934 resultados para Approximate filtering


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The improvement of the dynamics of flexible manipulators like log cranes often requires advanced control methods. This thesis discusses the vibration problems in the cranes used in commercial forestry machines. Two control methods, adaptive filtering and semi-active damping, are presented. The adaptive filter uses a part of the lowest natural frequency of the crane as a filtering frequency. The payload estimation algorithm, filtering of control signal and algorithm for calculation of the lowest natural frequency of the crane are presented. The semi-active damping method is basedon pressure feedback. The pressure vibration, scaled with suitable gain, is added to the control signal of the valve of the lift cylinder to suppress vibrations. The adaptive filter cuts off high frequency impulses coming from the operatorand semi-active damping suppresses the crane?s oscillation, which is often caused by some external disturbance. In field tests performed on the crane, a correctly tuned (25 % tuning) adaptive filter reduced pressure vibration by 14-17 % and semi-active damping correspondingly by 21-43%. Applying of these methods require auxiliary transducers, installed in specific points in the crane, and electronically controlled directional control valves.

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This thesis is about detection of local image features. The research topic belongs to the wider area of object detection, which is a machine vision and pattern recognition problem where an object must be detected (located) in an image. State-of-the-art object detection methods often divide the problem into separate interest point detection and local image description steps, but in this thesis a different technique is used, leading to higher quality image features which enable more precise localization. Instead of using interest point detection the landmark positions are marked manually. Therefore, the quality of the image features is not limited by the interest point detection phase and the learning of image features is simplified. The approach combines both interest point detection and local description into one phase for detection. Computational efficiency of the descriptor is therefore important, leaving out many of the commonly used descriptors as unsuitably heavy. Multiresolution Gabor features has been the main descriptor in this thesis and improving their efficiency is a significant part. Actual image features are formed from descriptors by using a classifierwhich can then recognize similar looking patches in new images. The main classifier is based on Gaussian mixture models. Classifiers are used in one-class classifier configuration where there are only positive training samples without explicit background class. The local image feature detection method has been tested with two freely available face detection databases and a proprietary license plate database. The localization performance was very good in these experiments. Other applications applying the same under-lying techniques are also presented, including object categorization and fault detection.

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Perceiving the world visually is a basic act for humans, but for computers it is still an unsolved problem. The variability present innatural environments is an obstacle for effective computer vision. The goal of invariant object recognition is to recognise objects in a digital image despite variations in, for example, pose, lighting or occlusion. In this study, invariant object recognition is considered from the viewpoint of feature extraction. Thedifferences between local and global features are studied with emphasis on Hough transform and Gabor filtering based feature extraction. The methods are examined with respect to four capabilities: generality, invariance, stability, and efficiency. Invariant features are presented using both Hough transform and Gabor filtering. A modified Hough transform technique is also presented where the distortion tolerance is increased by incorporating local information. In addition, methods for decreasing the computational costs of the Hough transform employing parallel processing and local information are introduced.

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Microphthalmia with linear skin defects (MLS) syndrome is an X-linked male-lethal disorder also known as MIDAS (microphthalmia, dermal aplasia, and sclerocornea). Additional clinical features include neurological and cardiac abnormalities. MLS syndrome is genetically heterogeneous given that heterozygous mutations in HCCS or COX7B have been identified in MLS-affected females. Both genes encode proteins involved in the structure and function of complexes III and IV, which form the terminal segment of the mitochondrial respiratory chain (MRC). However, not all individuals with MLS syndrome carry a mutation in either HCCS or COX7B. The majority of MLS-affected females have severe skewing of X chromosome inactivation, suggesting that mutations in HCCS, COX7B, and other as-yet-unidentified X-linked gene(s) cause selective loss of cells in which the mutated X chromosome is active. By applying whole-exome sequencing and filtering for X-chromosomal variants, we identified a de novo nonsense mutation in NDUFB11 (Xp11.23) in one female individual and a heterozygous 1-bp deletion in a second individual, her asymptomatic mother, and an affected aborted fetus of the subject's mother. NDUFB11 encodes one of 30 poorly characterized supernumerary subunits of NADH:ubiquinone oxidoreductase, known as complex I (cI), the first and largest enzyme of the MRC. By shRNA-mediated NDUFB11 knockdown in HeLa cells, we demonstrate that NDUFB11 is essential for cI assembly and activity as well as cell growth and survival. These results demonstrate that X-linked genetic defects leading to the complete inactivation of complex I, III, or IV underlie MLS syndrome. Our data reveal an unexpected role of cI dysfunction in a developmental phenotype, further underscoring the existence of a group of mitochondrial diseases associated with neurocutaneous manifestations.