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international conference on Signal Processing Proceedings (ICSP)

作者： M. Pawlak S.X. Liao Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada Department of Business Computing University of Winnipeg Winnipeg MAN Canada

We consider the problem of estimating a function f(x, y) on the unit disk {(x, y): x/sup 2/+y/sup 2/ /spl les/ 1}, given discrete and noisy data recorded on a regular square grid. An estimate of f(x, y) based on a class of orthogonal and complete functions over the unit disk is proposed. this class of functions has a distinctive property of being invariant to rotation of axes about the origin of coordinates, yielding, therefore a rotationally invariant and noise resistant reconstruction method. For radial functions, the orthogonal set has a particularly simple form being related to the classical Legendre polynomials. We give a detailed statistical accuracy analysis of the proposed estimate of f(x, y) in the sense of the L/sub 2/ metric. It is found that there is an inherent limitation in the precision of the estimate due to the geometric nature of a circular domain. this is explained by relating the accuracy issue to the celebrated problem in the analytic number theory called the lattice points of a circle. In fact, the obtained bounds for the mean integrated squared error are determined by the best known result so far on the problem of lattice points within the circular domain.

关键词： Polynomials Image analysis Jacobian matrices pattern recognition Lattices Data analysis pattern analysis Positrons Optical diffraction Optical devices

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pattern recognition techniques applied to electric power signal processing

Pattern recognition techniques applied to electric power sig...

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international conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

作者： Ghazi Bousaleh Mohamad Darwiche Fahed Hassoun Lebanese University Saida Lebanon Eseo Institute of Science and Technology Angers France France Telecom Nantes France

We propose in this paper an approach whose main objective is to detect disturbances that affect an electric power signal. the method allows us to locate the time occurrence of these disturbances. the signal processing consists of determining two distributions that are based on the energy of the wavelet signal decomposition: the deviation distribution and the deformation distribution. theses distributions are a signature of the disturbance and are able to provide an identification of the type of the problem. the method has been developed using the analysis by the Discrete Wavelet Transform (DWT). the electrical signal is decomposed into several levels by DWT. the different waveforms resolution levels allows us to detect any deviations from the sane signal. the energy distributions data obtained in the first step will be used as feature vectors for training an artificial neural network (ANN) with multilayer perceptrons (MLPs) and support vector machines (SVMs) to classify the Power Quality Disturbance (PQD).

关键词： Signal resolution Discrete wavelet transforms Harmonic analysis Energy resolution Power quality

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pattern recognition for Uterine Fibroid Detection

Pattern Recognition for Uterine Fibroid Detection

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IEEE Eurasia conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Wai Yie Leong Yuan Zhi Leong Wai San Leong INTI International University Persiaran Perdana BBN Putra Nilai Nilai Malaysia Schneider Electric Singapore Pte. Ltd. Singapore

ISBN: (数字)9798350396133

ISBN: (纸本)9798350396140

pattern recognition plays a significant role in the detection of uterine fibroids through various medical imaging modalities such as ultrasound, Magnetic Resonance Imaging (MRI), and computed tomography (CT) scans. Ultrasound imaging helps identify hypoechoic masses within the uterine muscle layer, while MRI provides detailed images showing well-defined masses with characteristic signal intensities on Tl-weighted and T2-weighted sequences. CT imaging, though less common, is used to detect fibroids based on hypodense masses within the uterus or pelvic cavity. Computer-aided diagnosis (CAD) systems and advanced machine-learning algorithms aid in automating the detection and characterization of fibroids by analyzing image features such as shape, texture, and intensity. these pattern recognition techniques facilitate early diagnosis and accurate assessment of uterine fibroids, enabling timely management and treatment of this prevalent gynecological condition.

关键词： Ultrasonic imaging Accuracy Shape Magnetic resonance imaging Computed tomography Uterus Streaming media pattern recognition Reliability Sustainable development

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Development and preliminary testing of a flexible control system for powered knee-ankle prostheses

Development and preliminary testing of a flexible control sy...

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IEEE/RAS-EMBS international conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Ann M. Simon Kimberly A. Ingraham John A. Spanias Aaron J. Young Levi J. Hargrove Department of Physical Medicine and Rehabilitation Northwestern University Chicago IL USA Rehabilitation Institute of Chicago Chicago IL USA Department of Mechanical Engineering University of Michigan Ann Arbor MI USA Department of Biomedical Engineering Northwestern University Evanston IL USA Human Neuromechanics Laboratory University of Michigan Ann Arbor MI USA

ISBN: (纸本)9781509032884

Recently developed motorized lower limb prostheses which provide joint power at the knee and/or ankle have been shown to assist users in performing a variety of ambulation tasks. Control of these devices, specifically creating seamless, natural, and accurate mode transitions, is challenging. the control system also needs to allow users the flexibility to perform these tasks with a wide range of variability. the goal of this study was to develop and test a flexible control system for a powered knee-ankle prosthesis. Specifically, a pattern recognition system was trained to recognize the transitions between six modes (standing, level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent) at various discrete time points during the gait cycle. Six transfemoral users were successful performing a variety of ambulation tasks including climbing a 3- and 4-step staircases, performing walking and standing transitions to and from the staircases, approaching the stairs and ramps at various angles, and ambulating at various speeds. Offline classification results demonstrate that system error rates 1.6% [1.0%], mean [standard deviation], at heel contact and 0.5% [0.2%] at toe off. the results of this study are encouraging and provide another step towards a clinically viable lower limb intent recognition system for a powered knee-ankle prosthesis.

关键词： Legged locomotion Prosthetics Steady-state Knee Mechanical sensors Performance evaluation pattern recognition

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Gait based Human Identification through Intra-Class Variations

Gait based Human Identification through Intra-Class Variatio...

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international conference on Inventive Computation Technologies (ICICT)

作者： Khaine Zar Chi Tun Sai Maung Maung Zaw Faculty of Computer Systems and Technologies University of Computer Studies Mandalay (UCSM) Mandalay Myanmar

ISBN: (数字)9781728185019

ISBN: (纸本)9781728185026

the Gait based human identification is the recently critical and challenging research problem for computer vision, visual surveillance and access control security applications. Human gait is behavioral biometrics that involves both the appearance of the body and the dynamics of motion for human walking. Intra-class variation, which involves different carrying condition, variation in clothes and normal walking with different variations in view angles that interfere affect the efficiency in recognition. the extraction of discriminatory and insightful characteristics from video frame sequences is also important. this paper proposes statistical gait characteristics to identify the biometric gait feature for human identification by combining statistical values of SURF and Haralick features. these features are appropriate for human identification to decrease the effects of “covariate variables” so that the accuracy of human identification is efficient. the discriminatory performance of gait pattern classification on CASIA-B (Multi-view Gait Dataset) is assessed by the K-Nearest Neighbor (KNN) classifier.

关键词： Legged locomotion Visualization Face recognition Surveillance Clothing pattern classification Iris recognition

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A geometrical approach to rejecting option in pattern recognition problem

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2015年 9339卷 231-243页

作者： Ciecierski, Jakub Dybisz, Bartomiej Jastrzebska, Agnieszka Pedrycz, Witold Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2G7 Canada

ISBN: (纸本)9783319243689

Frequently it happens that during symbols recognition, not all of them are the proper ones. this may cause deterioration of a classifying process. In this paper we present a way to "separate the wheat from the chaff", by constructing a rejector, based on geometrical figures enclosing "wheat" and excluding "chaff". We assume that entities of wheat, called native elements, are structured in some way and that there is no a priori knowledge about chaff, named foreign symbols. For the purpose of this study we present simple geometrical figures to generalize the distribution of symbols and to govern the rejection process. © IFIP international Federation for Information Processing 2015.

关键词： Geometry

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Unpaired Multisource Imagery Joint Learning via Lightweight Network for Tread Tire recognition

Unpaired Multisource Imagery Joint Learning via Lightweight ...

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Natural Language Processing (ICNLP), international conference on

作者： Yulu Zhong Jiasen Wang Jie Fang School of Communications and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Tread tire recognition has attracted much attention during the last decade since its vital practival application value in criminal investigation cases, but the feature representation from only one source imagery of tire pattern can not reflect its semantic attributes because of some unknown interfere noises, and hence influence the recognition performance. To address the aforementioned problem, this work presents two novel unpaired tire pattern imagery recognition frameworks, including a multisource joint learning framework and a cross-domain expansion learning framework, the former needs multisource images in training phase and testing phases while the latter needs multisource images in training phase and only one source images in testing phase. Specifically, the multisource joint learning framework utilizes a perception retrieval based fusion module to enhance the complementary information of surface pattern and tire mark to each other, then uses a two-stream lightweight network based on GhostNet to exploit the latent semantic information of multisource images and finalize the recognition. the cross-domain expansion learning framework uses a CycleGAN to generate corresponding image series of unavailable domain according to ones in existing domain, and utilizes the same two-stream classification network in multisource joint learning framework to finalize the recognition. Finally, the proposed two frameworks achieve competitive performances on the challenging CIIP-TPID dataset, which demonstrate their superiorities.

关键词： Training Image recognition Semantics Noise Tires Natural language processing pattern recognition Testing

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Weighting multi-block two-dimensional linear discriminant features for face verification

Weighting multi-block two-dimensional linear discriminant fe...

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international conference on Information, Communications and Signal Processing

作者： Jian-Gang Wang Eric Sung Wei-Yun Yau Institute for Infocomm Research Singapore School of Electrical & Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781424409822

In order to deal with the small sample size (SSS) problem in linear discriminant analysis (LDA), a two-dimensional linear discriminant analysis (2DLDA) has been proposed [16, 25]. However, some discriminant information could be lost because the 2DLDA can be viewed as operating on the image rows or columns independently [24]. We know that the spatial correlation between neighbouring pixels in both vertical and horizontal directions tend to be high. this motivates us to consider other decompositions of the image. In this paper, a multi-block 2DLDA (MB2DLDA) is developed to consider the spatial correlation in which an image is decomposed into blocks of pixels where some of this local correlations could be captured when the 2DLDA is applied to the subimages instead of the whole image. Moreover, different facial regions have different degrees of importance in face recognition, so we combine the 2DLDA features of the different facial regions based on their degrees of importance. this can be done by weighting the 2DLDA feature of a block. In doing so, the verification performance is improved. We evaluate the approach on the face verification on the XM2VTS database based on a standard protocol defined in the database. the results show that the weighted MB2DLDA outperforms the 2DLDA.

关键词： Linear discriminant analysis Face recognition Pixel Principal component analysis Spatial databases Feature extraction Scattering Covariance matrix Protocols pattern recognition

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Modulation pattern recognition Based on TR-Unet

Modulation Pattern Recognition Based on TR-Unet

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IEEE international conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Yanzhe Chai University of Jinan Jinan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Under the complex electromagnetic environment, Automatic Modulation recognition (AMR) needs to improve the recognition accuracy. To address such problems, we design a Transformer ResNet-Unet (TR-Unet) model, which combines the Transformer and the Unet model with identity shortcut connections. Identity shortcut connections are from the residual network (ResNet). the original in-phase and quadrature (IQ) signals are input directly into the network. We design multi-layer encoder and decoder to extract features and introduce identity shortcut connection to preserve feature details. We utilize the Transformer instead of the convolution in the Unet model. this is to provide more comprehensive feature information extraction capability for the model and to reduce misjudgment and omission of the modulated signal. the training process is conducted with RML2016.10b, while the testing process is conducted with RML2016.10a. the results of simulation experiments show the classification accuracy of our proposed model can reach 92% when the signal-to-noise Ratio (SNR) is 12dB. this proves the effectiveness and reliability of TR-Unet.

关键词： Training Visualization Accuracy Computational modeling Modulation Transformers Feature extraction pattern recognition Signal to noise ratio Testing

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survAIval: Survival Analysis with the Eyes of AI 1

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5th international conference on CHIRA 2021 and 6th international conference on CHIRA 2022

作者： Kowol, Kamil Bracke, Stefan Gottschalk, Hanno School of Mathematics and Natural Sciences IZMD University of Wuppertal Gaußstraße 20 Wuppertal Germany Chair of Reliability Engineering and Risk Analytics IZMD University of Wuppertal Gaußstraße 20 Wuppertal Germany Institute of Mathematics Technical University Berlin Straße des 17. Juni 135 Berlin Germany

ISBN: (数字)9783031419621

ISBN: (纸本)9783031419614

In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-critical corner cases in a short period of time, as already presented in [12]. Our results show that incorporating these corner cases during training improves the recognition of corner cases during testing, even though, they were recorded due to visual impairment. Using the corner case triggering pipeline developed in the previous work, we investigate the effectiveness of using expert models to overcome the domain gap due to different weather conditions and times of day, compared to a universal model from a development perspective. Our study reveals that expert models can provide significant benefits in terms of performance and efficiency, and can reduce the time and effort required for model training. Our results contribute to the progress of automated driving, providing a pathway for safer and more reliable autonomous vehicles on the road in the future. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： bioinformatics

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