STUDY BY FRACTAL SCALING ANALYSES." Fractals 14, no. 03 (2006): 187–204. http://dx.doi.org/10.1142/s0218348x06003210.
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Sea clutter refers to the radar returns from a patch of ocean surface. Accurate modeling of sea clutter and robust detection of low observable targets within sea clutter are important problems in remote sensing and radar signal processing applications. Due to lack of fundamental understanding of the nature of sea clutter, however, no simple and effective methods for detecting targets within sea clutter have been proposed. To help solve this important problem, we apply three types of fractal scaling analyses, fluctuation analysis (FA), detrended fluctuation analysis (DFA), and the wavelet-based fractal scaling analysis to study sea clutter. Our analyses show that sea clutter data exhibit fractal behaviors in the time scale range of about 0.01 seconds to a few seconds. The physical significance of these time scales is discussed. We emphasize that time scales characterizing fractal scaling break are among the most important features for detecting patterns using fractal theory. By systematically studying 392 sea clutter time series measured under various sea and weather conditions, we find very effective methods for detecting targets within sea clutter. Based on the data available to us, the accuracy of these methods is close to 100%.
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Kirichenko, Lyudmyla, Yulia Koval, Sergiy Yakovlev, and Dmytro Chumachenko. "Anomaly Detection in Fractal Time Series with LSTM Autoencoders." Mathematics 12, no. 19 (2024): 3079. http://dx.doi.org/10.3390/math12193079.
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This study explores the application of neural networks for anomaly detection in time series data exhibiting fractal properties, with a particular focus on changes in the Hurst exponent. The objective is to investigate whether changes in fractal properties can be identified by transitioning from the analysis of the original time series to the analysis of the sequence of Hurst exponent estimates. To this end, we employ an LSTM autoencoder neural network, demonstrating its effectiveness in detecting anomalies within synthetic fractal time series and real EEG signals by identifying deviations in the sequence of estimates. Whittle’s method was utilized for the precise estimation of the Hurst exponent, thereby enhancing the model’s ability to differentiate between normal and anomalous data. The findings underscore the potential of machine learning techniques for robust anomaly detection in complex datasets.
Dissertations / Theses on the topic "Fractal Detection"
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Stein, Gregory W. "Target Detection Using a Wavelet-Based Fractal Scheme." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/437.
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In this thesis, a target detection technique using a rotational invariant wavelet-based scheme is presented. The technique is evaluated on Synthetic Aperture Rader (SAR) imaging and compared with a previously developed fractal-based technique, namely the extended fractal (EF) model. Both techniques attempt to exploit the textural characteristics of SAR imagery. Recently, a wavelet-based fractal feature set, similar to the proposed one, was compared with the EF feature for a general texture classification problem. The wavelet-based technique yielded a lower classification error than EF, which motivated the comparison between the two techniques presented in this paper. Experimental results show that the proposed techniques feature map provides a lower false alarm rate than the previously developed method.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.
Full textAbstract:
Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." Thesis, The University of Sydney, 2003. http://hdl.handle.net/2123/586.
Full textAbstract:
Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Gras, Fabrice Roger Pierre Fernand. "Fractal segmentation using Fourier and wavelet spectra." Thesis, Queensland University of Technology, 1997.
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Quinlan, Brendan Robert. "A Model For The Absorption Of Thermal Radiation By Gold-Black." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/63888.
Full textAbstract:
The work presented here addresses an important topic in thermal radiation detection when gold-black is used as an absorber. Sought is a model to simulate the absorption of thermal radiation by gold-black.
Fractal geometry is created to simulate the topology of gold-black. Then electrical circuits based on the topology are identified that capture the physics of the interaction between the gold-black material and incident electro-magnetic radiation. Parameters of the model are then adjusted so results obtained are comparable to absorption data from the literature.
Potential next-generation radiometric instruments will likely involve thermal radiation detectors using gold-black as an absorbing medium. A model that accurately simulates gold-black absorption will be an important tool in their design.<br>Master of Science
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Relan, Devanjali. "Discovery of retinal biomarkers for vascular conditions through advancement of artery-vein detection and fractal analysis." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/23612.
Full textAbstract:
Research into automatic retina image analysis has become increasingly important, not just in ophthalmology but also in other clinical specialities such as cardiology and neurology. In the retina, blood vessels can be directly visualised non-invasively in-vivo, and hence it serves as a "window" to cardiovascular and neurovascular complications. Biomarker research, i.e. investigating associations between the morphology of the retinal vasculature (as a means of revealing microvascular health or disease) and particular conditions affecting the body or brain could play an important role in detecting disease early enough to impact on patient treatment and care. A fundamental requirement of biomarker research is access to large datasets to achieve sufficient power and significance when ascertaining associations between retinal measures and clinical characterisation of disease. Crucially, the vascular changes that appear can affect arteries and veins differently. An essential part of automatic systems for retinal morphology quantification and biomarker extraction is, therefore, a computational method for classifying vessels into arteries and veins. Artery-vein classification enables the efficient extraction of biomarkers such as the Arteriolar to Venular Ratio, which is a well-established predictor of stroke and other cardiovascular events. While structural parameters of the retinal vasculature such as vessels calibre, branching angle, and tortuosity may individually convey some information regarding specific aspects of the health of the retinal vascular network, they do not convey a global summary of the branching pattern and its state or condition. The retinal vascular tree can be considered a fractal structure as it has a branching pattern that exhibits the property of self-similarity. Fractal analysis, therefore, provides an additional means for the quantitative study of changes to the retinal vascular network and may be of use in detecting abnormalities related to retinopathy and systemic diseases. In this thesis, new developments to fully automated retinal vessel classification and fractal analysis were explored in order to extract potential biomarkers. These novel processes were tested and validated on several datasets of retinal images acquired with fundus cameras. The major contributions of this thesis include: 1) developing a fully automated retinal blood vessel classification technique, 2) developing a fractal analysis technique that quantifies regional as well as global branching complexity, 3) validating the methods using multiple datasets, and 4) applying the proposed methods in multiple retinal vasculature analysis studies.
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Benavides, Iglesias Alfonso. "Experimental time-domain controlled source electromagnetic induction for highly conductive targets detection and discrimination." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5810.
Full textAbstract:
The response of geological materials at the scale of meters and the response
of buried targets of different shapes and sizes using controlled-source electromagnetic
induction (CSEM) is investigated. This dissertation focuses on three topics; i) frac-
tal properties on electric conductivity data from near-surface geology and processing
techniques for enhancing man-made target responses, ii) non-linear inversion of spa-
tiotemporal data using continuation method, and iii) classification of CSEM transient
and spatiotemporal data.
In the first topic, apparent conductivity profiles and maps were studied to de-
termine self-affine properties of the geological noise and the effects of man-made con-
ductive metal targets. 2-D Fourier transform and omnidirectional variograms showed
that variations in apparent conductivity exhibit self-affinity, corresponding to frac-
tional Brownian motion. Self-affinity no longer holds when targets are buried in the
near-surface, making feasible the use of spectral methods to determine their pres-
ence. The difference between the geology and target responses can be exploited using
wavelet decomposition. A series of experiments showed that wavelet filtering is able
to separate target responses from the geological background.
In the second topic, a continuation-based inversion method approach is adopted,
based on path-tracking in model space, to solve the non-linear least squares prob-
lem for unexploded ordnance (UXO) data. The model corresponds to a stretched-
exponential decay of eddy currents induced in a magnetic spheroid. The fast inversion of actual field multi-receiver CSEM responses of inert, buried ordnance is also shown.
Software based on the continuation method could be installed within a multi-receiver
CSEM sensor and used for near-real-time UXO decision.
In the third topic, unsupervised self-organizing maps (SOM) were adapted for
data clustering and classification. The use of self-organizing maps (SOM) for central-
loop CSEM transients shows potential capability to perform classification, discrimi-
nating background and non-dangerous items (clutter) data from, for instance, unex-
ploded ordnance. Implementation of a merge SOM algorithm showed that clustering
and classification of spatiotemporal CSEM data is possible. The ability to extract tar-
get signals from a background-contaminated pattern is desired to avoid dealing with
forward models containing subsurface response or to implement processing algorithm
to remove, to some degree, the effects of background response and the target-host
interactions.
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Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16200/1/Luke_Rankine_Thesis.pdf.
Full textAbstract:
Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing.
The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG.
Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis.
A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG.
Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes.
The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit.
An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%).
In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.
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Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16200/.
Full textAbstract:
Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.
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Runhem, Lovisa, and Filip Schulze. "Evaluating a fractal features method for automatic detection of Alzheimer’s Disease in brain MRI scans : A quantitative study based on the method developed by Lahmiri and Boukadoum in 2013." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166408.
Full textAbstract:
The field of computer-aided diagnosis has recently made progress in the diagnosing of Alzheimer's disease (AD) from magnetic resonance images (MRI) of the brain. Lahmiri and Boukadoum (2013) have research this topic since 2011, and in 2013 they presented a system for automatic detection of AD based on machine learning classification. Their proposed system achieved a classification accuracy of 100% (2013, p. 1507) using support vector machines with quadratic kernel classifiers. The MRI scans were first translated to 1-dimensional signals, from which three features were extracted to measure the signals self-affinity. These three features were Hurst’s exponent, the total fluctuation energy of a detrended fluctuational analysis and the same analysis’ scaling exponent. The results of their study were validated using a dataset of 23 MRI scans from brains with AD and normal brains. This report makes an attempt at implementing the method proposed by Lahmiri and Boukadoum in 2013 and evaluating its accuracy on a dataset of 120 cases, out of which 60 are cases of AD and 60 are normal cases. The results were validated using both leave-one-out cross-validation and 3-fold cross-validation. A dataset of 23 cases consistent with Lahmiri and Boukadoum’s in size was considered and the larger dataset of 120 cases. The best classification accuracy for the small and large were obtained from the 3-fold cross-validation was 78,26% respectively 65,00%. The results of this study are to some extent similar to those of Lahmiri and Boukadoum’s, however this study fails to verify how their method performs on a larger dataset, as their results for a small dataset could not be reproduced in this implementation. Thus the results of this report are inconclusive in verifying the accuracy of the implemented method for a larger dataset. However this implementation of the method shows promise as the accuracy for the large dataset was fairly good when comparing to other research done in the field.
Books on the topic "Fractal Detection"
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Centre, Bhabha Atomic Research, ed. Seismic signal detection by fractal dimension approach. Bhabha Atomic Research Centre, 2003.
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Li, Ming. Multi-Fractal Traffic and Anomaly Detection in Computer Communications. Taylor & Francis Group, 2022.
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Multi-Fractal Traffic and Anomaly Detection in Computer Communications. Taylor & Francis Group, 2022.
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Li, Ming. Multi-Fractal Traffic and Anomaly Detection in Computer Communications. Taylor & Francis Group, 2022.
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Li, Ming. Multi-Fractal Traffic and Anomaly Detection in Computer Communications. Taylor & Francis Group, 2022.
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Li, Ming. Multi-Fractal Traffic and Anomaly Detection in Computer Communications. CRC Press LLC, 2022.
Find full textBook chapters on the topic "Fractal Detection"
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Li, Ming. "Fractal Time Series." In Multi-Fractal Traffic and Anomaly Detection in Computer Communications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003354987-2.
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Ganguli, Ranjan. "Fractal Dimension Based Damage Detection." In Structural Health Monitoring. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4988-5_8.
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Ehlers, Arne, Florian Baumann, and Bodo Rosenhahn. "Boosted Fractal Integral Paths for Object Detection." In Advances in Visual Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-14364-4_44.
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Borwankar, Saumya, Manmohan Dogra, and Jai Prakash Verma. "Fractal-Based Speech Emotion Detection Using CNN." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2709-5_56.
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Gong, Xueqing, Weining Qian, Shouke Qin, and Aoying Zhou. "Fractal Based Anomaly Detection over Data Streams." In Web Technologies and Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37401-2_54.
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Jayasuriya, Surani Anuradha, and Alan Wee-Chung Liew. "Fractal Analysis for Symmetry Plane Detection in Neuroimages." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_21.
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Stein, Michael C., and Warren G. Heller. "Fractal Methods for Flaw Detection in NDE Imagery." In Review of Progress in Quantitative Nondestructive Evaluation. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-0817-1_87.
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Chen, Fuze, Yonggang Zuo, Yuting Hu, et al. "Oil Fire Detection Technology Based on Fractal Geometry." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1064-5_7.
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Zhang, Liangbin, and Lifeng Xi. "A Novel Image Edge Detection Using Fractal Compression." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74827-4_20.
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Li, Ming. "Power Laws of Fractal Data in Cyber-Physical Networking Systems." In Multi-Fractal Traffic and Anomaly Detection in Computer Communications. CRC Press, 2022. http://dx.doi.org/10.1201/9781003354987-4.
Full textConference papers on the topic "Fractal Detection"
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Yuan, Linan, Qiangmin He, Shuli Dong, Wenbo Wu, Runxin Zhang, and Ying Wang. "Research on infrared detection method based on improved scale fractal feature." In Tenth Symposium on Novel Optoelectronic Detection Technology and Applications, edited by Chen Ping. SPIE, 2025. https://doi.org/10.1117/12.3056862.
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Zou, Kai, Yun Meng, Zifan Hao, et al. "Fractal superconducting nanowire single-photon detector coupled with multi-mode optical fiber." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jw2a.95.
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Optical absorptance of fractal SNSPDs is insensitive to the speckles in multi-mode fiber (MMF). We demonstrate 73% system detection efficiency at 1540 nm and 69 ps timing jitter with a MMF-coupled fractal SNSPD.
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Xia, Xuan, Weijie Lv, Xing He, Nan Li, Chuanqi Liu, and Ning Ding. "FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650356.
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Bhagath, Parabattina, Sambasiva Rao Chindam, Malempati Shanmukha, and Pradip K. Das. "Speech Signal Analysis Using Fractal Geometry for Phoneme Boundary Detection." In TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). IEEE, 2024. https://doi.org/10.1109/tencon61640.2024.10902725.
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Chen, Xiaowei, Xuebin Li, Tao Luo, et al. "Fractal properties of optical turbulence profiles." In Fourth Seminar on Novel Optoelectronic Detection Technology and Application, edited by Weiqi Jin and Ye Li. SPIE, 2018. http://dx.doi.org/10.1117/12.2306138.
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Yu-Qiu Sun, Xiao-Qiang Feng, Ling Li, Jin-Wen Tian, and Jian Liu. "Fractal-based infrared target detection." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212162.
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Zhang Xiaofei and Xu Dazhuan. "Singular signal detection with fractal." In Proceedings of 2003 International Conference on Neural Networks and Signal Processing. IEEE, 2003. http://dx.doi.org/10.1109/icnnsp.2003.1279347.
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Shen, Jun, Jie Li, and Tianxu Zhang. "Detection of image fractal signature." In Aerospace Remote Sensing '97, edited by Jacky Desachy and Shahram Tajbakhsh. SPIE, 1997. http://dx.doi.org/10.1117/12.295619.
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Luo, Huiguo, Yaoting Zhu, Guang-Xi Zhu, Faguang Wan, and Ping Zhang. "Fractal-based image edge detection." In Optical Engineering and Photonics in Aerospace Sensing, edited by Friedrich O. Huck and Richard D. Juday. SPIE, 1993. http://dx.doi.org/10.1117/12.150955.
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Sun, Huayan, and Xiaozhong Chen. "Fractal-based image target detection." In Multispectral Image Processing and Pattern Recognition, edited by Jun Shen, Sharatchandra Pankanti, and Runsheng Wang. SPIE, 2001. http://dx.doi.org/10.1117/12.441662.
Full textReports on the topic "Fractal Detection"
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Zhang, Renduo, and David Russo. Scale-dependency and spatial variability of soil hydraulic properties. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7587220.bard.
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Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.