this work presents a biometric approach for spider identification based on transform domain and Support Vector Machines as classifier. the dataset is composed by 185 images of spider web. the goal of this work is to u...
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ISBN:
(纸本)9781538630778
this work presents a biometric approach for spider identification based on transform domain and Support Vector Machines as classifier. the dataset is composed by 185 images of spider web. the goal of this work is to use the structure of spider web for identifying the kind of spider. the experiments were done using two different of segmentation blocks and the analysis of the whole and center of the spider web. the best accuracy is reached after to run the different combinations.
the morphogenetie engineering that learns biological morphogenesis has attracted our attention recently. In the paper, we first propose a new system that models pattern formation in biological morphogenesis. the syste...
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ISBN:
(纸本)9781538626337
the morphogenetie engineering that learns biological morphogenesis has attracted our attention recently. In the paper, we first propose a new system that models pattern formation in biological morphogenesis. the system is theoretically able to produce a variety of shapes similar to desired one. However, to realize that, it is necessary to optimize parameters of the system. So, we assume the use of an evolutionary algorithm for the optimization and examine a suitable fitness function for the optimization through simulations. the simulation results reveal that the suitable fitness function enables the evolutionary algorithm to find parameters that produce a variety of rough shapes including the desired one at the top priority and then to conduct fine-tuning of parameters for obtaining closer shapes to the desired one.
the dimensionality and the amount of data that need to be processed when intensive data streams are classified may occur prohibitively large. the aim of this paper is to analyze Johnson-Linden-strauss type random proj...
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ISBN:
(纸本)9783642132070
the dimensionality and the amount of data that need to be processed when intensive data streams are classified may occur prohibitively large. the aim of this paper is to analyze Johnson-Linden-strauss type random projections as an approach to dimensionality reduction in pattern classification based on K-nearest neighbors search. We show that in-class data clustering allows us to retain accuracy recognition rates obtained in the original high-dimensional space also after transformation to a lower dimension.
A categorization of important image/vision algorithms that can be efficiently implemented on a research multiprocessor called Viscom is presented. Extended C-language constructs are developed and used to specify ortho...
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A categorization of important image/vision algorithms that can be efficiently implemented on a research multiprocessor called Viscom is presented. Extended C-language constructs are developed and used to specify orthogonal multiprocessing algorithms. Several early vision algorithms are illustrated, including convolution, optical flow, and 2-D transforms. Motion analysis is modeled by an artificial neural network, which can also be efficiently mapped onto Viscom. Advantages of using Viscom for early vision and neural computing are discussed, and the associated hardware/software development experiences are reported.
Very often, the recognition of a pattern is accompanied by a cognitive process of interpretation and understanding. In the arts and sciences, as well as in our daily lives, we learned patterns from nature and create n...
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this Volume 1 of 3 of the conference proceedings contains 136 papers. Topics discussed include fuzzy filters for image processing, modeling with words, causality is undefinable toward a theory of hierarchical definabi...
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this Volume 1 of 3 of the conference proceedings contains 136 papers. Topics discussed include fuzzy filters for image processing, modeling with words, causality is undefinable toward a theory of hierarchical definability, patternrecognition and image processing, electronic and robotic systems, soft computing and hybrid systems, control systems and mathematics.
the pattern matching problem is imperative in diversified field of computer science, and it finds occurrences of the pattern string(s) of any arbitrary length "M" in the large text string of length "N&q...
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ISBN:
(纸本)9781538659069
the pattern matching problem is imperative in diversified field of computer science, and it finds occurrences of the pattern string(s) of any arbitrary length "M" in the large text string of length "N". Since past decades the solutions to a problem have been suggesting through efficient algorithms. Classical benchmark algorithms such as Knuth Morris Pratt and Boyer Moore locates such solution in O(N) time, equivalent quantum algorithms can utilize quantum computations which are inherently parallel and obtains computational speedup by providing the solution in O(root N) time. the quantum pattern matching algorithm uses Grover's search logic that finds an element existence in the large unstructured text data of "N" items in O(root N) time, instead classically it takes O(N) time. the article comprises of introductory pattern matching tactics, quantum basics, Grover's search method, quantum based pattern matching algorithms, their illustration followed by complexity analysis, and finally concludes withthe possible algorithmic variations and relevant applications.
Wind Power forecasts in the short and medium term time horizons are essential for overcoming variable energy productions caused by the changes in weather conditions and fluctuations in wind speed. Wind speed follows a...
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ISBN:
(纸本)9781538659069
Wind Power forecasts in the short and medium term time horizons are essential for overcoming variable energy productions caused by the changes in weather conditions and fluctuations in wind speed. Wind speed follows a time series pattern in time that can be exploited to forecast for power production. this paper uses a long short term (LSTM) neural network model to predict and validate wind power using time series measurements of an online available wind power measurements. Short term pattern prediction (for accuracy) and mid-term (for stability) are studied for wind power prediction. To increase the reliability of the model a genetic algorithm is used to optimize the window of time capture to alleviate problems of overfitting or an appropriate window size that does not lead to any loss of information. A root mean square error of 0.0957993 and 0.0929905 is obtained for the short term and medium predictions respectively
In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly. Currently, UAVs are widely used in IoT deployment and smart agriculture. At present, the main method for radar to identify UAV is analyz...
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ISBN:
(纸本)9781728186160
In recent years, unmanned aerial vehicle (UAV) technology has developed rapidly. Currently, UAVs are widely used in IoT deployment and smart agriculture. At present, the main method for radar to identify UAV is analyzing the Micro-Doppler effect. Passive radar technology based on 5G base stations can identify UAV in complex urban environments. In this paper, we propose a method combining micro Doppler effect and patternrecognition technology. Firstly, we processed the radar echo to get the Micro-Doppler feature image. then the Micro-Doppler feature image will be classified by patternrecognition in order to judge the number of UAV rotors. the simulation results show that the method combining the Micro-Doppler effect and patternrecognition can identify the UAV in real time and accurately.
the automatic identification of an individual using physiological/behavioral traits connected to a person is Biometrics. patternrecognition is used in biometric authentication (identification/verification) systems to...
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