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Arabic Islamic Manuscripts Digitization based on Hybrid K-NN/SVM Approach and Cloud Computing Technologies

Arabic Islamic Manuscripts Digitization based on Hybrid K-NN...

Taibah-University International Conference on Advances in Information Technology for the Holy Quran and Its Sciences (NOORIC)

作者： Hassen, Hamdi Khemakhem, Maher Taibah Univ Coll Sci & Arts Al Ola Dept Comp Sci Al Ola Saudi Arabia Univ Sfax FSEGS Mir Cl Lab Sfax 3018 Tunisia King Abdulaziz Univ Fac Comp & Technol Dept Comp Sci Jeddah Saudi Arabia

ISBN: (纸本)9781479928231

In many national libraries and archive centers, most of Islamic Manuscripts are still in their initial form and not digitized yet. These documents are indeed very rich in knowledge and constitute a part of the heritage of Muslims. The main reasons behind the digitization of these Islamic Manuscripts are, consequently and at least, to enhance and ease their accessibility by people who are interesting in order to exploit the corresponding knowledge and to improve their durability. Such a mission is not yet easy to achieve because of the weaknesses of the existing approaches and algorithms. Indeed, several researchers around the world have proposed a variety of approaches, algorithms and techniques in order to build a powerful system able to digitize such documents. Unfortunately, these efforts didn't succeed since they realized that such problem cannot be solved without the integration and cooperation of several strong complementary approaches, algorithms and techniques at the same time. But, such an idea and a system requires at the same time the knowledge of the good complementary approaches, algorithms and techniques which can lead to an acceptable recognition rate of the Arabic handwriting in one hand, and the adequate hardware infrastructure which can host such complex and greedy software to achieve the mission in a reasonable time on the other hand. Our idea consists on: in the first hand, to consider cloud computing as an infrastructure (IaaS) to deploy our combination of algorithms K-NN/SVM for Arabic Islamic Manuscripts recognition system AIMRS. In the second hand, to consider cloud Storage as a Service (SaaS) to store and retrieve large amounts of Arabic Islamic Manuscripts. Our approach provides indeed an adequate platform for the expected powerful digitization system based on the integration and cooperation of some strong complementary approaches. In addition, our approach offers a number of benefits, such as the ability to store and retrieve large amounts

关键词： Arabic Islamic Manuscripts Cloud Computing pattern recognition system

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Towards the Development of an Automated Blood Vessel Classification system 10

Towards the Development of an Automated Blood Vessel Classif...

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10th International Symposium on Communication systems, Networks and Digital Signal Processing (CSNDSP)

作者： Satney, W. Als, A. Carrington-Dyall, A. Scantlebury-Manning, T. Univ West Indies Comp Sci Math & Phys Dept Cave Hill Campus Bridgetown Barbados Univ West Indies Biol & Chem Sci Dept Cave Hill Campus Bridgetown Barbados

ISBN: (纸本)9781509025268

The implementation and optimization of a deterministic pattern recognition system to automatically classify venous and arterial signals is presented. CWT and STFT spectral analysis methods were applied to a pool of ninety-seven Doppler signals consisting of both arterial and venous signals. Subsequently, a classification algorithm was developed to examine each resulting spectral data to differentiate between the signal types. The CWT method proved more accurate than the STFT method because of its exceptional spectral resolution. The results of the study can be employed to medical systems in which distinguishing between venous and arterial signals is of primary importance.

关键词： pattern recognition system Doppler signals CWT STFT

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Development of an Artificial Neural Network system for Sulphate-reducing Bacteria Detection by using Model-based Design Technique

Development of an Artificial Neural Network System for Sulph...

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IEEE Asia Pacific Conference on Circuits and systems (APCCAS)

作者： Tan, Earn Tzeh Halim, Zaini Abdul Univ Sains Malaysia Sch Elect & Elect Engn George Town Malaysia Univ Sains Malaysia Collaborat Microelect Design Excellence Ctr George Town Malaysia

ISBN: (纸本)9781457717284;9781457717291

The paper demonstrates an approach for the implementation of artificial neural network (ANN) as a pattern recognition system in the development of electronic nose devices for sulphate-reducing bacteria (SRB) detection. The neural network algorithm was modeled using Xilinx system Generator and can be realized in Xilinx FPGA. The neural network was trained using the Levenberg-Marquardt back propagation algorithm in Matlab based on data collected from experiments, in which samples that contained either the broth medium with SRB or only the broth medium were used. In Matlab, sigmoid function is used as the activation function for the neurons in hidden and output layers. However, the block sets provided by Xilinx system Generator do not contain the sigmoid function. Thus, the sigmoid function was modeled through the expansion of the Maclaurin power series. Adders and multipliers blocks are used to calculate the series equation. This paper discusses on the similarity of sigmoid function generated using Matlab and approximation of sigmoid function generated using Maclaurin power series. The simulated results show that the absolute error between the values of sigmoid function based on Matlab and Xilinx system Generator using Maclaurin power series are not more than 1.38% when the inputs fall in the range between -0.5 to 0.5. Thus, the modeling of sigmoid function in Xilinx system Generator has been successfully modeled with the accuracy of the ANN to detect SRB is 100%.

关键词： Artificial neural networks Sigmoid Colon MATLAB xilinx system generator design techniques pattern recognition system Desulfobacter vibrioformis Power series

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A Study on the Method of Image Pre-Processing for recognition of Crop Diseases

A Study on the Method of Image Pre-Processing for Recognitio...

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International Conference on Advanced Computer Control

作者： Ying, Geng Miao, Li Yuan, Yuan Zelin, Hu Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China

ISBN: (纸本)9780769535166

Regarding the cucumber powdery mildew, speckle and downy mildew as examples, the method of image pre-processing for recognizing crop diseases was studied. This paper compared the effect of two filters-Simple filter and Median filter, and at last we chose median filter to wipe out the disturbance of noise effectively, and two-apex method was applied to separate the disease images from the background. Disease spots were separated through performing image edge detection and Snake model, and the latter got more desired result. Thus the image pre-processing made a good foundation for following effective characteristic parameters for the disease diagnoses and setting up pattern recognition system.

关键词： Plant health median filters pattern recognition system Pretreatment Image propamocarb Edge detection

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Memristor plasticity enables emergence of synchronization in neuromorphic networks

Memristor plasticity enables emergence of synchronization in...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Ascoli, Alon Tetzlaff, Ronald Lanza, Valentina Corinto, Fernando Gilli, Marco Tech Univ Dresden Inst Grundlagen Elektrotech & Elekt D-01062 Dresden Germany Univ Havre Appliquees Havre Lab Math Le Havre France Politecn Torino Dept Elect & Telecommun Turin Italy

ISBN: (纸本)9781479934324

Besides being at the core of novel ultra-high density low-power non-volatile memories and innovative pattern recognition systems based upon oscillatory associative and dynamic memories, the nano-scale memristor also has the potential to reproduce the behavior of a biological synapse more efficiently and accurately than any conventional electronic emulator. As in a living being the weight of a synapse is adapted by the ionic flow through it, so the conductance of a memristor is adjusted by the flux across it. This article is organized according to the regulations of the ISCAS2014 special session on the state-of-the-art in memristor-based nonlinear circuits and architectures. In this work we focus on arrays of oscillatory cells locally coupled through memristors. Our investigations shows how the nonlinear dynamics of the memristor plays a key role in the mechanisms underlying the synchronization properties of the networks. This work provides new insights into the nonlinear behavior of the still largely unexplored memristor element, which promises to revolutionize integrated circuit design in the incoming years.

关键词： Couplings Equations Manganese Mathematical model Memristors Neurons Synchronization Memristors Couplings Mathematical Model manganese Neurons pattern recognition system coupling Synapses Nonvolatile memory Emergence integrated circuit design

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Machine Learning Approach to Segment Saccharomyces cerevisiae Yeast Cells

Machine Learning Approach to Segment <i>Saccharomyces cerevi...

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International Conference on Advances in Biomedical Engineering (ICABME)

作者： Tleis, Mohamed Verbeek, Fons Leiden Univ LIACS Sect Imaging & BioInformat Leiden Netherlands

ISBN: (纸本)9781467365161

In biological studies, Saccharomyces cerevisiae yeast cells are used to study the behaviour of proteins. This is a time consuming and not completely objective process. Hence, image analysis platforms are developed to address these problems and to offer analysis per cell as well. The segmentation algorithms implemented in such platforms can segment the healthy cells, along with artefacts such as debris and dead cells that exist in the cultured medium. The novel idea in this work is to apply a machine learning approach to train the segmentation system in order to classify the healthy cell objects from the other objects. Such approach is based on the analysis of a set of relevant individual cell features extracted from the microscope images of yeast cells. These features include texture measurements and wavelet-based texture measurements, as well as moment invariant features. Those features were introduced to describe the intensity and morphology characteristics in a more sophisticated way. A set of classification systems, data sampling techniques, data normalization schemes and feature selection algorithms were tested and evaluated to build a classification model in order to be used within the segmentation module. The study picks the simple logistic classification model as the best approach to classify our dataset of 1380 cells. This system increases the performance level in our image and data analysis modules, improve the segmentation and consequently the analysis of the measurement results. This leads to a better pattern recognition system as well.

关键词： biological techniques biology computing cellular biophysics feature extraction image classification image segmentation image texture learning (artificial intelligence) microorganisms optical microscopy wavelet transforms S. cerevisiae cell segmentation Saccharomyces cerevisiae data analysis module data normalization schemes data sampling techniques feature selection algorithms image analysis module logistic classification model machine learning approach moment invariant features pattern recognition system segmentation system training wavelet based texture measurements yeast cell microscope images yeast cells Accuracy Biomedical measurement Image segmentation Logistics Machine learning algorithms Vegetation Saccharomyces cerevisiae image segmentation pattern recognition system biology computing cellular biophysics Machine learning algorithms Biological techniques image texture Image classification wavelet transforms Feature extraction Logistics optical microscopy biomedical measurement selection algorithm

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Non-weight-bearing neural control of a powered transfemoral prosthesis

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2013年第1期10卷 62-62页

作者： Hargrove, Levi J. Simon, Ann M. Lipschutz, Robert Finucane, Suzanne B. Kuiken, Todd A. Rehabil Inst Chicago Ctr Bion Med Chicago IL 60611 USA Northwestern Univ Dept Phys Med & Rehabil Chicago IL 60611 USA Northwestern Univ Dept Biomed Engn Evanston IL 60208 USA

Lower limb prostheses have traditionally been mechanically passive devices without electronic control systems. Microprocessor-controlled passive and powered devices have recently received much interest from the clinical and research communities. The control systems for these devices typically use finite-state controllers to interpret data measured from mechanical sensors embedded within the prosthesis. In this paper we investigated a control system that relied on information extracted from myoelectric signals to control a lower limb prosthesis while amputee patients were seated. Sagittal plane motions of the knee and ankle can be accurately (>90%) recognized and controlled in both a virtual environment and on an actuated transfemoral prosthesis using only myoelectric signals measured from nine residual thigh muscles. Patients also demonstrated accurate (similar to 90%) control of both the femoral and tibial rotation degrees of freedom within the virtual environment. A channel subset investigation was completed and the results showed that only five residual thigh muscles are required to achieve accurate control. This research is the first step in our long-term goal of implementing myoelectric control of lower limb prostheses during both weight-bearing and non-weight-bearing activities for individuals with transfemoral amputation.

关键词： Virtual Environment Controller Area Network Residual Limb pattern recognition system Transfemoral Amputee

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Using Deep Learning and Low-Cost RGB and Thermal Cameras to Detect Pedestrians in Aerial Images Captured by Multirotor UAV

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SENSORS 2018年第7期18卷 2244-2244页

作者： de Oliveira, Diulhio Candido Wehrmeister, Marco Aurelio Fed Univ Technol Parana UTFPR Comp Syst Engn Lab LESC BR-80230901 Curitiba Parana Brazil

The use of Unmanned Aerial Vehicles (UAV) has been increasing over the last few years in many sorts of applications due mainly to the decreasing cost of this technology. One can see the use of the UAV in several civilian applications such as surveillance and search and rescue. Automatic detection of pedestrians in aerial images is a challenging task. The computing vision system must deal with many sources of variability in the aerial images captured with the UAV, e.g., low-resolution images of pedestrians, images captured at distinct angles due to the degrees of freedom that a UAV can move, the camera platform possibly experiencing some instability while the UAV flies, among others. In this work, we created and evaluated different implementations of pattern recognition systems (PRS) aiming at the automatic detection of pedestrians in aerial images captured with multirotor UAV. The main goal is to assess the feasibility and suitability of distinct PRS implementations running on top of low-cost computing platforms, e.g., single-board computers such as the Raspberry Pi or regular laptops without a GPU. For that, we used four machine learning techniques in the feature extraction and classification steps, namely Haar cascade, LBP cascade, HOG + SVM and Convolutional Neural Networks (CNN). In order to improve the system performance (especially the processing time) and also to decrease the rate of false alarms, we applied the Saliency Map (SM) and Thermal Image Processing (TIP) within the segmentation and detection steps of the PRS. The classification results show the CNN to be the best technique with 99.7% accuracy, followed by HOG + SVM with 92.3%. In situations of partial occlusion, the CNN showed 71.1% sensitivity, which can be considered a good result in comparison with the current state-of-the-art, since part of the original image data is missing. As demonstrated in the experiments, by combining TIP with CNN, the PRS can process more than two frames per second (fps),

关键词： pedestrian detection aerial images Unmanned Aerial Vehicle (UAV) thermal camera deep learning convolutional neural network pattern recognition system performance assessment

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Progressive Data Transmission for Anatomical Landmark Detection in a Cloud

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METHODS OF INFORMATION IN MEDICINE 2012年第3期51卷 268-278页

作者： Sofka, M. Ralovich, K. Zhang, J. Zhou, S. K. Comaniciu, D. Siemens Corp Res Princeton NJ 08540 USA Tech Univ Munich D-85748 Garching Germany

Background: In the concept of cloud-computing-based systems, various authorized users have secure access to patient records from a number of care delivery organizations from any location. This creates a growing need for remote visualization, advanced image processing, state-of-the-art image analysis, and computer aided diagnosis. Objectives: This paper proposes a system of algorithms for automatic detection of anatomical landmarks in 3D volumes in the cloud computing environment. The system addresses the inherent problem of limited bandwidth between a (thin) client, data center, and data analysis server. Methods: The problem of limited bandwidth is solved by a hierarchical sequential detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request to detect a set of landmarks for region visualization or further analysis. The algorithm running on the data analysis server obtains a coarse level image from the data center and generates landmark location candidates. The candidates are then used to obtain image neighborhood regions at a finer resolution level for further detection. This way, the landmark locations are hierarchically and sequentially detected and refined. Results: Only image regions surrounding landmark location candidates need to be trans- mitted during detection. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data. Conclusions: The hierarchical sequential algorithm with progressive data transmission considerably reduces bandwidth requirements in cloud-based detection systems.

关键词： Cloud computing machine learning pattern recognition system computer-assisted image processing image compression

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pattern Analysis Towards Human Verification using Photoplethysmograph Signals

Pattern Analysis Towards Human Verification using Photopleth...

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Emerging Trends in Smart Technologies (ICETST), 2020 International Conference on

作者： Muhammad Umar Khan Sumair Aziz Syed Zohaib Hassan Naqvi Ahmed Zaib Aiman Maqsood University of Engineering and Technology Taxila Taxila Pakistan

ISBN: (数字)9781728171135

ISBN: (纸本)9781728171142

Photoplethysmogram (PPG) is a biomedical signal capable of detecting blood volume changes in the microvascular bed of tissues. As PPG signals are more intimate, keen, hard to replicate, and steel, they are dedicated to providing a more secure biometric approach for user recognition. This work presents a low cost, nonintellectual pattern recognition system for biometric authentication. In the proposed methodology, raw PPG signals obtained from 20 subjects are denoised using Empirical Mode Decomposition (EMD) and reconstructed using the first three IMFs. A combination of twenty time and frequency domain features is extracted from the preprocessed PPG signals. Subsequently, a range of different classifiers, including Support Vector Machines (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN), was used to classify the features extracted. When trained to quadratic SVM classifier using 10-fold cross-validation, the system achieves an accuracy of 93.10%.

关键词： Photoplethysmography pattern recognition system Empirical Mode Decomposition Support Vector Machines Feature Extraction pattern recognition system Support Vector Network Photoplethysmography Feature extraction pattern analysis SRGN gene Empirical mode decomposition signals Microcirculatory Bed Biometric Identification

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