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Sample records for networks based automatical

  1. Formal Specification Based Automatic Test Generation for Embedded Network Systems

    Directory of Open Access Journals (Sweden)

    Eun Hye Choi

    2014-01-01

    Full Text Available Embedded systems have become increasingly connected and communicate with each other, forming large-scaled and complicated network systems. To make their design and testing more reliable and robust, this paper proposes a formal specification language called SENS and a SENS-based automatic test generation tool called TGSENS. Our approach is summarized as follows: (1 A user describes requirements of target embedded network systems by logical property-based constraints using SENS. (2 Given SENS specifications, test cases are automatically generated using a SAT-based solver. Filtering mechanisms to select efficient test cases are also available in our tool. (3 In addition, given a testing goal by the user, test sequences are automatically extracted from exhaustive test cases. We’ve implemented our approach and conducted several experiments on practical case studies. Through the experiments, we confirmed the efficiency of our approach in design and test generation of real embedded air-conditioning network systems.

  2. AUTOMATIC APPROACH TO PRODUCT CONCEPTUAL DESIGN BASED ON CONSTRAINT NETWORK

    Institute of Scientific and Technical Information of China (English)

    Li Hongjie; Xiao Renbin

    2004-01-01

    Product design is considered as the constraint satisfaction problem (CSP), and a new design approach basedon constraint network is proposed and applied to make design automatic partially. By means of constraint extraction, constraint network construction and operation, constraint chains are acquired, and then the conceptual design scheme can be got by decoding the information stored in the design constraint chains, which provides a novelty method for mechanical product design automation. Shearing mechanism of shearing machine has been designed by this way.

  3. Automatic event detection based on artificial neural networks

    Science.gov (United States)

    Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef

    2015-04-01

    The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to

  4. Reinforcement-Based Fuzzy Neural Network ontrol with Automatic Rule Generation

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    A reinforcemen-based fuzzy neural network control with automatic rule generation RBFNNC) is pro-posed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based onthe state variables of object system. RBFNNC was applied to a cart-pole balancing system and simulation resultshows significant improvements on the rule generation.

  5. MINUTIAE EXTRACTION BASED ON ARTIFICIAL NEURAL NETWORKS FOR AUTOMATIC FINGERPRINT RECOGNITION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Necla ÖZKAYA

    2007-01-01

    Full Text Available Automatic fingerprint recognition systems are utilised for personal identification with the use of comparisons of local ridge characteristics and their relationships. Critical stages in personal identification are to extract features automatically, fast and reliably from the input fingerprint images. In this study, a new approach based on artificial neural networks to extract minutiae from fingerprint images is developed and introduced. The results have shown that artificial neural networks achieve the minutiae extraction from fingerprint images with high accuracy.

  6. Technical Note: Automatic river network generation for a physically-based river catchment model

    OpenAIRE

    2010-01-01

    SHETRAN is a physically-based distributed modelling system that gives detailed simulations in time and space of water flow and sediment and solute transport in river catchments. Standard algorithms for the automatic generation of river channel networks from digital elevation data are impossible to apply in SHETRAN and other similar models because the river channels are assumed to run along the edges of grid cells. In this work a new algorithm for the automatic generation of a river cha...

  7. Technical Note: Automatic river network generation for a physically-based river catchment model

    OpenAIRE

    2010-01-01

    SHETRAN is a physically-based distributed modelling system that gives detailed simulations in time and space of water flow and sediment and solute transport in river catchments. Standard algorithms for the automatic generation of river channel networks from digital elevation data are impossible to apply in SHETRAN and other similar models because the river channels are assumed to run along the edges of grid cells. In this work a new algorithm for the automatic generation of a river channel ne...

  8. Technical Note: Automatic river network generation for a physically-based river catchment model

    Directory of Open Access Journals (Sweden)

    S. J. Birkinshaw

    2010-09-01

    Full Text Available SHETRAN is a physically-based distributed modelling system that gives detailed simulations in time and space of water flow and sediment and solute transport in river catchments. Standard algorithms for the automatic generation of river channel networks from digital elevation data are impossible to apply in SHETRAN and other similar models because the river channels are assumed to run along the edges of grid cells. In this work a new algorithm for the automatic generation of a river channel network in SHETRAN is described and its use in an example catchment demonstrated.

  9. Technical Note: Automatic river network generation for a physically-based river catchment model

    Science.gov (United States)

    Birkinshaw, S. J.

    2010-09-01

    SHETRAN is a physically-based distributed modelling system that gives detailed simulations in time and space of water flow and sediment and solute transport in river catchments. Standard algorithms for the automatic generation of river channel networks from digital elevation data are impossible to apply in SHETRAN and other similar models because the river channels are assumed to run along the edges of grid cells. In this work a new algorithm for the automatic generation of a river channel network in SHETRAN is described and its use in an example catchment demonstrated.

  10. Technical Note: Automatic river network generation for a physically-based river catchment model

    Directory of Open Access Journals (Sweden)

    S. J. Birkinshaw

    2010-05-01

    Full Text Available SHETRAN is a physically-based distributed modelling system that gives detailed simulations in time and space of water flow and sediment and solute transport in river catchments. Standard algorithms for the automatic generation of river channel networks from digital elevation data are impossible to apply in SHETRAN and other similar models because the river channels are assumed to run along the edges of grid cells. In this work a new algorithm for the automatic generation of a river channel network in SHETRAN is described and its use in an example catchment demonstrated.

  11. Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks

    OpenAIRE

    Oliveira, Gonçalo; Frazão, Xavier; Pimentel, André; Ribeiro, Bernardete

    2016-01-01

    Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance in several generic object recognition tasks (PASCAL Visual Object Classes challenges). In par...

  12. An Automatic System of Vehicle Number-Plate Recognition Based on Neural Networks

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    This paper presents an automatic system of vehicle number-plate recognition based on neural networks. In this system, location of number-plate and recognition of characters in number-plate can be automatically completed. Pixel colors of Number-plate area are classified using neural network, then color features are extracted by analyzing scanning lines of the cross-section of number-plate. It takes full use of number-plate color features to locate number plate. Characters in number-plate can be effectively recognized using the neural networks. Experimental results show that the correct rate of number-plate location is close to 100%, and the time of number-plate location is less than 1 second. Moreover, recognition rate of characters is improved due to the known number-plate type. It is also observed that this system is not sensitive to variations of weather, illumination and vehicle speed. In addition, and also the size of number-plate need not to be known in prior. This system is of crucial significance to apply and spread the automatic system of vehicle number-plate recognition.

  13. Automatic layout feature extraction for lithography hotspot detection based on deep neural network

    Science.gov (United States)

    Matsunawa, Tetsuaki; Nojima, Shigeki; Kotani, Toshiya

    2016-03-01

    Lithography hotspot detection in the physical verification phase is one of the most important techniques in today's optical lithography based manufacturing process. Although lithography simulation based hotspot detection is widely used, it is also known to be time-consuming. To detect hotspots in a short runtime, several machine learning based methods have been proposed. However, it is difficult to realize highly accurate detection without an increase in false alarms because an appropriate layout feature is undefined. This paper proposes a new method to automatically extract a proper layout feature from a given layout for improvement in detection performance of machine learning based methods. Experimental results show that using a deep neural network can achieve better performance than other frameworks using manually selected layout features and detection algorithms, such as conventional logistic regression or artificial neural network.

  14. Automatic Identification of Axis Orbit Based on Both Wavelet Moment Invariants and Neural Network

    Institute of Scientific and Technical Information of China (English)

    FuXiang-qian; LiuGuang-lin; JiangJing; LiYou-ping

    2003-01-01

    Axis orbit is an important characteristic to be used in the condition monitoring and diagnosis system of rotating machine. The wavelet moment has the invariant to the translation, scaling and rotation. A method, which uses a neural network based on Radial Basis Function (RBF) and wavelet moment invariants to identify the orbit of shaft centerline of rotating machine is discussed in this paper. The principle and its application procedure of the method are introduced in detail. It gives simulation results of automatic identification for three typical axis orbits. It is proved that the method is effective and practicable.

  15. Automatic Identification of Axis Orbit Based on Both Wavelet Moment Invariants and Neural Network

    Institute of Scientific and Technical Information of China (English)

    Fu Xiang-qian; Liu Guang-lin; Jiang Jing; Li You-ping

    2003-01-01

    Axis orbit is an important characteristic to be used in the condition monitoring and diagnosis system of rota-ting machine. The wavelet moment has the invariant to the translation, scaling and rotation. A method, which uses a neural network based on Radial Basis Function (RBF) and wavelet moment invariants to identify the orbit of shaft centerline of rotating machine is discussed in this paper. The principle and its application procedure of the method are intro-duced in detail. It gives simulation results of automatic identi-fication for three typical axis orbits. It is proved that the method is effective and practicable.

  16. Sedimentary Micro-phase Automatic Recognition Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    龚声蓉; 王朝晖

    2004-01-01

    In the process of geologic prospecting and development, it is important to forecast the distribution of gritstone, master the regulation of physical parameter in the reserves mass level. Especially, it is more important to recognize to rock phase and sedimentary circumstance. In the land level, the study of sedimentary phase and micro-phase is important to prospect and develop. In this paper, an automatic approach based on ANN (Artificial Neural Networks) is proposed to recognize sedimentary phase, the corresponding system is designed after the character of well general curves is considered. Different from the approach extracting feature parameters, the proposed approach can directly process the input curves. The proposed method consists of two steps: The first step is called learning. In this step, the system creates automatically sedimentary micro-phase features by learning from the standard sedimentary micro-phase patterns such as standard electric current phase curves of the well and standard resistance rate curves of the well. The second step is called recognition. In this step, based the results of the learning step, the system classifies automatically by comparing the standard pattern curves of the well to unknown pattern curves of the well. The experiment has demonstrated that the proposed approach is more effective than those approaches used previously.

  17. Neural Network Control-Based Drive Design of Servomotor and Its Application to Automatic Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Ming-Shyan Wang

    2015-01-01

    Full Text Available An automatic guided vehicle (AGV is extensively used for productions in a flexible manufacture system with high efficiency and high flexibility. A servomotor-based AGV is designed and implemented in this paper. In order to steer the AGV to go along a predefined path with corner or arc, the conventional proportional-integral-derivative (PID control is used in the system. However, it is difficult to tune PID gains at various conditions. As a result, the neural network (NN control is considered to assist the PID control for gain tuning. The experimental results are first provided to verify the correctness of the neural network plus PID control for 400 W-motor control system. Secondly, the AGV includes two sets of the designed motor systems and CAN BUS transmission so that it can move along the straight line and curve paths shown in the taped videos.

  18. A Demonstration of Automatically Switched Optical Network

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    We build an automatically switched optical network (ASON) testbed with four optical cross-connect nodes. Many fundamental ASON features are demonstrated, which is implemented by control protocols based on generalized multi-protocol label switching (GMPLS) framework.

  19. Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-07-26

    In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution system operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.

  20. A new colour constancy algorithm based on automatic determination of gray framework parameters using neural network

    Indian Academy of Sciences (India)

    Mohammad Mehdi Faghih; Zeynab Khosravinia; Mohsen Ebrahimi Moghaddam

    2014-04-01

    Colour constancy is defined as the ability to estimate the actual colours of objects in an acquired image disregarding the colour of scene illuminant. Despite large variety of existing methods, no colour constancy algorithm can be considered as universal. Among the methods, the gray framework is one of the best-known and most used approaches. This framework has some parameters that should be set with appropriate values to achieve the best performance for each image. In this article, we propose a neural network-based algorithm that aims to automatically determine the best value of gray framework parameters for each image. It is a multi-level approach that estimates the optimal values for the gray framework parameters based on relevant features extracted from the input image. Experimental results on two popular colour constancy datasets show an acceptable improvement over state-of-the-art methods.

  1. Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ding, Fei [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhang, Yingchen [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-08-23

    In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

  2. Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

    Science.gov (United States)

    Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

    2008-01-01

    Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

  3. The Automatic Telescope Network (ATN)

    CERN Document Server

    Mattox, J R

    1999-01-01

    Because of the scheduled GLAST mission by NASA, there is strong scientific justification for preparation for very extensive blazar monitoring in the optical bands to exploit the opportunity to learn about blazars through the correlation of variability of the gamma-ray flux with flux at lower frequencies. Current optical facilities do not provide the required capability.Developments in technology have enabled astronomers to readily deploy automatic telescopes. The effort to create an Automatic Telescope Network (ATN) for blazar monitoring in the GLAST era is described. Other scientific applications of the networks of automatic telescopes are discussed. The potential of the ATN for science education is also discussed.

  4. FOUR-PARAMETER AUTOMATIC TRANSMISSION TECHNOLOGY FOR CONSTRUCTION VEHICLE BASED ON ELMAN RECURSIVE NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hongyan; ZHAO Dingxuan; TANG Xinxing; Ding Chunfeng

    2008-01-01

    From the viewpoint of energy saving and improving transmission efficiency, the ZL50E wheel loader is taken as the study object. And the system model is analyzed based on the transmission system of the construction vehicle. A new four-parameter shift schedule is presented, which can keep the torque converter working in the high efficiency area. The control algorithm based on the Elman recursive neural network is applied, and four-parameter control system is developed which is based on industrial computer. The system is used to collect data accurately and control 4D180 power-shift gearbox of ZL50E wheel loader shift timely. An experiment is done on automatic transmission test-bed, and the result indicates that the control system could reliably and safely work and improve the efficiency of hydraulic torque converter. Four-parameter shift strategy that takes into account the power consuming of the working pump has important operating significance and reflects the actual working status of construction vehicle.

  5. GIS Data Based Automatic High-Fidelity 3D Road Network Modeling

    Science.gov (United States)

    Wang, Jie; Shen, Yuzhong

    2011-01-01

    3D road models are widely used in many computer applications such as racing games and driving simulations_ However, almost all high-fidelity 3D road models were generated manually by professional artists at the expense of intensive labor. There are very few existing methods for automatically generating 3D high-fidelity road networks, especially those existing in the real world. This paper presents a novel approach thai can automatically produce 3D high-fidelity road network models from real 2D road GIS data that mainly contain road. centerline in formation. The proposed method first builds parametric representations of the road centerlines through segmentation and fitting . A basic set of civil engineering rules (e.g., cross slope, superelevation, grade) for road design are then selected in order to generate realistic road surfaces in compliance with these rules. While the proposed method applies to any types of roads, this paper mainly addresses automatic generation of complex traffic interchanges and intersections which are the most sophisticated elements in the road networks

  6. Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    A. T. Tzallas

    2007-01-01

    Full Text Available The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN, which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.

  7. Automatic diagnosis of premature ventricular contraction based on Lyapunov exponents and LVQ neural network.

    Science.gov (United States)

    Liu, Xiuling; Du, Haiman; Wang, Guanglei; Zhou, Suiping; Zhang, Hong

    2015-10-01

    Premature ventricular contraction (PVC) is a common type of abnormal heartbeat. Without early diagnosis and proper treatment, PVC may result in serious harms. Diagnosis of PVC is of great importance in goal-directed treatment and preoperation prognosis. This paper proposes a novel diagnostic method for PVC based on Lyapunov exponents of electrocardiogram (ECG) beats. The methodology consists of preprocessing, feature extraction and classification integrated into the system. PVC beats can be classified and differentiated from other types of abnormal heartbeats by analyzing Lyapunov exponents and training a learning vector quantization (LVQ) neural network. Our algorithm can obtain a good diagnostic result with little features by using single lead ECG data. The sensitivity, positive predictability, and the overall accuracy of the automatic diagnosis of PVC is 90.26%, 92.31%, and 98.90%, respectively. The effectiveness of the new method is validated through extensive tests using data from MIT-BIH database. The experimental results show that the proposed method is efficient and robust.

  8. A Routing Algorithm for WiFi-Based Wireless Sensor Network and the Application in Automatic Meter Reading

    OpenAIRE

    Li Li; Xiaoguang Hu; Baochang Zhang

    2013-01-01

    The Automatic Meter Reading (AMR) network for the next generation Smart Grid is required to possess many essential functions, such as data reading and writing, intelligent power transmission, and line damage detection. However, the traditional AMR network cannot meet the previous requirement. With the development of the WiFi sensor node in the low power cost, a new kind of wireless sensor network based on the WiFi technology can be used in application. In this paper, we have designed a new ar...

  9. Automatic Isolated-Word Arabic Sign Language Recognition System Based on Time Delay Neural Networks

    Directory of Open Access Journals (Sweden)

    Feras Fares Al Mashagba

    2014-03-01

    Full Text Available There have been a little number of attempts to develop an Arabic sign recognition system that can be used as a communication means between hearing-impaired and other people. This study introduces the first automatic isolated-word Arabic Sign Language (ArSL recognition system based on Time Delay Neural Networks (TDNN. The proposed vision-based recognition system that the user wears two simple but different colors gloves when performing the signs in the data sets within this study. The two colored regions are recognized and highlighted within each frame in the video to help in recognizing the signs. This research uses the multivariate Gaussian Mixture Model (GMM based on the characteristics of the well known Hue Saturation Lightness Model (HIS in determining the colors within the video frames. In this research the mean and covariance of the three colored region within the frames are determined and used to help us in segmenting each frame (picture into two colored regions and outlier region. Finally we propose, create and use the following four features as an input to the TDNN; the centroid position for each hand using the center of the upper area for each frame as references, the change in horizontal velocity of both hands across the frames, the change in vertical velocity of both hands across the frames and the area change for each hand across the frames. A large set of samples has been used to recognize 40 isolated words coded by 10 different signers from the Standard Arabic sign language signs. Our proposed system obtains a word recognition rate of 70.0% in testing set.

  10. Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network

    National Research Council Canada - National Science Library

    Ashraf A. Shahin

    2016-01-01

    .... This paper has proposed dynamic threshold based auto-scaling algorithms that predict required resources using Long Short-Term Memory Recurrent Neural Network and auto-scale virtual resources based on predicted values...

  11. Automatic facial feature extraction and expression recognition based on neural network

    CERN Document Server

    Khandait, S P; Khandait, P D

    2012-01-01

    In this paper, an approach to the problem of automatic facial feature extraction from a still frontal posed image and classification and recognition of facial expression and hence emotion and mood of a person is presented. Feed forward back propagation neural network is used as a classifier for classifying the expressions of supplied face into seven basic categories like surprise, neutral, sad, disgust, fear, happy and angry. For face portion segmentation and localization, morphological image processing operations are used. Permanent facial features like eyebrows, eyes, mouth and nose are extracted using SUSAN edge detection operator, facial geometry, edge projection analysis. Experiments are carried out on JAFFE facial expression database and gives better performance in terms of 100% accuracy for training set and 95.26% accuracy for test set.

  12. A Routing Algorithm for WiFi-Based Wireless Sensor Network and the Application in Automatic Meter Reading

    Directory of Open Access Journals (Sweden)

    Li Li

    2013-01-01

    Full Text Available The Automatic Meter Reading (AMR network for the next generation Smart Grid is required to possess many essential functions, such as data reading and writing, intelligent power transmission, and line damage detection. However, the traditional AMR network cannot meet the previous requirement. With the development of the WiFi sensor node in the low power cost, a new kind of wireless sensor network based on the WiFi technology can be used in application. In this paper, we have designed a new architecture of WiFi-based wireless sensor network, which is suitable for the next generation AMR system. We have also proposed a new routing algorithm called Energy Saving-Based Hybrid Wireless Mesh Protocol (E-HWMP on the premise of current algorithm, which can improve the energy saving of the HWMP and be suitable for the WiFi-based wireless sensor network. The simulation results show that the life cycle of network is extended.

  13. Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification

    Directory of Open Access Journals (Sweden)

    Ricardo Pérez-Aguila

    2013-01-01

    Full Text Available An approach for nonsupervised segmentation of Computed Tomography (CT brain slices which is based on the use of Vector Quantization Networks (VQNs is described. Images are segmented via a VQN in such way that tissue is characterized according to its geometrical and topological neighborhood. The main contribution rises from the proposal of a similarity metric which is based on the application of Discrete Compactness (DC which is a factor that provides information about the shape of an object. One of its main strengths lies in the sense of its low sensitivity to variations, due to noise or capture defects, in the shape of an object. We will present, compare, and discuss some examples of segmentation networks trained under Kohonen’s original algorithm and also under our similarity metric. Some experiments are established in order to measure the effectiveness and robustness, under our application of interest, of the proposed networks and similarity metric.

  14. Mobile large scale 3D coordinate measuring system based on network of rotating laser automatic theodolites

    Science.gov (United States)

    Liu, Zhigang; Liu, Zhongzheng; Wu, Jianwei; Xu, Yaozhong

    2010-08-01

    This paper presents a mobile 3D coordinate measuring system for large scale metrology. This system is composed of a network of rotating laser automatic theodolites (N-RLATs) and a portable touch probe. In the N-RLAT system, each RLAT consists of two laser fans which rotate about its own Z axis at a constant speed and scan the whole metrology space. The optical sensors mounted on the portable touch probe receive the sweeping laser fans and generate the corresponding pulse signals, which establish a relationship between rotating angle of laser fan and time, and then the space angle measurement is converted into the corresponding peak time precision measurement of pulse signal. The rotating laser fans are modeled mathematically as a time varying parametrical vector in its local framework. A two steps on-site calibration method for solving the parameters of each RLAT and coordinate transformation among the N-RLATs. The portable probe is composed of optical sensors array with specified geometrical features and a touch point, on which the coordinates of optical sensors is determined by the N-RLATs and the touch point is estimated by solving a non-linear system. A prototype mobile 3D coordinate measuring system is developed and experiment results show its validity.

  15. Optical implementation of a feature-based neural network with application to automatic target recognition

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1993-01-01

    An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

  16. A Novel Neural Network Based Method Developed for Digit Recognition Applied to Automatic Speed Sign Recognition

    Directory of Open Access Journals (Sweden)

    Hanene Rouabeh

    2016-02-01

    Full Text Available This Paper presents a new hybrid technique for digit recognition applied to the speed limit sign recognition task. The complete recognition system consists in the detection and recognition of the speed signs in RGB images. A pretreatment is applied to extract the pictogram from a detected circular road sign, and then the task discussed in this work is employed to recognize digit candidates. To realize a compromise between performances, reduced execution time and optimized memory resources, the developed method is based on a conjoint use of a Neural Network and a Decision Tree. A simple Network is employed firstly to classify the extracted candidates into three classes and secondly a small Decision Tree is charged to determine the exact information. This combination is used to reduce the size of the Network as well as the memory resources utilization. The evaluation of the technique and the comparison with existent methods show the effectiveness.

  17. A new approach for automatic sleep scoring: Combining Taguchi based complex-valued neural network and complex wavelet transform.

    Science.gov (United States)

    Peker, Musa

    2016-06-01

    Automatic classification of sleep stages is one of the most important methods used for diagnostic procedures in psychiatry and neurology. This method, which has been developed by sleep specialists, is a time-consuming and difficult process. Generally, electroencephalogram (EEG) signals are used in sleep scoring. In this study, a new complex classifier-based approach is presented for automatic sleep scoring using EEG signals. In this context, complex-valued methods were utilized in the feature selection and classification stages. In the feature selection stage, features of EEG data were extracted with the help of a dual tree complex wavelet transform (DTCWT). In the next phase, five statistical features were obtained. These features are classified using complex-valued neural network (CVANN) algorithm. The Taguchi method was used in order to determine the effective parameter values in this CVANN. The aim was to develop a stable model involving parameter optimization. Different statistical parameters were utilized in the evaluation phase. Also, results were obtained in terms of two different sleep standards. In the study in which a 2nd level DTCWT and CVANN hybrid model was used, 93.84% accuracy rate was obtained according to the Rechtschaffen & Kales (R&K) standard, while a 95.42% accuracy rate was obtained according to the American Academy of Sleep Medicine (AASM) standard. Complex-valued classifiers were found to be promising in terms of the automatic sleep scoring and EEG data.

  18. Automatic Network Reconstruction using ASP

    CERN Document Server

    Ostrowski, Max; Durzinsky, Markus; Marwan, Wolfgang; Wagler, Annegret

    2011-01-01

    Building biological models by inferring functional dependencies from experimental data is an im- portant issue in Molecular Biology. To relieve the biologist from this traditionally manual process, various approaches have been proposed to increase the degree of automation. However, available ap- proaches often yield a single model only, rely on specific assumptions, and/or use dedicated, heuris- tic algorithms that are intolerant to changing circumstances or requirements in the view of the rapid progress made in Biotechnology. Our aim is to provide a declarative solution to the problem by ap- peal to Answer Set Programming (ASP) overcoming these difficulties. We build upon an existing approach to Automatic Network Reconstruction proposed by part of the authors. This approach has firm mathematical foundations and is well suited for ASP due to its combinatorial flavor providing a characterization of all models explaining a set of experiments. The usage of ASP has several ben- efits over the existing heuristic a...

  19. Automatic detection of epileptiform events in EEG by a three-stage procedure based on artificial neural networks.

    Science.gov (United States)

    Acir, Nurettin; Oztura, Ibrahim; Kuntalp, Mehmet; Baklan, Bariş; Güzeliş, Cüneyt

    2005-01-01

    This paper introduces a three-stage procedure based on artificial neural networks for the automatic detection of epileptiform events (EVs) in a multichannel electroencephalogram (EEG) signal. In the first stage, two discrete perceptrons fed by six features are used to classify EEG peaks into three subgroups: 1) definite epileptiform transients (ETs); 2) definite non-ETs; and 3) possible ETs and possible non-ETs. The pre-classification done in the first stage not only reduces the computation time but also increases the overall detection performance of the procedure. In the second stage, the peaks falling into the third group are aimed to be separated from each other by a nonlinear artificial neural network that would function as a postclassifier whose input is a vector of 41 consecutive sample values obtained from each peak. Different networks, i.e., a backpropagation multilayer perceptron and two radial basis function networks trained by a hybrid method and a support vector method, respectively, are constructed as the postclassifier and then compared in terms of their classification performances. In the third stage, multichannel information is integrated into the system for contributing to the process of identifying an EV by the electroencephalographers (EEGers). After the integration of multichannel information, the overall performance of the system is determined with respect to EVs. Visual evaluation, by two EEGers, of 19 channel EEG records of 10 epileptic patients showed that the best performance is obtained with a radial basis support vector machine providing an average sensitivity of 89.1%, an average selectivity of 85.9%, and a false detection rate (per hour) of 7.5.

  20. Design of Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System

    Directory of Open Access Journals (Sweden)

    Yongxian Song

    2012-05-01

    Full Text Available  In view of the characteristics of greenhouse environment monitoring system, a system scheme based on wireless sensor network (WSN is presented, which adopts Atmega128L chip and CC2530 that is a low power RF chip from TI to design the sink node and sensor nodes in the WSN. The monitoring and management center can control the temperature and humidity of the greenhouse, measure the carbon dioxide content, and collect the information about intensity of illumination, and so on. And the system adopts multilevel energy memory. It combines energy management with energy transfer, which makes the energy collected by solar energy batteries be used reasonably. Therefore, the self-managing energy supply system is established. In addition, the nodes deployment method and time synchronization problem are analyzed in detail. The system can solve the problem of complex cabling with the advantages of low power consumption, low cost, good robustness, extended flexible and high reliability. An effective tool is provided for monitoring and analysis decision-making of the greenhouse environment.

  1. Automatic Change Detection for Road Networks from Images Based on GIS

    Institute of Scientific and Technical Information of China (English)

    SUI Haigang; LI Deren; GONG Jianya

    2003-01-01

    Up to now, detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images with low resolution andhigh resolution using old GIS data,and presents a buffer detection andtracing algorithm for detecting roadfrom low-resolution images and a newprofile tracing algorithm for detectingroad from high-resolution images. Forfeature-level change detection (FL-CD), a so-called buffer detection algo-rithm is proposed to detect changes offeatures. Some ideas and algorithms ofusing GIS prior information and somecontext information such as substructures of road in high-resolution imagesto assist road detection and extractionare described in detail.

  2. Neural network based automatic limit prediction and avoidance system and method

    Science.gov (United States)

    Calise, Anthony J. (Inventor); Prasad, Jonnalagadda V. R. (Inventor); Horn, Joseph F. (Inventor)

    2001-01-01

    A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

  3. Automatic Thesaurus Construction Using Bayesian Networks.

    Science.gov (United States)

    Park, Young C.; Choi, Key-Sun

    1996-01-01

    Discusses automatic thesaurus construction and characterizes the statistical behavior of terms by using an inference network. Highlights include low-frequency terms and data sparseness, Bayesian networks, collocation maps and term similarity, constructing a thesaurus from a collocation map, and experiments with test collections. (Author/LRW)

  4. Automatic River Network Extraction from LIDAR Data

    Science.gov (United States)

    Maderal, E. N.; Valcarcel, N.; Delgado, J.; Sevilla, C.; Ojeda, J. C.

    2016-06-01

    National Geographic Institute of Spain (IGN-ES) has launched a new production system for automatic river network extraction for the Geospatial Reference Information (GRI) within hydrography theme. The goal is to get an accurate and updated river network, automatically extracted as possible. For this, IGN-ES has full LiDAR coverage for the whole Spanish territory with a density of 0.5 points per square meter. To implement this work, it has been validated the technical feasibility, developed a methodology to automate each production phase: hydrological terrain models generation with 2 meter grid size and river network extraction combining hydrographic criteria (topographic network) and hydrological criteria (flow accumulation river network), and finally the production was launched. The key points of this work has been managing a big data environment, more than 160,000 Lidar data files, the infrastructure to store (up to 40 Tb between results and intermediate files), and process; using local virtualization and the Amazon Web Service (AWS), which allowed to obtain this automatic production within 6 months, it also has been important the software stability (TerraScan-TerraSolid, GlobalMapper-Blue Marble , FME-Safe, ArcGIS-Esri) and finally, the human resources managing. The results of this production has been an accurate automatic river network extraction for the whole country with a significant improvement for the altimetric component of the 3D linear vector. This article presents the technical feasibility, the production methodology, the automatic river network extraction production and its advantages over traditional vector extraction systems.

  5. Automatic Distribution Network Reconfiguration: An Event-Driven Approach

    Energy Technology Data Exchange (ETDEWEB)

    Ding, Fei; Jiang, Huaiguang; Tan, Jin

    2016-11-14

    This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observable and detectable.

  6. Automatic Metadata Generation using Associative Networks

    CERN Document Server

    Rodriguez, Marko A; Van de Sompel, Herbert

    2008-01-01

    In spite of its tremendous value, metadata is generally sparse and incomplete, thereby hampering the effectiveness of digital information services. Many of the existing mechanisms for the automated creation of metadata rely primarily on content analysis which can be costly and inefficient. The automatic metadata generation system proposed in this article leverages resource relationships generated from existing metadata as a medium for propagation from metadata-rich to metadata-poor resources. Because of its independence from content analysis, it can be applied to a wide variety of resource media types and is shown to be computationally inexpensive. The proposed method operates through two distinct phases. Occurrence and co-occurrence algorithms first generate an associative network of repository resources leveraging existing repository metadata. Second, using the associative network as a substrate, metadata associated with metadata-rich resources is propagated to metadata-poor resources by means of a discrete...

  7. Convolutional neural network-based automatic classification of midsagittal tongue gestural targets using B-mode ultrasound images.

    Science.gov (United States)

    Xu, Kele; Roussel, Pierre; Csapó, Tamás Gábor; Denby, Bruce

    2017-06-01

    Tongue gestural target classification is of great interest to researchers in the speech production field. Recently, deep convolutional neural networks (CNN) have shown superiority to standard feature extraction techniques in a variety of domains. In this letter, both CNN-based speaker-dependent and speaker-independent tongue gestural target classification experiments are conducted to classify tongue gestures during natural speech production. The CNN-based method achieves state-of-the-art performance, even though no pre-training of the CNN (with the exception of a data augmentation preprocessing) was carried out.

  8. Neural Network Classifier for Automatic Detection of Invasive Versus Noninvasive Airway Management Technique Based on Respiratory Monitoring Parameters in a Pediatric Anesthesia.

    Science.gov (United States)

    Gálvez, Jorge A; Jalali, Ali; Ahumada, Luis; Simpao, Allan F; Rehman, Mohamed A

    2017-08-23

    Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. We retrieved three data sets from patients receiving general anesthesia in 2015 with either mask, laryngeal mask airway or endotracheal tube. Patients underwent myringotomy, tonsillectomy, adenoidectomy or inguinal hernia repair procedures. We retrieved measurements for end-tidal carbon dioxide, tidal volume, and peak inspiratory pressure and calculated statistical features for each data element per patient. We applied machine learning algorithms (decision tree, support vector machine, and neural network) to classify patients into noninvasive or invasive airway device support. We identified 300 patients per group (mask, laryngeal mask airway, and endotracheal tube) for a total of 900 patients. The neural network classifier performed better than the boosted trees and support vector machine classifiers based on the test data sets. The sensitivity, specificity, and accuracy for neural network classification are 97.5%, 96.3%, and 95.8%. In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.

  9. Automatic decision support in heterogeneous sensor networks

    Science.gov (United States)

    Kozma, Robert; Tanigawa, Timothy; Furxhi, Orges; Consul, Sergi

    2012-06-01

    There is a need to model complementary aspects of various data channels in distributed sensor networks in order to provide efficient tools of decision support in rapidly changing, dynamic real life scenarios. Our aim is to develop an autonomous cyber-sensing system that supports decision support based on the integration of information from diverse sensory channels. Target scenarios include dismounts performing various peaceful and/or potentially malicious activities. The studied test bed includes Ku band high bandwidth radar for high resolution range data and K band low bandwidth radar for high Doppler resolution data. We embed the physical sensor network in cyber network domain to achieve robust and resilient operation in adversary conditions. We demonstrate the operation of the integrated sensor system using artificial neural networks for the classification of human activities.

  10. Establishment of Quantum Communication Network and Design of Quantum Switch Based on Automatic Switch Optical Network%基于ASON的量子通信网络构建和量子交换机设计

    Institute of Scientific and Technical Information of China (English)

    刘晓慧; 聂敏; 裴昌幸

    2011-01-01

    Based on current quantum communication technology,the core technique in automatic switch optical network(ASON)is introduced into quantum communication network,the 3 layers quantum communication network (QCN) model is proposed,in which transmission plane and control plane are separated each other. At the same time,the quantum switch based on the proposed 3 layers quantum communication network model is presented in the paper. Finally the scheme of quantum switch,which is as a key technology of carrying out quantum switching is presented in the paper. This scheme of QCN may play an important role in the establishment of quantum communication network in the large scale in the future.%在目前量子通信技术的基础上,将自动交换光网络核心思想引入量子通信网络中,提出一个传输面与控制面相分离的三层量子通信网络模型和基于该模型的量子交换机结构,并给出了作为实现量子交换机关键技术的基于纠缠的量子交换方案.研究结果表明,该网络模型对于未来大规模量子通信系统的构建具有十分重要的意义.

  11. 基于FIS和RBFN的预想事故自动选择%Automatic Contingency Selection Based on Fuzzy Inference System and Radial Basis Function Network

    Institute of Scientific and Technical Information of China (English)

    陈刚; 田志平

    2011-01-01

    针对电力系统预想事故自动选择问题,提出了一种基于模糊推理系统FIS(fuzzy inference system)和径向基函数网络RBFN(radial basis function network)算法.定义了一种有功行为指标PIpf,该指标添加了一个模糊补偿系数用以改善遮蔽现象;同时构造了一个三层的RBFN,该网络以发电机功率、负荷功率和网络拓扑结构作为输入,以PIpf作为输出,并通过离线潮流计算获得训练样本;对算例进行计算并与其他算法比较,结果显示该算法能使事故排序更为合理,且计算精度和速度都令人满意.%In view of the problems of automatic contingency selection of power system, an advanced algorithm is proposed, which is based on fuzzy inference system(FIS) and radial basis function network(RBFN). Firstly an active performance index is defined, which adds a fuzzy compensation factor coefficient to improve shelter phenomenon. Meanwhile a three-layer RBFN is constructed, which treats generator power, load power and network topology as inputs, while treats the active performance index as output. The results of off-line load flow calculation are used to train the RBFN. Finally, the proposed method is demonstrated by an example, compared with several other algorithms. And the results show that the ranking of contingency is much more reasonable, and the calculation accuracy and speed are satisfied.

  12. Sparse encoding of automatic visual association in hippocampal networks

    DEFF Research Database (Denmark)

    Hulme, Oliver J; Skov, Martin; Chadwick, Martin J

    2014-01-01

    Intelligent action entails exploiting predictions about associations between elements of ones environment. The hippocampus and mediotemporal cortex are endowed with the network topology, physiology, and neurochemistry to automatically and sparsely code sensori-cognitive associations that can...

  13. Automatic Road Network Map Update for the City of Dubai

    Science.gov (United States)

    Marti, Paula; Al Hammadi, Omran; Napiorkowska, Milena; Constantini, Fabiano; Callejas, Alberto; Smith, Garin; Petit, David

    2016-08-01

    Dubai is a modern city which is growing at a fast pace, constantly changing and developing. The application presented in this paper is part of the "Smart Application for Feature extraction & 3D modelling using high resolution satellite Imagery (SAFIY)" project, whose main aim is to develop satellite data applications to help the government in Dubai to get up-to-date information on features such as water bodies, vegetation areas, buildings and roads. In this paper we present an application to automatically update the road network map for the city of Dubai using very high resolution satellite imagery, Deimos-2 and DubaiSat-2 at 0.75 cm resolution. Deimos-2 and DubaiSat-2 have been recently included as third party contributors to the Copernicus constellation.The algorithms implemented use unsupervised and supervised classification techniques to extract the road pixels. Special road objects such as roundabouts or bridges are also detected using algorithms based on feature descriptors, designed to match the specific characteristics of those objects. Finally, the system identifies the changes in roads and objects from the current road network map in order to update the map. The city of Dubai is 4,114 km and has many different types of neighbourhoods from suburbs to very densely populated areas. Overall, we propose a novel end to end system that automatically updates a road network map and allows a user to manually correct them so that the final resulting map is usable.

  14. Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

    Science.gov (United States)

    Kim, Sungho

    2017-06-01

    Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

  15. SET UP OF THE NEW AUTOMATIC HYDROMETEOROLOGICAL NETWORK IN HUNGARY

    Directory of Open Access Journals (Sweden)

    J. NAGy

    2013-03-01

    Full Text Available The Hungarian Meteorological Service (OMSZ and General Directorate of Water Management (OVF in Hungary run conventional precipitation measurement networks consisting of at least 1000 stations. OMSZ automated its synoptic and climatological network in 90’s and now more than 100 automatic stations give data every 1-10 minutes via GPRS channel. In 2007 the experts from both institutions determined the requirements of a common network. The predecessor in title of OVF is general Directorate for Water and Environment gave a project proposal in 2008 for establishment of a new hydrometeorological network based on common aims for meteorology and hydrology. The new hydrometeorological network was set up in 2012 financed by KEOP project. This network has got 141 weighing precipitation gauges, 118 temperature - humidity sensors and 25 soil moisture and soil temperature instruments. Near by Tisza-Lake two wind sensors have been installed. The network is operated by OMSZ and OVF together. OVF and its institutions maintain the stations itself and support the electricity. OMSZ operates data collection and transmission, maintaines and calibrates the sensors. Using precipitation data of enhanced network the radar precipitation field quality may be more precise, which are input of run-off model. Thereby the time allowance may be increased in flood-control events. Based on soil moisture and temperature water balance in soil may be modelled and forecast can be produced in different conditions. It is very important task in drought and inland water conditions. Considering OMSZ investment project in which new Doppler dual polarisation radar and 14 disdrometers will be installed, the precipitation estimation may be improved since 2015.

  16. Automatic Network Fingerprinting through Single-Node Motifs

    CERN Document Server

    Echtermeyer, Christoph; Rodrigues, Francisco A; Kaiser, Marcus; 10.1371/journal.pone.0015765

    2011-01-01

    Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes...

  17. Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the electrode displacement signal features

    Science.gov (United States)

    Zhang, Hongjie; Hou, Yanyan; Zhao, Jian; Wang, Lijing; Xi, Tao; Li, Yafeng

    2017-02-01

    To develop an automatic welding quality classification method for the spot welding based on the Chernoff face image created by the electrode displacement signal features, an effective pattern feature extraction method was proposed by which the Chernoff face images were converted to binary ones, and each binary image could be characterized by a binary matrix. According to expression categories on the Chernoff face images, welding quality was classified into five levels and each level just corresponded to a kind of expression. The Hopfield associative memory neural network was used to build a welding quality classifier in which the pattern feature matrices of some weld samples with different welding quality levels were remembered as the stable states. When the pattern feature matrix of a test weld is input into the classifier, it can be converged to the most similar stable state through associative memory, thus, welding quality corresponding to this finally locked stable state can represent the welding quality of the test weld. The classification performance test results show that the proposed method significantly improves the applicability and efficiency of the Chernoff faces technique for spot welding quality evaluation and it is feasible, effective and reliable.

  18. Automatic Security Assessment for Next Generation Wireless Mobile Networks

    Directory of Open Access Journals (Sweden)

    Francesco Palmieri

    2011-01-01

    Full Text Available Wireless networks are more and more popular in our life, but their increasing pervasiveness and widespread coverage raises serious security concerns. Mobile client devices potentially migrate, usually passing through very light access control policies, between numerous and heterogeneous wireless environments, bringing with them software vulnerabilities as well as possibly malicious code. To cope with these new security threats the paper proposes a new active third party authentication, authorization and security assessment strategy in which, once a device enters a new Wi-Fi environment, it is subjected to analysis by the infrastructure, and if it is found to be dangerously insecure, it is immediately taken out from the network and denied further access until its vulnerabilities have been fixed. The security assessment module, that is the fundamental component of the aforementioned strategy, takes advantage from a reliable knowledge base containing semantically-rich information about the mobile node under examination, dynamically provided by network mapping and configuration assessment facilities. It implements a fully automatic security analysis framework, based on AHP, which has been conceived to be flexible and customizable, to provide automated support for real-time execution of complex security/risk evaluation tasks which depends on the results obtained from different kind of analysis tools and methodologies. Encouraging results have been achieved utilizing a proof-of-concept model based on current technology and standard open-source networking tools.

  19. Potential Energy Surface-Based Automatic Deduction of Conformational Transition Networks and Its Application on Quantum Mechanical Landscapes of d-Glucose Conformers.

    Science.gov (United States)

    Satoh, Hiroko; Oda, Tomohiro; Nakakoji, Kumiyo; Uno, Takeaki; Tanaka, Hiroaki; Iwata, Satoru; Ohno, Koichi

    2016-11-08

    This paper describes our approach that is built upon the potential energy surface (PES)-based conformational analysis. This approach automatically deduces a conformational transition network, called a conformational reaction route map (r-map), by using the Scaled Hypersphere Search of the Anharmonic Downward Distortion Following method (SHS-ADDF). The PES-based conformational search has been achieved by using large ADDF, which makes it possible to trace only low transition state (TS) barriers while restraining bond lengths and structures with high free energy. It automatically performs sampling the minima and TS structures by simply taking into account the mathematical feature of PES without requiring any a priori specification of variable internal coordinates. An obtained r-map is composed of equilibrium (EQ) conformers connected by reaction routes via TS conformers, where all of the reaction routes are already confirmed during the process of the deduction using the intrinsic reaction coordinate (IRC) method. The postcalculation analysis of the deduced r-map is interactively carried out using the RMapViewer software we have developed. This paper presents computational details of the PES-based conformational analysis and its application to d-glucose. The calculations have been performed for an isolated glucose molecule in the gas phase at the RHF/6-31G level. The obtained conformational r-map for α-d-glucose is composed of 201 EQ and 435 TS conformers and that for β-d-glucose is composed of 202 EQ and 371 TS conformers. For the postcalculation analysis of the conformational r-maps by using the RMapViewer software program we have found multiple minimum energy paths (MEPs) between global minima of (1)C4 and (4)C1 chair conformations. The analysis using RMapViewer allows us to confirm the thermodynamic and kinetic predominance of (4)C1 conformations; that is, the potential energy of the global minimum of (4)C1 is lower than that of (1)C4 (thermodynamic predominance

  20. Toward automatic time-series forecasting using neural networks.

    Science.gov (United States)

    Yan, Weizhong

    2012-07-01

    Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

  1. Automatic Construction of Hierarchical Road Networks

    Science.gov (United States)

    Yang, Weiping

    2016-06-01

    This paper describes an automated method of constructing a hierarchical road network given a single dataset, without the presence of thematic attributes. The method is based on a pattern graph which maintains nodes and paths as junctions and through-traffic roads. The hierarchy is formed incrementally in a top-down fashion for highways, ramps, and major roads directly connected to ramps; and bottom-up for the rest of major and minor roads. Through reasoning and analysis, ramps are identified as unique characteristics for recognizing and assembling high speed roads. The method makes distinctions on the types of ramps by articulating their connection patterns with highways. Major and minor roads will be identified by both quantitative and qualitative analysis of spatial properties and by discovering neighbourhood patterns revealed in the data. The result of the method would enrich data description and support comprehensive queries on sorted exit or entry points on highways and their related roads. The enrichment on road network data is important to a high successful rate of feature matching for road networks and to geospatial data integration.

  2. Automatic Web-Based, Radio-Network System To Monitor And Control Equipment For Investigating Gas Flux At Water - Air Interfaces

    Science.gov (United States)

    Duc, N. T.; Silverstein, S.; Wik, M.; Beckman, P.; Crill, P. M.; Bastviken, D.; Varner, R. K.

    2015-12-01

    Aquatic ecosystems are major sources of greenhouse gases (GHG). Robust measurements of natural GHG emissions are vital for evaluating regional to global carbon budgets and for assessing climate feedbacks on natural emissions to improve climate models. Diffusive and ebullitive (bubble) transport are two major pathways of gas release from surface waters. To capture the high temporal variability of these fluxes in a well-defined footprint, we designed and built an inexpensive automatic device that includes an easily mobile diffusive flux chamber and a bubble counter, all in one. Besides a function of automatically collecting gas samples for subsequent various analyses in the laboratory, this device utilizes low cost CO2 sensor (SenseAir, Sweden) and CH4 sensor (Figaro, Japan) to measure GHG fluxes. To measure the spatial variability of emissions, each of the devices is equipped with an XBee module to enable a local radio communication DigiMesh network for time synchronization and data readout at a server-controller station on the lakeshore. Software of this server-controller is operated on a low cost Raspberry Pi computer which has a 3G connection for remote monitoring - controlling functions from anywhere in the world. From field studies in Abisko, Sweden in summer 2014 and 2015, the system has resulted in measurements of GHG fluxes comparable to manual methods. In addition, the deployments have shown the advantage of a low cost automatic network system to study GHG fluxes on lakes in remote locations.

  3. Automatic Generation of Network Protocol Gateways

    DEFF Research Database (Denmark)

    Bromberg, Yérom-David; Réveillère, Laurent; Lawall, Julia

    2009-01-01

    , however, requires an intimate knowledge of the relevant protocols and a substantial understanding of low-level network programming, which can be a challenge for many application programmers. This paper presents a generative approach to gateway construction, z2z, based on a domain-specific language...... for describing protocol behaviors, message structures, and the gateway logic.  Z2z includes a compiler that checks essential correctness properties and produces efficient code. We have used z2z to develop a number of gateways, including SIP to RTSP, SLP to UPnP, and SMTP to SMTP via HTTP, involving a range...... of issues common to protocols used in the home.  Our evaluation of these gateways shows that z2z enables communication between incompatible devices without increasing the overall resource usage or response time....

  4. Automatic mapping of valley networks on Mars

    Science.gov (United States)

    Molloy, I.; Stepinski, T. F.

    2007-06-01

    Martian valley networks bear some resemblance to terrestrial drainage systems, but their precise origin remains an active research topic. A limited number of valley networks have been manually mapped from images, but the vast majority remains unmapped because standard drainage mapping algorithms are inapplicable to valleys that are poorly organized and lack spatial integration. In this paper, we present a novel drainage delineation algorithm specially designed for mapping the valley networks from digital elevation data. It first identifies landforms characterized by convex tangential curvature, and then uses a series of image processing operations to separate valleys from other features having a convex form. The final map is produced by reconnecting all valley segments along drainage directions. Eight test sites on Mars are selected and manually mapped for valley networks. The algorithm is applied to the test sites and delineated networks are compared to mapped networks using a series of quantitative quality factors. We have found a good agreement between delineated and mapped networks. In the process of comparing manual and delineated networks some shortcomings of manual mapping became apparent. We argue that delineated networks are indeed of better quality than the networks manually mapped from images. Although the algorithm has been developed to study Martian surface, it may also be relevant to terrestrial geomorphology.

  5. Semi-automatic simulation model generation of virtual dynamic networks for production flow planning

    Science.gov (United States)

    Krenczyk, D.; Skolud, B.; Olender, M.

    2016-08-01

    Computer modelling, simulation and visualization of production flow allowing to increase the efficiency of production planning process in dynamic manufacturing networks. The use of the semi-automatic model generation concept based on parametric approach supporting processes of production planning is presented. The presented approach allows the use of simulation and visualization for verification of production plans and alternative topologies of manufacturing network configurations as well as with automatic generation of a series of production flow scenarios. Computational examples with the application of Enterprise Dynamics simulation software comprising the steps of production planning and control for manufacturing network have been also presented.

  6. 基于电话网的多功能煤气管道自动抄表系统%Automatical Reading Meter System of Multifunction Gas Pipings Based on Telephone Network

    Institute of Scientific and Technical Information of China (English)

    刘桂兰

    2011-01-01

    为了保证远程自动抄表系统的准确性和可靠性,介绍了一种基于电话网用单片机控制的多功能煤气管道远程自动抄表系统.整个系统由主控端和客户多功能煤气表端组成,主控端与客户端通过电话线网实现自动抄表.该系统布线简单,数据传输的可靠性和安全性都较好.%With the development of sensor, electron, automatic control and computer technologies, the long-range automatical reading meter system is emerging rapidly. In order to guarantee its accuracy and reliability, a kind of long-range automatical reading meter system of multifunction gas pipings, controlled by a single chip microcomputer and based on telephone network, is introduced. The overall system consists of the main control terminal and multifunction gas meter at client. The automalic reading meter is realized automatically by telephone wires network.

  7. Neuro-fuzzy system modeling based on automatic fuzzy clustering

    Institute of Scientific and Technical Information of China (English)

    Yuangang TANG; Fuchun SUN; Zengqi SUN

    2005-01-01

    A neuro-fuzzy system model based on automatic fuzzy clustering is proposed.A hybrid model identification algorithm is also developed to decide the model structure and model parameters.The algorithm mainly includes three parts:1) Automatic fuzzy C-means (AFCM),which is applied to generate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which the complexity of system design is reducesd greatly at the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which is employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy values according to the back propagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to validate the feasibility of the method.

  8. Automatic Generation of Network Protocol Gateways

    Science.gov (United States)

    Bromberg, Yérom-David; Réveillère, Laurent; Lawall, Julia L.; Muller, Gilles

    The emergence of networked devices in the home has made it possible to develop applications that control a variety of household functions. However, current devices communicate via a multitude of incompatible protocols, and thus gateways are needed to translate between them. Gateway construction, however, requires an intimate knowledge of the relevant protocols and a substantial understanding of low-level network programming, which can be a challenge for many application programmers.

  9. AUTOMATIC CONTROL OF INTELLECTUAL RIGHTS IN THE GLOBAL COMPUTER NETWORKS

    Directory of Open Access Journals (Sweden)

    Anatoly P. Yakimaho

    2013-01-01

    Full Text Available The problems of use of subjects of intellectual property in the global computer networks are stated. The main attention is focused on the ways of problems solutions arising during the work in computer networks. Legal problems of information society are considered. The analysis of global computer networks as places for the organization of collective management by copyrights in the world scale is carried out. Issues of creation of a system of automatic control of property rights of authors and owners in the global computer networks are taken up.

  10. Automatic GPRS Rainfall Detecting Set Based on P89C669

    Institute of Scientific and Technical Information of China (English)

    Yang,Lei; Wu,Kun

    2005-01-01

    A new kind of remote and automatic GPRS rainfall detecting network system is established and developed. As the main unit of the network system, automatic rainfall detecting set based on P89C669 is used to acquire rainfall information automatically. GPRS station, combined with mobile wireless communication and internet technology is used to achieve the objective of dynamically share and display the meteorological information via internet.

  11. Intelligent neural network classifier for automatic testing

    Science.gov (United States)

    Bai, Baoxing; Yu, Heping

    1996-10-01

    This paper is concerned with an application of a multilayer feedforward neural network for the vision detection of industrial pictures, and introduces a high characteristics image processing and recognizing system which can be used for real-time testing blemishes, streaks and cracks, etc. on the inner walls of high-accuracy pipes. To take full advantage of the functions of the artificial neural network, such as the information distributed memory, large scale self-adapting parallel processing, high fault-tolerance ability, this system uses a multilayer perceptron as a regular detector to extract features of the images to be inspected and classify them.

  12. Phoneme vs Grapheme Based Automatic Speech Recognition

    OpenAIRE

    Magimai.-Doss, Mathew; Dines, John; Bourlard, Hervé; Hermansky, Hynek

    2004-01-01

    In recent literature, different approaches have been proposed to use graphemes as subword units with implicit source of phoneme information for automatic speech recognition. The major advantage of using graphemes as subword units is that the definition of lexicon is easy. In previous studies, results comparable to phoneme-based automatic speech recognition systems have been reported using context-independent graphemes or context-dependent graphemes with decision trees. In this paper, we study...

  13. Automatic Campus Network Management using GPS

    Directory of Open Access Journals (Sweden)

    Jayakumar.S

    2012-05-01

    Full Text Available The Organization Network is the place where large number of attacks is happening. The attackers are using different methodologies to capture the information from the end user without the knowledge of the end-user. This paper introduces the concepts of Campus Management and Emergency log by using Medium Access Control (MAC and Global Positioning System (GPS. By using the IP address of an attacker, the MAC address can be found and the attackers machine can be blocked access with the help of firewall. Using the GPS we can be able to navigate the attackers position with the help of the position log. The log keeps updating for each and every 10 seconds. The attacker can be identified as if he used his own system or victim (3rd party system. An emergency response log has been created to record each emergency incident response process. The role of the log is more important with an increasing accumulation of information with the log; Network Engineer/Administrator can determine the type of inevitable emergency incidents grouped into evitable events, in order to improve the system reliability of emergency response.

  14. Integration of wireless sensor networks into automatic irrigation scheduling of a center pivot

    Science.gov (United States)

    A six-span center pivot system was used as a platform for testing two wireless sensor networks (WSN) of infrared thermometers. The cropped field was a semi-circle, divided into six pie shaped sections of which three were irrigated manually and three were irrigated automatically based on the time tem...

  15. Automatic Image-Based Pencil Sketch Rendering

    Institute of Scientific and Technical Information of China (English)

    王进; 鲍虎军; 周伟华; 彭群生; 徐迎庆

    2002-01-01

    This paper presents an automatic image-based approach for converting greyscale images to pencil sketches, in which strokes follow the image features. The algorithm first extracts a dense direction field automatically using Logical/Linear operators which embody the drawing mechanism. Next, a reconstruction approach based on a sampling-and-interpolation scheme is introduced to generate stroke paths from the direction field. Finally, pencil strokes are rendered along the specified paths with consideration of image tone and artificial illumination.As an important application, the technique is applied to render portraits from images with little user interaction. The experimental results demonstrate that the approach can automatically achieve compelling pencil sketches from reference images.

  16. Morphological self-organizing feature map neural network with applications to automatic target recognition

    Institute of Scientific and Technical Information of China (English)

    Shijun Zhang; Zhongliang Jing; Jianxun Li

    2005-01-01

    @@ The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and realworld infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

  17. Automatic Amharic text news classification: Aneural networks ...

    African Journals Online (AJOL)

    Document Frequency (TF*IDF), are used to weight the features or keywords in news documents. Based on the ... C. Problem of Amharic Writing System. There are a number of .... outcome is the assignment of classes for news items that are not ...

  18. New Network of Automatic Stations integrated in the CSNs Environmental Radiological Surveillance Network; Nueva Red de Estaciones Automaticas integrada en la Red de Vigilancia Radiologica Ambiental del CSN

    Energy Technology Data Exchange (ETDEWEB)

    Parages Perez del Yerro, C.; Garcia Cadierno, J. P.; Calvin Cuartero, M.

    2016-05-01

    In 1992, the Council put into operation a network comprising 25 automatic stations for continuous monitoring of the radiological quality of the air and the detection of anomalous situations. It has now decided to undertake the renewal and modernisation of these installations, incorporating sensors and automatic connection and communication systems based on the best technology currently available. (Author)

  19. Automatic discovery of the communication network topology for building a supercomputer model

    Science.gov (United States)

    Sobolev, Sergey; Stefanov, Konstantin; Voevodin, Vadim

    2016-10-01

    The Research Computing Center of Lomonosov Moscow State University is developing the Octotron software suite for automatic monitoring and mitigation of emergency situations in supercomputers so as to maximize hardware reliability. The suite is based on a software model of the supercomputer. The model uses a graph to describe the computing system components and their interconnections. One of the most complex components of a supercomputer that needs to be included in the model is its communication network. This work describes the proposed approach for automatically discovering the Ethernet communication network topology in a supercomputer and its description in terms of the Octotron model. This suite automatically detects computing nodes and switches, collects information about them and identifies their interconnections. The application of this approach is demonstrated on the "Lomonosov" and "Lomonosov-2" supercomputers.

  20. The automatic calibration of Korean VLBI Network data

    CERN Document Server

    Hodgson, Jeffrey A; Zhao, Guang-Yao; Algaba, Juan-Carlos; Yun, Youngjoo; Jung, Taehyun; Byun, Do-Young

    2016-01-01

    The calibration of Very Long Baseline Interferometry (VLBI) data has long been a time consuming process. The Korean VLBI Network (KVN) is a simple array consisting of three identical antennas. Because four frequencies are observed simultaneously, phase solutions can be transferred from lower frequencies to higher frequencies in order to improve phase coherence and hence sensitivity at higher frequencies. Due to the homogeneous nature of the array, the KVN is also well suited for automatic calibration. In this paper we describe the automatic calibration of single-polarisation KVN data using the KVN Pipeline and comparing the results against VLBI data that has been manually reduced. We find that the pipelined data using phase transfer produces better results than a manually reduced dataset not using the phase transfer. Additionally we compared the pipeline results with a manually reduced phase-transferred dataset and found the results to be identical.

  1. The Automatic Calibration of Korean VLBI Network Data

    Science.gov (United States)

    Hodgson, Jeffrey A.; Lee, Sang-Sung; Zhao, Guang-Yao; Algaba, Juan-Carlos; Yun, Youngjoo; Jung, Taehyun; Byun, Do-Young

    2016-08-01

    The calibration of Very Long Baseline Interferometry (VLBI) data has long been a time consuming process. The Korean VLBI Network (KVN) is a simple array consisting of three identical antennas. Because four frequencies are observed simultaneously, phase solutions can be transferred from lower frequencies to higher frequencies in order to improve phase coherence and hence sensitivity at higher frequencies. Due to the homogeneous nature of the array, the KVN is also well suited for automatic calibration. In this paper we describe the automatic calibration of single-polarisation KVN data using the KVN Pipeline and comparing the results against VLBI data that has been manually reduced. We find that the pipelined data using phase transfer produces better results than a manually reduced dataset not using the phase transfer. Additionally we compared the pipeline results with a manually reduced phase-transferred dataset and found the results to be identical.

  2. 基于无线传感器网络的远程自动抄表系统设计%Application of Remote Automatic Meter Reading System Based on Wireless Sensor Network

    Institute of Scientific and Technical Information of China (English)

    傅仁轩

    2011-01-01

    针对居民小区抄表系统的技术要求,设计了一种基于无线传感器网络的的远程自动抄表系统方案,该系统将有线通信与无线通信、ZigBee短距离通信与GPRS/CDMA/3G远距离通信有机地结合起来,实现了居民小区的远程自动抄表.给出了组网结构图,阐述了抄袁管理中心的软件结构框图和主要功能.与传统的实现方案相比,该方案的通信设计有明显的优势.%To satisfy the technical requirement of the meter reading system, a remote automatic meter reading system based on wireless sensor network was designed.The system combined wire communication, wireless communication ZigBee short distance communication and GPRS/CDMA/3G remote communication to achieve the remote automatic meter reading system in a residential area.The network structure is proposed, the software structure diagram and main function of meter management center are elaborated.The communication design of the scheme has obvious advantages compared with the traditional scheme.

  3. Automatic analysis of attack data from distributed honeypot network

    Science.gov (United States)

    Safarik, Jakub; Voznak, MIroslav; Rezac, Filip; Partila, Pavol; Tomala, Karel

    2013-05-01

    There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.

  4. Consistently Trained Artificial Neural Network for Automatic Ship Berthing Control

    Directory of Open Access Journals (Sweden)

    Y.A. Ahmed

    2015-09-01

    Full Text Available In this paper, consistently trained Artificial Neural Network controller for automatic ship berthing is discussed. Minimum time course changing manoeuvre is utilised to ensure such consistency and a new concept named ‘virtual window’ is introduced. Such consistent teaching data are then used to train two separate multi-layered feed forward neural networks for command rudder and propeller revolution output. After proper training, several known and unknown conditions are tested to judge the effectiveness of the proposed controller using Monte Carlo simulations. After getting acceptable percentages of success, the trained networks are implemented for the free running experiment system to judge the network’s real time response for Esso Osaka 3-m model ship. The network’s behaviour during such experiments is also investigated for possible effect of initial conditions as well as wind disturbances. Moreover, since the final goal point of the proposed controller is set at some distance from the actual pier to ensure safety, therefore a study on automatic tug assistance is also discussed for the final alignment of the ship with actual pier.

  5. AUTOMATIC RECOVERING NODE FAILURE IN WIRELESS SENSOR ACTOR NETWORKS

    Directory of Open Access Journals (Sweden)

    C.A.Subasini

    2015-02-01

    Full Text Available Automatic recovering node failure in wireless sensor actor network to identify the cutvertex and to meet to the node failure. The network consists of many nodes that are constructed into a tree structure. Once the tree has been constructed the shortest path is found by the Aodv protocol.If a node failure occurs in the shortest path then the cut-vertex could be recovered and the data can be securely passed on to the destination in an alternative route obtained from the routing table. Heartbeat messages acknowledge the node failure. The feasible path is not found by the protocol, and then the network is divided into two or many parts. The MLeDir algorithm is used to identify the network failure and disjoint block of the network. The disjoint block is identified by the MLeDir algorithm and to rectify the disjoint block of failure network nodes. MLeDir algorithm is moving only the respective nodes from source to destination.

  6. PLC Based Automatic Multistoried Car Parking System

    OpenAIRE

    2014-01-01

    This project work presents the study and design of PLC based Automatic Multistoried Car Parking System. Multistoried car parking is an arrangement which is used to park a large number of vehicles in least possible place. For making this arrangement in a real plan very high technological instruments are required. In this project a prototype of such a model is made. This prototype model is made for accommodating twelve cars at a time. Availability of the space for parking is detecte...

  7. Automatic Clustering of Rolling Element Bearings Defects with Artificial Neural Network

    Science.gov (United States)

    Antonini, M.; Faglia, R.; Pedersoli, M.; Tiboni, M.

    2006-06-01

    The paper presents the optimization of a methodology for automatic clustering based on Artificial Neural Networks to detect the presence of defects in rolling bearings. The research activity was developed in co-operation with an Italian company which is expert in the production of water pumps for automotive use (Industrie Saleri Italo). The final goal of the work is to develop a system for the automatic control of the pumps, at the end of the production line. In this viewpoint, we are gradually considering the main elements of the water pump, which can cause malfunctioning. The first elements we have considered are the rolling bearing, a very critic component for the system. The experimental activity is based on the vibration measuring of rolling bearings opportunely damaged; vibration signals are in the second phase elaborated; the third and last phase is an automatic clustering. Different signal elaboration techniques are compared to optimize the methodology.

  8. Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

    Directory of Open Access Journals (Sweden)

    Chunhua Li

    2017-01-01

    Full Text Available Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

  9. AUTOMATIC REMOTE SENSING IMAGE CLASSIFICATION ALGORITHM BASED ONFCM AND BP NEURAL NETWORK%基于模糊C均值和BP神经网络的遥感影像自动分类算法

    Institute of Scientific and Technical Information of China (English)

    黄奇瑞

    2015-01-01

    针对非监督分类算法分类精度不高、监督法分类算法的训练样本需要人工选择且容易误选的问题,提出了一种基于模糊C均值聚类( FCM)和BP神经网络相结合的遥感影像自动分类算法. 首先利用FCM对影像进行初始聚类,然后根据聚类结果,由该算法自动选取其中的纯净像元作为训练样本,并送入BP网络进行学习,用最终训练得到的BP神经网络分类器对TM遥感影像进行分类,实验结果表明该算法具有较高的分类精度,能够满足大尺度地物类别判定的需要.%As for the problems that low classification accuracy of non-supervise classification algorithm and training sample of super-vise classification algorithm needs manual selection which is easy to be made wrongly, there is an automatic classfication algorithm of remote sensing image which is based on the combination of FCM and BP neural network. First, this paper uses FCM to make initial clusters of images. Then in accordance with the results of clusters, this paper picks out the endmembers which are automatically select-ed by the algorithm as the traaning samples, sends the samples to study in BP network and uses the BP neural network classifier which is got from the final training to classify the TM remote sensing images. The result shows that the algorithm owns high accuracy which could meet the requirements of determination of object types in a large scale.

  10. Automatic Facial Expression Recognition Based on Hybrid Approach

    Directory of Open Access Journals (Sweden)

    Ali K. K. Bermani

    2012-12-01

    Full Text Available The topic of automatic recognition of facial expressions deduce a lot of researchers in the late last century and has increased a great interest in the past few years. Several techniques have emerged in order to improve the efficiency of the recognition by addressing problems in face detection and extraction features in recognizing expressions. This paper has proposed automatic system for facial expression recognition which consists of hybrid approach in feature extraction phase which represent a combination between holistic and analytic approaches by extract 307 facial expression features (19 features by geometric, 288 feature by appearance. Expressions recognition is performed by using radial basis function (RBF based on artificial neural network to recognize the six basic emotions (anger, fear, disgust, happiness, surprise, sadness in addition to the natural.The system achieved recognition rate 97.08% when applying on person-dependent database and 93.98% when applying on person-independent.

  11. An automatic fault management model for distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Lehtonen, M.; Haenninen, S. [VTT Energy, Espoo (Finland); Seppaenen, M. [North-Carelian Power Co (Finland); Antila, E.; Markkila, E. [ABB Transmit Oy (Finland)

    1998-08-01

    An automatic computer model, called the FI/FL-model, for fault location, fault isolation and supply restoration is presented. The model works as an integrated part of the substation SCADA, the AM/FM/GIS system and the medium voltage distribution network automation systems. In the model, three different techniques are used for fault location. First, by comparing the measured fault current to the computed one, an estimate for the fault distance is obtained. This information is then combined, in order to find the actual fault point, with the data obtained from the fault indicators in the line branching points. As a third technique, in the absence of better fault location data, statistical information of line section fault frequencies can also be used. For combining the different fault location information, fuzzy logic is used. As a result, the probability weights for the fault being located in different line sections, are obtained. Once the faulty section is identified, it is automatically isolated by remote control of line switches. Then the supply is restored to the remaining parts of the network. If needed, reserve connections from other adjacent feeders can also be used. During the restoration process, the technical constraints of the network are checked. Among these are the load carrying capacity of line sections, voltage drop and the settings of relay protection. If there are several possible network topologies, the model selects the technically best alternative. The FI/IL-model has been in trial use at two substations of the North-Carelian Power Company since November 1996. This chapter lists the practical experiences during the test use period. Also the benefits of this kind of automation are assessed and future developments are outlined

  12. AUTOMATIC RECOGNITION OF BOTH INTER AND INTRA CLASSES OF DIGITAL MODULATED SIGNALS USING ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    JIDE JULIUS POPOOLA

    2014-04-01

    Full Text Available In radio communication systems, signal modulation format recognition is a significant characteristic used in radio signal monitoring and identification. Over the past few decades, modulation formats have become increasingly complex, which has led to the problem of how to accurately and promptly recognize a modulation format. In addressing these challenges, the development of automatic modulation recognition systems that can classify a radio signal’s modulation format has received worldwide attention. Decision-theoretic methods and pattern recognition solutions are the two typical automatic modulation recognition approaches. While decision-theoretic approaches use probabilistic or likelihood functions, pattern recognition uses feature-based methods. This study applies the pattern recognition approach based on statistical parameters, using an artificial neural network to classify five different digital modulation formats. The paper deals with automatic recognition of both inter-and intra-classes of digitally modulated signals in contrast to most of the existing algorithms in literature that deal with either inter-class or intra-class modulation format recognition. The results of this study show that accurate and prompt modulation recognition is possible beyond the lower bound of 5 dB commonly acclaimed in literature. The other significant contribution of this paper is the usage of the Python programming language which reduces computational complexity that characterizes other automatic modulation recognition classifiers developed using the conventional MATLAB neural network toolbox.

  13. Study on control algorithm for Automatic Train Operation based on neural network%基于神经网络的列车自动驾驶控制算法研究

    Institute of Scientific and Technical Information of China (English)

    费洋; 吴永城

    2014-01-01

    对列车自动驾驶系统的速度控制过程进行了分析,针对传统PID算法缺乏自适应能力的缺点,在传统神经网络算法的基础之上对神经元的学习算法作出改进,设计出了基于RBF网络辨识的单神经元自适应PID控制器,仿真结果表明该控制器能够满足列车自动驾驶系统速度控制要求。%The speed control process for Automatic Train Operation is analyzed, and as the traditional PID algorithm lacks adaptability, a single neuron adaptive PID controler based on RBF neural network is designed. The result of simulation shows that the controler can meet the speed control requirements for Automatic Train Operation.

  14. 基于ZigBee的城市交通中的塞车状况自动预警系统%Automatic warning system of city traffic congestion based on ZigBee wireless network

    Institute of Scientific and Technical Information of China (English)

    姜云国

    2013-01-01

    根据现今的交通系统中驾驶员不能获取塞车状况的实时信息的情况,设计出了基于ZigBee无线组网城市交通中的塞车状况的自动预警系统.本系统可以为某一时刻正在或将要出行车辆和出行人员提供实时的路况信息,从而自动分流出行车辆与人员,减轻了车辆过于拥挤的现状并极大地缓解了交通道路的压力.%According to contemporary transportation system,the driver cannot obtain the real-time information of traffic situation,this paper designs an automatic warning system of city traffic congestion base on ZigBee wreless network. The system can provide real-time traffic information for people who are or will be going out at a time.lt can automatic shunt vehicles and personnel and reduce the status of the vehicle overcrowded and greatly ease the traffic pressure of the road.

  15. Reinforcement Based Fuzzy Neural Network Control with Automatic Rule Generation%基于增强型算法并能自动生成规则的模糊神经网络控制器

    Institute of Scientific and Technical Information of China (English)

    吴耿锋; 傅忠谦

    2001-01-01

    A reinforcement based fuzzy neural network controller (RBFNNC) is proposed. A set of optimised fuzzy control rules can be automatically generated through reinforcement learning based on the state variables of object system. RBFNNC was applied to a cart-pole balancing system and shows significant improvements on the rule generation.%给出了一种基于增强型算法并能自动生成控制规则的模糊神经网络控制器RBFNNC(reinforcements based fuzzy neural network controller).该控制器能根据被控对象的状态通过增强型学习自动生成模糊控制规则.RBFNNC用于倒立摆小车平衡系统控制的仿真实验表明了该系统的结构及增强型学习算法是有效和成功的.

  16. 基于物联网技术的设施农业自动控制系统%Networking technology based automatic control system of agricultural facilities

    Institute of Scientific and Technical Information of China (English)

    李金莹; 张日升; 杨宏业; 宣传忠

    2014-01-01

    针对农户对基于物联网技术农业大棚的迫切需求,通过农业大棚与物联网技术、WIFI与3G等通信技术相互结合的方式,完成了农业大棚系统总体构架设计的每个模块功能与器件选型,实现了设施农业自动控制系统的整体功能,这样既减少了用户的人力与精力投入,又提高了产品的质量与品质,为用户获得了更多的增值创收;同时通过基于物联网技术的设施农业大棚的推广应用,给经销商带来了源源不断的收益;也促使了当地政府更好的服务于三农,很好的塑造了地方农业品牌与产业升级,更大程度的惠及了广大农户。%Aiming at the urgent demand of farmers on the Internet of things technology based agricultural greenhouse, the greenhouse and the technology of the Internet of things, WIFI and 3G communication technology in conjunction with each other, to complete the function of architecture design of agricultural greenhouse system each module and device selection, the facility agriculture overall function of the automatic control system, thus reducing human and energy user input, and improve the quality of products and quality, for the user to gain more value-added income;at the same time, through the promotion of application of the Internet of things technology facilities based on agricultural greenhouse, brought a revenue stream to the dealer; also prompted the local government to better serve the agriculture, good shape. Brand agricultural and industrial upgrading, greater benefit farmers.

  17. ON AUTOMATIC MODULATION RECOGNITION BASED ON UNSUPERVISED LEARNING NEURAL NETWORKS AND ITS IMPLEMENTATION%基于非监督学习神经网络的自动调制识别研究与实现

    Institute of Scientific and Technical Information of China (English)

    徐毅琼; 葛临东; 王波; 叶健

    2011-01-01

    以非监督学习神经网络为主要研究对象,描述自组织网络的基本模型,分析传统自组织网络的训练算法,提出了一种基于自组织特征映射SOFM(Self-Organizing Feature Map)神经网络的通信信号自动调制识别方法.方法改进了训练算法中的学习率函数和邻域函数,提高了算法的收敛速度和性能,并将其应用在通信信号调制识别中.仿真实验检验基于SOFM神经网络的调制识别方法的性能,并与后向反馈(BP)神经网络加以比较,结果表明SOFM神经网络的调制识别方法具有较高的识别精度,改进后的训练算法提高了识别的有效性.%This paper focuses on the unsupervised learning neural networks.Firstly ,the basic structure of self-organised neural network is described.Then the traditional training algorithm of self-organised neural network is analysed, and the automatic modulation recognition method for communication signals based on self-organised feature map (SOFM) neural network is presented.The method improves the learning rate function and neighbourhood function of the training algorithm,enhances the convergence speed and performance of the algorithm,and has been applied in the modulation recognition of communication signals.Simulations test checks the performance of SOFM neural network based modulation recognition method, and compares it with the back-propagation (BP) neural network.Results illustrate that the modulation recognition method based on SOFM neural network has higher recognition precision, and the improved training algorithm has ameliorated its effectiveness of recognition.

  18. Automatic MR prostate segmentation by deep learning with holistically-nested networks

    Science.gov (United States)

    Cheng, Ruida; Roth, Holger R.; Lay, Nathan; Lu, Le; Turkbey, Baris; Gandler, William; McCreedy, Evan S.; Choyke, Peter; Summers, Ronald M.; McAuliffe, Matthew J.

    2017-02-01

    Accurate automatic prostate magnetic resonance image (MRI) segmentation is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. The proposed method performs end-to- end segmentation by integrating holistically nested edge detection with fully convolutional neural networks. Holistically-nested networks (HNN) automatically learn the hierarchical representation that can improve prostate boundary detection. Quantitative evaluation is performed on the MRI scans of 247 patients in 5-fold cross-validation. We achieve a mean Dice Similarity Coefficient of 88.70% and a mean Jaccard Similarity Coefficient of 80.29% without trimming any erroneous contours at apex and base.

  19. Automatic calibration and neural networks for robot guidance

    Science.gov (United States)

    Sethuramasamyraja, Balaji; Ghaffari, Masoud; Hall, Ernest L.

    2003-10-01

    An autonomous robot must be able to sense its environment and react appropriately in a variable environment. The University of Cincinnati Robot team is actively involved in building a small, unmanned, autonomously guided vehicle for the International Ground Robotics Contest organized by Association for Unmanned Vehicle Systems International (AUVSI) each year. The unmanned vehicle is supposed to follow an obstacle course bounded by two white/yellow lines, which are four inches thick and 10 feet apart. The navigation system for one of the University of Cincinnati"s designs, Bearcat, uses 2 CCD cameras and an image-tracking device for the front end processing of the image captured by the cameras. The three dimensional world co-ordinates were reduced to two dimensional image coordinates as a result of the transformations taking place from the ground plane to the image plane. A novel automatic calibration system was designed to transform the image co-ordinates back to world co-ordinates for navigation purposes. The purpose of this paper is to simplify this tedious calibration using an artificial neural network. Image processing is used to automatically detect calibration points. Then a back projection neural algorithm is used to learn the relationships between the image coordinates and three-dimensional coordinates. This transformation is the main focus of this study. Using these algorithms, the robot built with this design is able to track and follow the lines successfully.

  20. Automatic Induction of Rule Based Text Categorization

    Directory of Open Access Journals (Sweden)

    D.Maghesh Kumar

    2010-12-01

    Full Text Available The automated categorization of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuingneed to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. This paper describes, a novel method for the automatic induction of rule-based text classifiers. This method supports a hypothesis language of the form "if T1, … or Tn occurs in document d, and none of T1+n,... Tn+m occurs in d, then classify d under category c," where each Ti is a conjunction of terms. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. Issues pertaining tothree different problems, namely, document representation, classifier construction, and classifier evaluation were discussed in detail.

  1. Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

    Science.gov (United States)

    Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

    2012-09-01

    The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

  2. Network patterns recognition for automatic dermatologic images classification

    Science.gov (United States)

    Grana, Costantino; Daniele, Vanini; Pellacani, Giovanni; Seidenari, Stefania; Cucchiara, Rita

    2007-03-01

    In this paper we focus on the problem of automatic classification of melanocytic lesions, aiming at identifying the presence of reticular patterns. The recognition of reticular lesions is an important step in the description of the pigmented network, in order to obtain meaningful diagnostic information. Parameters like color, size or symmetry could benefit from the knowledge of having a reticular or non-reticular lesion. The detection of network patterns is performed with a three-steps procedure. The first step is the localization of line points, by means of the line points detection algorithm, firstly described by Steger. The second step is the linking of such points into a line considering the direction of the line at its endpoints and the number of line points connected to these. Finally a third step discards the meshes which couldn't be closed at the end of the linking procedure and the ones characterized by anomalous values of area or circularity. The number of the valid meshes left and their area with respect to the whole area of the lesion are the inputs of a discriminant function which classifies the lesions into reticular and non-reticular. This approach was tested on two balanced (both sets are formed by 50 reticular and 50 non-reticular images) training and testing sets. We obtained above 86% correct classification of the reticular and non-reticular lesions on real skin images, with a specificity value never lower than 92%.

  3. Neural network for automatic analysis of motility data

    DEFF Research Database (Denmark)

    Jakobsen, Erik; Kruse-Andersen, S; Kolberg, Jens Godsk

    1994-01-01

    events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means...... of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were...

  4. Automatic detection of esophageal pressure events. Is there an alternative to rule-based criteria?

    DEFF Research Database (Denmark)

    Kruse-Andersen, S; Rütz, K; Kolberg, Jens Godsk

    1995-01-01

    curves generated by muscular contractions, rule-based criteria do not always select the pressure events most relevant for further analysis. We have therefore been searching for a new concept for automatic event recognition. The present study describes a new system, based on the method of neurocomputing.......79-0.99 and accuracies of 0.89-0.98, depending on the recording level within the esophageal lumen. The neural networks often recognized peaks that clearly represented true contractions but that had been rejected by a rule-based system. We conclude that neural networks have potentials for automatic detections...

  5. AUTOMATIC TEXT SUMMARIZATION BASED ON TEXTUAL COHESION

    Institute of Scientific and Technical Information of China (English)

    Chen Yanmin; Liu Bingquan; Wang Xiaolong

    2007-01-01

    This paper presents two different algorithms that derive the cohesion structure in the form of lexical chains from two kinds of language resources HowNet and TongYiCiCiLin.The research that connects the cohesion structure of a text to the derivation of its summary is displayed.A novel model of automatic text summarization is devised,based on the data provided by lexicai chains from original texts.Moreover,the construction rules of lexical chains are modified according to characteristics of the knowledge database in order to be more suitable for Chinese suIninarization.Evaluation results show that high quality indicative summaries are produced from Chinese texts.

  6. Automatic Distinguishing Oil Bearing Reservoirs from Water Bearing Reservoirs Based on Neural Networks and Image Process Technology%基于神经网络与图象处理技术的油水层综合判别

    Institute of Scientific and Technical Information of China (English)

    许少华; 梁久祯; 麻成斗; 孙文德

    2001-01-01

    提出了一种基于神经网络与图象处理技术相结合的油水层综合判别方法。首先将数字化测井曲线和地层参数经预处理转化为二值点阵图象模式,经过点阵数据编码压缩提取和记忆曲线所表征的地层模式特征,然后利用BP算法与遗传算法相结合的方法训练多层前馈神经网络。所得神经网络稳定、学习收敛速度快,同时有很强的记忆能力和推广能力,此模型对解决油水层综合判别问题具有良好的适应性。通过对大庆油田采油八厂升平油田葡萄花油层8口井的资料处理,取得了很好的效果。%In this paper we propose an automatic distinguishing oil bearing reservoirs from water bearing reservoirs based on neural networks and image process technology. First, we translate digital well measure curves and stratum parameters into binary image modes. Second, through contracting binary data codes, we distill and store stratum mode characters token by well measure curves. Last, we combine BP algorithm and genetic algorithm to train a multilayers forward neural network. The neural network keeps properties of being stable, fast learning, awfully memorable and generalized ability. This model is suitable to solve issues of Automatic distinguishing oil bearing reservoirs from water bearing reservoirs . Testing on 8 wells data of Putaohua oil layer in the eighth plant of Daqing oil field, we obtain nice results.

  7. Integrating the automatic and the controlled: strategies in semantic priming in an attractor network with latching dynamics.

    Science.gov (United States)

    Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

    2014-01-01

    Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. Copyright © 2014 Cognitive Science Society, Inc.

  8. Expert knowledge for automatic detection of bullies in social networks

    OpenAIRE

    Dadvar, Maral; Trieschnigg, Dolf; Jong, de, F.

    2013-01-01

    Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of YouTube users‟ behaviour and their characteristics through expert knowledge. Based on experts‟ knowledge, the system assigns a score to the users, which represents their level of “bulliness” based on th...

  9. Automatic Speech Segmentation Based on HMM

    Directory of Open Access Journals (Sweden)

    M. Kroul

    2007-06-01

    Full Text Available This contribution deals with the problem of automatic phoneme segmentation using HMMs. Automatization of speech segmentation task is important for applications, where large amount of data is needed to process, so manual segmentation is out of the question. In this paper we focus on automatic segmentation of recordings, which will be used for triphone synthesis unit database creation. For speech synthesis, the speech unit quality is a crucial aspect, so the maximal accuracy in segmentation is needed here. In this work, different kinds of HMMs with various parameters have been trained and their usefulness for automatic segmentation is discussed. At the end of this work, some segmentation accuracy tests of all models are presented.

  10. UMLS-based automatic image indexing.

    Science.gov (United States)

    Sneiderman, C; Sneiderman, Charles Alan; Demner-Fushman, D; Demner-Fushman, Dina; Fung, K W; Fung, Kin Wah; Bray, B; Bray, Bruce

    2008-01-01

    To date, most accurate image retrieval techniques rely on textual descriptions of images. Our goal is to automatically generate indexing terms for an image extracted from a biomedical article by identifying Unified Medical Language System (UMLS) concepts in image caption and its discussion in the text. In a pilot evaluation of the suggested image indexing method by five physicians, a third of the automatically identified index terms were found suitable for indexing.

  11. PLC Based Automatic Multistoried Car Parking System

    Directory of Open Access Journals (Sweden)

    Swanand S .Vaze

    2014-12-01

    Full Text Available This project work presents the study and design of PLC based Automatic Multistoried Car Parking System. Multistoried car parking is an arrangement which is used to park a large number of vehicles in least possible place. For making this arrangement in a real plan very high technological instruments are required. In this project a prototype of such a model is made. This prototype model is made for accommodating twelve cars at a time. Availability of the space for parking is detected by optical proximity sensor which is placed on the pallet. A motor controlled elevator is used to lift the cars. Elevator status is indicated by LED which is placed on ground floor. Controlling of the platforms and checking the vacancies is done by PLC. For unparking of car, keyboard is interfaced with the model for selection of required platform. Automation is done to reduce requirement of space and also to reduce human errors, which in-turn results in highest security and greatest flexibility. Due to these advantages, this system can be used in hotels, railway stations, airports where crowding of car is more.

  12. Automatic generalization of metro maps based on dynamic segmentation

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    A metro map is usually optimized for the readability of connections and transportation networks structure.In order to assure good readability and meet aesthetic considerations,a set of principles for good metro map layout are proposed.According to these principles,a new methodology based on dynamic segmentation is presented to produce the metro maps automatically.Firstly,routes are constructed according to the line attribute similarity and geometry continuity.Then a set of cartographic generalization methods about the shape,angle,length,and topology are presented for these routes.This method is validated by Beijing metro plan map.From the experiment results,it can be concluded that this new method is more effective than the static segmentation method to produce metro maps with better readability for route plans.

  13. An Automatic Networking and Routing Algorithm for Mesh Network in PLC System

    Science.gov (United States)

    Liu, Xiaosheng; Liu, Hao; Liu, Jiasheng; Xu, Dianguo

    2017-05-01

    Power line communication (PLC) is considered to be one of the best communication technologies in smart grid. However, the topology of low voltage distribution network is complex, meanwhile power line channel has characteristics of time varying and attenuation, which lead to the unreliability of power line communication. In this paper, an automatic networking and routing algorithm is introduced which can be adapted to the "blind state" topology. The results of simulation and test show that the scheme is feasible, the routing overhead is small, and the load balance performance is good, which can achieve the establishment and maintenance of network quickly and effectively. The scheme is of great significance to improve the reliability of PLC.

  14. Automatic Fingerprint Classification by GA-Based Neural Network%基于遗传算法的神经网络指纹自动分类

    Institute of Scientific and Technical Information of China (English)

    黄席樾; 马笑潇; 沈志熙; 汪鹏; 周欣

    2001-01-01

    研究指纹的自动分类问题对解决大容量指纹库的匹配实时性有着重要的意义。笔者提出了一种新的指纹自动分类方法。该方法通过求取指纹方向图抽取了指纹的纹形特征,并将其送入神经网络进行分类识别,网络连接权系数采用遗传算法进行学习寻优,克服了单纯BP算法训练时间长、易陷入局部极值的缺点,同时提高了网络全局收敛的效率。测试结果表明,基于遗传算法的多层前向神经网络分类器对指纹图象的分类有良好的性能。%Fingerprint classification can provide an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint-matching time for large database. In the paper, by combining genetic algorithm and neural network is presented a fingerprint classification algorithm which is able to achieve an accurate classification. By inputting the global feature represented by directional image to three layer neural network trained by genetic algorithm, the fingerprints were classified into six categories: whorl, right loop, left loop, arch, double loop and undiscerning type successfully.

  15. [Research on automatic external defibrillator based on DSP].

    Science.gov (United States)

    Jing, Jun; Ding, Jingyan; Zhang, Wei; Hong, Wenxue

    2012-10-01

    Electrical defibrillation is the most effective way to treat the ventricular tachycardia (VT) and ventricular fibrillation (VF). An automatic external defibrillator based on DSP is introduced in this paper. The whole design consists of the signal collection module, the microprocessor controlingl module, the display module, the defibrillation module and the automatic recognition algorithm for VF and non VF, etc. This automatic external defibrillator has achieved goals such as ECG signal real-time acquisition, ECG wave synchronous display, data delivering to U disk and automatic defibrillate when shockable rhythm appears, etc.

  16. An automatic layout system for OMT-based object diagram

    Energy Technology Data Exchange (ETDEWEB)

    Nakashima, Satoshi; Ali, Jauhar; Tanaka, Jiro [Univ. of Tsukuba (Japan)

    1996-12-31

    In this paper, we propose an automatic layout method for the object diagram, the event trace diagram and the state diagram based on OMT (Object Modeling Technique) methodology. In our automatic layout system, when the elements of model (classes, associations etc.) are entered, an arrangement for them is computed, and the object model automatically appears in the editor`s window. We adopted Messinger`s algorithm using the rule of divide-and-conquer for the layout algorithm of the object diagram. Furthermore, diagrams can be maintained easily with the capabilities of automatic modification and direct manipulation interface.

  17. automatic data collection design for neural networks detection of ...

    African Journals Online (AJOL)

    Dr Obe

    University of Nigeria, Nsukka. E-mail: ... paper examines some formal procedures for data collection and proposes designing an automatic .... 4.2 Proposed Architecture for Automatic .... specific application through a learning process. ... space R. D to match (represent) regions that include relatively large amount of samples.

  18. Automatic acquisition and classification system for agricultural network information based on Web data%基于Web数据的农业网络信息自动采集与分类系统

    Institute of Scientific and Technical Information of China (English)

    段青玲; 魏芳芳; 张磊; 肖晓琰

    2016-01-01

    The purpose of this study is to obtain agricultural web information efficiently, and to provide users with personalized service through the integration of agricultural resources scattered in different sites and the fusion of heterogeneous environmental data. The research in this paper has improved some key information technologies, which are agricultural web data acquisition and extraction technologies, text classification based on support vector machine (SVM) and heterogeneous data collection based on the Internet of things (IOT). We first add quality target seed site into the system, and get website URL (uniform resource locator) and category information. The web crawler program can save original pages. The de-noised web page can be obtained through HTML parser and regular expressions, which create custom Node Filter objects. Therefore, the system builds a document object model (DOM) tree before digging out data area. According to filtering rules, the target data area can be identified from a plurality of data regions with repeated patterns. Next, the structured data can be extracted after property segmentation. Secondly, we construct linear SVM classification model, and realize agricultural text classification automatically. The procedures of our model include 4 steps. First of all, we use segment tool ICTCLAS to carry out the word segment and part-of-speech (POS) tagging, followed by combining agricultural key dictionary and document frequency adjustment rule to choose feature words, and building a feature vector and calculating inverse document frequency (IDF) weight value for feature words; lastly we design adaptive classifier of SVM algorithm. Finally, the perception data of different format collected by the sensor are transmitted to the designated server as the source data through the wireless sensor network. Relational database in accordance with specified acquisition frequency can be achieved through data conversion and data filtering. The key step of

  19. Designing a Knowledge Base for Automatic Book Classification.

    Science.gov (United States)

    Kim, Jeong-Hyen; Lee, Kyung-Ho

    2002-01-01

    Reports on the design of a knowledge base for an automatic classification in the library science field by using the facet classification principles of colon classification. Discusses inputting titles or key words into the computer to create class numbers through automatic subject recognition and processing title key words. (Author/LRW)

  20. Supervisory control system based on PC applied to substations automatization of and to regional operation centers of distribution networks; Sistema de control supervisorio basado en PC aplicado en automatizacion de subestaciones y centros de operacion regional de redes de distribucion

    Energy Technology Data Exchange (ETDEWEB)

    Picasso B, Cuitlahuac [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico); Astorga Q, Clemente [Luz y Fuerza del Centro (Mexico)

    2003-07-01

    In order to increase the services quality in the electrical energy, to improve the process of identification of energy losses, to increase the equipment efficiency, to count on more complete statistics of profiles of load consumptions, among other applications, the Compania de Luz y Fuerza del Centro (LyFC) carries out the modernization in automatization of substations and regional operation centers of distribution networks. The technological base to make these programs requires automating the operative schemes of the substation and distribution centers that supervise the events of the electrical process. In this article the main results of the development and integration of master stations based on PC, that were made in the Instituto de Investigaciones Electricas (IIE) and that have been integrated in the electrical company LyFC are presented. [Spanish] Con el proposito de incrementar la calidad de los servicios en la energia electrica, mejorar el proceso de identificacion de perdidas de energia, aumentar la eficiencia de los equipos, contar con estadisticas mas completas de perfiles de consumos de carga, entre otras aplicaciones, la Compania de Luz y Fuerza del Centro (LyFC) lleva a cabo la modernizacion en automatizacion de subestaciones y centros de operacion regional de redes electricas de distribucion. La base tecnologica para realizar estos programas, requiere de automatizar los esquemas operativos de la subestacion y centros de distribucion que supervisan los eventos del proceso electrico. En este articulo se presentan los principales resultados del desarrollo e integracion de estaciones maestras basadas en PC, que se realizaron en el Instituto de Investigaciones Electricas (IIE) y que se han integrado en la compania electrica LyFC.

  1. Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks

    Directory of Open Access Journals (Sweden)

    Mazo Ilya

    2007-07-01

    Full Text Available Abstract Background Uncovering cellular roles of a protein is a task of tremendous importance and complexity that requires dedicated experimental work as well as often sophisticated data mining and processing tools. Protein functions, often referred to as its annotations, are believed to manifest themselves through topology of the networks of inter-proteins interactions. In particular, there is a growing body of evidence that proteins performing the same function are more likely to interact with each other than with proteins with other functions. However, since functional annotation and protein network topology are often studied separately, the direct relationship between them has not been comprehensively demonstrated. In addition to having the general biological significance, such demonstration would further validate the data extraction and processing methods used to compose protein annotation and protein-protein interactions datasets. Results We developed a method for automatic extraction of protein functional annotation from scientific text based on the Natural Language Processing (NLP technology. For the protein annotation extracted from the entire PubMed, we evaluated the precision and recall rates, and compared the performance of the automatic extraction technology to that of manual curation used in public Gene Ontology (GO annotation. In the second part of our presentation, we reported a large-scale investigation into the correspondence between communities in the literature-based protein networks and GO annotation groups of functionally related proteins. We found a comprehensive two-way match: proteins within biological annotation groups form significantly denser linked network clusters than expected by chance and, conversely, densely linked network communities exhibit a pronounced non-random overlap with GO groups. We also expanded the publicly available GO biological process annotation using the relations extracted by our NLP technology

  2. Automatic malware analysis an emulator based approach

    CERN Document Server

    Yin, Heng

    2012-01-01

    Malicious software (i.e., malware) has become a severe threat to interconnected computer systems for decades and has caused billions of dollars damages each year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving becoming more sophisticated and evasive to strike against current malware analysis and defense systems. Automatic Malware Analysis presents a virtualized malware analysis framework that addresses common challenges in malware analysis. In regards to this new analysis framework, a series of analysis techniques for automatic malware analy

  3. Automatic incrementalization of Prolog based static analyses

    DEFF Research Database (Denmark)

    Eichberg, Michael; Kahl, Matthias; Saha, Diptikalyan;

    2007-01-01

    Modem development environments integrate various static analyses into the build process. Analyses that analyze the whole project whenever the project changes are impractical in this context. We present an approach to automatic incrementalization of analyses that are specified as tabled logic prog...

  4. 应用改进遗传神经网络识别种蛋蛋形试验%Experiment on automatic shape identification of hatching eggs based on improved genetic algorithm neural network

    Institute of Scientific and Technical Information of China (English)

    郁志宏; 王栓巧; 张平; 贾超

    2009-01-01

    Shape inspection of hatching eggs is an important and hard work in farms, manual inspection lacks the objectivity and is time-consuming. In order to solve problems mentioned above, an automatic shape identification method was proposed based on machine vision, moment technique and improved genentic algorithm-neural network (GA-NN) algorithm. Egg shape index and radius differences were extracted as eggs shape feature parameters. An improved immune genentic algorithm was put forward to optimize topology structure of levenberg-marquardt back progagation-neural network (LMBP-NN). After egg shape index was identified , radius differences were used as inputs of LMBP-NN and its outputs were used to determine the hatching egg shape normal or not. The results indicated that the classification accuracy of this method reached 97.1% for longer eggs, 95.59% for shorter eggs, 94.87% for abnormal eggs and 95.75% for normal eggs' respectively. It is significant for shape identification of hatching eggs automatically, which can improve detection accuracy and efficiency. The neural network system for shape identification of hatching eggs has high accuracy and generalization ability, and the algorithm is feasible and robust.%针对人工检测种蛋蛋形劳动强度大,缺乏客观性,检测效率低,研究了自动快速、准确地识别鸡种蛋蛋形的方法.以蛋形指数和蛋径差为形状特征参数,利用机器视觉技术、矩技术和提出的改进遗传神经网络算法剔除畸形蛋.基于机器视觉和矩技术提取种蛋的长短径,剔除蛋形指数不合格种蛋后,再通过构建合理的遗传神经网络模型,以蛋径差作为神经网络输入参数,根据网络输出值识别种蛋蛋形.对过圆蛋、过尖蛋、畸形蛋和正常蛋检测准确率分别达到了97.10%、95.59%、94.87%和95.75%.研究种蛋蛋形自动识别方法对提高种蛋蛋形检测准确率和工作效率具有重要意义,试验结果表明提出的种蛋蛋形评价

  5. Analysis of the influence of tectonics on the evolution valley network based on the SRTM DEM and the relationship of automatically extracted lineaments and the tectonic faults, Jemma River basin, Ethiopia

    Science.gov (United States)

    Kusák, Michal

    2016-04-01

    The Ethiopian Highland is good example of high plateau landscape formed by combination of tectonic uplift and episodic volcanism (Kazmin, 1975; Pik et al., 2003; Gani et al., 2009). Deeply incised gorges indicate active fluvial erosion which leads to instabilities of over-steepened slopes. In this study we focus on Jemma River basin which is a left tributary of Abay - Blue Nile to assess the influence of neotectonics on the evolution of its river and valley network. Tectonic lineaments, shape of valley networks, direction of river courses and intensity of fluvial erosion were compared in six subregions which were delineate beforehand by means of morphometric analysis. The influence of tectonics on the valley network is low in the older deep and wide canyons and in the and on the high plateau covered with Tertiary lava flows while younger upper part of the canyons it is high. Furthermore, the coincidence of the valley network with the tectonic lineaments differs in the subregions. The fluvial erosion along the main tectonic zones (NE-SW) direction made the way for backward erosion possible to reach far distant areas in E for the fluvial erosion. This tectonic zone also separates older areas in the W from the youngest landscape evolution subregions in the E, next to the Rift Valley. We studied the functions that can automatically extract lineaments in programs ArcGIS 10.1 and PCI Geomatica. The values of input parameters and their influence of the final shape and number of lineaments. A map of automated extracted lineaments was created and compared with 1) the tectonic faults by Geology Survey of Ethiopia (1996); and 2) the lineaments based on visual interpretation of by the author. The comparation of lineaments by automated visualization in GIS and visual interpretation of lineaments by the author proves that both sets of lineaments are in the same azimuth (NE-SW) - the same direction as the orientation of the rift. But it the mapping of lineaments by automated

  6. Automatic Representation and Segmentation of Video Sequences via a Novel Framework Based on the nD-EVM and Kohonen Networks

    Directory of Open Access Journals (Sweden)

    José-Yovany Luis-García

    2016-01-01

    Full Text Available Recently in the Computer Vision field, a subject of interest, at least in almost every video application based on scene content, is video segmentation. Some of these applications are indexing, surveillance, medical imaging, event analysis, and computer-guided surgery, for naming some of them. To achieve their goals, these applications need meaningful information about a video sequence, in order to understand the events in its corresponding scene. Therefore, we need semantic information which can be obtained from objects of interest that are present in the scene. In order to recognize objects we need to compute features which aid the finding of similarities and dissimilarities, among other characteristics. For this reason, one of the most important tasks for video and image processing is segmentation. The segmentation process consists in separating data into groups that share similar features. Based on this, in this work we propose a novel framework for video representation and segmentation. The main workflow of this framework is given by the processing of an input frame sequence in order to obtain, as output, a segmented version. For video representation we use the Extreme Vertices Model in the n-Dimensional Space while we use the Discrete Compactness descriptor as feature and Kohonen Self-Organizing Maps for segmentation purposes.

  7. Improving Cluster Analysis with Automatic Variable Selection Based on Trees

    Science.gov (United States)

    2014-12-01

    ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES by Anton D. Orr December 2014 Thesis Advisor: Samuel E. Buttrey Second Reader...DATES COVERED Master’s Thesis 4. TITLE AND SUBTITLE IMPROVING CLUSTER ANALYSIS WITH AUTOMATIC VARIABLE SELECTION BASED ON TREES 5. FUNDING NUMBERS 6...2006 based on classification and regression trees to address problems with determining dissimilarity. Current algorithms do not simultaneously address

  8. An automatic weighting system for wild animals based in an artificial neural network: how to weigh wild animals without causing stress.

    Science.gov (United States)

    Larios, Diego Francisco; Rodríguez, Carlos; Barbancho, Julio; Baena, Manuel; Angel, Miguel Leal; Marín, Jesús; León, Carlos; Bustamante, Javier

    2013-02-28

    This paper proposes a novel and autonomous weighing system for wild animals. It allows evaluating changes in the body weight of animals in their natural environment without causing stress. The proposed system comprises a smart scale designed to estimate individual body weights and their temporal evolution in a bird colony. The system is based on computational intelligence, and offers valuable large amount of data to evaluate the relationship between long-term changes in the behavior of individuals and global change. The real deployment of this system has been for monitoring a breeding colony of lesser kestrels (Falco naumanni) in southern Spain. The results show that it is possible to monitor individual weight changes during the breeding season and to compare the weight evolution in males and females.

  9. Intelligent Storage System Based on Automatic Identification

    Directory of Open Access Journals (Sweden)

    Kolarovszki Peter

    2014-09-01

    Full Text Available This article describes RFID technology in conjunction with warehouse management systems. Article also deals with automatic identification and data capture technologies and each processes, which are used in warehouse management system. It describes processes from entering goods into production to identification of goods and also palletizing, storing, bin transferring and removing goods from warehouse. Article focuses on utilizing AMP middleware in WMS processes in Nowadays, the identification of goods in most warehouses is carried through barcodes. In this article we want to specify, how can be processes described above identified through RFID technology. All results are verified by measurement in our AIDC laboratory, which is located at the University of Žilina, and also in Laboratory of Automatic Identification Goods and Services located in GS1 Slovakia. The results of our research bring the new point of view and indicate the ways using of RFID technology in warehouse management system.

  10. Generating IDS Attack Pattern Automatically Based on Attack Tree

    Institute of Scientific and Technical Information of China (English)

    向尕; 曹元大

    2003-01-01

    Generating attack pattern automatically based on attack tree is studied. The extending definition of attack tree is proposed. And the algorithm of generating attack tree is presented. The method of generating attack pattern automatically based on attack tree is shown, which is tested by concrete attack instances. The results show that the algorithm is effective and efficient. In doing so, the efficiency of generating attack pattern is improved and the attack trees can be reused.

  11. Automobile Transmission Shift Control Based on MMAS and BP Networks

    Directory of Open Access Journals (Sweden)

    Jianxue Chen

    2013-08-01

    Full Text Available The neural network control model of automobile automatic transmission has been developed, which make the optimum shift decision based on the vehicle velocity, the vehicle acceleration and the throttle opening. The MAX-MIN ant syste (MMAS is introduced to train the neural network weights and thresholds. The basic theory and steps of MMAS algorithm are given, and applied in the automatic transmission shift control. Experimental results show that the automatic transmission shift control system based on MMAS, comparing to the system based on ACO-BP, has better capability of gear recognition, and can make shift decision promptly and effectively.

  12. Image analysis techniques associated with automatic data base generation.

    Science.gov (United States)

    Bond, A. D.; Ramapriyan, H. K.; Atkinson, R. J.; Hodges, B. C.; Thomas, D. T.

    1973-01-01

    This paper considers some basic problems relating to automatic data base generation from imagery, the primary emphasis being on fast and efficient automatic extraction of relevant pictorial information. Among the techniques discussed are recursive implementations of some particular types of filters which are much faster than FFT implementations, a 'sequential similarity detection' technique of implementing matched filters, and sequential linear classification of multispectral imagery. Several applications of the above techniques are presented including enhancement of underwater, aerial and radiographic imagery, detection and reconstruction of particular types of features in images, automatic picture registration and classification of multiband aerial photographs to generate thematic land use maps.

  13. Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, N.; Havinga, P.J.M.

    2008-01-01

    Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a

  14. Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a

  15. Automatic Structure-Based Code Generation from Coloured Petri Nets

    DEFF Research Database (Denmark)

    Kristensen, Lars Michael; Westergaard, Michael

    2010-01-01

    Automatic code generation based on Coloured Petri Net (CPN) models is challenging because CPNs allow for the construction of abstract models that intermix control flow and data processing, making translation into conventional programming constructs difficult. We introduce Process-Partitioned CPNs....... The viability of our approach is demonstrated by applying it to automatically generate an Erlang implementation of the Dynamic MANET On-demand (DYMO) routing protocol specified by the Internet Engineering Task Force (IETF)....

  16. JAPS: an automatic parallelizing system based on JAVA

    Institute of Scientific and Technical Information of China (English)

    杜建成; 陈道蓄; 谢立

    1999-01-01

    JAPS is an automatic parallelizing system based on JAVA running on NOW. It implements the automatic process from dependence analysis to parallel execution. The current version of JAPS can exploit functional parallelism and the detection of data parallelism will be incorporated in the new version, which is underway. The framework and key techniques of JAPS are presented. Specific topics discussed are task partitioning, summary information collection, data dependence analysis, pre-scheduling and dynamic scheduling, etc.

  17. Automatic Implementation of Ttethernet-Based Time-Triggered Avionics Applications

    Science.gov (United States)

    Gorcitz, Raul Adrian; Carle, Thomas; Lesens, David; Monchaux, David; Potop-Butucaruy, Dumitru; Sorel, Yves

    2015-09-01

    The design of safety-critical embedded systems such as those used in avionics still involves largely manual phases. But in avionics the definition of standard interfaces embodied in standards such as ARINC 653 or TTEthernet should allow the definition of fully automatic code generation flows that reduce the costs while improving the quality of the generated code, much like compilers have done when replacing manual assembly coding. In this paper, we briefly present such a fully automatic implementation tool, called Lopht, for ARINC653-based time-triggered systems, and then explain how it is currently extended to include support for TTEthernet networks.

  18. Automatic network-adaptive ultra-low-bit-rate video coding

    Science.gov (United States)

    Chien, Wei-Jung; Lam, Tuyet-Trang; Abousleman, Glen P.; Karam, Lina J.

    2006-05-01

    This paper presents a software-only, real-time video coder/decoder (codec) for use with low-bandwidth channels where the bandwidth is unknown or varies with time. The codec incorporates a modified JPEG2000 core and interframe predictive coding, and can operate with network bandwidths of less than 1 kbits/second. The encoder and decoder establish two virtual connections over a single IP-based communications link. The first connection is UDP/IP guaranteed throughput, which is used to transmit the compressed video stream in real time, while the second is TCP/IP guaranteed delivery, which is used for two-way control and compression parameter updating. The TCP/IP link serves as a virtual feedback channel and enables the decoder to instruct the encoder to throttle back the transmission bit rate in response to the measured packet loss ratio. It also enables either side to initiate on-the-fly parameter updates such as bit rate, frame rate, frame size, and correlation parameter, among others. The codec also incorporates frame-rate throttling whereby the number of frames decoded is adjusted based upon the available processing resources. Thus, the proposed codec is capable of automatically adjusting the transmission bit rate and decoding frame rate to adapt to any network scenario. Video coding results for a variety of network bandwidths and configurations are presented to illustrate the vast capabilities of the proposed video coding system.

  19. Multiagent system in automatic light power balance in optical networks

    Science.gov (United States)

    Å lapák, Martin; Hůla, Miloslav

    2013-09-01

    This article deals with automatic power balancing along an optical line. For optimal transmission of an optical signal it is important to achieve certain parameters such as the signal to noise ratio or chromatic dispersion and also the sufficient output power level of in-line amplifiers. Pump diodes in amplifiers suffer from aging of material and therefore the driving current of pump diodes has to be accordingly increased to achieve the same gain as in the moment when the pump diodes were new. The use of a minimal required driving current leads to the longer lifetime of optical pumps. Therefore an automatic power balance is one of the methods used to achieve these goals.

  20. Automatic Image Registration Algorithm Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    LIU Qiong; NI Guo-qiang

    2006-01-01

    An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the least-squares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.

  1. Man-system interface based on automatic speech recognition: integration to a virtual control desk

    Energy Technology Data Exchange (ETDEWEB)

    Jorge, Carlos Alexandre F.; Mol, Antonio Carlos A.; Pereira, Claudio M.N.A.; Aghina, Mauricio Alves C., E-mail: calexandre@ien.gov.b, E-mail: mol@ien.gov.b, E-mail: cmnap@ien.gov.b, E-mail: mag@ien.gov.b [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil); Nomiya, Diogo V., E-mail: diogonomiya@gmail.co [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2009-07-01

    This work reports the implementation of a man-system interface based on automatic speech recognition, and its integration to a virtual nuclear power plant control desk. The later is aimed to reproduce a real control desk using virtual reality technology, for operator training and ergonomic evaluation purpose. An automatic speech recognition system was developed to serve as a new interface with users, substituting computer keyboard and mouse. They can operate this virtual control desk in front of a computer monitor or a projection screen through spoken commands. The automatic speech recognition interface developed is based on a well-known signal processing technique named cepstral analysis, and on artificial neural networks. The speech recognition interface is described, along with its integration with the virtual control desk, and results are presented. (author)

  2. Developing an intelligent control system of automatic window motor with diverse wireless sensor network devices

    Indian Academy of Sciences (India)

    Yao-Chiang Kan; Hsueh-Chun Lin; Wen-Pei Sung

    2014-08-01

    This invention system involves hardware, firmware and software to develop an intelligent control system of automatic window motor with diverse wireless sensor network (WSN) devices for health and environmental monitoring. The parts of this invention are improved by implementing the WSN mote into environmental sensors that may detect temperature, humility, toxic gas, smog or aerosol, etc. With embedded system design, these sensors are capable of delivering WSN signal packets based on ZigBee protocol that follows the IEEE 802.14.4 standards. The primary hardware of the system is the window motor with circuit design by integrating micro control unit (MCU), radio frequency (RF) and WSN antenna to receive command. The firmware developed under embedded system can bridge hardware and software to control the window at the specified position. At the back end, the control system software can manage diverse sensor data and provide the interface for remote monitoring.

  3. Effective and fully automatic image segmentation using quantum entropy and pulse-coupled neural networks

    Science.gov (United States)

    Du, Songlin; Yan, Yaping; Ma, Yide

    2015-03-01

    A novel image segmentation algorithm which uses quantum entropy and pulse-coupled neural networks (PCNN) is proposed in this paper. Optimal iteration of the PCNN is one of the key factors affecting segmentation accuracy. We borrow quantum entropy from quantum information to act as a criterion in determining optimal iteration of the PCNN. Optimal iteration is captured while total quantum entropy of the segments reaches a maximum. Moreover, compared with other PCNN-employed algorithms, the proposed algorithm works without any manual intervention, because all parameters of the PCNN are set automatically. Experimental results prove that the proposed method can achieve much lower probabilities of error segmentation than other PCNN-based image segmentation algorithms, and this suggests that higher image segmentation quality is achieved by the proposed method.

  4. A novel spectral index to automatically extract road networks from WorldView-2 satellite imagery

    Directory of Open Access Journals (Sweden)

    Kaveh Shahi

    2015-06-01

    Full Text Available This research develops a spectral index to automatically extract asphalt road networks named road extraction index (REI. This index uses WorldView-2 (WV-2 imagery, which has high spatial resolution and is multispectral. To determine the best bands for WV-2, field spectral data using a field spectroradiometer were collected. These data were then analyzed statistically. The bands were selected through the methodology of stepwise discriminant analysis. The appropriate WV-2 bands were distinguished from one another as per significant wavelengths. The proposed index is based on this classification. By applying REI to WV-2 imagery, we can extract asphalt roads accurately. Results demonstrate that REI is automated, transferable, and efficient in asphalt road extraction from high-resolution satellite imagery.

  5. Automatic classification of DMSA scans using an artificial neural network

    Science.gov (United States)

    Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

    2014-04-01

    DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

  6. The research of an automatic monitoring system based on wireless sensor network and RFID in the reservoir%基于无线传感器网络与RFID技术的水库自动监测系统研究

    Institute of Scientific and Technical Information of China (English)

    张琳; 胡金杭

    2012-01-01

    The automatic monitoring system of reservoir based on wireless sensor network and RFID can keep inspection personnel of reservoir arrive in time, and monitor the change of various parameters in real time. It doesn't only provide full data base for reservoir regulation and management, but also improves the informatization construction of reservoir, meanwhile realizes the automatic monitoring scheduling.%该文研究的基于无线传感器网络与RFID技术的水库自动监测系统能有效保证水库巡检人员及时到岗,实时监控水库中各种参数变化.不仅为水库调度与管理提供了充分的数据基础,而且提高了水库信息化建设,实现了监控调度自动化.

  7. Automatic aeroponic irrigation system based on Arduino’s platform

    Science.gov (United States)

    Montoya, A. P.; Obando, F. A.; Morales, J. G.; Vargas, G.

    2017-06-01

    The recirculating hydroponic culture techniques, as aeroponics, has several advantages over traditional agriculture, aimed to improve the efficiently and environmental impact of agriculture. These techniques require continuous monitoring and automation for proper operation. In this work was developed an automatic monitored aeroponic-irrigation system based on the Arduino’s free software platform. Analog and digital sensors for measuring the temperature, flow and level of a nutrient solution in a real greenhouse were implemented. In addition, the pH and electric conductivity of nutritive solutions are monitored using the Arduino’s differential configuration. The sensor network, the acquisition and automation system are managed by two Arduinos modules in master-slave configuration, which communicate one each other wireless by Wi-Fi. Further, data are stored in micro SD memories and the information is loaded on a web page in real time. The developed device brings important agronomic information when is tested with an arugula culture (Eruca sativa Mill). The system also could be employ as an early warning system to prevent irrigation malfunctions.

  8. Forest point processes for the automatic extraction of networks in raster data

    Science.gov (United States)

    Schmidt, Alena; Lafarge, Florent; Brenner, Claus; Rottensteiner, Franz; Heipke, Christian

    2017-04-01

    In this paper, we propose a new stochastic approach for the automatic detection of network structures in raster data. We represent a network as a set of trees with acyclic planar graphs. We embed this model in the probabilistic framework of spatial point processes and determine the most probable configuration of trees by stochastic sampling. That is, different configurations are constructed randomly by modifying the graph parameters and by adding or removing nodes and edges to/ from the current trees. Each configuration is evaluated based on the probabilities for these changes and an energy function describing the conformity with a predefined model. By using the Reversible jump Markov chain Monte Carlo sampler, an approximation of the global optimum of the energy function is iteratively reached. Although our main target application is the extraction of rivers and tidal channels in digital terrain models, experiments with other types of networks in images show the transferability to further applications. Qualitative and quantitative evaluations demonstrate the competitiveness of our approach with respect to existing algorithms.

  9. Automatic Selection of Open Source Multimedia Softwares Using Error Back-Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Deepika

    2015-07-01

    Full Text Available Open source opens a new era to provide license of the software for the user at free of cost which is advantage over paid licensed software. In Multimedia applications there are many versions of software are available and there is a problem for the user to select compatible software for their own system. Most of the time while surfing for software a huge list of software opens in response. The selection of particular software which is pretty suitable for the system from a real big list is the biggest challenge that is faced by the users. This work has been done that focuses on the existing open source software that are widely used and to design an automatic system for selection of particular open source software according to the compatibility of users own system. In this work, error back-propagation based neural network is designed in MATLAB for automatic selection of open source software. The system provides the open source software name after taking the information from user. Regression coefficient of 0.93877 is obtained and the results shown are up to the mark and can be utilized for the fast and effective software search.

  10. An automatic method to generate domain-specific investigator networks using PubMed abstracts

    Directory of Open Access Journals (Sweden)

    Gwinn Marta

    2007-06-01

    Full Text Available Abstract Background Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. Results We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8% and from 94.2% of HuGE PubMed records (accuracy 87.0. We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit, indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70–90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. Conclusion We successfully created a

  11. Knowledge Automatic Indexing Based on Concept Lexicon and Segmentation Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Lan-cheng; JIANG Dan; LE Jia-jin

    2005-01-01

    This paper is based on two existing theories about automatic indexing of thematic knowledge concept. The prohibit-word table with position information has been designed. The improved Maximum Matching-Minimum Backtracking method has been researched. Moreover it has been studied on improved indexing algorithm and application technology based on rules and thematic concept word table.

  12. Automatic detection of intruders using a neural network

    Science.gov (United States)

    Carvalho, Fernando D.; Novo, Pedro; Pais, Cassiano P.; Rodrigues, Fernando C.; Rego, Toste

    1992-09-01

    A system is presented that applies a neural network to a video surveillance system. It consists of a pre-processing unit that extract high level information from images and introduces it in the neural network. This system can learn in operational conditions while under the supervision of an unskilled operator. It uses the error backpropagation learning algorithm in a multilayer perceptron structure. The results obtained show that the system performs well, and with a high degree of efficiency.

  13. Wireless Mid-Infrared Spectroscopy Sensor Network for Automatic Carbon Dioxide Fertilization in a Greenhouse Environment

    Directory of Open Access Journals (Sweden)

    Jianing Wang

    2016-11-01

    Full Text Available In this paper, a wireless mid-infrared spectroscopy sensor network was designed and implemented for carbon dioxide fertilization in a greenhouse environment. A mid-infrared carbon dioxide (CO2 sensor based on non-dispersive infrared (NDIR with the functionalities of wireless communication and anti-condensation prevention was realized as the sensor node. Smart transmission power regulation was applied in the wireless sensor network, according to the Received Signal Strength Indication (RSSI, to realize high communication stability and low-power consumption deployment. Besides real-time monitoring, this system also provides a CO2 control facility for manual and automatic control through a LabVIEW platform. According to simulations and field tests, the implemented sensor node has a satisfying anti-condensation ability and reliable measurement performance on CO2 concentrations ranging from 30 ppm to 5000 ppm. As an application, based on the Fuzzy proportional, integral, and derivative (PID algorithm realized on a LabVIEW platform, the CO2 concentration was regulated to some desired concentrations, such as 800 ppm and 1200 ppm, in 30 min with a controlled fluctuation of <±35 ppm in an acre of greenhouse.

  14. Wireless Mid-Infrared Spectroscopy Sensor Network for Automatic Carbon Dioxide Fertilization in a Greenhouse Environment.

    Science.gov (United States)

    Wang, Jianing; Niu, Xintao; Zheng, Lingjiao; Zheng, Chuantao; Wang, Yiding

    2016-11-18

    In this paper, a wireless mid-infrared spectroscopy sensor network was designed and implemented for carbon dioxide fertilization in a greenhouse environment. A mid-infrared carbon dioxide (CO₂) sensor based on non-dispersive infrared (NDIR) with the functionalities of wireless communication and anti-condensation prevention was realized as the sensor node. Smart transmission power regulation was applied in the wireless sensor network, according to the Received Signal Strength Indication (RSSI), to realize high communication stability and low-power consumption deployment. Besides real-time monitoring, this system also provides a CO₂ control facility for manual and automatic control through a LabVIEW platform. According to simulations and field tests, the implemented sensor node has a satisfying anti-condensation ability and reliable measurement performance on CO₂ concentrations ranging from 30 ppm to 5000 ppm. As an application, based on the Fuzzy proportional, integral, and derivative (PID) algorithm realized on a LabVIEW platform, the CO₂ concentration was regulated to some desired concentrations, such as 800 ppm and 1200 ppm, in 30 min with a controlled fluctuation of <±35 ppm in an acre of greenhouse.

  15. Automatic theory generation from analyst text files using coherence networks

    Science.gov (United States)

    Shaffer, Steven C.

    2014-05-01

    This paper describes a three-phase process of extracting knowledge from analyst textual reports. Phase 1 involves performing natural language processing on the source text to extract subject-predicate-object triples. In phase 2, these triples are then fed into a coherence network analysis process, using a genetic algorithm optimization. Finally, the highest-value sub networks are processed into a semantic network graph for display. Initial work on a well- known data set (a Wikipedia article on Abraham Lincoln) has shown excellent results without any specific tuning. Next, we ran the process on the SYNthetic Counter-INsurgency (SYNCOIN) data set, developed at Penn State, yielding interesting and potentially useful results.

  16. Facilitate generation connections on Orkney by automatic distribution network management

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-07-01

    This report summarises the results of a study assessing the capability and limitations of the Orkney Network under a variety of conditions of demand, generation connections, network configuration, and reactive compensation). A conceptual active management scheme (AMS) suitable for the conditions on Orkney is developed and evaluated. Details are given of a proposed framework for the design and evaluation of future active management schemes, logic control sequences for managed generation units, and a proposed evaluation method for the active management scheme. Implications of introducing the proposed AMS are examined, and the commercial aspects of an AMS and system security are considered. The existing Orkney network is described; and an overview of the SHEPDL (Scottish Hydro Electric Power Distribution Ltd.) SCADA system is presented with a discussion of AMS identification, selection, and development.

  17. Facilitate generation connections on Orkney by automatic distribution network management

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2004-07-01

    This report summarises the results of a study assessing the capability and limitations of the Orkney Network under a variety of conditions of demand, generation connections, network configuration, and reactive compensation). A conceptual active management scheme (AMS) suitable for the conditions on Orkney is developed and evaluated. Details are given of a proposed framework for the design and evaluation of future active management schemes, logic control sequences for managed generation units, and a proposed evaluation method for the active management scheme. Implications of introducing the proposed AMS are examined, and the commercial aspects of an AMS and system security are considered. The existing Orkney network is described; and an overview of the SHEPDL (Scottish Hydro Electric Power Distribution Ltd.) SCADA system is presented with a discussion of AMS identification, selection, and development.

  18. Automatically assessing Wikipedia article quality by exploiting article-editor networks

    NARCIS (Netherlands)

    Li, X.; Tang, J.; Wang, T.; Luo, Z.; de Rijke, M.; Hanbury, A.; Kazai, G.; Rauber, A.; Fuhr, N.

    2015-01-01

    We consider the problem of automatically assessing Wikipedia article quality. We develop several models to rank articles by using the editing relations between articles and editors. First, we create a basic model by modeling the article-editor network. Then we design measures of an editor’s

  19. Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network

    NARCIS (Netherlands)

    Hiemstra, P.H.; Pebesma, E.J.; Twenhöfel, C.J.W.; Heuvelink, G.B.M.

    2009-01-01

    Detection of radiological accidents and monitoring the spread of the contamination is of great importance. Following the Chernobyl accident many European countries have installed monitoring networks to perform this task. Real-time availability of automatically interpolated maps showing the spread of

  20. The guitar chord-generating algorithm based on complex network

    Science.gov (United States)

    Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

    2016-02-01

    This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

  1. Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

    Science.gov (United States)

    Zhang, Junming; Wu, Yan

    2017-02-21

    Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

  2. Expert knowledge for automatic detection of bullies in social networks

    NARCIS (Netherlands)

    Dadvar, Maral; Trieschnigg, Dolf; Jong, de Franciska

    2013-01-01

    Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of YouTu

  3. Expert knowledge for automatic detection of bullies in social networks

    NARCIS (Netherlands)

    Dadvar, M.; Trieschnigg, Rudolf Berend; de Jong, Franciska M.G.

    2013-01-01

    Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of YouTu

  4. Super pixel density based clustering automatic image classification method

    Science.gov (United States)

    Xu, Mingxing; Zhang, Chuan; Zhang, Tianxu

    2015-12-01

    The image classification is an important means of image segmentation and data mining, how to achieve rapid automated image classification has been the focus of research. In this paper, based on the super pixel density of cluster centers algorithm for automatic image classification and identify outlier. The use of the image pixel location coordinates and gray value computing density and distance, to achieve automatic image classification and outlier extraction. Due to the increased pixel dramatically increase the computational complexity, consider the method of ultra-pixel image preprocessing, divided into a small number of super-pixel sub-blocks after the density and distance calculations, while the design of a normalized density and distance discrimination law, to achieve automatic classification and clustering center selection, whereby the image automatically classify and identify outlier. After a lot of experiments, our method does not require human intervention, can automatically categorize images computing speed than the density clustering algorithm, the image can be effectively automated classification and outlier extraction.

  5. Automatic gear sorting system based on monocular vision

    Directory of Open Access Journals (Sweden)

    Wenqi Wu

    2015-11-01

    Full Text Available An automatic gear sorting system based on monocular vision is proposed in this paper. A CCD camera fixed on the top of the sorting system is used to obtain the images of the gears on the conveyor belt. The gears׳ features including number of holes, number of teeth and color are extracted, which is used to categorize the gears. Photoelectric sensors are used to locate the gears׳ position and produce the trigger signals for pneumatic cylinders. The automatic gear sorting is achieved by using pneumatic actuators to push different gears into their corresponding storage boxes. The experimental results verify the validity and reliability of the proposed method and system.

  6. AUTOMATIC MULTILEVEL IMAGE SEGMENTATION BASED ON FUZZY REASONING

    Directory of Open Access Journals (Sweden)

    Liang Tang

    2011-05-01

    Full Text Available An automatic multilevel image segmentation method based on sup-star fuzzy reasoning (SSFR is presented. Using the well-known sup-star fuzzy reasoning technique, the proposed algorithm combines the global statistical information implied in the histogram with the local information represented by the fuzzy sets of gray-levels, and aggregates all the gray-levels into several classes characterized by the local maximum values of the histogram. The presented method has the merits of determining the number of the segmentation classes automatically, and avoiding to calculating thresholds of segmentation. Emulating and real image segmentation experiments demonstrate that the SSFR is effective.

  7. 基于遗传-BP神经网络的沉积微相自动识别%Automatic sedimentary facies identification method based on genetic-BP neural networks

    Institute of Scientific and Technical Information of China (English)

    许少华; 陈可为; 梁久祯; 郑生民

    2001-01-01

    提出了一种基于神经网络与图象处理技术相结合的沉积微相自动识别方法.该方法是先将数字化测井曲线和地层参数预处理转化为二值点阵图象模式,经过点阵数据编码压缩提取和记忆曲线所表征的地层模式特征,然后利用超线性BP算法与遗传算法相结合的方法训练多层前馈神经网络.所得神经网络稳定、学习收敛速度快,同时具有很强的记忆能力和推广能力,此模型对解决沉积微相自动识别问题具有良好的适应性.%We propose an automatic sedimentary facies identification methodbased on combination of neural network with image process technology. First, we translate digital well logging curves and stratum parameters into binary image modes. Second, through contracting binary data codes, we distill and store stratum mode characters token by well logging curves. Last, we combine BP algorithm with genetic algorithm to train a multilayers forward neural network. The neural network has the advantages of being stable, fast learning, awfully memorable and generalized ability. This model is suitable to solve problems of sedimentary facies identification.

  8. Automatic SIMD parallelization of embedded applications based on pattern recognition

    NARCIS (Netherlands)

    Manniesing, R.; Karkowski, I.P.; Corporaal, H.

    2000-01-01

    This paper investigates the potential for automatic mapping of typical embedded applications to architectures with multimedia instruction set extensions. For this purpose a (pattern matching based) code transformation engine is used, which involves a three-step process of matching, condition checkin

  9. Automatic determination of recrystallization parameters based on EBSD mapping

    DEFF Research Database (Denmark)

    Wu, Guilin; Juul Jensen, Dorte

    2008-01-01

    A new automatic algorithm for determining the recrystallization parameters V-V, S-V and based on EBSD mapping is presented in this paper. The algorithm is validated on aluminium deformed to high strains. The algorithm is also compared with other methods using the exact same sets of samples...

  10. Automatic SIMD parallelization of embedded applications based on pattern recognition

    NARCIS (Netherlands)

    Manniesing, R.; Karkowski, I.P.; Corporaal, H.

    2000-01-01

    This paper investigates the potential for automatic mapping of typical embedded applications to architectures with multimedia instruction set extensions. For this purpose a (pattern matching based) code transformation engine is used, which involves a three-step process of matching, condition

  11. Automatic Recognition of Object Use Based on Wireless Motion Sensors

    NARCIS (Netherlands)

    Bosch, S.; Marin Perianu, Raluca; Havinga, Paul J.M.; Marin Perianu, Mihai; Horst, Arie; Vasilescu, Andrei

    2010-01-01

    In this paper, we present a method for automatic, online detection of a user’s interaction with objects. This represents an essential building block for improving the performance of distributed activity recognition systems. Our method is based on correlating features extracted from motion sensors

  12. Simulation of the Surface Hydrology of Dalinghe Watershed Automatically Based on SRTM DEM

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    [Objective] The aim was to simulate the surface water flow of Dalinghe watershed based on SRTM DEM.[Method] By using ArcGIS ModelBuilder,and SRTM DEM data of Dalinghe watershed as input data,the model to simulate the water flow of Dalinghe watershed was set up.[Result] The model realized automatic division of Dalinghe watershed area and extraction of stream network.In the meantime,it also made the choice of threshold during filling DEM and extracting stream network much easier.The division of the Dalinghe w...

  13. Automatic labeling of molecular biomarkers on a cell-by-cell basis in immunohistochemistry images using convolutional neural networks

    Science.gov (United States)

    Sheikhzadeh, Fahime; Carraro, Anita; Korbelik, Jagoda; MacAulay, Calum; Guillaud, Martial; Ward, Rabab K.

    2016-03-01

    This paper addresses the problem of classifying cells expressing different biomarkers. A deep learning based method that can automatically localize and count the cells expressing each of the different biomarkers is proposed. To classify the cells, a Convolutional Neural Network (CNN) was employed. Images of Immunohistochemistry (IHC) stained slides that contain these cells were digitally scanned. The images were taken from digital scans of IHC stained cervical tissues, acquired for a clinical trial. More than 4,500 RGB images of cells were used to train the CNN. To evaluate our method, the cells were first manually labeled based on the expressing biomarkers. Then we performed the classification on 156 randomly selected images of cells that were not used in training the CNN. The accuracy of the classification was 92% in this preliminary data set. The results have shown that this method has a good potential in developing an automatic method for immunohistochemical analysis.

  14. Information Model for Connection Management in Automatic Switched Optical Network

    Institute of Scientific and Technical Information of China (English)

    Xu Yunbin(徐云斌); Song Hongsheng; Gui Xuan; Zhang Jie; Gu Wanyi

    2004-01-01

    The three types of connections (Permanent Connection, Soft Permanent Connection and Switched Connection) provided by ASON can adapt the requirement of different network services. Management and maintenance of these three connections are the most important aspect of ASON management. The information models proposed in this paper are used for the purpose of ASON connection management. Firstly a new information model is proposed to meet the requirement for the control plane introduced by ASON. In this model, a new class ControlNE is given, and the relationship between the ControlNE and the transport NE (network element) is also defined. Then this paper proposes information models for the three types of connections for the first time, and analyzes the relationship between the three kinds of connections and the basic network transport entities. Finally, the paper defines some CORBA interfaces for the management of the three connections. In these interfaces, some operations such as create or release a connection are defined, and some other operations can manage the performance of the three kinds of connections, which is necessary for a distributed management system.

  15. An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images

    Directory of Open Access Journals (Sweden)

    Rosalia Leonardi

    2009-01-01

    Full Text Available Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged.

  16. A new approach to the automatic identification of organism evolution using neural networks.

    Science.gov (United States)

    Kasperski, Andrzej; Kasperska, Renata

    2016-01-01

    Automatic identification of organism evolution still remains a challenging task, which is especially exiting, when the evolution of human is considered. The main aim of this work is to present a new idea to allow organism evolution analysis using neural networks. Here we show that it is possible to identify evolution of any organisms in a fully automatic way using the designed EvolutionXXI program, which contains implemented neural network. The neural network has been taught using cytochrome b sequences of selected organisms. Then, analyses have been carried out for the various exemplary organisms in order to demonstrate capabilities of the EvolutionXXI program. It is shown that the presented idea allows supporting existing hypotheses, concerning evolutionary relationships between selected organisms, among others, Sirenia and elephants, hippopotami and whales, scorpions and spiders, dolphins and whales. Moreover, primate (including human), tree shrew and yeast evolution has been reconstructed.

  17. Social Network Analysis Based on Network Motifs

    OpenAIRE

    2014-01-01

    Based on the community structure characteristics, theory, and methods of frequent subgraph mining, network motifs findings are firstly introduced into social network analysis; the tendentiousness evaluation function and the importance evaluation function are proposed for effectiveness assessment. Compared with the traditional way based on nodes centrality degree, the new approach can be used to analyze the properties of social network more fully and judge the roles of the nodes effectively. I...

  18. Edge Segment-Based Automatic Video Surveillance

    Directory of Open Access Journals (Sweden)

    Oksam Chae

    2007-12-01

    Full Text Available This paper presents a moving-object segmentation algorithm using edge information as segment. The proposed method is developed to address challenges due to variations in ambient lighting and background contents. We investigated the suitability of the proposed algorithm in comparison with the traditional-intensity-based as well as edge-pixel-based detection methods. In our method, edges are extracted from video frames and are represented as segments using an efficiently designed edge class. This representation helps to obtain the geometric information of edge in the case of edge matching and moving-object segmentation; and facilitates incorporating knowledge into edge segment during background modeling and motion tracking. An efficient approach for background initialization and robust method of edge matching is presented, to effectively reduce the risk of false alarm due to illumination change and camera motion while maintaining the high sensitivity to the presence of moving object. Detected moving edges are utilized along with watershed algorithm for extracting video object plane (VOP with more accurate boundary. Experiment results with real image sequence reflect that the proposed method is suitable for automated video surveillance applications in various monitoring systems.

  19. Model-Based Reasoning in Humans Becomes Automatic with Training.

    Directory of Open Access Journals (Sweden)

    Marcos Economides

    2015-09-01

    Full Text Available Model-based and model-free reinforcement learning (RL have been suggested as algorithmic realizations of goal-directed and habitual action strategies. Model-based RL is more flexible than model-free but requires sophisticated calculations using a learnt model of the world. This has led model-based RL to be identified with slow, deliberative processing, and model-free RL with fast, automatic processing. In support of this distinction, it has recently been shown that model-based reasoning is impaired by placing subjects under cognitive load--a hallmark of non-automaticity. Here, using the same task, we show that cognitive load does not impair model-based reasoning if subjects receive prior training on the task. This finding is replicated across two studies and a variety of analysis methods. Thus, task familiarity permits use of model-based reasoning in parallel with other cognitive demands. The ability to deploy model-based reasoning in an automatic, parallelizable fashion has widespread theoretical implications, particularly for the learning and execution of complex behaviors. It also suggests a range of important failure modes in psychiatric disorders.

  20. Looking for underlying features in automatic and reviewed seismic bulletins through a neural network

    Science.gov (United States)

    Carluccio, R.; Console, R.; Chiappini, M.; Chiappini, S.

    2009-12-01

    SEL1 bulletins are, among all IDC products, a fundamental tool for NDCs in their task of national assessment of compliance with the CTBT. This is because SEL1s are expected to be disseminated within 2 hours from the occurrence of any detected waveform event, and the National Authorities are supposed to take a political decision in nearly real time, especially in the case when the event could triggers the request for an on site inspection. In this context not only the rapidity, but also the reliability of the SEL1 is a fundamental requirement. Our last years experience gained in the comparison between SEL1 and Italian Seismic Bulletin events has shown that SEL1s usually contain a big fraction of bogus events (sometimes close to 50%). This is due to many factors, all related to the availability of processing data and to the fast automatic algorithms involved. On the other hand, REBs are much more reliable as proved by our experience. Therefore, in spite of their relevant time delay by which they are distributed, which prevents their real-time use, REBs can be still useful in a retrospective way as reference information for comparison with SEL1s. This study tries to set up a sort of logical filter on the SEL1s that, while maintaining the rapidity requirements, improves their reliability. Our idea is based on the assumption that the SEL1s are produced by systematic algorithm of phase association and therefore some patterns among the input and output data could exist and be recognized. Our approach was initially based on a set of rules suggested by human experts on their personal experience, and its application on large datasets on a global scale. Other approaches not involving human interaction (data mining techniques) do exist. This study refers specifically to a semi-automatic approach: fitting of multi-parametric relationships hidden in the data set, through the application of neural networks by an algorithm of supervised learning. Full SEL1 and REB bulletins from

  1. Automatic Camera Viewfinder Based on TI DaVinci

    Institute of Scientific and Technical Information of China (English)

    WANG Hai-gang; XIAO Zhi-tao; GENG Lei

    2009-01-01

    Presented is an automatic camera viewfinder based on TI DaVinci digital platform and discussed mainly is the scheme of software system based on linux. This system can give an alarm and save the picture when the set features appear in the view, and the saved pictures can be downloaded and zoomed out. All functions are operated in OSD menu. It is well established for its flexible operations, powerful functions, multitasking and stable performance.

  2. Dissociable changes in functional network topology underlie early category learning and development of automaticity.

    Science.gov (United States)

    Soto, Fabian A; Bassett, Danielle S; Ashby, F Gregory

    2016-11-01

    Recent work has shown that multimodal association areas-including frontal, temporal, and parietal cortex-are focal points of functional network reconfiguration during human learning and performance of cognitive tasks. On the other hand, neurocomputational theories of category learning suggest that the basal ganglia and related subcortical structures are focal points of functional network reconfiguration during early learning of some categorization tasks but become less so with the development of automatic categorization performance. Using a combination of network science and multilevel regression, we explore how changes in the connectivity of small brain regions can predict behavioral changes during training in a visual categorization task. We find that initial category learning, as indexed by changes in accuracy, is predicted by increasingly efficient integrative processing in subcortical areas, with higher functional specialization, more efficient integration across modules, but a lower cost in terms of redundancy of information processing. The development of automaticity, as indexed by changes in the speed of correct responses, was predicted by lower clustering (particularly in subcortical areas), higher strength (highest in cortical areas), and higher betweenness centrality. By combining neurocomputational theories and network scientific methods, these results synthesize the dissociative roles of multimodal association areas and subcortical structures in the development of automaticity during category learning.

  3. Automatic web services classification based on rough set theory

    Institute of Scientific and Technical Information of China (English)

    陈立; 张英; 宋自林; 苗壮

    2013-01-01

    With development of web services technology, the number of existing services in the internet is growing day by day. In order to achieve automatic and accurate services classification which can be beneficial for service related tasks, a rough set theory based method for services classification was proposed. First, the services descriptions were preprocessed and represented as vectors. Elicited by the discernibility matrices based attribute reduction in rough set theory and taking into account the characteristic of decision table of services classification, a method based on continuous discernibility matrices was proposed for dimensionality reduction. And finally, services classification was processed automatically. Through the experiment, the proposed method for services classification achieves approving classification result in all five testing categories. The experiment result shows that the proposed method is accurate and could be used in practical web services classification.

  4. Automatic Recognition Method for Optical Measuring Instruments Based on Machine Vision

    Institute of Scientific and Technical Information of China (English)

    SONG Le; LIN Yuchi; HAO Liguo

    2008-01-01

    Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope (UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation (BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dynamically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.

  5. UAV-BASED AUTOMATIC TREE GROWTH MEASUREMENT FOR BIOMASS ESTIMATION

    Directory of Open Access Journals (Sweden)

    M. Karpina

    2016-06-01

    Full Text Available Manual in-situ measurements of geometric tree parameters for the biomass volume estimation are time-consuming and economically non-effective. Photogrammetric techniques can be deployed in order to automate the measurement procedure. The purpose of the presented work is an automatic tree growth estimation based on Unmanned Aircraft Vehicle (UAV imagery. The experiment was conducted in an agriculture test field with scots pine canopies. The data was collected using a Leica Aibotix X6V2 platform equipped with a Nikon D800 camera. Reference geometric parameters of selected sample plants were measured manually each week. In situ measurements were correlated with the UAV data acquisition. The correlation aimed at the investigation of optimal conditions for a flight and parameter settings for image acquisition. The collected images are processed in a state of the art tool resulting in a generation of dense 3D point clouds. The algorithm is developed in order to estimate geometric tree parameters from 3D points. Stem positions and tree tops are identified automatically in a cross section, followed by the calculation of tree heights. The automatically derived height values are compared to the reference measurements performed manually. The comparison allows for the evaluation of automatic growth estimation process. The accuracy achieved using UAV photogrammetry for tree heights estimation is about 5cm.

  6. Uav-Based Automatic Tree Growth Measurement for Biomass Estimation

    Science.gov (United States)

    Karpina, M.; Jarząbek-Rychard, M.; Tymków, P.; Borkowski, A.

    2016-06-01

    Manual in-situ measurements of geometric tree parameters for the biomass volume estimation are time-consuming and economically non-effective. Photogrammetric techniques can be deployed in order to automate the measurement procedure. The purpose of the presented work is an automatic tree growth estimation based on Unmanned Aircraft Vehicle (UAV) imagery. The experiment was conducted in an agriculture test field with scots pine canopies. The data was collected using a Leica Aibotix X6V2 platform equipped with a Nikon D800 camera. Reference geometric parameters of selected sample plants were measured manually each week. In situ measurements were correlated with the UAV data acquisition. The correlation aimed at the investigation of optimal conditions for a flight and parameter settings for image acquisition. The collected images are processed in a state of the art tool resulting in a generation of dense 3D point clouds. The algorithm is developed in order to estimate geometric tree parameters from 3D points. Stem positions and tree tops are identified automatically in a cross section, followed by the calculation of tree heights. The automatically derived height values are compared to the reference measurements performed manually. The comparison allows for the evaluation of automatic growth estimation process. The accuracy achieved using UAV photogrammetry for tree heights estimation is about 5cm.

  7. Automatic identification of pump unit axis orbit based on invariant moments features and neural networks%基于不变矩和神经网络的泵机组轴心轨迹自动识别

    Institute of Scientific and Technical Information of China (English)

    陈坚; 叶渊杰; 陈抒; 陈光大; 于永海; 王建明

    2011-01-01

    To meet the needs of signal processing on pump unit fault diagnosis, the principle of invariant moment theory was introduced. In addition, the neural network modeling as well as the sample acquisition in detail was discussed. As the shape of axis orbit responded the pump unit operation is related to a variety of fault, the real-time detection swing signals of axis on invariant moment were processed according to the invariant features of translation, scaling and rotation of invariant moment. And then the shape of axis orbit was determined by using BP neural network on pattern recognize. The combination of numerical simulation and on-site test were used to compensate the shortage of neural network training samples. All samples of both processed on invariant moment and the corresponding actual shape of the samples are of the neural network training ones. After network training completed, the output was compared with the actual shape of axis loci to validate this method. Taken the fault detection and diagnosis of Dayudu Pump Station in Shanxi for example, 10 sets of data of the sample were selectd to be compared, and the results show that the neural network recognition of the results are accurate. The method can provide the basis for orbit shape automatic identification and realizing fault diagnosis system intellectualization of pump unit.%基于泵机组故障信号处理的需要,介绍了不变矩原理,同时对神经网络建模,包括其样本获取进行了详细讨论;由于泵机组的多种故障与表征其运行状态的轴心轨迹形状有关,根据不变矩的平移、伸缩和旋转不变性特征,对实时检测的轴心摆度信号进行不变矩处理,利用BP型神经网络对其进行模式识别,进而判断出轴心轨迹的形状.为了弥补泵机组用于神经网络训练样本的不足,采用数值模拟与现场测试相结合的方法,将获取的所有样本进行求不变矩处理,并连同样本对应的实际形状作为神经网络

  8. MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Ge Guangying; Chen Lili; Xu Jianjian

    2005-01-01

    Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.

  9. Personal Information Leaks with Automatic Login in Mobile Social Network Services

    Directory of Open Access Journals (Sweden)

    Jongwon Choi

    2015-06-01

    Full Text Available To log in to a mobile social network service (SNS server, users must enter their ID and password to get through the authentication process. At that time, if the user sets up the automatic login option on the app, a sort of security token is created on the server based on the user’s ID and password. This security token is called a credential. Because such credentials are convenient for users, they are utilized by most mobile SNS apps. However, the current state of credential management for the majority of Android SNS apps is very weak. This paper demonstrates the possibility of a credential cloning attack. Such attacks occur when an attacker extracts the credential from the victim’s smart device and inserts it into their own smart device. Then, without knowing the victim’s ID and password, the attacker can access the victim’s account. This type of attack gives access to various pieces of personal information without authorization. Thus, in this paper, we analyze the vulnerabilities of the main Android-based SNS apps to credential cloning attacks, and examine the potential leakage of personal information that may result. We then introduce effective countermeasures to resolve these problems.

  10. Automatic Three-Dimensional Measurement of Large-Scale Structure Based on Vision Metrology

    Directory of Open Access Journals (Sweden)

    Zhaokun Zhu

    2014-01-01

    Full Text Available All relevant key techniques involved in photogrammetric vision metrology for fully automatic 3D measurement of large-scale structure are studied. A new kind of coded target consisting of circular retroreflective discs is designed, and corresponding detection and recognition algorithms based on blob detection and clustering are presented. Then a three-stage strategy starting with view clustering is proposed to achieve automatic network orientation. As for matching of noncoded targets, the concept of matching path is proposed, and matches for each noncoded target are found by determination of the optimal matching path, based on a novel voting strategy, among all possible ones. Experiments on a fixed keel of airship have been conducted to verify the effectiveness and measuring accuracy of the proposed methods.

  11. A fully automatic system for acid-base coulometric titrations

    OpenAIRE

    1990-01-01

    An automatic system for acid-base titrations by electrogeneration of H+ and OH- ions, with potentiometric end-point detection, was developed. The system includes a PC-compatible computer for instrumental control, data acquisition and processing, which allows up to 13 samples to be analysed sequentially with no human intervention. The system performance was tested on the titration of standard solutions, which it carried out with low errors and RSD. It was subsequently applied to the analysis o...

  12. Automatic Identification of Tomato Maturation Using Multilayer Feed Forward Neural Network with Genetic Algorithms (GA)

    Institute of Scientific and Technical Information of China (English)

    FANG Jun-long; ZHANG Chang-li; WANG Shu-wen

    2004-01-01

    We set up computer vision system for tomato images. By using this system, the RGB value of tomato image was converted into HIS value whose H was used to acquire the color character of the surface of tomato. To use multilayer feed forward neural network with GA can finish automatic identification of tomato maturation. The results of experiment showed that the accuracy was upto 94%.

  13. Automatic labeling and characterization of objects using artificial neural networks

    Science.gov (United States)

    Campbell, William J.; Hill, Scott E.; Cromp, Robert F.

    1989-01-01

    Existing NASA supported scientific data bases are usually developed, managed and populated in a tedious, error prone and self-limiting way in terms of what can be described in a relational Data Base Management System (DBMS). The next generation Earth remote sensing platforms, i.e., Earth Observation System, (EOS), will be capable of generating data at a rate of over 300 Mbs per second from a suite of instruments designed for different applications. What is needed is an innovative approach that creates object-oriented databases that segment, characterize, catalog and are manageable in a domain-specific context and whose contents are available interactively and in near-real-time to the user community. Described here is work in progress that utilizes an artificial neural net approach to characterize satellite imagery of undefined objects into high-level data objects. The characterized data is then dynamically allocated to an object-oriented data base where it can be reviewed and assessed by a user. The definition, development, and evolution of the overall data system model are steps in the creation of an application-driven knowledge-based scientific information system.

  14. Automatic evolution of heat exchanger networks with simultaneous heat exchanger design

    Energy Technology Data Exchange (ETDEWEB)

    Liporace, F.S.; Pessoa, F.L.P.; Queiroz, E.M. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Escola de Quimica. Dept. de Engenharia Quimica]. E-mail: lipo@h2o.eq.ufrj.br; lipo@hexanet.com.br

    1999-03-01

    Recently, a new software (AtHENS) that automatically synthesizes a heat exchanger network with minima consumption of utilities was developed. This work deals with the next step, which represents the evolution of the initial network. Hence, new procedures to identify and break loops are incorporated, for which a new algorithm is proposed. Also, a heat exchanger design procedure which uses the available pressure drop to determine the film coefficient on the tube side and shell side is added, providing the utilization of more realistic heat exchangers in the network during its optimization. Results obtained from a case study point to the possibility of equipment design having a strong influence on the network synthesis. (author)

  15. AUTOMATIC EVOLUTION OF HEAT EXCHANGER NETWORKS WITH SIMULTANEOUS HEAT EXCHANGER DESIGN

    Directory of Open Access Journals (Sweden)

    F.S. LIPORACE

    1999-03-01

    Full Text Available Recently, a new software (AtHENS that automatically synthesizes a heat exchanger network with minima consumption of utilities was developed. This work deals with the next step, which represents the evolution of the initial network. Hence, new procedures to identify and break loops are incorporated, for which a new algorithm is proposed. Also, a heat exchanger design procedure which uses the available pressure drop to determine the film coefficient on the tube side and shell side is added, providing the utilization of more realistic heat exchangers in the network during its optimization. Results obtained from a case study point to the possibility of equipment design having a strong influence on the network synthesis.

  16. Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks.

    Science.gov (United States)

    Rodrigues, Pedro L; Rodrigues, Nuno F; Pinho, A C M; Fonseca, Jaime C; Correia-Pinto, Jorge; Vilaça, João L

    2014-10-01

    Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization. Copyright © 2014 IPEM. Published by Elsevier Ltd. All rights reserved.

  17. Automatic landing system using neural networks and radio-technical subsystems

    Directory of Open Access Journals (Sweden)

    Romulus Lungu

    2017-02-01

    Full Text Available The paper focuses on the design of a new automatic landing system (ALS in longitudinal plane; the new ALS controls the aircraft trajectory and longitudinal velocity. Aircraft control is achieved by means of a proportional-integral (PI controller and the instrumental landing system – the first phase of landing (the glide slope and a proportional-integral-derivative (PID controller together with a radio-altimeter – the second phase of landing (the flare; both controllers modify the reference model associated with aircraft pitch angle. The control of the pitch angle and longitudinal velocity is performed by a neural network adaptive control system, based on the dynamic inversion concept, having the following as components: a linear dynamic compensator, a linear observer, reference models, and a Pseudo control hedging (PCH block. The theoretical results are software implemented and validated by complex numerical simulations; compared with other ALSs having the same radio-technical subsystems but with conventional or fuzzy controllers for the control of aircraft pitch angle and longitudinal velocity, the architecture designed in this paper is characterized by much smaller overshoots and stationary errors.

  18. Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

    Science.gov (United States)

    Rodrigues, Pedro L.; Moreira, António H. J.; Rodrigues, Nuno F.; Pinho, ACM; Fonseca, Jaime C.; Correia-Pinto, Jorge; Vilaça, João. L.

    2014-03-01

    Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82+/-5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7+/-4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

  19. Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Prucnal Monika

    2017-06-01

    Full Text Available EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD, discrete wavelet transform (DWT and empirical mode decomposition (EMD in the automatic classification of sleep stages by an artificial neural network (ANN. 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM, were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA. Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD appeared to be the most effective feature extraction method in the analysed problem.

  20. Automatic relational database compression scheme design based on swarm evolution

    Institute of Scientific and Technical Information of China (English)

    HU Tian-lei; CHEN Gang; LI Xiao-yan; DONG Jin-xiang

    2006-01-01

    Compression is an intuitive way to boost the performance of a database system. However, compared with other physical database design techniques, compression consumes large amount of CPU power. There is a trade-off between the reduction of disk access and the overhead of CPU processing. Automatic design and adaptive administration of database systems are widely demanded, and the automatic selection of compression schema to compromise the trade-off is very important. In this paper,we present a model with novel techniques to integrate a rapidly convergent agent-based evolution framework, i.e. the SWAF (SWarm Algorithm Framework), into adaptive attribute compression for relational database. The model evolutionally consults statistics of CPU load and IO bandwidth to select compression schemas considering both aspects of the trade-off. We have implemented a prototype model on Oscar RDBMS with experiments highlighting the correctness and efficiency of our techniques.

  1. Automatic Image Segmentation based on MRF-MAP

    CERN Document Server

    Qiyang, Zhao

    2012-01-01

    Solving the Maximum a Posteriori on Markov Random Field, MRF-MAP, is a prevailing method in recent interactive image segmentation tools. Although mathematically explicit in its computational targets, and impressive for the segmentation quality, MRF-MAP is hard to accomplish without the interactive information from users. So it is rarely adopted in the automatic style up to today. In this paper, we present an automatic image segmentation algorithm, NegCut, based on the approximation to MRF-MAP. First we prove MRF-MAP is NP-hard when the probabilistic models are unknown, and then present an approximation function in the form of minimum cuts on graphs with negative weights. Finally, the binary segmentation is taken from the largest eigenvector of the target matrix, with a tuned version of the Lanczos eigensolver. It is shown competitive at the segmentation quality in our experiments.

  2. Automatic training sample selection for a multi-evidence based crop classification approach

    DEFF Research Database (Denmark)

    Chellasamy, Menaka; Ferre, Ty; Greve, Mogens Humlekrog

    three Multi-Layer Perceptron (MLP) neural networks trained separately with spectral, texture and vegetation indices; classification labels were then assigned based on Endorsement Theory. The present study proposes an approach to feed this ensemble classifier with automatically selected training samples......An approach to use the available agricultural parcel information to automatically select training samples for crop classification is investigated. Previous research addressed the multi-evidence crop classification approach using an ensemble classifier. This first produced confidence measures using....... Thus this approach uses the spectral, texture and indices domains in an ensemble framework to iteratively remove the mislabeled pixels from the crop clusters declared by the farmers. Once the clusters are refined, the selected border samples are used for final learning and the unknown samples...

  3. Automatic self-configuration of the logical network using distributed software agents

    OpenAIRE

    Marzo i Lázaro, Josep Lluís; Vilà Talleda, Pere; Bueno Delgado, Antonio; Fàbrega i Soler, Lluís; Calle Ortega, Eusebi

    2004-01-01

    We present a system for dynamic network resource configuration in environments with bandwidth reservation. The proposed system is completely distributed and automates the mechanisms for adapting the logical network to the offered load. The system is able to manage dynamically a logical network such as a virtual path network in ATM or a label switched path network in MPLS or GMPLS. The system design and implementation is based on a multi-agent system (MAS) which make the decisions of when and ...

  4. Automatic learning-based beam angle selection for thoracic IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Amit, Guy; Marshall, Andrea [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Purdie, Thomas G., E-mail: tom.purdie@rmp.uhn.ca; Jaffray, David A. [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9 (Canada); Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2 (Canada); Techna Institute, University Health Network, Toronto, Ontario M5G 1P5 (Canada); Levinshtein, Alex [Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4 (Canada); Hope, Andrew J.; Lindsay, Patricia [Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario M5G 2M9, Canada and Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5S 3E2 (Canada); Pekar, Vladimir [Philips Healthcare, Markham, Ontario L6C 2S3 (Canada)

    2015-04-15

    Purpose: The treatment of thoracic cancer using external beam radiation requires an optimal selection of the radiation beam directions to ensure effective coverage of the target volume and to avoid unnecessary treatment of normal healthy tissues. Intensity modulated radiation therapy (IMRT) planning is a lengthy process, which requires the planner to iterate between choosing beam angles, specifying dose–volume objectives and executing IMRT optimization. In thorax treatment planning, where there are no class solutions for beam placement, beam angle selection is performed manually, based on the planner’s clinical experience. The purpose of this work is to propose and study a computationally efficient framework that utilizes machine learning to automatically select treatment beam angles. Such a framework may be helpful for reducing the overall planning workload. Methods: The authors introduce an automated beam selection method, based on learning the relationships between beam angles and anatomical features. Using a large set of clinically approved IMRT plans, a random forest regression algorithm is trained to map a multitude of anatomical features into an individual beam score. An optimization scheme is then built to select and adjust the beam angles, considering the learned interbeam dependencies. The validity and quality of the automatically selected beams evaluated using the manually selected beams from the corresponding clinical plans as the ground truth. Results: The analysis included 149 clinically approved thoracic IMRT plans. For a randomly selected test subset of 27 plans, IMRT plans were generated using automatically selected beams and compared to the clinical plans. The comparison of the predicted and the clinical beam angles demonstrated a good average correspondence between the two (angular distance 16.8° ± 10°, correlation 0.75 ± 0.2). The dose distributions of the semiautomatic and clinical plans were equivalent in terms of primary target volume

  5. A learning-based automatic spinal MRI segmentation

    Science.gov (United States)

    Liu, Xiaoqing; Samarabandu, Jagath; Garvin, Greg; Chhem, Rethy; Li, Shuo

    2008-03-01

    Image segmentation plays an important role in medical image analysis and visualization since it greatly enhances the clinical diagnosis. Although many algorithms have been proposed, it is still challenging to achieve an automatic clinical segmentation which requires speed and robustness. Automatically segmenting the vertebral column in Magnetic Resonance Imaging (MRI) image is extremely challenging as variations in soft tissue contrast and radio-frequency (RF) in-homogeneities cause image intensity variations. Moveover, little work has been done in this area. We proposed a generic slice-independent, learning-based method to automatically segment the vertebrae in spinal MRI images. A main feature of our contributions is that the proposed method is able to segment multiple images of different slices simultaneously. Our proposed method also has the potential to be imaging modality independent as it is not specific to a particular imaging modality. The proposed method consists of two stages: candidate generation and verification. The candidate generation stage is aimed at obtaining the segmentation through the energy minimization. In this stage, images are first partitioned into a number of image regions. Then, Support Vector Machines (SVM) is applied on those pre-partitioned image regions to obtain the class conditional distributions, which are then fed into an energy function and optimized with the graph-cut algorithm. The verification stage applies domain knowledge to verify the segmented candidates and reject unsuitable ones. Experimental results show that the proposed method is very efficient and robust with respect to image slices.

  6. Automatic Seamline Network Generation for Urban Orthophoto Mosaicking with the Use of a Digital Surface Model

    Directory of Open Access Journals (Sweden)

    Qi Chen

    2014-12-01

    Full Text Available Intelligent seamline selection for image mosaicking is an area of active research in the fields of massive data processing, computer vision, photogrammetry and remote sensing. In mosaicking applications for digital orthophoto maps (DOMs, the visual transition in mosaics is mainly caused by differences in positioning accuracy, image tone and relief displacement of high ground objects between overlapping DOMs. Among these three factors, relief displacement, which prevents the seamless mosaicking of images, is relatively more difficult to address. To minimize visual discontinuities, many optimization algorithms have been studied for the automatic selection of seamlines to avoid high ground objects. Thus, a new automatic seamline selection algorithm using a digital surface model (DSM is proposed. The main idea of this algorithm is to guide a seamline toward a low area on the basis of the elevation information in a DSM. Given that the elevation of a DSM is not completely synchronous with a DOM, a new model, called the orthoimage elevation synchronous model (OESM, is derived and introduced. OESM can accurately reflect the elevation information for each DOM unit. Through the morphological processing of the OESM data in the overlapping area, an initial path network is obtained for seamline selection. Subsequently, a cost function is defined on the basis of several measurements, and Dijkstra’s algorithm is adopted to determine the least-cost path from the initial network. Finally, the proposed algorithm is employed for automatic seamline network construction; the effective mosaic polygon of each image is determined, and a seamless mosaic is generated. The experiments with three different datasets indicate that the proposed method meets the requirements for seamline network construction. In comparative trials, the generated seamlines pass through fewer ground objects with low time consumption.

  7. Modeling and monitoring of pipelines and networks advanced tools for automatic monitoring and supervision of pipelines

    CERN Document Server

    Torres, Lizeth

    2017-01-01

    This book focuses on the analysis and design of advanced techniques for on-line automatic computational monitoring of pipelines and pipe networks. It discusses how to improve the systems’ security considering mathematical models of the flow, historical flow rate and pressure data, with the main goal of reducing the number of sensors installed along a pipeline. The techniques presented in the book have been implemented in digital systems to enhance the abilities of the pipeline network’s operators in recognizing anomalies. A real leak scenario in a Mexican water pipeline is used to illustrate the benefits of these techniques in locating the position of a leak. Intended for an interdisciplinary audience, the book addresses researchers and professionals in the areas of mechanical, civil and control engineering. It covers topics on fluid mechanics, instrumentation, automatic control, signal processing, computing, construction and diagnostic technologies.

  8. CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis.

    Science.gov (United States)

    Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

    2014-09-01

    Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

  9. Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

    Science.gov (United States)

    Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

    2014-03-01

    This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative

  10. Automatic Foreground Extraction Based on Difference of Gaussian

    Directory of Open Access Journals (Sweden)

    Yubo Yuan

    2014-01-01

    Full Text Available A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm.

  11. Automatic Vehicle License Recognition Based on Video Vehicular Detection System

    Institute of Scientific and Technical Information of China (English)

    YANG Zhaoxuan; CHEN Yang; HE Yinghua; WU Jun

    2006-01-01

    Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system.Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented.Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold.The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%.When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy circumstance or uneven illumination.

  12. A semi-automatic method for extracting thin line structures in images as rooted tree network

    Energy Technology Data Exchange (ETDEWEB)

    Brazzini, Jacopo [Los Alamos National Laboratory; Dillard, Scott [Los Alamos National Laboratory; Soille, Pierre [EC - JRC

    2010-01-01

    This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.

  13. Associating fuzzy logic, neural networks and multivariable statistic methodologies in the automatic identification of oil reservoir lithologies through well logs

    Energy Technology Data Exchange (ETDEWEB)

    Carrasquilla, Abel [Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF), Macae, RJ (Brazil). Lab. de Engenharia e Exploracao de Petroleo]. E-mail: abel@lenep.uenf.br; Silva, Jadir da [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Dept. de Geologia; Flexa, Roosevelt [Baker Hughes do Brasil Ltda, Macae, RJ (Brazil)

    2008-07-01

    In this article, we present a new approach to the automatic identification of lithologies using only well log data, which associates fuzzy logic, neural networks and multivariable statistic methods. Firstly, we chose well log data that represents lithological types, as gamma rays (GR) and density (RHOB), and, immediately, we applied a fuzzy logic algorithm to determine optimal number of clusters. In the following step, a competitive neural network is developed, based on Kohonen's learning rule, where the input layer is composed of two neurons, which represent the same number of used logs. On the other hand, the competitive layer is composed by several neurons, which have the same number of clusters as determined by the fuzzy logic algorithm. Finally, some data bank elements of the lithological types are selected at random to be the discriminate variables, which correspond to the input data of the multigroup discriminate analysis program. In this form, with the application of this methodology, the lithological types were automatically identified throughout the a well of the Namorado Oil Field, Campos Basin, which presented some difficulty in the results, mainly because of geological complexity of this field. (author)

  14. Invariant wavelet transform-based automatic target recognition

    Science.gov (United States)

    Sadovnik, Lev S.; Rashkovskiy, Oleg; Tebelev, Igor

    1995-03-01

    The authors' previous work (SPIE Vol. 2237) on scale-, rotation- and shift-invariant wavelet transform is extended to accommodate multiple objects in the scene and a nonuniform background. After background elimination and segmentation, a set of windows each containing a single object are analyzed based on an invariant wavelet feature extraction algorithm and neural network-based classifier.

  15. Detecting danger labels with RAM-based neural networks

    DEFF Research Database (Denmark)

    Jørgensen, T.M.; Christensen, S.S.; Andersen, A.W.

    1996-01-01

    An image processing system for the automatic location of danger labels on the back of containers is presented. The system uses RAM-based neural networks to locate and classify labels after a pre-processing step involving specially designed non-linear edge filters and RGB-to-HSV conversion. Results...

  16. Automatic defense against zero-day polymorphic worms in communication networks

    CERN Document Server

    Mohammed, Mohssen

    2013-01-01

    Able to propagate quickly and change their payload with each infection, polymorphic worms have been able to evade even the most advanced intrusion detection systems (IDS). And, because zero-day worms require only seconds to launch flooding attacks on your servers, using traditional methods such as manually creating and storing signatures to defend against these threats is just too slow. Bringing together critical knowledge and research on the subject, Automatic Defense Against Zero-day Polymorphic Worms in Communication Networks details a new approach for generating automated signatures for un

  17. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.

    Science.gov (United States)

    Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; van Hamersvelt, Robbert W; Viergever, Max A; Išgum, Ivana

    2016-12-01

    The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be used for this purpose. We present a method for automatic CAC quantification in CCTA. This method uses supervised learning to directly identify and quantify CAC without a need for coronary artery extraction commonly used in existing methods. The study included cardiac CT exams of 250 patients for whom both a CCTA and a CSCT scan were available. To restrict the volume-of-interest for analysis, a bounding box around the heart is automatically determined. The bounding box detection algorithm employs a combination of three ConvNets, where each detects the heart in a different orthogonal plane (axial, sagittal, coronal). These ConvNets were trained using 50 cardiac CT exams. In the remaining 200 exams, a reference standard for CAC was defined in CSCT and CCTA. Out of these, 100 CCTA scans were used for training, and the remaining 100 for evaluation of a voxel classification method for CAC identification. The method uses ConvPairs, pairs of convolutional neural networks (ConvNets). The first ConvNet in a pair identifies voxels likely to be CAC, thereby discarding the majority of non-CAC-like voxels such as lung and fatty tissue. The identified CAC-like voxels are further classified by the second ConvNet in the pair, which distinguishes between CAC and CAC-like negatives. Given the different task of each ConvNet, they share their architecture, but not their weights. Input patches are either 2.5D or 3D. The ConvNets are purely convolutional, i.e. no pooling layers are present and fully connected layers are implemented as convolutions, thereby allowing efficient voxel classification. The performance of individual 2.5D and 3D ConvPairs with input sizes of 15 and 25 voxels, as well as the performance of ensembles of these Conv

  18. FURTHER CONSIDERATIONS ON SPREADSHEET-BASED AUTOMATIC TREND LINES

    Directory of Open Access Journals (Sweden)

    DANIEL HOMOCIANU

    2015-12-01

    Full Text Available Most of the nowadays business applications working with data sets allow exports to the spreadsheet format. This fact is related to the experience of common business users with such products and to the possibility to couple what they have with something containing many models, functions and possibilities to process and represent data, by that getting something in dynamics and much more than a simple static less useful report. The purpose of Business Intelligence is to identify clusters, profiles, association rules, decision trees and many other patterns or even behaviours, but also to generate alerts for exceptions, determine trends and make predictions about the future based on historical data. In this context, the paper shows some practical results obtained after testing both the automatic creation of scatter charts and trend lines corresponding to the user’s preferences and the automatic suggesting of the most appropriate trend for the tested data mostly based on the statistical measure of how close they are to the regression function.

  19. Resilient Disaster Network Based on Software Defined Cognitive Wireless Network Technology

    Directory of Open Access Journals (Sweden)

    Goshi Sato

    2015-01-01

    Full Text Available In order to temporally recover the information network infrastructure in disaster areas from the Great East Japan Earthquake in 2011, various wireless network technologies such as satellite IP network, 3G, and Wi-Fi were effectively used. However, since those wireless networks are individually introduced and installed but not totally integrated, some of networks were congested due to the sudden network traffic generation and unbalanced traffic distribution, and eventually the total network could not effectively function. In this paper, we propose a disaster resilient network which integrates various wireless networks into a cognitive wireless network that users can use as an access network to the Internet at the serious disaster occurrence. We designed and developed the disaster resilient network based on software defined network (SDN technology to automatically select the best network link and route among the possible access networks to the Internet by periodically monitoring their network states and evaluate those using extended AHP method. In order to verify the usefulness of our proposed system, a prototype system is constructed and its performance is evaluated.

  20. Automatic speech signal segmentation based on the innovation adaptive filter

    Directory of Open Access Journals (Sweden)

    Makowski Ryszard

    2014-06-01

    Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modified version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

  1. Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT

    Science.gov (United States)

    Lessmann, Nikolas; Išgum, Ivana; Setio, Arnaud A. A.; de Vos, Bob D.; Ciompi, Francesco; de Jong, Pim A.; Oudkerk, Matthjis; Mali, Willem P. Th. M.; Viergever, Max A.; van Ginneken, Bram

    2016-03-01

    The amount of calcifications in the coronary arteries is a powerful and independent predictor of cardiovascular events and is used to identify subjects at high risk who might benefit from preventive treatment. Routine quantification of coronary calcium scores can complement screening programs using low-dose chest CT, such as lung cancer screening. We present a system for automatic coronary calcium scoring based on deep convolutional neural networks (CNNs). The system uses three independently trained CNNs to estimate a bounding box around the heart. In this region of interest, connected components above 130 HU are considered candidates for coronary artery calcifications. To separate them from other high intensity lesions, classification of all extracted voxels is performed by feeding two-dimensional 50 mm × 50 mm patches from three orthogonal planes into three concurrent CNNs. The networks consist of three convolutional layers and one fully-connected layer with 256 neurons. In the experiments, 1028 non-contrast-enhanced and non-ECG-triggered low-dose chest CT scans were used. The network was trained on 797 scans. In the remaining 231 test scans, the method detected on average 194.3 mm3 of 199.8 mm3 coronary calcifications per scan (sensitivity 97.2 %) with an average false-positive volume of 10.3 mm3 . Subjects were assigned to one of five standard cardiovascular risk categories based on the Agatston score. Accuracy of risk category assignment was 84.4 % with a linearly weighted κ of 0.89. The proposed system can perform automatic coronary artery calcium scoring to identify subjects undergoing low-dose chest CT screening who are at risk of cardiovascular events with high accuracy.

  2. Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning.

    Science.gov (United States)

    Wang, Guan; Sun, Yu; Wang, Jianxin

    2017-01-01

    Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation. Using the apple black rot images in the PlantVillage dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. The performances of shallow networks trained from scratch and deep models fine-tuned by transfer learning are evaluated systemically in this paper. The best model is the deep VGG16 model trained with transfer learning, which yields an overall accuracy of 90.4% on the hold-out test set. The proposed deep learning model may have great potential in disease control for modern agriculture.

  3. Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network

    Directory of Open Access Journals (Sweden)

    K. Gayathri Devi

    2015-01-01

    Full Text Available Purpose. Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer. Methods. The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect. Results. Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate. Conclusions. The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result.

  4. Proportional directional valve based automatic steering system for tractors

    Institute of Scientific and Technical Information of China (English)

    Jin-yi LIU; Jing-quan TAN; En-rong MAO; Zheng-he SONG; Zhong-xiang ZHU‡

    2016-01-01

    Most automatic steering systems for large tractors are designed with hydraulic systems that run on either constant flow or constant pressure. Such designs are limited in adaptability and applicability. Moreover, their control valves can unload in the neutral position and eventually lead to serious hydraulic leakage over long operation periods. In response to the problems noted above, a multifunctional automatic hydraulic steering circuit is presented. The system design is composed of a 5-way-3-position proportional directional valve, two pilot-controlled check valves, a pressure-compensated directional valve, a pressure-compensated flow regulator valve, a load shuttle valve, and a check valve, among other components. It is adaptable to most open-center systems with constant flow supply and closed-center systems with load feedback. The design maintains the lowest pressure under load feedback and stays at the neutral position during unloading, thus meeting the requirements for steering. The steering controller is based on proportional-integral-derivative (PID) running on a 51-microcontroller-unit master control chip. An experimental platform is developed to establish the basic characteristics of the system subject to stepwise inputs and sinusoi-dal tracking. Test results show that the system design demonstrates excellent control accuracy, fast response, and negligible leak during long operation periods.

  5. Automatic Ration Material Distributions Based on GSM and RFID Technology

    Directory of Open Access Journals (Sweden)

    S.Valarmathy

    2013-10-01

    Full Text Available Now a day ration card is very important for every home and used for various field such as family members details, to get gas connection, it act as address proof for various purposes etc. All the people having a ration card to buy the various materials (sugar, rice, oil, kerosene, etc from the ration shops. But in this system having two draw backs, first one is weight of the material may be inaccurate due to human mistakes and secondly, if not buy the materials at the end of the month, they will sale to others without any intimation to the government and customers. In this paper, proposed an Automatic Ration Materials Distribution Based on GSM (Global System for Mobile and RFID (Radio Frequency Identification technology instead of ration cards. To get the materials in ration shops need to show the RFID tag into the RFID reader, then controller check the customer codes and details of amounts in the card. After verification, these systems show the amount details. Then customer need to enter they required materials by using keyboard, after receiving materials controller send the information to government office and customer through GSM technology. In this system provides the materials automatically without help of humans.

  6. Ontology-Based Automatically Hidden Web Portal Index

    Institute of Scientific and Technical Information of China (English)

    SONGHui; PANLeyun; MAFanyuan

    2004-01-01

    Many valuable databases on the Web have non-crawlable contents that are “hidden” behind the search forms. Information is available only by filling out HTML forms manually to query the underlying databases. For accessing data behind forms by automated agents, the critical task is having the corresponding query interfaces of the hidden databases that can be understood by machine. This paper presents an automatic approach of hidden Web portal index for various domains. It discovers and scrapes the query forms from Web pages based the tag-tree presentation, and then interpret them into the uniform mediate interfaces with the aid of domain ontology definition. To achieve high transformation accuracy, the domain ontology is also used to filter out the interfaces that are not related to the specific domain. The query interfaces gained finally represented with common concepts can automatically be indexed and retrieved by program. The experiments indicate that the algorithms used are efficient and the system is materially useful for information system or personalized Web search system to retrieval contents from hidden Web.

  7. Automatic indexing of news video for content-based retrieval

    Science.gov (United States)

    Yang, Myung-Sup; Yoo, Cheol-Jung; Chang, Ok-Bae

    1998-06-01

    Since it is impossible to automatically parse a general video, we investigated an integrated solution for the content-based news video indexing and the retrieval. Thus, a specific structural video such as news video is parsed, because it is included both temporal and spatial characteristics that the news event with an anchor-person is iteratively appeared, a news icon and a caption are involved in some frame, respectively. To extract automatically the key frames by using the structured knowledge of news, the model used in this paper is consisted of the news event segmentation, caption recognition and search browser module. The following are three main modules represented in this paper: (1) The news event segmentation module (NESM) for both the recognition and the division of an anchor-person shot. (2) The caption recognition module (CRM) for the detection of the caption-frames in a news event, the extraction of their caption region in the frame by using split-merge method, and the recognition of the region as a text with OCR software. 3) The search browser module (SBM) for the display of the list of news events and news captions, which are included in selected news event. However, the SBM can be caused various searching mechanisms.

  8. Forensic Automatic Speaker Recognition Based on Likelihood Ratio Using Acoustic-phonetic Features Measured Automatically

    Directory of Open Access Journals (Sweden)

    Huapeng Wang

    2015-01-01

    Full Text Available Forensic speaker recognition is experiencing a remarkable paradigm shift in terms of the evaluation framework and presentation of voice evidence. This paper proposes a new method of forensic automatic speaker recognition using the likelihood ratio framework to quantify the strength of voice evidence. The proposed method uses a reference database to calculate the within- and between-speaker variability. Some acoustic-phonetic features are extracted automatically using the software VoiceSauce. The effectiveness of the approach was tested using two Mandarin databases: A mobile telephone database and a landline database. The experiment's results indicate that these acoustic-phonetic features do have some discriminating potential and are worth trying in discrimination. The automatic acoustic-phonetic features have acceptable discriminative performance and can provide more reliable results in evidence analysis when fused with other kind of voice features.

  9. Embedded Processor Based Automatic Temperature Control of VLSI Chips

    Directory of Open Access Journals (Sweden)

    Narasimha Murthy Yayavaram

    2009-01-01

    Full Text Available This paper presents embedded processor based automatic temperature control of VLSI chips, using temperature sensor LM35 and ARM processor LPC2378. Due to the very high packing density, VLSI chips get heated very soon and if not cooled properly, the performance is very much affected. In the present work, the sensor which is kept very near proximity to the IC will sense the temperature and the speed of the fan arranged near to the IC is controlled based on the PWM signal generated by the ARM processor. A buzzer is also provided with the hardware, to indicate either the failure of the fan or overheating of the IC. The entire process is achieved by developing a suitable embedded C program.

  10. Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm

    KAUST Repository

    Abbas, Ahmed

    2013-01-07

    A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into p-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx. © 2013

  11. Automatic peak selection by a Benjamini-Hochberg-based algorithm.

    Directory of Open Access Journals (Sweden)

    Ahmed Abbas

    Full Text Available A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into [Formula: see text]-values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx.

  12. Laplace domain automatic data assimilation of contaminant transport using a Wireless Sensor Network

    Science.gov (United States)

    Barnhart, K.; Illangasekare, T. H.

    2011-12-01

    Emerging in situ sensors and distributed network technologies have the potential to monitor dynamic hydrological and environmental processes more effectively than traditional monitoring and data acquisition techniques by sampling at greater spatial and temporal resolutions. In particular, Wireless Sensor Networks, the combination of low-power telemetry and energy-harvesting with miniaturized sensors, could play a large role in monitoring the environment on nature's time scale. Since sensor networks supply data with little or no delay, applications exist where automatic or real-time assimilation of this data would be useful, for example during smart remediation procedures where tracking of the plume response will reinforce real-time decisions. As a foray into this new data context, we consider the estimation of hydraulic conductivity when incorporating subsurface plume concentration data. Current practice optimizes the model in the time domain, which is often slow and overly sensitive to data anomalies. Instead, we perform model inversion in Laplace space and are able to do so because data gathered using new technologies can be sampled densely in time. An intermediate-scale synthetic aquifer is used to illustrate the developed technique. Data collection and model (re-)optimization are automatic. Electric conductivity values of passing sodium bromide plumes are sent through a wireless sensor network, stored in a database, scrubbed and passed to a modeling server which transforms the data and assimilates it into a Laplace domain model. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000

  13. 基于GSM网络的家居自动监测控制系统的设计与实现%Design and Implementation of Home Automatic Monitoring and Control System Based on GSM Network

    Institute of Scientific and Technical Information of China (English)

    殷美琳; 周亦敏

    2013-01-01

    Combined with the current concept of the "Internet of Things", the application of innovation as a core aim is to achieve things and things, things and people, and all items connected to the network to facilitate the identification, management and control, home automation based on GSM network monitoring control systemis one of the focus of development.This system uses multiple sensors. Such as the temperature and humidity sensor, the flammable gas sensor, the infrared sensor. This device will test multiple indicators of local. The GMS network is a remote control function. When the abnormal data to be detected, it will notice the remote users at once. The communication mode will use the short message. The remote user also can send the specific short-interest-instruction to the device. It can control multi-channel switch on the local device. Wireless control also includes a handheld remote control to complete.%结合当下“物联网”的概念,以应用创新作为核心,目的是实现物与物、物与人,所有的物品与网络的连接,方便识别、管理和控制,基于GSM网络的家居自动监测控制系统正是其中发展的重点.本次设计中采用了多路传感器,包括温湿度、可燃性气体、红外热释电等,它们可以对本地的多个指标进行测试.对于GSM网络的远程控制功能是可以在发生异常的时候及时通知远程用户,采用的方式为短息形式.远程用户也可以使用编写特定的短息指令对本地的多路开关实施控制,无线控制还包括采用手持遥控器完成.

  14. Design and implementation of farmland information automatic detection system based on wireless sensor networks%基于无线传感器网络农田信息自动检测系统的设计与实现

    Institute of Scientific and Technical Information of China (English)

    蔡晓艳; 王缓缓

    2013-01-01

    The basis for the implementation of precision agriculture is to rely on farmland information's timely and accurate obtaining. Based on the application of wireless sensor networks in the collection of farmland information, this paper proposed the need of design of small size, low cost, low power, long duration of work of wireless sensor network nodes used in farmland information collection. This system uses low-power processors, Atmel ATmega1281 and AT86RF231 RF chip, eventually achieve a low-power, low cost, low complexity detection system. By environmental factors such as temperature and humidity testing, the system can achieve real-time monitoring of crop environmental requirements.%农田信息的及时准确获取是精准农业实施的基础.基于当前无线传感器网络在农田信息采集中的应用现状,提出了设计体积小、成本低、低功耗、工作持续时间长的农田信息采集无线传感器网络节点的必要性.系统采用Atmel公司的低功耗处理器芯片ATmega 1281和AT86RF231射频芯片,最终实现了低功耗、低成本、低复杂度的检测系统,通过对温湿度等环境因子的检测,能够达到对作物种植环境进行实时监测的要求.

  15. Automatic fracture density update using smart well data and artificial neural networks

    Science.gov (United States)

    Al-Anazi, A.; Babadagli, T.

    2010-03-01

    This paper presents a new methodology to continuously update and improve fracture network models. We begin with a hypothetical model whose fracture network parameters and geological information are known. After generating the "exact" fracture network with known characteristics, the data were exported to a reservoir simulator and simulations were run over a period of time. Intelligent wells equipped with downhole multiple pressure and flow sensors were placed throughout the reservoir and put into production. These producers were completed in different fracture zones to create a representative pressure and production response. We then considered a number of wells of which static (cores and well logs) and dynamic (production) data were used to model well fracture density. As new wells were opened, historical static and dynamic data from previous wells and static data from the new wells were used to update the fracture density using Artificial Neural Networks (ANN). The accuracy of the prediction model depends significantly on the representation of the available data of the existing fracture network. The importance of conventional data (surface production data) and smart well data prediction capability was also investigated. Highly sensitive input data were selected through a forward selection scheme to train the ANN. Well geometric locations were included as a new link in the ANN regression process. Once the relationship between fracture network parameters and well performance data was established, the ANN model was used to predict fracture density at newly drilled locations. Finally, an error analysis through a correlation coefficient and percentage absolute relative error performance was performed to examine the accuracy of the proposed inverse modeling methodology. It was shown that fracture dominated production performance data collected from both conventional and smart wells allow for automatically updating the fracture network model. The proposed technique helps

  16. Caption detection from video sequence based on fuzzy neural networks

    Science.gov (United States)

    Gao, Xinbo; Xin, Hong; Li, Jie

    2001-09-01

    Caption graphically superimposed in video frames can provide important indexing information. The automatic detection and recognition of video captions can be of great help in querying topics of interest in digital news library. To detect the caption from video sequence, we present algorithms based on fuzzy clustering neural networks. Since neural networks have the capabilities of learning and self-organizing and parallel computing mechanism, with the great increasing of digital images and video databases, neural networks based techniques become more efficient and popular tools for multimedia processing. Experimental results show that our caption detection scheme is effective and robust.

  17. Weighted ensemble based automatic detection of exudates in fundus photographs.

    Science.gov (United States)

    Prentasic, Pavle; Loncaric, Sven

    2014-01-01

    Diabetic retinopathy (DR) is a visual complication of diabetes, which has become one of the leading causes of preventable blindness in the world. Exudate detection is an important problem in automatic screening systems for detection of diabetic retinopathy using color fundus photographs. In this paper, we present a method for detection of exudates in color fundus photographs, which combines several preprocessing and candidate extraction algorithms to increase the exudate detection accuracy. The first stage of the method consists of an ensemble of several exudate candidate extraction algorithms. In the learning phase, simulated annealing is used to determine weights for combining the results of the ensemble candidate extraction algorithms. The second stage of the method uses a machine learning-based classification for detection of exudate regions. The experimental validation was performed using the DRiDB color fundus image set. The validation has demonstrated that the proposed method achieved higher accuracy in comparison to state-of-the art methods.

  18. Spline-based automatic path generation of welding robot

    Institute of Scientific and Technical Information of China (English)

    Niu Xuejuan; Li Liangyu

    2007-01-01

    This paper presents a flexible method for the representation of welded seam based on spline interpolation. In this method, the tool path of welding robot can be generated automatically from a 3D CAD model. This technique has been implemented and demonstrated in the FANUC Arc Welding Robot Workstation. According to the method, a software system is developed using VBA of SolidWorks 2006. It offers an interface between SolidWorks and ROBOGUIDE, the off-line programming software of FANUC robot. It combines the strong modeling function of the former and the simulating function of the latter. It also has the capability of communication with on-line robot. The result data have shown its high accuracy and strong reliability in experiments. This method will improve the intelligence and the flexibility of the welding robot workstation.

  19. Spike Detection Based on Normalized Correlation with Automatic Template Generation

    Directory of Open Access Journals (Sweden)

    Wen-Jyi Hwang

    2014-06-01

    Full Text Available A novel feedback-based spike detection algorithm for noisy spike trains is presented in this paper. It uses the information extracted from the results of spike classification for the enhancement of spike detection. The algorithm performs template matching for spike detection by a normalized correlator. The detected spikes are then sorted by the OSortalgorithm. The mean of spikes of each cluster produced by the OSort algorithm is used as the template of the normalized correlator for subsequent detection. The automatic generation and updating of templates enhance the robustness of the spike detection to input trains with various spike waveforms and noise levels. Experimental results show that the proposed algorithm operating in conjunction with OSort is an efficient design for attaining high detection and classification accuracy for spike sorting.

  20. Automatic target recognition based on cross-plot.

    Directory of Open Access Journals (Sweden)

    Kelvin Kian Loong Wong

    Full Text Available Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository.

  1. Toward a multi-sensor-based approach to automatic text classification

    Energy Technology Data Exchange (ETDEWEB)

    Dasigi, V.R. [Sacred Heart Univ., Fairfield, CT (United States); Mann, R.C. [Oak Ridge National Lab., TN (United States)

    1995-10-01

    Many automatic text indexing and retrieval methods use a term-document matrix that is automatically derived from the text in question. Latent Semantic Indexing is a method, recently proposed in the Information Retrieval (IR) literature, for approximating a large and sparse term-document matrix with a relatively small number of factors, and is based on a solid mathematical foundation. LSI appears to be quite useful in the problem of text information retrieval, rather than text classification. In this report, we outline a method that attempts to combine the strength of the LSI method with that of neural networks, in addressing the problem of text classification. In doing so, we also indicate ways to improve performance by adding additional {open_quotes}logical sensors{close_quotes} to the neural network, something that is hard to do with the LSI method when employed by itself. The various programs that can be used in testing the system with TIPSTER data set are described. Preliminary results are summarized, but much work remains to be done.

  2. Location based Network Optimizations for Mobile Wireless Networks

    DEFF Research Database (Denmark)

    Nielsen, Jimmy Jessen

    selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...

  3. Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

    Science.gov (United States)

    Sun, Wenqing; Zheng, Bin; Qian, Wei

    2017-04-13

    This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

  4. Knowledge-based system for automatic MBR control.

    Science.gov (United States)

    Comas, J; Meabe, E; Sancho, L; Ferrero, G; Sipma, J; Monclús, H; Rodriguez-Roda, I

    2010-01-01

    MBR technology is currently challenging traditional wastewater treatment systems and is increasingly selected for WWTP upgrading. MBR systems typically are constructed on a smaller footprint, and provide superior treated water quality. However, the main drawback of MBR technology is that the permeability of membranes declines during filtration due to membrane fouling, which for a large part causes the high aeration requirements of an MBR to counteract this fouling phenomenon. Due to the complex and still unknown mechanisms of membrane fouling it is neither possible to describe clearly its development by means of a deterministic model, nor to control it with a purely mathematical law. Consequently the majority of MBR applications are controlled in an "open-loop" way i.e. with predefined and fixed air scour and filtration/relaxation or backwashing cycles, and scheduled inline or offline chemical cleaning as a preventive measure, without taking into account the real needs of membrane cleaning based on its filtration performance. However, existing theoretical and empirical knowledge about potential cause-effect relations between a number of factors (influent characteristics, biomass characteristics and operational conditions) and MBR operation can be used to build a knowledge-based decision support system (KB-DSS) for the automatic control of MBRs. This KB-DSS contains a knowledge-based control module, which, based on real time comparison of the current permeability trend with "reference trends", aims at optimizing the operation and energy costs and decreasing fouling rates. In practice the automatic control system proposed regulates the set points of the key operational variables controlled in MBR systems (permeate flux, relaxation and backwash times, backwash flows and times, aeration flow rates, chemical cleaning frequency, waste sludge flow rate and recycle flow rates) and identifies its optimal value. This paper describes the concepts and the 3-level architecture

  5. Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding.

    Science.gov (United States)

    Jati, Arindam; Singh, Garima; Mukherjee, Rashmi; Ghosh, Madhumala; Konar, Amit; Chakraborty, Chandan; Nagar, Atulya K

    2014-03-01

    The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.

  6. Cloud networking understanding cloud-based data center networks

    CERN Document Server

    Lee, Gary

    2014-01-01

    Cloud Networking: Understanding Cloud-Based Data Center Networks explains the evolution of established networking technologies into distributed, cloud-based networks. Starting with an overview of cloud technologies, the book explains how cloud data center networks leverage distributed systems for network virtualization, storage networking, and software-defined networking. The author offers insider perspective to key components that make a cloud network possible such as switch fabric technology and data center networking standards. The final chapters look ahead to developments in architectures

  7. Enhancing Automaticity through Task-Based Language Learning

    Science.gov (United States)

    De Ridder, Isabelle; Vangehuchten, Lieve; Gomez, Marta Sesena

    2007-01-01

    In general terms automaticity could be defined as the subconscious condition wherein "we perform a complex series of tasks very quickly and efficiently, without having to think about the various components and subcomponents of action involved" (DeKeyser 2001: 125). For language learning, Segalowitz (2003) characterised automaticity as a…

  8. Enhancing Automaticity through Task-Based Language Learning

    Science.gov (United States)

    De Ridder, Isabelle; Vangehuchten, Lieve; Gomez, Marta Sesena

    2007-01-01

    In general terms automaticity could be defined as the subconscious condition wherein "we perform a complex series of tasks very quickly and efficiently, without having to think about the various components and subcomponents of action involved" (DeKeyser 2001: 125). For language learning, Segalowitz (2003) characterised automaticity as a…

  9. 海量网络监控数据的自动融合和关联分析%MASS NETWORK MONITORING DATA AUTOMATIC FUSION AND CORRELATION ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    张立涓

    2011-01-01

    Based on SOA, a scheme is designed for mass network monitoring data automatic fusion and correlation analysis. The anomaly detection model and network threat warning model are emphasized upon for thorough discussion and analysis.%基于SOA设计一个对海量网络监控数据的自动融合和关联分析的方案.着重对异常检测模型和网络威胁预警模型做了深入的探讨和分析.

  10. Automatic classification of sentences to support Evidence Based Medicine

    Directory of Open Access Journals (Sweden)

    Martinez David

    2011-03-01

    Full Text Available Abstract Aim Given a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels. Method We constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome. We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF for classification. Results For the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences. Conclusions Of the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.

  11. Remanufacturing system based on totally automatic MIG surfacing via robot

    Institute of Scientific and Technical Information of China (English)

    ZHU Sheng; GUO Ying-chun; YANG Pei

    2005-01-01

    Remanufacturing system is a term of green system project which conforms to the national sustainable development strategy. With the demand of the high adaptability of the varieties of waste machining parts, the short product cycle, the low machining cost and the high product quality are offered. Each step of the remanufacturing system from the beginning of the scanning to the accomplishment of the welding was investigted. Aiming at building a remanufacturing system based on totally automatic MIG surfacing via robot, advanced information technology, remanufacturing technology and management, through the control of the pretreatment and the optimization to minimize the time of remanufacturing and realize the remanufacturing on the terminal products of varieties, were applied. The steps mainly include: 1) using the visual sensor which is installed at the end of the Robot to rapidly get the outline data of the machining part and the pretreatment of the data; 2) rebuilding the curved surface based on the outline data and the integrated CAD material object model; 3) building the remanufacturing model based on the CAD material object model and projecting the remanufacturing process; and 4) accomplishing the remanufacture of the machining part by the technology of MIG surfacing.

  12. SU-E-J-16: Automatic Image Contrast Enhancement Based On Automatic Parameter Optimization for Radiation Therapy Setup Verification

    Energy Technology Data Exchange (ETDEWEB)

    Qiu, J [Taishan Medical University, Taian, Shandong (China); Washington University in St Louis, St Louis, MO (United States); Li, H. Harlod; Zhang, T; Yang, D [Washington University in St Louis, St Louis, MO (United States); Ma, F [Taishan Medical University, Taian, Shandong (China)

    2015-06-15

    Purpose: In RT patient setup 2D images, tissues often cannot be seen well due to the lack of image contrast. Contrast enhancement features provided by image reviewing software, e.g. Mosaiq and ARIA, require manual selection of the image processing filters and parameters thus inefficient and cannot be automated. In this work, we developed a novel method to automatically enhance the 2D RT image contrast to allow automatic verification of patient daily setups as a prerequisite step of automatic patient safety assurance. Methods: The new method is based on contrast limited adaptive histogram equalization (CLAHE) and high-pass filtering algorithms. The most important innovation is to automatically select the optimal parameters by optimizing the image contrast. The image processing procedure includes the following steps: 1) background and noise removal, 2) hi-pass filtering by subtracting the Gaussian smoothed Result, and 3) histogram equalization using CLAHE algorithm. Three parameters were determined through an iterative optimization which was based on the interior-point constrained optimization algorithm: the Gaussian smoothing weighting factor, the CLAHE algorithm block size and clip limiting parameters. The goal of the optimization is to maximize the entropy of the processed Result. Results: A total 42 RT images were processed. The results were visually evaluated by RT physicians and physicists. About 48% of the images processed by the new method were ranked as excellent. In comparison, only 29% and 18% of the images processed by the basic CLAHE algorithm and by the basic window level adjustment process, were ranked as excellent. Conclusion: This new image contrast enhancement method is robust and automatic, and is able to significantly outperform the basic CLAHE algorithm and the manual window-level adjustment process that are currently used in clinical 2D image review software tools.

  13. Conditions for use of APV automatic reclosing units in surface mine distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhidkov, V.O.; Polozkov, A.V.; Kotov, V.P.

    1986-08-01

    Assesses the potential for use of APV automatic reclosing units in 6-10 kV electrical networks for surface mining equipment, with particular reference to Kuzbass conditions (Kemerovougol'association). These units automatically restore power after single phase grounds caused by damage to cables, etc. There are two major problems with APV units: the need to check insulation before restoration of power so as to prevent more serious injury to electrocuted persons, and the danger of asynchronous start-up of synchronous electric motors in which rundown lasts several tens of seconds. The first of these problems can be overcome with the aid of KBU insulation monitoring units. Tests have been performed at VostNII to determine the optimum parameters, with measurements of actual cable insulation resistance at mines. The minimum insulation resistance may be set at about 100 kohm. The second problem requires a no current condition before the APV for as long as 60 s to ensure full rundown of motors. An equation for determining the duration of this condition is given. Field suppression devices should be fitted to synchronous motors.

  14. Automatic Estimation of the Dynamics of Channel Conductance Using a Recurrent Neural Network

    Directory of Open Access Journals (Sweden)

    Masaaki Takahashi

    2009-01-01

    Full Text Available In order to simulate neuronal electrical activities, we must estimate the dynamics of channel conductances from physiological experimental data. However, this approach requires the formulation of differential equations that express the time course of channel conductance. On the other hand, if the dynamics are automatically estimated, neuronal activities can be easily simulated. By using a recurrent neural network (RNN, it is possible to estimate the dynamics of channel conductances without formulating the differential equations. In the present study, we estimated the dynamics of the Na+ and K+ conductances of a squid giant axon using two different fully connected RNNs and were able to reproduce various neuronal activities of the axon. The reproduced activities were an action potential, a threshold, a refractory phenomenon, a rebound action potential, and periodic action potentials with a constant stimulation. RNNs can be trained using channels other than the Na+ and K+ channels. Therefore, using our RNN estimation method, the dynamics of channel conductance can be automatically estimated and the neuronal activities can be simulated using the channel RNNs. An RNN can be a useful tool to estimate the dynamics of the channel conductance of a neuron, and by using the method presented here, it is possible to simulate neuronal activities more easily than by using the previous methods.

  15. Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

    Science.gov (United States)

    Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

    2017-03-01

    Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

  16. Automatic Tamil lyric generation based on ontological interpretation for semantics

    Indian Academy of Sciences (India)

    Rajeswari Sridhar; D Jalin Gladis; Kameswaran Ganga; G Dhivya Prabha

    2014-02-01

    This system proposes an -gram based approach to automatic Tamil lyric generation, by the ontological semantic interpretation of the input scene. The approach is based on identifying the semantics conveyed in the scenario, thereby making the system understand the situation and generate lyrics accordingly. The heart of the system includes the ontological interpretation of the scenario, and the selection of the appropriate tri-grams for generating the lyrics. To fulfill this, we have designed a new ontology with weighted edges, where the edges correspond to a set of sentences, which indicate a relationship, and are represented as a tri-gram. Once the appropriate tri-grams are selected, the root words from these tri-grams are sent to the morphological generator, to form words in their packed form. These words are then assembled to form the final lyrics. Parameters of poetry like rhyme, alliteration, simile, vocative words, etc., are also taken care of by the system. Using this approach, we achieved an average accuracy of 77.3% with respect to the exact semantic details being conveyed in the generated lyrics.

  17. A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing

    Science.gov (United States)

    Yu, Ning; Xiao, Chenxian; Wu, Yinfeng; Feng, Renjian

    2016-01-01

    Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users’ smartphone sensors, the indoor anchors (doors) are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users’ explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established. PMID:27070623

  18. A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing

    Directory of Open Access Journals (Sweden)

    Ning Yu

    2016-04-01

    Full Text Available Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users’ smartphone sensors, the indoor anchors (doors are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users’ explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established.

  19. Automatic color based reassembly of fragmented images and paintings.

    Science.gov (United States)

    Tsamoura, Efthymia; Pitas, Ioannis

    2010-03-01

    The problem of reassembling image fragments arises in many scientific fields, such as forensics and archaeology. In the field of archaeology, the pictorial excavation findings are almost always in the form of painting fragments. The manual execution of this task is very difficult, as it requires great amount of time, skill and effort. Thus, the automation of such a work is very important and can lead to faster, more efficient, painting reassembly and to a significant reduction in the human effort involved. In this paper, an integrated method for automatic color based 2-D image fragment reassembly is presented. The proposed 2-D reassembly technique is divided into four steps. Initially, the image fragments which are probably spatially adjacent, are identified utilizing techniques employed in content based image retrieval systems. The second operation is to identify the matching contour segments for every retained couple of image fragments, via a dynamic programming technique. The next step is to identify the optimal transformation in order to align the matching contour segments. Many registration techniques have been evaluated to this end. Finally, the overall image is reassembled from its properly aligned fragments. This is achieved via a novel algorithm, which exploits the alignment angles found during the previous step. In each stage, the most robust algorithms having the best performance are investigated and their results are fed to the next step. We have experimented with the proposed method using digitally scanned images of actual torn pieces of paper image prints and we produced very satisfactory reassembly results.

  20. Signature prediction for model-based automatic target recognition

    Science.gov (United States)

    Keydel, Eric R.; Lee, Shung W.

    1996-06-01

    The moving and stationary target recognition (MSTAR) model- based automatic target recognition (ATR) system utilizes a paradigm which matches features extracted form an unknown SAR target signature against predictions of those features generated from models of the sensing process and candidate target geometries. The candidate target geometry yielding the best match between predicted and extracted features defines the identify of the unknown target. MSTAR will extend the current model-based ATR state-of-the-art in a number of significant directions. These include: use of Bayesian techniques for evidence accrual, reasoning over target subparts, coarse-to-fine hypothesis search strategies, and explicit reasoning over target articulation, configuration, occlusion, and lay-over. These advances also imply significant technical challenges, particularly for the MSTAR feature prediction module (MPM). In addition to accurate electromagnetics, the MPM must provide traceback between input target geometry and output features, on-line target geometry manipulation, target subpart feature prediction, explicit models for local scene effects, and generation of sensitivity and uncertainty measures for the predicted features. This paper describes the MPM design which is being developed to satisfy these requirements. The overall module structure is presented, along with the specific deign elements focused on MSTAR requirements. Particular attention is paid to design elements that enable on-line prediction of features within the time constraints mandated by model-driven ATR. Finally, the current status, development schedule, and further extensions in the module design are described.

  1. A Review of Methods of Instance-based Automatic Image Annotation

    Directory of Open Access Journals (Sweden)

    Morad Derakhshan

    2016-12-01

    Full Text Available Today, to use automatic image annotation in order to fill the semantic gap between low level features of images and understanding their information in retrieving process has become popular. Since automatic image annotation is crucial in understanding digital images several methods have been proposed to automatically annotate an image. One of the most important of these methods is instance-based image annotation. As these methods are vastly used in this paper, the most important instance-based image annotation methods are analyzed. First of all the main parts of instance-based automatic image annotation are analyzed. Afterwards, the main methods of instance-based automatic image annotation are reviewed and compared based on various features. In the end the most important challenges and open-ended fields in instance-based image annotation are analyzed.

  2. FULLY AUTOMATIC IMAGE-BASED REGISTRATION OF UNORGANIZED TLS DATA

    Directory of Open Access Journals (Sweden)

    M. Weinmann

    2012-09-01

    Full Text Available The estimation of the transformation parameters between different point clouds is still a crucial task as it is usually followed by scene reconstruction, object detection or object recognition. Therefore, the estimates should be as accurate as possible. Recent developments show that it is feasible to utilize both the measured range information and the reflectance information sampled as image, as 2D imagery provides additional information. In this paper, an image-based registration approach for TLS data is presented which consists of two major steps. In the first step, the order of the scans is calculated by checking the similarity of the respective reflectance images via the total number of SIFT correspondences between them. Subsequently, in the second step, for each SIFT correspondence the respective SIFT features are filtered with respect to their reliability concerning the range information and projected to 3D space. Combining the 3D points with 2D observations on a virtual plane yields 3D-to-2D correspondences from which the coarse transformation parameters can be estimated via a RANSAC-based registration scheme including the EPnP algorithm. After this coarse registration, the 3D points are again checked for consistency by using constraints based on the 3D distance, and, finally, the remaining 3D points are used for an ICP-based fine registration. Thus, the proposed methodology provides a fast, reliable, accurate and fully automatic image-based approach for the registration of unorganized point clouds without the need of a priori information about the order of the scans, the presence of regular surfaces or human interaction.

  3. Size-based protocol optimization using automatic tube current modulation and automatic kV selection in computed tomography.

    Science.gov (United States)

    MacDougall, Robert D; Kleinman, Patricia L; Callahan, Michael J

    2016-01-08

    Size-based diagnostic reference ranges (DRRs) for contrast-enhanced pediatric abdominal computed tomography (CT) have been published in order to establish practical upper and lower limits of CTDI, DLP, and SSDE. Based on these DRRs, guidelines for establishing size-based SSDE target levels from the SSDE of a standard adult by applying a linear correction factor have been published and provide a great reference for dose optimization initiatives. The necessary step of designing manufacturer-specific CT protocols to achieve established SSDE targets is the responsibility of the Qualified Medical Physicist. The task is straightforward if fixed-mA protocols are used, however, more difficult when automatic exposure control (AEC) and automatic kV selection are considered. In such cases, the physicist must deduce the operation of AEC algorithms from technical documentation or through testing, using a wide range of phantom sizes. Our study presents the results of such testing using anthropomorphic phantoms ranging in size from the newborn to the obese adult. The effect of each user-controlled parameter was modeled for a single-manufacturer AEC algorithm (Siemens CARE Dose4D) and automatic kV selection algorithm (Siemens CARE kV). Based on the results presented in this study, a process for designing mA-modulated, pediatric abdominal CT protocols that achieve user-defined SSDE and kV targets is described.

  4. Automatic Road Extraction Based on Integration of High Resolution LIDAR and Aerial Imagery

    Science.gov (United States)

    Rahimi, S.; Arefi, H.; Bahmanyar, R.

    2015-12-01

    In recent years, the rapid increase in the demand for road information together with the availability of large volumes of high resolution Earth Observation (EO) images, have drawn remarkable interest to the use of EO images for road extraction. Among the proposed methods, the unsupervised fully-automatic ones are more efficient since they do not require human effort. Considering the proposed methods, the focus is usually to improve the road network detection, while the roads' precise delineation has been less attended to. In this paper, we propose a new unsupervised fully-automatic road extraction method, based on the integration of the high resolution LiDAR and aerial images of a scene using Principal Component Analysis (PCA). This method discriminates the existing roads in a scene; and then precisely delineates them. Hough transform is then applied to the integrated information to extract straight lines; which are further used to segment the scene and discriminate the existing roads. The roads' edges are then precisely localized using a projection-based technique, and the round corners are further refined. Experimental results demonstrate that our proposed method extracts and delineates the roads with a high accuracy.

  5. A superpixel-based framework for automatic tumor segmentation on breast DCE-MRI

    Science.gov (United States)

    Yu, Ning; Wu, Jia; Weinstein, Susan P.; Gaonkar, Bilwaj; Keller, Brad M.; Ashraf, Ahmed B.; Jiang, YunQing; Davatzikos, Christos; Conant, Emily F.; Kontos, Despina

    2015-03-01

    Accurate and efficient automated tumor segmentation in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is highly desirable for computer-aided tumor diagnosis. We propose a novel automatic segmentation framework which incorporates mean-shift smoothing, superpixel-wise classification, pixel-wise graph-cuts partitioning, and morphological refinement. A set of 15 breast DCE-MR images, obtained from the American College of Radiology Imaging Network (ACRIN) 6657 I-SPY trial, were manually segmented to generate tumor masks (as ground truth) and breast masks (as regions of interest). Four state-of-the-art segmentation approaches based on diverse models were also utilized for comparison. Based on five standard evaluation metrics for segmentation, the proposed framework consistently outperformed all other approaches. The performance of the proposed framework was: 1) 0.83 for Dice similarity coefficient, 2) 0.96 for pixel-wise accuracy, 3) 0.72 for VOC score, 4) 0.79 mm for mean absolute difference, and 5) 11.71 mm for maximum Hausdorff distance, which surpassed the second best method (i.e., adaptive geodesic transformation), a semi-automatic algorithm depending on precise initialization. Our results suggest promising potential applications of our segmentation framework in assisting analysis of breast carcinomas.

  6. Sinc-function based Network

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1999-01-01

    The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...

  7. Sinc-function based Network

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1998-01-01

    The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...

  8. Automatic Generation of Setup for CNC Spring Coiler Based on Case-based Reasoning

    Institute of Scientific and Technical Information of China (English)

    KU Xiangchen; WANG Runxiao; LI Jishun; WANG Dongbo

    2006-01-01

    When producing special-shape spring in CNC spring coiler, the setup of the coiler is often a manual work using a trial-and-error method. As a result, the setup of coiler consumes so much time and becomes the bottleneck of the spring production process. In order to cope with this situation, this paper proposes an automatic generation system of setup for CNC spring coiler using case-based reasoning (CBR). The core of the study contains: (1) integrated reasoning model of CBR system;(2) spatial shape describe of special-shape spring based on feature;(3) coiling case representation using shape feature matrix; and (4) case similarity measure algorithm. The automatic generation system has implemented with C++ Builder 6.0 and is helpful in improving the automaticity and efficiency of spring coiler.

  9. Automatic Mapping of Martian Landforms Using Segmentation-based Classification

    Science.gov (United States)

    Ghosh, S.; Stepinski, T. F.; Vilalta, R.

    2007-03-01

    We use terrain segmentation and classification techniques to automatically map landforms on Mars. The method is applied to six sites to obtain geomorphic maps geared toward rapid characterization of impact craters.

  10. A computer program to automatically generate state equations and macro-models. [for network analysis and design

    Science.gov (United States)

    Garrett, S. J.; Bowers, J. C.; Oreilly, J. E., Jr.

    1978-01-01

    A computer program, PROSE, that produces nonlinear state equations from a simple topological description of an electrical or mechanical network is described. Unnecessary states are also automatically eliminated, so that a simplified terminal circuit model is obtained. The program also prints out the eigenvalues of a linearized system and the sensitivities of the eigenvalue of largest magnitude.

  11. Automatic Parameters Selection for SVM Based on PSO

    Institute of Scientific and Technical Information of China (English)

    ZHANG Mingfeng; ZHU Yinghua; ZHENG Xu; LIU Yu

    2007-01-01

    Motivated by the fact that automatic parameters selection for Support Vector Machine (SVM) is an important issue to make SVM practically useful and the common used Leave-One-Out (LOO) method is complex calculation and time consuming,an effective strategy for automatic parameters selection for SVM is proposed by using the Particle Swarm Optimization (PSO) in this paper.Simulation results of practice data model demonstrate the effectiveness and high efficiency of the proposed approach.

  12. Automatically inferred Markov network models for classification of chromosomal band pattern structures.

    Science.gov (United States)

    Granum, E; Thomason, M G

    1990-01-01

    A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonlinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles." A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise normalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementations can process one cell in less than 1 min on current standard

  13. Toward next-generation optical networks: a network operator perspective based on experimental tests and economic analysis

    Science.gov (United States)

    Xiao, Xiaojun; Du, Chunsheng; Zhou, Rongsheng

    2004-04-01

    As a result of data traffic"s exponential growth, network is currently evolving from fixed circuit switched services to dynamic packet switched services, which has brought unprecedented changes to the existing transport infrastructure. It is generally agreed that automatic switched optical network (ASON) is one of the promising solutions for the next generation optical networks. In this paper, we present the results of our experimental tests and economic analysis on ASON. The intention of this paper is to present our perspective, in terms of evolution strategy toward ASON, on next generation optical networks. It is shown through experimental tests that the performance of current Pre-standard ASON enabled equipments satisfies the basic requirements of network operators and is ready for initial deployment. The results of the economic analysis show that network operators can be benefit from the deployment of ASON from three sides. Firstly, ASON can reduce the CAPEX for network expanding by integrating multiple ADM & DCS into one box. Secondly, ASON can reduce the OPEX for network operation by introducing automatic resource control scheme. Finally, ASON can increase margin revenue by providing new optical network services such as Bandwidth on Demand, optical VPN etc. Finally, the evolution strategy is proposed as our perspective toward next generation optical networks. We hope the evolution strategy introduced may be helpful for the network operators to gracefully migrate their fixed ring based legacy networks to next generation dynamic mesh based network.

  14. Automatic Language Identification with Discriminative Language Characterization Based on SVM

    Science.gov (United States)

    Suo, Hongbin; Li, Ming; Lu, Ping; Yan, Yonghong

    Robust automatic language identification (LID) is the task of identifying the language from a short utterance spoken by an unknown speaker. The mainstream approaches include parallel phone recognition language modeling (PPRLM), support vector machine (SVM) and the general Gaussian mixture models (GMMs). These systems map the cepstral features of spoken utterances into high level scores by classifiers. In this paper, in order to increase the dimension of the score vector and alleviate the inter-speaker variability within the same language, multiple data groups based on supervised speaker clustering are employed to generate the discriminative language characterization score vectors (DLCSV). The back-end SVM classifiers are used to model the probability distribution of each target language in the DLCSV space. Finally, the output scores of back-end classifiers are calibrated by a pair-wise posterior probability estimation (PPPE) algorithm. The proposed language identification frameworks are evaluated on 2003 NIST Language Recognition Evaluation (LRE) databases and the experiments show that the system described in this paper produces comparable results to the existing systems. Especially, the SVM framework achieves an equal error rate (EER) of 4.0% in the 30-second task and outperforms the state-of-art systems by more than 30% relative error reduction. Besides, the performances of proposed PPRLM and GMMs algorithms achieve an EER of 5.1% and 5.0% respectively.

  15. Automatic comic page image understanding based on edge segment analysis

    Science.gov (United States)

    Liu, Dong; Wang, Yongtao; Tang, Zhi; Li, Luyuan; Gao, Liangcai

    2013-12-01

    Comic page image understanding aims to analyse the layout of the comic page images by detecting the storyboards and identifying the reading order automatically. It is the key technique to produce the digital comic documents suitable for reading on mobile devices. In this paper, we propose a novel comic page image understanding method based on edge segment analysis. First, we propose an efficient edge point chaining method to extract Canny edge segments (i.e., contiguous chains of Canny edge points) from the input comic page image; second, we propose a top-down scheme to detect line segments within each obtained edge segment; third, we develop a novel method to detect the storyboards by selecting the border lines and further identify the reading order of these storyboards. The proposed method is performed on a data set consisting of 2000 comic page images from ten printed comic series. The experimental results demonstrate that the proposed method achieves satisfactory results on different comics and outperforms the existing methods.

  16. Semi-automatic video semantic annotation based on active learning

    Science.gov (United States)

    Song, Yan; Hua, Xian-Sheng; Dai, Li-Rong; Wang, Ren-Hua

    2005-07-01

    In this paper, we propose a novel semi-automatic annotation scheme for home videos based on active learning. It is well-known that there is a large gap between semantics and low-level features. To narrow down this gap, relevance feedback has been introduced in a number of literatures. Furthermore, to accelerate the convergence to the optimal result, several active learning schemes, in which the most informative samples are chosen to be annotated, have been proposed in literature instead of randomly selecting samples. In this paper, a representative active learning method is proposed, which local consistency of video content is effectively taken into consideration. The main idea is to exploit the global and local statistical characteristics of videos, and the temporal relationship between shots. The global model is trained on a smaller pre-labeled video dataset, and the local information is obtained online in the process of active learning, and will be used to adjust the initial global model adaptively. The experiment results show that the proposed active learning scheme has significantly improved the annotation performance compared with random selecting and common active learning method.

  17. Performance of data acceptance criteria over 50 months from an automatic real-time environmental radiation surveillance network

    Energy Technology Data Exchange (ETDEWEB)

    Casanovas, R., E-mail: ramon.casanovas@urv.cat [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Morant, J.J. [Servei de Proteccio Radiologica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Lopez, M. [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Servei de Proteccio Radiologica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Hernandez-Giron, I. [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain); Batalla, E. [Servei de Coordinacio d' Activitats Radioactives, Departament d' Economia i Finances, Generalitat de Catalunya, ES-08018 Barcelona (Spain); Salvado, M. [Unitat de Fisica Medica, Facultat de Medicina i Ciencies de la Salut, Universitat Rovira i Virgili, ES-43201 Reus (Tarragona) (Spain)

    2011-08-15

    The automatic real-time environmental radiation surveillance network of Catalonia (Spain) comprises two subnetworks; one with 9 aerosol monitors and the other with 8 Geiger monitors together with 2 water monitors located in the Ebre river. Since September 2006, several improvements were implemented in order to get better quality and quantity of data, allowing a more accurate data analysis. However, several causes (natural causes, equipment failure, artificial external causes and incidents in nuclear power plants) may produce radiological measured values mismatched with the own station background, whether spurious without significance or true radiological values. Thus, data analysis for a 50-month period was made and allowed to establish an easily implementable statistical criterion to find those values that require special attention. This criterion proved a very useful tool for creating a properly debugged database and to give a quick response to equipment failures or possible radiological incidents. This paper presents the results obtained from the criterion application, including the figures for the expected, raw and debugged data, percentages of missing data grouped by causes and radiological measurements from the networks. Finally, based on the discussed information, recommendations for the improvement of the network are identified to obtain better radiological information and analysis capabilities. - Highlights: > Causes producing data mismatching with the own stations background are described. > Causes may be natural, equipment failure, external or nuclear plants incidents. > These causes can produce either spurious or true radiological data. > A criterion to find these data was implemented and tested for a 50-month period. > Recommendations for the improvement of the network are identified.

  18. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution

    Science.gov (United States)

    Hu, Peijun; Wu, Fa; Peng, Jialin; Liang, Ping; Kong, Dexing

    2016-12-01

    The detection and delineation of the liver from abdominal 3D computed tomography (CT) images are fundamental tasks in computer-assisted liver surgery planning. However, automatic and accurate segmentation, especially liver detection, remains challenging due to complex backgrounds, ambiguous boundaries, heterogeneous appearances and highly varied shapes of the liver. To address these difficulties, we propose an automatic segmentation framework based on 3D convolutional neural network (CNN) and globally optimized surface evolution. First, a deep 3D CNN is trained to learn a subject-specific probability map of the liver, which gives the initial surface and acts as a shape prior in the following segmentation step. Then, both global and local appearance information from the prior segmentation are adaptively incorporated into a segmentation model, which is globally optimized in a surface evolution way. The proposed method has been validated on 42 CT images from the public Sliver07 database and local hospitals. On the Sliver07 online testing set, the proposed method can achieve an overall score of 80.3+/- 4.5 , yielding a mean Dice similarity coefficient of 97.25+/- 0.65 % , and an average symmetric surface distance of 0.84+/- 0.25 mm. The quantitative validations and comparisons show that the proposed method is accurate and effective for clinical application.

  19. Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    G. Trejo-Caballero

    2015-01-01

    Full Text Available Curve fitting is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of data points, possibly noisy, the goal is to build a compact representation of the curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Despite the large number of methods available to tackle this problem, it remains challenging and elusive. In this paper, a new method to tackle such problem using strictly a linear combination of radial basis functions (RBFs is proposed. To be more specific, we divide the parameter search space into linear and nonlinear parameter subspaces. We use a hierarchical genetic algorithm (HGA to minimize a model selection criterion, which allows us to automatically and simultaneously determine the nonlinear parameters and then, by the least-squares method through Singular Value Decomposition method, to compute the linear parameters. The method is fully automatic and does not require subjective parameters, for example, smooth factor or centre locations, to perform the solution. In order to validate the efficacy of our approach, we perform an experimental study with several tests on benchmarks smooth functions. A comparative analysis with two successful methods based on RBF networks has been included.

  20. An Automatic Prediction of Epileptic Seizures Using Cloud Computing and Wireless Sensor Networks.

    Science.gov (United States)

    Sareen, Sanjay; Sood, Sandeep K; Gupta, Sunil Kumar

    2016-11-01

    Epilepsy is one of the most common neurological disorders which is characterized by the spontaneous and unforeseeable occurrence of seizures. An automatic prediction of seizure can protect the patients from accidents and save their life. In this article, we proposed a mobile-based framework that automatically predict seizures using the information contained in electroencephalography (EEG) signals. The wireless sensor technology is used to capture the EEG signals of patients. The cloud-based services are used to collect and analyze the EEG data from the patient's mobile phone. The features from the EEG signal are extracted using the fast Walsh-Hadamard transform (FWHT). The Higher Order Spectral Analysis (HOSA) is applied to FWHT coefficients in order to select the features set relevant to normal, preictal and ictal states of seizure. We subsequently exploit the selected features as input to a k-means classifier to detect epileptic seizure states in a reasonable time. The performance of the proposed model is tested on Amazon EC2 cloud and compared in terms of execution time and accuracy. The findings show that with selected HOS based features, we were able to achieve a classification accuracy of 94.6 %.

  1. Network-based functional enrichment

    Directory of Open Access Journals (Sweden)

    Poirel Christopher L

    2011-11-01

    Full Text Available Abstract Background Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. Results Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i determine which functions are enriched in a given network, ii given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. Conclusions We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are

  2. Development of Filtered Bispectrum for EEG Signal Feature Extraction in Automatic Emotion Recognition Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Prima Dewi Purnamasari

    2017-05-01

    Full Text Available The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN, and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction

  3. Neural networks for action representation underlying automatic mimicry: A functional magnetic-resonance imaging and dynamic causal modeling study

    Directory of Open Access Journals (Sweden)

    Akihiro T Sasaki

    2012-08-01

    Full Text Available Automatic mimicry is based on the tight linkage between motor and perception action representations in which internal models play a key role. Based on the anatomical connection, we hypothesized that the direct effective connectivity from the posterior superior temporal sulcus (pSTS to the ventral premotor area (PMv formed an inverse internal model, converting visual representation into a motor plan, and that reverse connectivity formed a forward internal model, converting the motor plan into a sensory outcome of action. To test this hypothesis, we employed dynamic causal-modeling analysis with functional magnetic-resonance imaging. Twenty-four normal participants underwent a change-detection task involving two visually-presented balls that were either manually rotated by the investigator’s right hand (‘Hand’ or automatically rotated. The effective connectivity from the pSTS to the PMv was enhanced by hand observation and suppressed by execution, corresponding to the inverse model. Opposite effects were observed from the PMv to the pSTS, suggesting the forward model. Additionally, both execution and hand observation commonly enhanced the effective connectivity from the pSTS to the inferior parietal lobule (IPL, the IPL to the primary sensorimotor cortex (S/M1, the PMv to the IPL, and the PMv to the S/M1. Representation of the hand action therefore was implemented in the motor system including the S/M1. During hand observation, effective connectivity toward the pSTS was suppressed whereas that toward the PMv and S/M1 was enhanced. Thus the action-representation network acted as a dynamic feedback-control system during action observation.

  4. Gap-free segmentation of vascular networks with automatic image processing pipeline.

    Science.gov (United States)

    Hsu, Chih-Yang; Ghaffari, Mahsa; Alaraj, Ali; Flannery, Michael; Zhou, Xiaohong Joe; Linninger, Andreas

    2017-03-01

    Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes.

  5. An Automatic Image Inpainting Algorithm Based on FCM

    Directory of Open Access Journals (Sweden)

    Jiansheng Liu

    2014-01-01

    Full Text Available There are many existing image inpainting algorithms in which the repaired area should be manually determined by users. Aiming at this drawback of the traditional image inpainting algorithms, this paper proposes an automatic image inpainting algorithm which automatically identifies the repaired area by fuzzy C-mean (FCM algorithm. FCM algorithm classifies the image pixels into a number of categories according to the similarity principle, making the similar pixels clustering into the same category as possible. According to the provided gray value of the pixels to be inpainted, we calculate the category whose distance is the nearest to the inpainting area and this category is to be inpainting area, and then the inpainting area is restored by the TV model to realize image automatic inpainting.

  6. Profiling School Shooters: Automatic Text-Based Analysis

    Directory of Open Access Journals (Sweden)

    Yair eNeuman

    2015-06-01

    Full Text Available School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various charateristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by six school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters' texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/priorization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology.

  7. Hopfield neural network based on ant system

    Institute of Scientific and Technical Information of China (English)

    洪炳镕; 金飞虎; 郭琦

    2004-01-01

    Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters.This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.

  8. Automatic HDL firmware generation for FPGA-based reconfigurable measurement and control systems with mezzanines in FMC standard

    Science.gov (United States)

    Wojenski, Andrzej; Kasprowicz, Grzegorz; Pozniak, Krzysztof T.; Romaniuk, Ryszard

    2013-10-01

    The paper describes a concept of automatic firmware generation for reconfigurable measurement systems, which uses FPGA devices and measurement cards in FMC standard. Following sections are described in details: automatic HDL code generation for FPGA devices, automatic communication interfaces implementation, HDL drivers for measurement cards, automatic serial connection between multiple measurement backplane boards, automatic build of memory map (address space), automatic generated firmware management. Presented solutions are required in many advanced measurement systems, like Beam Position Monitors or GEM detectors. This work is a part of a wider project for automatic firmware generation and management of reconfigurable systems. Solutions presented in this paper are based on previous publication in SPIE.

  9. The research and realization about automatic abstracting based on text clustering and natural language understanding

    Institute of Scientific and Technical Information of China (English)

    GUO Qing-lin; FAN Xiao-zhong; LIU Chang-an

    2006-01-01

    A method of realization of automatic abstracting based on text clustering and natural language understanding is explored,aimed at overcoming shortages of some current methods.The method makes use of text clustering and can realize automatic abstracting of multi-documents.The algorithm of twice word segmentation based on the title and first sentences in paragraphs is investigated..Its precision and recall is above 95%.For a specific domain on plastics,an automatic abstracting system named TCAAS is implemented.The precision and recall of multi-document's automatic abstracting is above 75%.Also,the experiments prove that it is feasible to use the method to develop a domain automatic abstracting system,which is valuable for further in-depth study.

  10. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  11. Building an Image-Based System to automatically Score psoriasis

    DEFF Research Database (Denmark)

    G{'o}mez, D. Delgado; Carstensen, Jens Michael; Ersbøll, Bjarne Kjær

    2003-01-01

    the images. The system is tested on patients with the dermatological disease psoriasis. Temporal series of images are taken for each patient and the lesions are automatically extracted. Results indicate that to the images obtained are a good source for obtaining derived variables to track the lesion....

  12. Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

    Science.gov (United States)

    2014-03-27

    SALIENT FEATURE IDENTIFICATION AND ANALYSIS USING KERNEL-BASED CLASSIFICATION TECHNIQUES FOR SYNTHETIC APERTURE RADAR AUTOMATIC TARGET RECOGNITION...FEATURE IDENTIFICATION AND ANALYSIS USING KERNEL-BASED CLASSIFICATION TECHNIQUES FOR SYNTHETIC APERTURE RADAR AUTOMATIC TARGET RECOGNITION THESIS Presented...SALIENT FEATURE IDENTIFICATION AND ANALYSIS USING KERNEL-BASED CLASSIFICATION TECHNIQUES FOR SYNTHETIC APERTURE RADAR AUTOMATIC TARGET RECOGNITION

  13. 自动拨测在集团客户短信业务网络监控中的应用研究%Application study on the measurement of network monitoring in the message service of the group customer based on the automatic dialing

    Institute of Scientific and Technical Information of China (English)

    李建荣

    2015-01-01

    In this paper, starting from the theoretical study of the measurement of network monitoring in the message service of the group customer, the difficulty of its network monitoring is summarized. The use of automatic dialing measuring principle for the end-to-end network performance monitoring is provided. The end-to-end network performance and quality data is obtained by setting up automatic dialing measurement system, which play a better role for proactive monitoring, fault pretreatment, fault location, testing and other aspects of the business after business networks and network adjustment. So as to better monitor the health of the end-to-end network, which improve the service perception of group customer.%从集团客户短信业务网络特点出发,总结其网络监控的难点,提出利用自动拨测原理进行短信业务的端到端网络性能监控.通过搭建短信业务自动拨测系统,对短信业务网络进行轮询测试,获取端到端网络性能质量数据,能够在主动监控、故障预处理、故障定位、业务入网及网络调整后业务测试等方面发挥较好的作用,从而更好的监测网络的运行状况,提升客户服务感知.

  14. Deep residual networks for automatic segmentation of laparoscopic videos of the liver

    Science.gov (United States)

    Gibson, Eli; Robu, Maria R.; Thompson, Stephen; Edwards, P. Eddie; Schneider, Crispin; Gurusamy, Kurinchi; Davidson, Brian; Hawkes, David J.; Barratt, Dean C.; Clarkson, Matthew J.

    2017-03-01

    Motivation: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. Method: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. Results: The CNN yielded segmentations with Dice scores >=0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. Conclusion: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance.

  15. Using stochastic activity networks to study the energy feasibility of automatic weather stations

    Energy Technology Data Exchange (ETDEWEB)

    Cassano, Luca [Dipartimento di Elettronica, Informatica e Bioingegneria, Politecnico di Milano (Italy); Cesarini, Daniel [Scuola Superiore Sant’Anna, Pisa (Italy); Avvenuti, Marco [Dipartimento di Ingegneria dell’Informazione, University of Pisa (Italy)

    2015-03-10

    Automatic Weather Stations (AWSs) are systems equipped with a number of environmental sensors and communication interfaces used to monitor harsh environments, such as glaciers and deserts. Designing such systems is challenging, since designers have to maximize the amount of sampled and transmitted data while considering the energy needs of the system that, in most cases, is powered by rechargeable batteries and exploits energy harvesting, e.g., solar cells and wind turbines. To support designers of AWSs in the definition of the software tasks and of the hardware configuration of the AWS we designed and implemented an energy-aware simulator of such systems. The simulator relies on the Stochastic Activity Networks (SANs) formalism and has been developed using the Möbius tool. In this paper we first show how we used the SAN formalism to model the various components of an AWS, we then report results from an experiment carried out to validate the simulator against a real-world AWS and we finally show some examples of usage of the proposed simulator.

  16. Implementation of Automatic Recovery In Packet Loss For Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Mr. Devaraya Vijay Kumar Arjun

    2014-03-01

    Full Text Available This paper describes with an idea of how the automatic recovery paradigm will work for audio/video streaming data packets in Wireless Sensor Networks. In recent works observed that compressed sensing theory can obtain all the signal information from far fewer measurements by means of non-adaptive linear projection, and can recover the signal information using non-linear reconstruction technique. In this paper according to the compressive sensing theory, a new video codec system has been developed. In the encoding process, the audio/video frame sequences are divided into groups, each group include intra and inter frames. A random measurement matrix is constructed to measure different frames. Then the measurements are quantized, the quantization codes are transmitted on the channel. In decoding process, each frame sequence is reconstructed using the St OMP algorithm and processed in the present system then experimental results shown that the proposed method exhibits better results over the traditional video codec with keeping the same quality of the video image, and it can reduce sampling number significantly, realize easily, encode/decode more efficiently.

  17. Network design and quality checks in automatic orientation of close-range photogrammetric blocks.

    Science.gov (United States)

    Dall'Asta, Elisa; Thoeni, Klaus; Santise, Marina; Forlani, Gianfranco; Giacomini, Anna; Roncella, Riccardo

    2015-04-03

    Due to the recent improvements of automatic measurement procedures in photogrammetry, multi-view 3D reconstruction technologies are becoming a favourite survey tool. Rapidly widening structure-from-motion (SfM) software packages offer significantly easier image processing workflows than traditional photogrammetry packages. However, while most orientation and surface reconstruction strategies will almost always succeed in any given task, estimating the quality of the result is, to some extent, still an open issue. An assessment of the precision and reliability of block orientation is necessary and should be included in every processing pipeline. Such a need was clearly felt from the results of close-range photogrammetric surveys of in situ full-scale and laboratory-scale experiments. In order to study the impact of the block control and the camera network design on the block orientation accuracy, a series of Monte Carlo simulations was performed. Two image block configurations were investigated: a single pseudo-normal strip and a circular highly-convergent block. The influence of surveying and data processing choices, such as the number and accuracy of the ground control points, autofocus and camera calibration was investigated. The research highlights the most significant aspects and processes to be taken into account for adequate in situ and laboratory surveys, when modern SfM software packages are used, and evaluates their effect on the quality of the results of the surface reconstruction.

  18. Network Design and Quality Checks in Automatic Orientation of Close-Range Photogrammetric Blocks

    Directory of Open Access Journals (Sweden)

    Elisa Dall'Asta

    2015-04-01

    Full Text Available Due to the recent improvements of automatic measurement procedures in photogrammetry, multi-view 3D reconstruction technologies are becoming a favourite survey tool. Rapidly widening structure-from-motion (SfM software packages offer significantly easier image processing workflows than traditional photogrammetry packages. However, while most orientation and surface reconstruction strategies will almost always succeed in any given task, estimating the quality of the result is, to some extent, still an open issue. An assessment of the precision and reliability of block orientation is necessary and should be included in every processing pipeline. Such a need was clearly felt from the results of close-range photogrammetric surveys of in situ full-scale and laboratory-scale experiments. In order to study the impact of the block control and the camera network design on the block orientation accuracy, a series of Monte Carlo simulations was performed. Two image block configurations were investigated: a single pseudo-normal strip and a circular highly-convergent block. The influence of surveying and data processing choices, such as the number and accuracy of the ground control points, autofocus and camera calibration was investigated. The research highlights the most significant aspects and processes to be taken into account for adequate in situ and laboratory surveys, when modern SfM software packages are used, and evaluates their effect on the quality of the results of the surface reconstruction.

  19. National automatic network of environmental radiological monitoring (RENAMORA); Red Nacional automatica de monitoreo radiologico ambiental (RENAMORA)

    Energy Technology Data Exchange (ETDEWEB)

    Gonzalez M, J.L.; Sanchez H, L. [CNSNS, Dr. Barragan 779, Col. Narvarte, 03020 Mexico D.F. (Mexico)]. e-mail: jlgonzalez@cnsns.gob.mx

    2003-07-01

    Inside the programs of Environmental Radiological Surveillance that it carries out the National Commission of Nuclear Security and Safeguards (CNSNS), it develops an National Automatic Network of Environmental Radiological Monitoring (RENAMORA), where it is carried out a registration of speed of environmental dose in continuous and simultaneous forms with the same moment of the measurement. This net allows to account with the meticulous and opportune information that will help to characterize, in dynamics form, the radiological conditions of diverse geographical zones of the country, including the sites that by normative require bigger surveillance, like its are the Laguna Verde Nuclear power station (CNLV), the Nuclear Center of Mexico (ININ) and the Radioactive waste storage center (CADER). This net is in its first development stage; three points inside the state of Veracruz, in the surroundings of the CNLV, already its are operating; the obtained data of rapidity of environmental dose are being stored in a database inside a primary data center located in the facilities of the CNSNS in Mexico city and its will be analyzed according to the project advances. At the moment, its are installing the first ten teams corresponding to the first phase of the RENAMORA (three stages); its are carried out operation tests, transmission, reception and administration of data. The obtained data will be interpreted, analyzed and inter compared to evaluate the risk levels to that it would be hold the population and to determine thresholds that allow to integrate the alarm systems that its had considered for emergency situations. (Author)

  20. Automatic script identification from images using cluster-based templates

    Energy Technology Data Exchange (ETDEWEB)

    Hochberg, J.; Kerns, L.; Kelly, P.; Thomas, T.

    1995-02-01

    We have developed a technique for automatically identifying the script used to generate a document that is stored electronically in bit image form. Our approach differs from previous work in that the distinctions among scripts are discovered by an automatic learning procedure, without any handson analysis. We first develop a set of representative symbols (templates) for each script in our database (Cyrillic, Roman, etc.). We do this by identifying all textual symbols in a set of training documents, scaling each symbol to a fixed size, clustering similar symbols, pruning minor clusters, and finding each cluster`s centroid. To identify a new document`s script, we identify and scale a subset of symbols from the document and compare them to the templates for each script. We choose the script whose templates provide the best match. Our current system distinguishes among the Armenian, Burmese, Chinese, Cyrillic, Ethiopic, Greek, Hebrew, Japanese, Korean, Roman, and Thai scripts with over 90% accuracy.

  1. Early automatic detection of Parkinson's disease based on sleep recordings

    DEFF Research Database (Denmark)

    Kempfner, Jacob; Sorensen, Helge B D; Nikolic, Miki;

    2014-01-01

    SUMMARY: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs...... the number of outliers during REM sleep was used as a quantitative measure of muscle activity. RESULTS: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder. CONCLUSION: The proposed work is considered...... during REM sleep. PURPOSE: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria. METHOD: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification...

  2. Automatic infarct planimetry by means of swarm-based clustering

    OpenAIRE

    Van Vuuren, Pieter A.; Van Vuuren, Derick

    2014-01-01

    Infarct planimetry is an important tool in cardiology research. At present this technique entails that infarct size is manually determined from scanned images of prepared heart sections. Existing attempts at automating infarct planimetry are limited in that they require user input in the form of starting points for region growing algorithms or template values for classification algorithms. In this paper a new automatic infarct planimetry (AIP) algorithm is presented. The ...

  3. A multi-algorithm-based automatic person identification system

    Science.gov (United States)

    Monwar, Md. Maruf; Gavrilova, Marina

    2010-04-01

    Multimodal biometric is an emerging area of research that aims at increasing the reliability of biometric systems through utilizing more than one biometric in decision-making process. In this work, we develop a multi-algorithm based multimodal biometric system utilizing face and ear features and rank and decision fusion approach. We use multilayer perceptron network and fisherimage approaches for individual face and ear recognition. After face and ear recognition, we integrate the results of the two face matchers using rank level fusion approach. We experiment with highest rank method, Borda count method, logistic regression method and Markov chain method of rank level fusion approach. Due to the better recognition performance we employ Markov chain approach to combine face decisions. Similarly, we get combined ear decision. These two decisions are combined for final identification decision. We try with 'AND'/'OR' rule, majority voting rule and weighted majority voting rule of decision fusion approach. From the experiment results, we observed that weighted majority voting rule works better than any other decision fusion approaches and hence, we incorporate this fusion approach for the final identification decision. The final results indicate that using multi algorithm based can certainly improve the recognition performance of multibiometric systems.

  4. Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach.

    Science.gov (United States)

    Ren, Xiang; El-Kishky, Ahmed; Wang, Chi; Han, Jiawei

    2015-08-01

    In today's computerized and information-based society, we are soaked with vast amounts of text data, ranging from news articles, scientific publications, product reviews, to a wide range of textual information from social media. To unlock the value of these unstructured text data from various domains, it is of great importance to gain an understanding of entities and their relationships. In this tutorial, we introduce data-driven methods to recognize typed entities of interest in massive, domain-specific text corpora. These methods can automatically identify token spans as entity mentions in documents and label their types (e.g., people, product, food) in a scalable way. We demonstrate on real datasets including news articles and tweets how these typed entities aid in knowledge discovery and management.

  5. A web based semi automatic frame work for astrobiological researches

    Directory of Open Access Journals (Sweden)

    P.V. Arun

    2013-12-01

    Full Text Available Astrobiology addresses the possibility of extraterrestrial life and explores measures towards its recognition. Researches in this context are founded upon the premise that indicators of life encountered in space will be recognizable. However, effective recognition can be accomplished through a universal adaptation of life signatures without restricting solely to those attributes that represent local solutions to the challenges of survival. The life indicators should be modelled with reference to temporal and environmental variations specific to each planet and time. In this paper, we investigate a semi-automatic open source frame work for the accurate detection and interpretation of life signatures by facilitating public participation, in a similar way as adopted by SETI@home project. The involvement of public in identifying patterns can bring a thrust to the mission and is implemented using semi-automatic framework. Different advanced intelligent methodologies may augment the integration of this human machine analysis. Automatic and manual evaluations along with dynamic learning strategy have been adopted to provide accurate results. The system also helps to provide a deep public understanding about space agency’s works and facilitate a mass involvement in the astrobiological studies. It will surely help to motivate young eager minds to pursue a career in this field.

  6. Automatic reconstruction of fault networks from seismicity catalogs including location uncertainty

    CERN Document Server

    Wang, Yaming; Woessner, Jochen; Sornette, Didier; Husen, Stephan

    2013-01-01

    We introduce the Anisotropic Clustering of Location Uncertainty Distributions (ACLUD) method to reconstruct active fault networks on the basis of both earthquake locations and their estimated individual uncertainties. After a massive search through the large solution space of possible reconstructed fault networks, we apply six different validation procedures in order to select the corresponding best fault network. Two of the validation steps (cross-validation and Bayesian Information Criterion (BIC) process the fit residuals, while the four others look for solutions that provide the best agreement with independently observed focal mechanisms. Tests on synthetic catalogs allow us to qualify the performance of the fitting method and of the various validation procedures. The ACLUD method is able to provide solutions that are close to the expected ones, especially for the BIC and focal mechanismbased techniques. The clustering method complemented by the validation step based on focal mechanisms provides good solu...

  7. System Identification and Automatic Mass Balancing of Ground-Based Three-Axis Spacecraft Simulator

    Science.gov (United States)

    2006-08-01

    System Identification and Automatic Mass Balancing of Ground-Based Three-Axis Spacecraft Simulator Jae-Jun Kim∗ and Brij N. Agrawal † Department of...TITLE AND SUBTITLE System Identification and Automatic Mass Balancing of Ground-Based Three-Axis Spacecraft Simulator 5a. CONTRACT NUMBER 5b...and Dynamics, Vol. 20, No. 4, July-August 1997, pp. 625-632. 6Schwartz, J. L. and Hall, C. D., “ System Identification of a Spherical Air-Bearing

  8. Neural Network based Consumption Forecasting

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    2016-01-01

    This paper describe a Neural Network based method for consumption forecasting. This work has been financed by the The ENCOURAGE project. The aims of The ENCOURAGE project is to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable...

  9. Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech.

    Science.gov (United States)

    Agarwalla, Swapna; Sarma, Kandarpa Kumar

    2016-06-01

    Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn class information obtained using clustering and manual labeling. DNNs are also used to extract specific sentence types. Initially, from a large storage, relevant samples are selected and assimilated. Next, a few conventional methods are used for feature extraction of a few selected types. The features comprise of both spectral and prosodic types. These are applied to Recurrent Neural Network (RNN) and Fully Focused Time Delay Neural Network (FFTDNN) structures to evaluate their performance in recognizing mood, dialect, speaker and gender variations in dialectal Assamese speech. The system is tested under several background noise conditions by considering the recognition rates (obtained using confusion matrices and manually) and computation time

  10. Self-organized topology of recurrence-based complex networks.

    Science.gov (United States)

    Yang, Hui; Liu, Gang

    2013-12-01

    With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., "what is the self-organizing geometry of a recurrence network?" and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

  11. Self-organized topology of recurrence-based complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Hui, E-mail: huiyang@usf.edu; Liu, Gang [Complex Systems Monitoring, Modeling and Analysis Laboratory, University of South Florida, Tampa, Florida 33620 (United States)

    2013-12-15

    With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks.

  12. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression

    Science.gov (United States)

    Mousavi, S. Mostafa; Horton, Stephen, P.; Langston, Charles A.; Samei, Borhan

    2016-07-01

    We develop an automated strategy for discriminating deep microseismic events from shallow ones on the basis of the waveforms recorded on a limited number of surface receivers. Machine-learning techniques are employed to explore the relationship between event hypocenters and seismic features of the recorded signals in time, frequency, and time-frequency domains. We applied the technique to 440 microearthquakes -1.7deep and shallow events based on the knowledge gained from existing patterns. The cross validation test showed that events with depth shallower than 250 m can be discriminated from events with hypocentral depth between 1000 to 2000 m with 88% and 90.7% accuracy using logistic regression (LR) and artificial neural network (ANN) models, respectively. Similar results were obtained using single station seismograms. The results show that the spectral features have the highest correlation to source depth. Spectral centroids and 2D cross-correlations in the time-frequency domain are two new seismic features used in this study that showed to be promising measures for seismic event classification. The used machine learning techniques have application for efficient automatic classification of low energy signals recorded at one or more seismic stations.

  13. Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression

    Science.gov (United States)

    Mousavi, S. Mostafa; Horton, Stephen P.; Langston, Charles A.; Samei, Borhan

    2016-10-01

    We develop an automated strategy for discriminating deep microseismic events from shallow ones on the basis of the waveforms recorded on a limited number of surface receivers. Machine-learning techniques are employed to explore the relationship between event hypocentres and seismic features of the recorded signals in time, frequency and time-frequency domains. We applied the technique to 440 microearthquakes -1.7 train the system to discriminate between deep and shallow events based on the knowledge gained from existing patterns. The cross-validation test showed that events with depth shallower than 250 m can be discriminated from events with hypocentral depth between 1000 and 2000 m with 88 per cent and 90.7 per cent accuracy using logistic regression and artificial neural network models, respectively. Similar results were obtained using single station seismograms. The results show that the spectral features have the highest correlation to source depth. Spectral centroids and 2-D cross-correlations in the time-frequency domain are two new seismic features used in this study that showed to be promising measures for seismic event classification. The used machine-learning techniques have application for efficient automatic classification of low energy signals recorded at one or more seismic stations.

  14. Automatic proximate analyzer of coal based on isothermal thermogravimetric analysis (TGA) with twin-furnace

    Energy Technology Data Exchange (ETDEWEB)

    Xiong, Youhui; Jiang, Taiyi; Zou, Xianhong [National Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, Hubei 430074 (China)

    2003-12-17

    A new type of rapid and automatic proximate analyzer for coal based on isothermal thermogravimetric analysis (TGA) with twin-furnace is introduced in this paper. This automatic proximate analyzer was developed by combination with some novel technologies, such as the automatic weighting method for multi-samples in a high temperature and dynamic gas flow circumstance, the self-protection system for the electric balance, and the optimal method and procedure for coal analysis process. Additionally, the comparison between standard values and the measurement values derived from the new instrument of standard coals was presented.

  15. MatchGUI: A Graphical MATLAB-Based Tool for Automatic Image Co-Registration

    Science.gov (United States)

    Ansar, Adnan I.

    2011-01-01

    MatchGUI software, based on MATLAB, automatically matches two images and displays the match result by superimposing one image on the other. A slider bar allows focus to shift between the two images. There are tools for zoom, auto-crop to overlap region, and basic image markup. Given a pair of ortho-rectified images (focused primarily on Mars orbital imagery for now), this software automatically co-registers the imagery so that corresponding image pixels are aligned. MatchGUI requires minimal user input, and performs a registration over scale and inplane rotation fully automatically

  16. Telephone Network Automatic Distributing Software System with GIS Technology%基于GIS的市话自动配线系统

    Institute of Scientific and Technical Information of China (English)

    李辉; 李小兵

    2001-01-01

    根据编码学原理,对配线资源进行了编码,设计了主要配线资源的编码格式,描述了主干电缆、配线电缆、配线箱盒的数据库结构,并给出了配线路由的逻辑表达式;提出了生成配区电子地图的方法,设计了自动配线系统的总体结构和软件/硬件平台,并采用GIS开发工具MAPINFO设计了基于电子地图的自动配线流程。%A new method based on GIS technology is introduced for telephone network distributing in this paper. According to coding principles, the network resources are coded to be processed in computer. The data structure and logic relationship of four main network resources are given. The hardware and software platform of whole automatic distributing system are designed in detail. In final, the automatic distributing flow on e-Map is generalized.

  17. Automatic Plating of Single-line Diagrams for Power Transmission Network Online Theoretical Line Loss Analysis Based on Ant Colony Algorithm%基于蚁群的在线理论线损分析用输电网单线图自动布局

    Institute of Scientific and Technical Information of China (English)

    卢志刚; 李学平

    2011-01-01

    输电网在线理论线损分析有时需要根据公共信息模型自动生成电网单线图,此时必须实现电网布局的自动生成;对于大电网,需要较短的求解时间。自动布局一般存在容易陷入局部最优解和求解时间长2种问题。文中将输电网单线图布局转化为二次分配问题,并且采用蚁群算法和3-opt优化,解决了以上问题。考虑可能的并行计算扩展,算法忽略各蚂蚁间的信息素更新,选择局部最优解和全局最优解更新信息素。仿真结果布局清晰,求解时间短,能够满足输电网在线理论线损分析要求。%To meet occasional needs for automatic generation of power network single-line diagrams from the common information model(CIM) in power transmission network online theoretical line loss analysis,it is necessary for the big power network to automatically generate its network layout and ask for rather short solving-time.In view of the problems with automatic plating,namely,local optimum and the long solving time,the plating of single-line diagrams for power transmission networks is transformed into a quadratic assignment problem using the ant colony algorithm with 3-opt optimization.By taking into consideration potential parallel computation,local optimum and global optimum are chosen to update the pheromone while ignoring pheromone updating between ants.Simulation results show that the requirement of the power transmission network online theoretical line loss analysis is met by clear layout and short solving time. This work is supported by National Natural Science Foundation of China(No.61071201) and Natural Science Foundation of Hebei Province(No.F2010001319).

  18. Automatic SIMD vectorization of SSA-based control flow graphs

    CERN Document Server

    Karrenberg, Ralf

    2015-01-01

    Ralf Karrenberg presents Whole-Function Vectorization (WFV), an approach that allows a compiler to automatically create code that exploits data-parallelism using SIMD instructions. Data-parallel applications such as particle simulations, stock option price estimation or video decoding require the same computations to be performed on huge amounts of data. Without WFV, one processor core executes a single instance of a data-parallel function. WFV transforms the function to execute multiple instances at once using SIMD instructions. The author describes an advanced WFV algorithm that includes a v

  19. Improved Support Vector Machine Approach Based on Determining Thresholds Automatically

    Institute of Scientific and Technical Information of China (English)

    WANG Xiao-hua; YAN Xue-mei; WANG Xiao-guang

    2007-01-01

    To improve the training speed of support vector machine (SVM), a method called improved center distance ratio method (ICDRM) with determining thresholds automatically is presented here without reduce the identification rate. In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance. The number of training samples is reduced greatly and the training speed is improved. This method is used to the identification for license plate characters. Experimental results show that the improved SVM method-ICDRM does well at identification rate and training speed.

  20. Pavement crack identification based on automatic threshold iterative method

    Science.gov (United States)

    Lu, Guofeng; Zhao, Qiancheng; Liao, Jianguo; He, Yongbiao

    2017-01-01

    Crack detection is an important issue in concrete infrastructure. Firstly, the accuracy of crack geometry parameters measurement is directly affected by the extraction accuracy, the same as the accuracy of the detection system. Due to the properties of unpredictability, randomness and irregularity, it is difficult to establish recognition model of crack. Secondly, various image noise, caused by irregular lighting conditions, dark spots, freckles and bump, exerts an influence on the crack detection accuracy. Peak threshold selection method is improved in this paper, and the processing of enhancement, smoothing and denoising is conducted before iterative threshold selection, which can complete the automatic selection of the threshold value in real time and stability.

  1. Implementation of a microcontroller-based semi-automatic coagulator.

    Science.gov (United States)

    Chan, K; Kirumira, A; Elkateeb, A

    2001-01-01

    The coagulator is an instrument used in hospitals to detect clot formation as a function of time. Generally, these coagulators are very expensive and therefore not affordable by a doctors' office and small clinics. The objective of this project is to design and implement a low cost semi-automatic coagulator (SAC) prototype. The SAC is capable of assaying up to 12 samples and can perform the following tests: prothrombin time (PT), activated partial thromboplastin time (APTT), and PT/APTT combination. The prototype has been tested successfully.

  2. Combining network and array waveform coherence for automatic location: examples from induced seismicity monitoring

    Science.gov (United States)

    Sick, Benjamin; Joswig, Manfred

    2017-03-01

    Events from induced seismicity suffer from low signal-to-noise ratios and noise spikes due to the industrial setting. Low magnitude thresholds are needed for traffic light warning systems. Conventional automatic location methods rely on independent picking of first arrivals from seismic wave onsets at recordings of single stations. Picking is done separately and without feedback from the actual location algorithm. If the recording network is small or only few phases can be associated, single wrong associations can lead to large errors in hypocentre locations and magnitude. Event location by source scanning which was established in the last two decades can provide more robust results. This study investigates how source-scanning can be extended and improved by integrating information from seismic arrays, that is, waveform stacking and Fisher ratio. These array methods rely on the coherency of the raw filtered waveforms while traditional source scanning uses a characteristic function to obtain coherency from otherwise incoherent waveforms between distant stations. Short-term/long-term average (STA/LTA) serves as the characteristic function and single station vertical-component traces for P-phases and radial and transverse components for S-phases are used. For array stations, the STA/LTA of the stacked vertical seismogram which is furthermore weighted by the STA/LTA of the Fisher ratio, dependent on backazimuth and slowness, is utilized for P-phases. The new method is tested on two diverse data sets from induced seismicity monitoring. In the chosen examples, the extension by array-processing techniques can reduce mean hypocentre errors up to a factor of 2.9, resolve ambiguities and further restrain the location.

  3. Expert Knowledge-Based Automatic Sleep Stage Determination by Multi-Valued Decision Making Method

    Science.gov (United States)

    Wang, Bei; Sugi, Takenao; Kawana, Fusae; Wang, Xingyu; Nakamura, Masatoshi

    In this study, an expert knowledge-based automatic sleep stage determination system working on a multi-valued decision making method is developed. Visual inspection by a qualified clinician is adopted to obtain the expert knowledge database. The expert knowledge database consists of probability density functions of parameters for various sleep stages. Sleep stages are determined automatically according to the conditional probability. Totally, four subjects were participated. The automatic sleep stage determination results showed close agreements with the visual inspection on sleep stages of awake, REM (rapid eye movement), light sleep and deep sleep. The constructed expert knowledge database reflects the distributions of characteristic parameters which can be adaptive to variable sleep data in hospitals. The developed automatic determination technique based on expert knowledge of visual inspection can be an assistant tool enabling further inspection of sleep disorder cases for clinical practice.

  4. Digital Image-Based Automatic Tracking Capability Project

    Data.gov (United States)

    National Aeronautics and Space Administration — OPTRA proposes to develop an Automated Optical Tracking Capability tailored to NASA's network of optical tracking stations at the Kennedy Space Center. This will be...

  5. SVM-based automatic diagnosis method for keratoconus

    Science.gov (United States)

    Gao, Yuhong; Wu, Qiang; Li, Jing; Sun, Jiande; Wan, Wenbo

    2017-06-01

    Keratoconus is a progressive cornea disease that can lead to serious myopia and astigmatism, or even to corneal transplantation, if it becomes worse. The early detection of keratoconus is extremely important to know and control its condition. In this paper, we propose an automatic diagnosis algorithm for keratoconus to discriminate the normal eyes and keratoconus ones. We select the parameters obtained by Oculyzer as the feature of cornea, which characterize the cornea both directly and indirectly. In our experiment, 289 normal cases and 128 keratoconus cases are divided into training and test sets respectively. Far better than other kernels, the linear kernel of SVM has sensitivity of 94.94% and specificity of 97.87% with all the parameters training in the model. In single parameter experiment of linear kernel, elevation with 92.03% sensitivity and 98.61% specificity and thickness with 97.28% sensitivity and 97.82% specificity showed their good classification abilities. Combining elevation and thickness of the cornea, the proposed method can reach 97.43% sensitivity and 99.19% specificity. The experiments demonstrate that the proposed automatic diagnosis method is feasible and reliable.

  6. Automatic age estimation based on facial aging patterns.

    Science.gov (United States)

    Geng, Xin; Zhou, Zhi-Hua; Smith-Miles, Kate

    2007-12-01

    While recognition of most facial variations, such as identity, expression and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual' s face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.

  7. Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields

    Directory of Open Access Journals (Sweden)

    Sheng-hui Liao

    2015-01-01

    Full Text Available An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.

  8. Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields.

    Science.gov (United States)

    Liao, Sheng-hui; Liu, Shi-jian; Zou, Bei-ji; Ding, Xi; Liang, Ye; Huang, Jun-hui

    2015-01-01

    An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.

  9. Why discourse structures in medical reports matter for the validity of automatically generated text knowledge bases.

    Science.gov (United States)

    Hahn, U; Romacker, M; Schulz, S

    1998-01-01

    The automatic analysis of medical full-texts currently suffers from neglecting text coherence phenomena such as reference relations between discourse units. This has unwarranted effects on the description adequacy of medical knowledge bases automatically generated from texts. The resulting representation bias can be characterized in terms of artificially fragmented, incomplete and invalid knowledge structures. We discuss three types of textual phenomena (pronominal and nominal anaphora, as well as textual ellipsis) and outline basic methodologies how to deal with them.

  10. Network fingerprint: a knowledge-based characterization of biomedical networks

    Science.gov (United States)

    Cui, Xiuliang; He, Haochen; He, Fuchu; Wang, Shengqi; Li, Fei; Bo, Xiaochen

    2015-01-01

    It can be difficult for biomedical researchers to understand complex molecular networks due to their unfamiliarity with the mathematical concepts employed. To represent molecular networks with clear meanings and familiar forms for biomedical researchers, we introduce a knowledge-based computational framework to decipher biomedical networks by making systematic comparisons to well-studied “basic networks”. A biomedical network is characterized as a spectrum-like vector called “network fingerprint”, which contains similarities to basic networks. This knowledge-based multidimensional characterization provides a more intuitive way to decipher molecular networks, especially for large-scale network comparisons and clustering analyses. As an example, we extracted network fingerprints of 44 disease networks in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The comparisons among the network fingerprints of disease networks revealed informative disease-disease and disease-signaling pathway associations, illustrating that the network fingerprinting framework will lead to new approaches for better understanding of biomedical networks. PMID:26307246

  11. FishCam - A semi-automatic video-based monitoring system of fish migration

    Science.gov (United States)

    Kratzert, Frederik; Mader, Helmut

    2016-04-01

    One of the main objectives of the Water Framework Directive is to preserve and restore the continuum of river networks. Regarding vertebrate migration, fish passes are widely used measure to overcome anthropogenic constructions. Functionality of this measure needs to be verified by monitoring. In this study we propose a newly developed monitoring system, named FishCam, to observe fish migration especially in fish passes without contact and without imposing stress on fish. To avoid time and cost consuming field work for fish pass monitoring, this project aims to develop a semi-automatic monitoring system that enables a continuous observation of fish migration. The system consists of a detection tunnel and a high resolution camera, which is mainly based on the technology of security cameras. If changes in the image, e.g. by migrating fish or drifting particles, are detected by a motion sensor, the camera system starts recording and continues until no further motion is detectable. An ongoing key challenge in this project is the development of robust software, which counts, measures and classifies the passing fish. To achieve this goal, many different computer vision tasks and classification steps have to be combined. Moving objects have to be detected and separated from the static part of the image, objects have to be tracked throughout the entire video and fish have to be separated from non-fish objects (e.g. foliage and woody debris, shadows and light reflections). Subsequently, the length of all detected fish needs to be determined and fish should be classified into species. The object classification in fish and non-fish objects is realized through ensembles of state-of-the-art classifiers on a single image per object. The choice of the best image for classification is implemented through a newly developed "fish benchmark" value. This value compares the actual shape of the object with a schematic model of side-specific fish. To enable an automatization of the

  12. Host Event Based Network Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Jonathan Chugg

    2013-01-01

    The purpose of INL’s research on this project is to demonstrate the feasibility of a host event based network monitoring tool and the effects on host performance. Current host based network monitoring tools work on polling which can miss activity if it occurs between polls. Instead of polling, a tool could be developed that makes use of event APIs in the operating system to receive asynchronous notifications of network activity. Analysis and logging of these events will allow the tool to construct the complete real-time and historical network configuration of the host while the tool is running. This research focused on three major operating systems commonly used by SCADA systems: Linux, WindowsXP, and Windows7. Windows 7 offers two paths that have minimal impact on the system and should be seriously considered. First is the new Windows Event Logging API, and, second, Windows 7 offers the ALE API within WFP. Any future work should focus on these methods.

  13. Automatic optimisation of gamma dose rate sensor networks: The DETECT Optimisation Tool

    DEFF Research Database (Denmark)

    Helle, K.B.; Müller, T.O.; Astrup, Poul;

    2014-01-01

    chosen using regular grids or according to administrative constraints. Nowadays, however, the choice can be based on more realistic risk assessment, as it is possible to simulate potential radioactive plumes. To support sensor planning, we developed the DETECT Optimisation Tool (DOT) within the scope...... monitoring network for early detection of radioactive plumes or for the creation of dose maps. The DOT is implemented as a stand-alone easy-to-use JAVA-based application with a graphical user interface and an R backend. Users can run evaluations and optimisations, and display, store and download the results...

  14. Multiquadric Spline-Based Interactive Segmentation of Vascular Networks.

    Science.gov (United States)

    Meena, Sachin; Surya Prasath, V B; Kassim, Yasmin M; Maude, Richard J; Glinskii, Olga V; Glinsky, Vladislav V; Huxley, Virginia H; Palaniappan, Kannappan

    2016-08-01

    Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches.

  15. Differential evolution algorithm based automatic generation control for interconnected power systems with

    Directory of Open Access Journals (Sweden)

    Banaja Mohanty

    2014-09-01

    Full Text Available This paper presents the design and performance analysis of Differential Evolution (DE algorithm based Proportional–Integral (PI and Proportional–Integral–Derivative (PID controllers for Automatic Generation Control (AGC of an interconnected power system. Initially, a two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. In the proposed approach, the design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions are used for the design purpose. The superiority of the proposed approach has been shown by comparing the results with a recently published Craziness based Particle Swarm Optimization (CPSO technique for the same interconnected power system. It is noticed that, the dynamic performance of DE optimized PI controller is better than CPSO optimized PI controllers. Additionally, controller parameters are tuned at different loading conditions so that an adaptive gain scheduling control strategy can be employed. The study is further extended to a more realistic network of two-area six unit system with different power generating units such as thermal, hydro, wind and diesel generating units considering boiler dynamics for thermal plants, Generation Rate Constraint (GRC and Governor Dead Band (GDB non-linearity.

  16. Spectral phase-based automatic calibration scheme for swept source-based optical coherence tomography systems

    Science.gov (United States)

    Ratheesh, K. M.; Seah, L. K.; Murukeshan, V. M.

    2016-11-01

    The automatic calibration in Fourier-domain optical coherence tomography (FD-OCT) systems allows for high resolution imaging with precise depth ranging functionality in many complex imaging scenarios, such as microsurgery. However, the accuracy and speed of the existing automatic schemes are limited due to the functional approximations and iterative operations used in their procedures. In this paper, we present a new real-time automatic calibration scheme for swept source-based optical coherence tomography (SS-OCT) systems. The proposed automatic calibration can be performed during scanning operation and does not require an auxiliary interferometer for calibration signal generation and an additional channel for its acquisition. The proposed method makes use of the spectral component corresponding to the sample surface reflection as the calibration signal. The spectral phase function representing the non-linear sweeping characteristic of the frequency-swept laser source is determined from the calibration signal. The phase linearization with improved accuracy is achieved by normalization and rescaling of the obtained phase function. The fractional-time indices corresponding to the equidistantly spaced phase intervals are estimated directly from the resampling function and are used to resample the OCT signals. The proposed approach allows for precise calibration irrespective of the path length variation induced by the non-planar topography of the sample or galvo scanning. The conceived idea was illustrated using an in-house-developed SS-OCT system by considering the specular reflection from a mirror and other test samples. It was shown that the proposed method provides high-performance calibration in terms of axial resolution and sensitivity without increasing computational and hardware complexity.

  17. CNEM: Cluster Based Network Evolution Model

    Directory of Open Access Journals (Sweden)

    Sarwat Nizamani

    2015-01-01

    Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks

  18. Model Considerations for Memory-based Automatic Music Transcription

    Science.gov (United States)

    Albrecht, Štěpán; Šmídl, Václav

    2009-12-01

    The problem of automatic music description is considered. The recorded music is modeled as a superposition of known sounds from a library weighted by unknown weights. Similar observation models are commonly used in statistics and machine learning. Many methods for estimation of the weights are available. These methods differ in the assumptions imposed on the weights. In Bayesian paradigm, these assumptions are typically expressed in the form of prior probability density function (pdf) on the weights. In this paper, commonly used assumptions about music signal are summarized and complemented by a new assumption. These assumptions are translated into pdfs and combined into a single prior density using combination of pdfs. Validity of the model is tested in simulation using synthetic data.

  19. Automatic code generation from the OMT-based dynamic model

    Energy Technology Data Exchange (ETDEWEB)

    Ali, J.; Tanaka, J.

    1996-12-31

    The OMT object-oriented software development methodology suggests creating three models of the system, i.e., object model, dynamic model and functional model. We have developed a system that automatically generates implementation code from the dynamic model. The system first represents the dynamic model as a table and then generates executable Java language code from it. We used inheritance for super-substate relationships. We considered that transitions relate to states in a state diagram exactly as operations relate to classes in an object diagram. In the generated code, each state in the state diagram becomes a class and each event on a state becomes an operation on the corresponding class. The system is implemented and can generate executable code for any state diagram. This makes the role of the dynamic model more significant and the job of designers even simpler.

  20. Building an Image-Based System to automatically Score psoriasis

    DEFF Research Database (Denmark)

    G{'o}mez, D. Delgado; Carstensen, Jens Michael; Ersbøll, Bjarne Kjær

    2003-01-01

    Nowadays the medical tracking of dermatological diseases is imprecise. The main reason is the lack of suitable objective methods to evaluate the lesion. The severity of the disease is scored by doctors just through their visual examination. In this work, a system to take accurate images...... of dermatological lesions has been developed. Mathematical methods can be applied to these images to obtain values that summarize the lesion and help to track its evolution. The system is composed of two elements. A precise image acquisition equipment and a statistical procedure to extract the lesions from...... the images. The system is tested on patients with the dermatological disease psoriasis. Temporal series of images are taken for each patient and the lesions are automatically extracted. Results indicate that to the images obtained are a good source for obtaining derived variables to track the lesion....

  1. Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

    Directory of Open Access Journals (Sweden)

    Chi Zhang

    2015-01-01

    Full Text Available Electroencephalogram (EEG is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA, wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

  2. Semi-Automatic Anatomical Tree Matching for Landmark-Based Elastic Registration of Liver Volumes

    Directory of Open Access Journals (Sweden)

    Klaus Drechsler

    2010-01-01

    Full Text Available One promising approach to register liver volume acquisitions is based on the branching points of the vessel trees as anatomical landmarks inherently available in the liver. Automated tree matching algorithms were proposed to automatically find pair-wise correspondences between two vessel trees. However, to the best of our knowledge, none of the existing automatic methods are completely error free. After a review of current literature and methodologies on the topic, we propose an efficient interaction method that can be employed to support tree matching algorithms with important pre-selected correspondences or after an automatic matching to manually correct wrongly matched nodes. We used this method in combination with a promising automatic tree matching algorithm also presented in this work. The proposed method was evaluated by 4 participants and a CT dataset that we used to derive multiple artificial datasets.

  3. An estimation-based automatic vehicle location system for public transport vehicles

    OpenAIRE

    Morenz, Tino; MEIER, RENE

    2008-01-01

    PUBLISHED Public transport vehicles often share a road network with other road users making their journeys susceptive to changing road conditions and especially to congestion. Travelers using such public transport increasingly depend on real-time information to plan their journeys. While such information can be provided by Automatic Vehicle Location (AVL) systems, AVLs depend heavily on large-scale deployment of designated sensory equipment, which may prevent their ...

  4. Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network

    Science.gov (United States)

    Vasuki, Perumal; Roomi, S. Mohamed Mansoor

    2013-01-01

    Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.

  5. Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

    Science.gov (United States)

    Jiménez del Toro, Oscar; Atzori, Manfredo; Otálora, Sebastian; Andersson, Mats; Eurén, Kristian; Hedlund, Martin; Rönnquist, Peter; Müller, Henning

    2017-03-01

    The Gleason grading system was developed for assessing prostate histopathology slides. It is correlated to the outcome and incidence of relapse in prostate cancer. Although this grading is part of a standard protocol performed by pathologists, visual inspection of whole slide images (WSIs) has an inherent subjectivity when evaluated by different pathologists. Computer aided pathology has been proposed to generate an objective and reproducible assessment that can help pathologists in their evaluation of new tissue samples. Deep convolutional neural networks are a promising approach for the automatic classification of histopathology images and can hierarchically learn subtle visual features from the data. However, a large number of manual annotations from pathologists are commonly required to obtain sufficient statistical generalization when training new models that can evaluate the daily generated large amounts of pathology data. A fully automatic approach that detects prostatectomy WSIs with high-grade Gleason score is proposed. We evaluate the performance of various deep learning architectures training them with patches extracted from automatically generated regions-of-interest rather than from manually segmented ones. Relevant parameters for training the deep learning model such as size and number of patches as well as the inclusion or not of data augmentation are compared between the tested deep learning architectures. 235 prostate tissue WSIs with their pathology report from the publicly available TCGA data set were used. An accuracy of 78% was obtained in a balanced set of 46 unseen test images with different Gleason grades in a 2-class decision: high vs. low Gleason grade. Grades 7-8, which represent the boundary decision of the proposed task, were particularly well classified. The method is scalable to larger data sets with straightforward re-training of the model to include data from multiple sources, scanners and acquisition techniques. Automatically

  6. Monitoring the urban heat island of Bucharest (Romania) through a network of automatic meteorological sensors - first results

    Science.gov (United States)

    Cheval, Sorin; Lucaschi, Bogdan; Ioja, Cristian; Dumitrescu, Alexandru; Manea, Ancuta; Radulescu, Adrian; Dumitrache, Catalin; Tudorache, George; Vanau, Gabriel; Onose, Diana

    2015-04-01

    Extreme warm temperatures and heat waves represent one of the major climate hazards which impact the city of Bucharest (Romania), favoured by the climate background and by the urban characteristics. Previous studies based either on sparse ground sensors or satellite remote sensing indicate that the average differences between the monthly temperature of the built area and the neighbouring rural buffers of Bucharest can reach 3-4°C, but instantaneous values are certainly higher. Since the city shelters about 2 million residents, as well as the major administrative and economic facilities of the country, the hazard management should receive a vivid attention. The meteorological monitoring of the city is currently performed in a systematic manner by the National Meteorological Administration (NMA) through 3 ground-based stations following the standards of the World Meteorological Organization, and through radar and satellite remote sensing. In 2014, NMA set up 7 automatic sensors in specific urban conditions, while the University of Bucharest deployed 30 mobile sensors in a joint effort for enhancing the accuracy of the urban heat island monitoring. Both sensor devices are designed for continuous monitoring (24/7). This presentation focuses on the technical characteristics of the recently implemented network (1), and brings to the public the first results of the monitoring (2), including the implementation experience, the observed benefits and plans for development and applications. The data obtained are compared with the existing data sets from meteorological stations and satellite products, and they are currently integrated in a common database, providing valuable information about the Bucharest's urban heat island. The results have been obtained within the project UCLIMESA (Urban Heat Island Monitoring under Present and Future Climate), ongoing between 2013 and 2015 in the framework of the Programme for Research-Development-Innovation for Space Technology and

  7. On Tree-Based Phylogenetic Networks.

    Science.gov (United States)

    Zhang, Louxin

    2016-07-01

    A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree-based networks. We present a simple necessary and sufficient condition for tree-based networks and prove that a universal tree-based network exists for any number of taxa that contains as its base every phylogenetic tree on the same set of taxa. This answers two problems posted by Francis and Steel recently. A byproduct is a computer program for generating random binary phylogenetic networks under the uniform distribution model.

  8. SAR Automatic Target Recognition Based on Numerical Scattering Simulation and Model-based Matching

    Directory of Open Access Journals (Sweden)

    Zhou Yu

    2015-12-01

    Full Text Available This study proposes a model-based Synthetic Aperture Radar (SAR automatic target recognition algorithm. Scattering is computed offline using the laboratory-developed Bidirectional Analytic Ray Tracing software and the same system parameter settings as the Moving and Stationary Target Acquisition and Recognition (MSTAR datasets. SAR images are then created by simulated electromagnetic scattering data. Shape features are extracted from the measured and simulated images, and then, matches are searched. The algorithm is verified using three types of targets from MSTAR data and simulated SAR images, and it is shown that the proposed approach is fast and easy to implement with high accuracy.

  9. Automatic approach to stabilization and control for multi robot teams by multilayer network operator

    Directory of Open Access Journals (Sweden)

    Diveev Askhat

    2016-01-01

    Full Text Available The paper describes a novel methodology for synthesis a high-level control of autonomous multi robot teams. The approach is based on multilayer network operator method that belongs to a symbolic regression class. Synthesis is accomplished in three steps: stabilizing robots about some given position in a state space, finding optimal trajectories of robots’ motion as sets of stabilizing points and then approximating all the points of optimal trajectories by some multi-dimensional function of state variables. The feasibility and effectiveness of the proposed approach is verified on simulations of the task of control synthesis for three mobile robots parking in the constrained space.

  10. An Efficient Method for Automatic Road Extraction Based on Multiple Features from LiDAR Data

    Science.gov (United States)

    Li, Y.; Hu, X.; Guan, H.; Liu, P.

    2016-06-01

    The road extraction in urban areas is difficult task due to the complicated patterns and many contextual objects. LiDAR data directly provides three dimensional (3D) points with less occlusions and smaller shadows. The elevation information and surface roughness are distinguishing features to separate roads. However, LiDAR data has some disadvantages are not beneficial to object extraction, such as the irregular distribution of point clouds and lack of clear edges of roads. For these problems, this paper proposes an automatic road centerlines extraction method which has three major steps: (1) road center point detection based on multiple feature spatial clustering for separating road points from ground points, (2) local principal component analysis with least squares fitting for extracting the primitives of road centerlines, and (3) hierarchical grouping for connecting primitives into complete roads network. Compared with MTH (consist of Mean shift algorithm, Tensor voting, and Hough transform) proposed in our previous article, this method greatly reduced the computational cost. To evaluate the proposed method, the Vaihingen data set, a benchmark testing data provided by ISPRS for "Urban Classification and 3D Building Reconstruction" project, was selected. The experimental results show that our method achieve the same performance by less time in road extraction using LiDAR data.

  11. Location-Based Services in Vehicular Networks

    Science.gov (United States)

    Wu, Di

    2013-01-01

    Location-based services have been identified as a promising communication paradigm in highly mobile and dynamic vehicular networks. However, existing mobile ad hoc networking cannot be directly applied to vehicular networking due to differences in traffic conditions, mobility models and network topologies. On the other hand, hybrid architectures…

  12. Sensitivity Based Segmentation and Identification in Automatic Speech Recognition.

    Science.gov (United States)

    1984-03-30

    by a network constructed from phonemic, phonetic , and phonological rules. Regardless of the speech processing system used, Klatt 1 2 has described...analysis, and its use in the segmentation and identification of the phonetic units of speech, that was initiated during the 1982 Summer Faculty Research...practicable framework for incorporation of acoustic- phonetic variance as well as time and talker normalization. XOI iF- ? ’:: .:- .- . . l ] 2 D

  13. Automatic Trading Agent. RMT based Portfolio Theory and Portfolio Selection

    CERN Document Server

    Snarska, M; Snarska, Malgorzata; Krzych, Jakub

    2006-01-01

    Portfolio theory is a very powerful tool in the modern investment theory. It is helpful in estimating risk of an investor's portfolio, which arises from our lack of information, uncertainty and incomplete knowledge of reality, which forbids a perfect prediction of future price changes. Despite of many advantages this tool is not known and is not widely used among investors on Warsaw Stock Exchange. The main reason for abandoning this method is a high level of complexity and immense calculations. The aim of this paper is to introduce an automatic decision - making system, which allows a single investor to use such complex methods of Modern Portfolio Theory (MPT). The key tool in MPT is an analysis of an empirical covariance matrix. This matrix, obtained from historical data is biased by such a high amount of statistical uncertainty, that it can be seen as random. By bringing into practice the ideas of Random Matrix Theory (RMT), the noise is removed or significantly reduced, so the future risk and return are b...

  14. Image automatic mosaics based on contour phase correlation

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jing; HU Zhiping; LIU Zhitai; OU Zongying

    2007-01-01

    The image planar mosaics is studied,and an image automatic mosaics algorithm on the basis of contour phase correlation is proposed in this paper.To begin with,by taking into account mere translations and rotations between images,a contour phase correlation algorithm is used to realize the preliminary alignments of images,and the initial projective transformation matrices are obtained.Then,an optimization algorithm is used to optimize the initial projective transformation matrices,and complete the precise image mosaics.The contour phase correlation is an improvement on the conventional phase correlation in two aspects:First,the contours of images are extracted,and the phase correlation is applied to the contours of images instead of the whole original images;Second,when there are multiple peak values approximate to the maximum peak value in the δ function array,their corresponding translations can be regarded as candidate translations and calculated separately,and the best translation can be determined by the optimization of conformability of two images in the overlapping area.The running results show that the proposed algorithm can consistently yield high-quality mosaics,even in the cases of poor or differential lighting conditions,existences of minor rotations,and other complicated displacements between images.

  15. Using a CLIPS expert system to automatically manage TCP/IP networks and their components

    Science.gov (United States)

    Faul, Ben M.

    1991-01-01

    A expert system that can directly manage networks components on a Transmission Control Protocol/Internet Protocol (TCP/IP) network is described. Previous expert systems for managing networks have focused on managing network faults after they occur. However, this proactive expert system can monitor and control network components in near real time. The ability to directly manage network elements from the C Language Integrated Production System (CLIPS) is accomplished by the integration of the Simple Network Management Protocol (SNMP) and a Abstract Syntax Notation (ASN) parser into the CLIPS artificial intelligence language.

  16. Automatic classification of sleep stages based on the time-frequency image of EEG signals.

    Science.gov (United States)

    Bajaj, Varun; Pachori, Ram Bilas

    2013-12-01

    In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals.

  17. Automatic Optimization of Focal Point Position in CO2 Laser Welding with Neural Network in A Focus Control System

    DEFF Research Database (Denmark)

    Gong, Hui; Olsen, Flemming Ove

    CO2 lasers are increasingly being utilized for quality welding in production. Considering the high cost of equipment, the start-up time and the set-up time should be minimized. Ideally the parameters should be set up and optimized more or less automatically. In this paper a control system......-learning mechanism - neural network as the essence of the control system is trained with the photo diode signals extracted from various welding processes with the changes on the laser power, translation speed, material and thickness of the plate, shielding gas type and flow rate, and welding configuration...

  18. Towards Automatic Extraction of Social Networks of Organizations in PubMed Abstracts

    CERN Document Server

    Jonnalagadda, Siddhartha; Gonzalez, Graciela

    2010-01-01

    Social Network Analysis (SNA) of organizations can attract great interest from government agencies and scientists for its ability to boost translational research and accelerate the process of converting research to care. For SNA of a particular disease area, we need to identify the key research groups in that area by mining the affiliation information from PubMed. This not only involves recognizing the organization names in the affiliation string, but also resolving ambiguities to identify the article with a unique organization. We present here a process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. We demonstrate the application of the method by analyzing organizations involved in angiogenensis treatment, and demonstrating the utility of the results for researchers in the pharmaceutical and biotechnology industries or national funding agencies.

  19. 农业机器人轨迹优化自动控制研究-基于 BP 神经网络与计算力矩%Automatic Control of Trajectory Optimization for Agricultural Robot-Based on BP Neural Network and Computational Torque

    Institute of Scientific and Technical Information of China (English)

    袁铸; 申一歌

    2017-01-01

    In the trajectory optimization of precision agriculture robot , taking automatic control as the goal , it introduced the optimization algorithm combined with BP neural network and the computed torque method of automatic controller , which intended to reduce motion errors during the work and improve the work efficiency .In this paper , it first established mathematical model of agricultural robot , kinematics and dynamics analysis;then, it designed the agricultural robot mo-tion control system by using BP neural network to uncertain dynamics factors to judge , and put forward the solution to the factor of adaptive learning method .Finally the system used MATLAB simulation .Experimental result shows that the com-bined with BP neural network and the computed torque method of automatic controller , which can effectively optimize the robot motion path , and improve the overall operation efficiency of the robot , the system is stable and reliable , and the ex-ternal environment interference factors with strong adaptive ability to learn .%以农业机器人精密轨迹优化自动控制为目标,在优化算法中引入BP神经网络与计算力矩法结合的自动控制器,旨在减少作业过程中的运动误差,提高其工作效率。首先,建立农业机器人数学模型,分析其运动学和动力学原理;然后,设计了农业机器人运动控制系统,引入BP 神经网络对不确定动力学因素进行判断,并提出解决该因素的自适应学习法;最后,对该系统运用 MatLab 进行了仿真。试验表明:以 BP 神经网络与计算力矩法结合的自动控制器可以有效优化机器人运动路径,提高机器人整体作业效率,系统运行稳定、可靠性强,且对外部环境的干扰因素具有较强的自适应学习能力。

  20. Automatic target validation based on neuroscientific literature mining for tractography

    Directory of Open Access Journals (Sweden)

    Xavier eVasques

    2015-05-01

    Full Text Available Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human. We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision, meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

  1. Router Agent Technology for Policy-Based Network Management

    Science.gov (United States)

    Chow, Edward T.; Sudhir, Gurusham; Chang, Hsin-Ping; James, Mark; Liu, Yih-Chiao J.; Chiang, Winston

    2011-01-01

    This innovation can be run as a standalone network application on any computer in a networked environment. This design can be configured to control one or more routers (one instance per router), and can also be configured to listen to a policy server over the network to receive new policies based on the policy- based network management technology. The Router Agent Technology transforms the received policies into suitable Access Control List syntax for the routers it is configured to control. It commits the newly generated access control lists to the routers and provides feedback regarding any errors that were faced. The innovation also automatically generates a time-stamped log file regarding all updates to the router it is configured to control. This technology, once installed on a local network computer and started, is autonomous because it has the capability to keep listening to new policies from the policy server, transforming those policies to router-compliant access lists, and committing those access lists to a specified interface on the specified router on the network with any error feedback regarding commitment process. The stand-alone application is named RouterAgent and is currently realized as a fully functional (version 1) implementation for the Windows operating system and for CISCO routers.

  2. A Method for Modeling the Virtual Instrument Automatic Test System Based on the Petri Net

    Institute of Scientific and Technical Information of China (English)

    MA Min; CHEN Guang-ju

    2005-01-01

    Virtual instrument is playing the important role in automatic test system. This paper introduces a composition of a virtual instrument automatic test system and takes the VXIbus based a test software platform which is developed by CAT lab of the UESTC as an example. Then a method to model this system based on Petri net is proposed. Through this method, we can analyze the test task scheduling to prevent the deadlock or resources conflict. At last, this paper analyzes the feasibility of this method.

  3. Automatic Compilation from High-Level Biologically-Oriented Programming Language to Genetic Regulatory Networks

    Science.gov (United States)

    Beal, Jacob; Lu, Ting; Weiss, Ron

    2011-01-01

    Background The field of synthetic biology promises to revolutionize our ability to engineer biological systems, providing important benefits for a variety of applications. Recent advances in DNA synthesis and automated DNA assembly technologies suggest that it is now possible to construct synthetic systems of significant complexity. However, while a variety of novel genetic devices and small engineered gene networks have been successfully demonstrated, the regulatory complexity of synthetic systems that have been reported recently has somewhat plateaued due to a variety of factors, including the complexity of biology itself and the lag in our ability to design and optimize sophisticated biological circuitry. Methodology/Principal Findings To address the gap between DNA synthesis and circuit design capabilities, we present a platform that enables synthetic biologists to express desired behavior using a convenient high-level biologically-oriented programming language, Proto. The high level specification is compiled, using a regulatory motif based mechanism, to a gene network, optimized, and then converted to a computational simulation for numerical verification. Through several example programs we illustrate the automated process of biological system design with our platform, and show that our compiler optimizations can yield significant reductions in the number of genes () and latency of the optimized engineered gene networks. Conclusions/Significance Our platform provides a convenient and accessible tool for the automated design of sophisticated synthetic biological systems, bridging an important gap between DNA synthesis and circuit design capabilities. Our platform is user-friendly and features biologically relevant compiler optimizations, providing an important foundation for the development of sophisticated biological systems. PMID:21850228

  4. Network based automation for SMEs

    DEFF Research Database (Denmark)

    Shahabeddini Parizi, Mohammad; Radziwon, Agnieszka

    2017-01-01

    could be obtained through network interaction. Based on two extreme cases of SMEs representing low-tech industry and an in-depth analysis of their manufacturing facilities this paper presents how collaboration between firms embedded in a regional ecosystem could result in implementation of new...... automation solutions. The empirical data collection involved application of a combination of comparative case study method with action research elements. This article provides an outlook over the challenges in implementing technological improvements and the way how it could be resolved in collaboration......, this paper develops and discusses a set of guidelines for systematic productivity improvement within an innovative collaboration in regards to automation processes in SMEs....

  5. A Language-Based Approach for Improving the Robustness of Network Application Protocol Implementations

    CERN Document Server

    Laurent, Burgy; Lawall, Julia; Muller, Gilles

    2007-01-01

    The secure and robust functioning of a network relies on the defect-free implementation of network applications. As network protocols have become increasingly complex, however, hand-writing network message processing code has become increasingly error-prone. In this paper, we present a domain-specific language, Zebu, for describing protocol message formats and related processing constraints. From a Zebu specification, a compiler automatically generates stubs to be used by an application to parse network messages. Zebu is easy to use, as it builds on notations used in RFCs to describe protocol grammars. Zebu is also efficient, as the memory usage is tailored to application needs and message fragments can be specified to be processed on demand. Finally, Zebu-based applications are robust, as the Zebu compiler automatically checks specification consistency and generates parsing stubs that include validation of the message structure. Using a mutation analysis in the context of SIP and RTSP, we show that Zebu sign...

  6. A network approach based on cliques

    Science.gov (United States)

    Fadigas, I. S.; Pereira, H. B. B.

    2013-05-01

    The characterization of complex networks is a procedure that is currently found in several research studies. Nevertheless, few studies present a discussion on networks in which the basic element is a clique. In this paper, we propose an approach based on a network of cliques. This approach consists not only of a set of new indices to capture the properties of a network of cliques but also of a method to characterize complex networks of cliques (i.e., some of the parameters are proposed to characterize the small-world phenomenon in networks of cliques). The results obtained are consistent with results from classical methods used to characterize complex networks.

  7. Automatic Quality Measurement and Parameter Selection for Example-based Texture Synthesis

    DEFF Research Database (Denmark)

    Laursen, Lasse Farnung; Clemmensen, Line Katrine Harder; Bærentzen, Jakob Andreas

    cover research to directly estimate specific texture synthesis parameters, such as patch size and iteration convergence, based on input textures. We also examine various similarity measures and evaluate their effectiveness. The goal for each measure is to properly evaluate how well the resulting...... far, automatically selecting parameters suitable for synthesis has been a relatively unexplored topic. In effect, this makes texture synthesis supervised rather than fully automatic. In this technical paper, we propose automatic parameter optimization methods for example based texture synthesis. We...... synthesis compares to the original input. A good similarity measure will enable the search for the optimal texture synthesis parameters by maximizing the quality of the synthesis as a function of parameters. We apply presented methods to a state of the art texture synthesis algorithm, namely the one...

  8. Automatic Generation Control Strategy Based on Balance of Daily Electric Energy

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    An automatic generation control strategy based on balance of daily total electric energy is put forward. It makes the balance between actual total generated energy controlled by automatic generation system and planned total energy on base of area control error, and makes the actual 24-hour active power load curve to approach the planned load curve. The generated energy is corrected by velocity weighting factor so that it conducts dynamic regulation and reaches the speed of response. Homologous strategy is used according to the real-time data in the operation of automatic generation control. Results of simulation are perfect and power energy compensation control with ideal effect can be achieved in the particular duration.

  9. Hierarchical Model-Based Activity Recognition With Automatic Low-Level State Discovery

    Directory of Open Access Journals (Sweden)

    Justin Muncaster

    2007-09-01

    Full Text Available Activity recognition in video streams is increasingly important for both the computer vision and artificial intelligence communities. Activity recognition has many applications in security and video surveillance. Ultimately in such applications one wishes to recognize complex activities, which can be viewed as combination of simple activities. In this paper, we present a general framework of a Dlevel dynamic Bayesian network to perform complex activity recognition. The levels of the network are constrained to enforce state hierarchy while the Dth level models the duration of simplest event. Moreover, in this paper we propose to use the deterministic annealing clustering method to automatically define the simple activities, which corresponds to the low level states of observable levels in a Dynamic Bayesian Networks. We used real data sets for experiments. The experimental results show the effectiveness of our proposed method.

  10. Neural Network Based Parking via Google Map Guidance

    Directory of Open Access Journals (Sweden)

    A.Saranya

    2015-02-01

    Full Text Available Intelligent transportation systems (ITS focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.

  11. A Network Coding Based Routing Protocol for Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xin Guan

    2012-04-01

    Full Text Available Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs. Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR.We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime.

  12. Sensor-based navigation of a mobile robot using automatically constructed fuzzy rules

    Energy Technology Data Exchange (ETDEWEB)

    Watanabe, Y.; Pin, F.G.

    1993-10-01

    A system for automatic generation of fuzzy rules is proposed which is based on a new approach, called ``Fuzzy Behaviorist,`` and on its associated formalism for rule base development in behavior-based robot control systems. The automated generator of fuzzy rules automatically constructs the set of rules and the associated membership functions that implement reasoning schemes that have been expressed in qualitative terms. The system also checks for completeness of the rule base and independence and/or redundancy of the rules to ensure that the requirements of the formalism are satisfied. Examples of the automatic generation of fuzzy rules for cases involving suppression and/or inhibition of fuzzy behaviors are given and discussed. Experimental results obtained with the automated fuzzy rule generator applied to the domain of sensor-based navigation in a priori unknown environments using one of our autonomous test-bed robots are then presented and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using our proposed ``Fuzzy Behaviorist`` approach.

  13. Automatic generation of fuzzy rules for the sensor-based navigation of a mobile robot

    Energy Technology Data Exchange (ETDEWEB)

    Pin, F.G.; Watanabe, Y.

    1994-10-01

    A system for automatic generation of fuzzy rules is proposed which is based on a new approach, called {open_quotes}Fuzzy Behaviorist,{close_quotes} and on its associated formalism for rule base development in behavior-based robot control systems. The automated generator of fuzzy rules automatically constructs the set of rules and the associated membership functions that implement reasoning schemes that have been expressed in qualitative terms. The system also checks for completeness of the rule base and independence and/or redundancy of the rules to ensure that the requirements of the formalism are satisfied. Examples of the automatic generation of fuzzy rules for cases involving suppression and/or inhibition of fuzzy behaviors are given and discussed. Experimental results obtained with the automated fuzzy rule generator applied to the domain of sensor-based navigation in a priori unknown environments using one of our autonomous test-bed robots are then presented and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using our proposed {open_quotes}Fuzzy Behaviorist{close_quotes} approach.

  14. Network Traffic Prediction based on Particle Swarm BP Neural Network

    Directory of Open Access Journals (Sweden)

    Yan Zhu

    2013-11-01

    Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.

  15. Towards automatic model based controller design for reconfigurable plants

    DEFF Research Database (Denmark)

    Michelsen, Axel Gottlieb; Stoustrup, Jakob; Izadi-Zamanabadi, Roozbeh

    2008-01-01

    This paper introduces model-based Plug and Play Process Control, a novel concept for process control, which allows a model-based control system to be reconfigured when a sensor or an actuator is plugged into a controlled process. The work reported in this paper focuses on composing a monolithic m...

  16. Knowledge-based segmentation for automatic Map interpretation

    NARCIS (Netherlands)

    Hartog, J. den; Kate, T. ten; Gerbrands, J.

    1996-01-01

    In this paper, a knowledge-based framework for the top-down interpretation and segmentation of maps is presented. The interpretation is based on a priori knowledge about map objects, their mutual spatial relationships and potential segmentation problems. To reduce computational costs, a global segme

  17. Towards From Manual to Automatic Semantic Annotation: Based on Ontology Elements and Relationships

    Directory of Open Access Journals (Sweden)

    Alaa Qasim Mohammed Salih

    2013-05-01

    Full Text Available Automatic annotation is offering a standards base for retrieving information from web services. It hasbeen observed that many existing protocol e.g. Annotea did not support the fully automatic annotationdirectly or the process to be carried out needs professional developers (i.e. non-trivial protocol such asKIM.In this paper a description of the architecture of the proposed system is given and a figurative structure issupplied. The diagrams that represent the structure will be described along with the main resources usage.

  18. Design and Implementation of FAQ Automatic Return System Based on Similarity Computation

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    FAQ (frequently asked question) is widely used on the Internet, but most FAQ's asking and answering are not automatic. This paper introduces the design and implementation of a FAQ automatic return system based on semantic similarity computation, including computation model choosing, FAQ characters analyzing, FAQ data formal expressing, feature vector indexing, and weight computing and so on. According to FAQ features of sentence length short, two mapping,strong domain characteristics etc. Vector Space Model with special semantic process was selected in system, and corresponding algorithm of similarity computation was proposed too. Experiment shows that the system has a good performance for high frequent and common questions.

  19. Formal Specification and Automatic Analysis of Business Processes under Authorization Constraints: An Action-Based Approach

    Science.gov (United States)

    Armando, Alessandro; Giunchiglia, Enrico; Ponta, Serena Elisa

    We present an approach to the formal specification and automatic analysis of business processes under authorization constraints based on the action language \\cal{C}. The use of \\cal{C} allows for a natural and concise modeling of the business process and the associated security policy and for the automatic analysis of the resulting specification by using the Causal Calculator (CCALC). Our approach improves upon previous work by greatly simplifying the specification step while retaining the ability to perform a fully automatic analysis. To illustrate the effectiveness of the approach we describe its application to a version of a business process taken from the banking domain and use CCALC to determine resource allocation plans complying with the security policy.

  20. Analysis and Design of PLC-based Control System for Automatic Beverage Filling Machine

    Directory of Open Access Journals (Sweden)

    Yundan Lu

    2015-01-01

    Full Text Available Automatic filling system is the main equipment in the food machinery industry. With the development of beverage industry and increasing demand of the filling system. The relay control method in traditional Filling machine has low automation and integration level and cannot satisfy the rapid development of automatic production. PLC control method has advantages of simple programming, strong anti-interference and high working reliability, has gradually replace the relay control method. In this study, hardware and software for the automatic filling system based on PLC control is designed, especially the injection section servo control system which adopts the servo motor driver metering pump is carefully analyzed and the filling precision is highly improved.

  1. Automatic Detection of Steel Ball's Surface Flaws Based on Image Processing

    Institute of Scientific and Technical Information of China (English)

    YU Zheng-lin; TAN Wei; YANG Dong-lin; CAO Guo-hua

    2007-01-01

    A new method to detect steel ball's surface flaws is presented based on computer techniques of image processing and pattern recognition. The steel ball's surface flaws is the primary factor causing bearing failure. The high efficient and precision detections for the surface flaws of steel ball can be conducted by the presented method, including spot, abrasion, burn, scratch and crack, etc. The design of main components of the detecting system is described in detail including automatic feeding mechanism, automatic spreading mechanism of steel ball's surface, optical system of microscope, image acquisition system, image processing system. The whole automatic system is controlled by an industrial control computer, which can carry out the recognition of flaws of steel ball's surface effectively.

  2. Automatic textual annotation of video news based on semantic visual object extraction

    Science.gov (United States)

    Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

    2003-12-01

    In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

  3. Automatic Model-Based Generation of Parameterized Test Cases Using Data Abstraction

    NARCIS (Netherlands)

    Calamé, Jens R.; Ioustinova, Natalia; Pol, van de Jaco; Romijn, J.M.T.; Smith, G.; Pol, van de J.C.

    2007-01-01

    Developing test suites is a costly and error-prone process. Model-based test generation tools facilitate this process by automatically generating test cases from system models. The applicability of these tools, however, depends on the size of the target systems. Here, we propose an approach to gener

  4. Automatic detection of suspicious behavior of pickpockets with track-based features in a shopping mall

    NARCIS (Netherlands)

    Bouma, H.; Baan, J.; Burghouts, G.J.; Eendebak, P.T.; Huis, J.R. van; Dijk, J.; Rest, J.H.C. van

    2014-01-01

    Proactive detection of incidents is required to decrease the cost of security incidents. This paper focusses on the automatic early detection of suspicious behavior of pickpockets with track-based features in a crowded shopping mall. Our method consists of several steps: pedestrian tracking, feature

  5. Evaluating Automatic Speech Recognition-Based Language Learning Systems: A Case Study

    Science.gov (United States)

    van Doremalen, Joost; Boves, Lou; Colpaert, Jozef; Cucchiarini, Catia; Strik, Helmer

    2016-01-01

    The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to…

  6. A Model-Based Method for Content Validation of Automatically Generated Test Items

    Science.gov (United States)

    Zhang, Xinxin; Gierl, Mark

    2016-01-01

    The purpose of this study is to describe a methodology to recover the item model used to generate multiple-choice test items with a novel graph theory approach. Beginning with the generated test items and working backward to recover the original item model provides a model-based method for validating the content used to automatically generate test…

  7. Evaluating Automatic Speech Recognition-Based Language Learning Systems: A Case Study

    Science.gov (United States)

    van Doremalen, Joost; Boves, Lou; Colpaert, Jozef; Cucchiarini, Catia; Strik, Helmer

    2016-01-01

    The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to…

  8. ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Chaudhari

    2011-06-01

    Full Text Available A Network Management System (NMS plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors & maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring & maintaining networks like improving QoS & saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware. We propose a Decision Management System (DMS framework which uses a machine learning technique called Bayesian Belief Networks (BBN, to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.

  9. Automatic hearing loss detection system based on auditory brainstem response

    Energy Technology Data Exchange (ETDEWEB)

    Aldonate, J; Mercuri, C; Reta, J; Biurrun, J; Bonell, C; Gentiletti, G; Escobar, S; Acevedo, R [Laboratorio de Ingenieria en Rehabilitacion e Investigaciones Neuromusculares y Sensoriales (Argentina); Facultad de Ingenieria, Universidad Nacional de Entre Rios, Ruta 11 - Km 10, Oro Verde, Entre Rios (Argentina)

    2007-11-15

    Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time, it can affect the nervous system and cause problems in speech, language and cognitive development. The recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory brainstem response (ABR). In this work, the design and implementation of an automated system based on ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory.

  10. Automatic hearing loss detection system based on auditory brainstem response

    Science.gov (United States)

    Aldonate, J.; Mercuri, C.; Reta, J.; Biurrun, J.; Bonell, C.; Gentiletti, G.; Escobar, S.; Acevedo, R.

    2007-11-01

    Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time, it can affect the nervous system and cause problems in speech, language and cognitive development. The recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory brainstem response (ABR). In this work, the design and implementation of an automated system based on ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory.

  11. Global Location-Based Access to Web Applications Using Atom-Based Automatic Update

    Science.gov (United States)

    Singh, Kulwinder; Park, Dong-Won

    We propose an architecture which enables people to enquire about information available in directory services by voice using regular phones. We implement a Virtual User Agent (VUA) which mediates between the human user and a business directory service. The system enables the user to search for the nearest clinic, gas station by price, motel by price, food / coffee, banks/ATM etc. and fix an appointment, or automatically establish a call between the user and the business party if the user prefers. The user also has an option to receive appointment confirmation by phone, SMS, or e-mail. The VUA is accessible by a toll free DID (Direct Inward Dialing) number using a phone by anyone, anywhere, anytime. We use the Euclidean formula for distance measurement. Since, shorter geodesic distances (on the Earth’s surface) correspond to shorter Euclidean distances (measured by a straight line through the Earth). Our proposed architecture uses Atom XML syndication format protocol for data integration, VoiceXML for creating the voice user interface (VUI) and CCXML for controlling the call components. We also provide an efficient algorithm for parsing Atom feeds which provide data to the system. Moreover, we describe a cost-effective way for providing global access to the VUA based on Asterisk (an open source IP-PBX). We also provide some information on how our system can be integrated with GPS for locating the user coordinates and therefore efficiently and spontaneously enhancing the system response. Additionally, the system has a mechanism for validating the phone numbers in its database, and it updates the number and other information such as daily price of gas, motel etc. automatically using an Atom-based feed. Currently, the commercial directory services (Example 411) do not have facilities to update the listing in the database automatically, so that why callers most of the times get out-of-date phone numbers or other information. Our system can be integrated very easily

  12. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    Science.gov (United States)

    Liu, Fei; Zhang, Shao-Wu; Guo, Wei-Feng; Wei, Ze-Gang; Chen, Luonan

    2016-08-01

    The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce

  13. A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks.

    Science.gov (United States)

    Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

    2016-01-01

    Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay.

  14. A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hao Wang

    2016-09-01

    Full Text Available Underwater Acoustic Sensor Networks (UASNs have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay.

  15. A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks

    Science.gov (United States)

    Wang, Hao; Wang, Shilian; Zhang, Eryang; Zou, Jianbin

    2016-01-01

    Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay. PMID:27618044

  16. The research of automatic speed control algorithm based on Green CBTC

    Science.gov (United States)

    Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

    2017-06-01

    Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

  17. Competition Based Neural Networks for Assignment Problems

    Institute of Scientific and Technical Information of China (English)

    李涛; LuyuanFang

    1991-01-01

    Competition based neural networks have been used to solve the generalized assignment problem and the quadratic assignment problem.Both problems are very difficult and are ε approximation complete.The neural network approach has yielded highly competitive performance and good performance for the quadratic assignment problem.These neural networks are guaranteed to produce feasible solutions.

  18. Durer-pentagon-based complex network

    Directory of Open Access Journals (Sweden)

    Rui Hou

    2016-04-01

    Full Text Available A novel Durer-pentagon-based complex network was constructed by adding a centre node. The properties of the complex network including the average degree, clustering coefficient, average path length, and fractal dimension were determined. The proposed complex network is small-world and fractal.

  19. Invariant-Based Automatic Testing of Modern Web Applications

    NARCIS (Netherlands)

    Mesbah, A.; Van Deursen, A.; Roest, D.

    2011-01-01

    AJAX-based Web 2.0 applications rely on stateful asynchronous client/server communication, and client-side run-time manipulation of the DOM tree. This not only makes them fundamentally different from traditional web applications, but also more error-prone and harder to test. We propose a method for

  20. Automatic Video-based Analysis of Human Motion

    DEFF Research Database (Denmark)

    Fihl, Preben

    received great interest from both industry and research communities. The focus of this thesis is on video-based analysis of human motion and the thesis presents work within three overall topics, namely foreground segmentation, action recognition, and human pose estimation. Foreground segmentation is often...... foreground camouflage, shadows, and moving backgrounds. The method continuously updates the background model to maintain high quality segmentation over long periods of time. Within action recognition the thesis presents work on both recognition of arm gestures and gait types. A key-frame based approach...... range of gait which deals with an inherent ambiguity of gait types. Human pose estimation does not target a specific action but is considered as a good basis for the recognition of any action. The pose estimation work presented in this thesis is mainly concerned with the problems of interacting people...

  1. Automatic Adjustment of Wide-Base Google Street View Panoramas

    Science.gov (United States)

    Boussias-Alexakis, E.; Tsironisa, V.; Petsa, E.; Karras, G.

    2016-06-01

    This paper focuses on the issue of sparse matching in cases of extremely wide-base panoramic images such as those acquired by Google Street View in narrow urban streets. In order to effectively use affine point operators for bundle adjustment, panoramas must be suitably rectified to simulate affinity. To this end, a custom piecewise planar projection (triangular prism projection) is applied. On the assumption that the image baselines run parallel to the street façades, the estimated locations of the vanishing lines of the façade plane allow effectively removing projectivity and applying the ASIFT point operator on panorama pairs. Results from comparisons with multi-panorama adjustment, based on manually measured image points, and ground truth indicate that such an approach, if further elaborated, may well provide a realistic answer to the matching problem in the case of demanding panorama configurations.

  2. SABATPG-A Structural Analysis Based Automatic Test Generation System

    Institute of Scientific and Technical Information of China (English)

    李忠诚; 潘榆奇; 闵应骅

    1994-01-01

    A TPG system, SABATPG, is given based on a generic structural model of large circuits. Three techniques of partial implication, aftereffect of identified undetectable faults and shared sensitization with new concepts of localization and aftereffect are employed in the system to improve FAN algorithm. Experiments for the 10 ISCAS benchmark circuits show that the computing time of SABATPG for test generation is 19.42% less than that of FAN algorithm.

  3. FLOOD ROUTING BASED ON NETWORK CODING (NCF)

    OpenAIRE

    HOSSEIN BALOOCHIAN; MOZAFAR BAGMOHAMMADI

    2010-01-01

    Most of the energy in a sensor network is used for transmission of data packets. For this reason, optimization of energy consumption is of utmost importance in these networks. This paper presents NCF, a flood routing protocol based on network coding. Simulations show that in addition to eliminating the drawbacks of traditional flooding methods, like the explosion phenomenon, NCF increases the lifetime of the network by at least 20% and decreases the number of packet transmissions. Another adv...

  4. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  5. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box.

    Science.gov (United States)

    Ciompi, Francesco; de Hoop, Bartjan; van Riel, Sarah J; Chung, Kaman; Scholten, Ernst Th; Oudkerk, Matthijs; de Jong, Pim A; Prokop, Mathias; van Ginneken, Bram

    2015-12-01

    In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts.

  6. [A medical image semantic modeling based on hierarchical Bayesian networks].

    Science.gov (United States)

    Lin, Chunyi; Ma, Lihong; Yin, Junxun; Chen, Jianyu

    2009-04-01

    A semantic modeling approach for medical image semantic retrieval based on hierarchical Bayesian networks was proposed, in allusion to characters of medical images. It used GMM (Gaussian mixture models) to map low-level image features into object semantics with probabilities, then it captured high-level semantics through fusing these object semantics using a Bayesian network, so that it built a multi-layer medical image semantic model, aiming to enable automatic image annotation and semantic retrieval by using various keywords at different semantic levels. As for the validity of this method, we have built a multi-level semantic model from a small set of astrocytoma MRI (magnetic resonance imaging) samples, in order to extract semantics of astrocytoma in malignant degree. Experiment results show that this is a superior approach.

  7. Automatic reconstruction of molecular and genetic networks from discrete time series data.

    Science.gov (United States)

    Durzinsky, Markus; Wagler, Annegret; Weismantel, Robert; Marwan, Wolfgang

    2008-09-01

    We apply a mathematical algorithm which processes discrete time series data to generate a complete list of Petri net structures containing the minimal number of nodes required to reproduce the data set. The completeness of the list as guaranteed by a mathematical proof allows to define a minimal set of experiments required to discriminate between alternative network structures. This in principle allows to prove all possible minimal network structures by disproving all alternative candidate structures. The dynamic behaviour of the networks in terms of a switching rule for the transitions of the Petri net is part of the result. In addition to network reconstruction, the algorithm can be used to determine how many yet undetected components at least must be involved in a certain process. The algorithm also reveals all alternative structural modifications of a network that are required to generate a predefined behaviour.

  8. Development of a digital signal processor-based new 12-lead synchronization electrocardiogram automatic analysis system.

    Science.gov (United States)

    Yang, Yuxing; Yin, Dongyuan; Freyer, Richard

    2002-07-01

    This paper presents a digital signal processor (DSP)-based new multichannel electrocardiogram (ECG) system for 12-lead synchronization ECG automatic analysis in real-time with high sampling rate at 1000 Hz and 12-bits precision. Using the hardware structure of double-CPU based on Microprocessor (MPU) 89C55 and DSP TMS320F206 combines the powerful control ability of MPU with DSPs fast computation ability. Fully utilizing the double-CPUs resource, the system can distribute the reasonable CPU-time for the real-time tasks of multichannel synchronization ECG sampling, digital filter, data storing, waveform automatic analysis and print at high sampling rate. The digital ECG system has the advantages of simple structure, sampling with high speed and precision, powerful real-time processing ability and good quality. The paper discusses the system's principle and the skilful hardware design, also gives the ECG processing using the fast simple integer-coefficient filter method and the automatic calculation algorithms of the ECG parameters such as heart rate, P-R interval, Q-T interval and deflexion angle of ECG-axis etc. The system had been successfully tested and used in the ECG automatic analysis instrument.

  9. An improved, SSH-based method to automatically identify mesoscale eddies in the ocean

    Institute of Scientific and Technical Information of China (English)

    WANG Xin; DU Yun-yan; ZHOU Cheng-hu; FAN Xing; YI Jia-wei

    2013-01-01

      Mesoscale eddies are an important component of oceanic features. How to automatically identify these mesoscale eddies from available data has become an important research topic. Through careful examination of existing methods, we propose an improved, SSH-based automatic identification method. Using the inclusion relation of enclosed SSH contours, the mesoscale eddy boundary and core(s) can be automatically identified. The time evolution of eddies can be examined by a threshold search algorithm and a tracking algorithm based on similarity. Sea-surface height (SSH) data from Naval Research Laboratory Layered Ocean Model (NLOM) and sea-level anomaly (SLA) data from altimeter are used in the many experiments, in which different automatic identification methods are compared. Our results indicate that the improved method is able to extract the mesoscale eddy boundary more precisely, retaining the multiple-core structure. In combination with the tracking algorithm, this method can capture complete mesoscale eddy processes. It can thus provide reliable information for further study of reconstructing eddy dynamics, merging, splitting, and evolution of a multi-core structure.

  10. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  11. Uav Visual Autolocalizaton Based on Automatic Landmark Recognition

    Science.gov (United States)

    Silva Filho, P.; Shiguemori, E. H.; Saotome, O.

    2017-08-01

    Deploying an autonomous unmanned aerial vehicle in GPS-denied areas is a highly discussed problem in the scientific community. There are several approaches being developed, but the main strategies yet considered are computer vision based navigation systems. This work presents a new real-time computer-vision position estimator for UAV navigation. The estimator uses images captured during flight to recognize specific, well-known, landmarks in order to estimate the latitude and longitude of the aircraft. The method was tested in a simulated environment, using a dataset of real aerial images obtained in previous flights, with synchronized images, GPS and IMU data. The estimated position in each landmark recognition was compatible with the GPS data, stating that the developed method can be used as an alternative navigation system.

  12. Automatic key frame selection using a wavelet-based approach

    Science.gov (United States)

    Campisi, Patrizio; Longari, Andrea; Neri, Alessandro

    1999-10-01

    In a multimedia framework, digital image sequences (videos) are by far the most demanding as far as storage, search, browsing and retrieval requirements are concerned. In order to reduce the computational burden associated to video browsing and retrieval, a video sequence is usually decomposed into several scenes (shots) and each of them is characterized by means of some key frames. The proper selection of these key frames, i.e. the most representative frames in the scene, is of paramount importance for computational efficiency. In this contribution a novel key frame extraction technique based on the wavelet analysis is presented. Experimental results show the capability of the proposed algorithm to select key frames properly summarizing the shot.

  13. RELAY ALGORITHM BASED ON NETWORK CODING IN WIRELESS LOCAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Wang Qi; Wang Qingshan; Wang Dongxue

    2013-01-01

    The network coding is a new technology in the field of information in 21st century.It could enhance the network throughput and save the energy consumption,and is mainly based on the single transmission rate.However,with the development of wireless network and equipment,wireless local network MAC protocols have already supported the multi-rate transmission.This paper investigates the optimal relay selection problem based on network coding.Firstly,the problem is formulated as an optimization problem.Moreover,a relay algorithm based on network coding is proposed and the transmission time gain of our algorithm over the traditional relay algorithm is analyzed.Lastly,we compare total transmission time and the energy consumption of our proposed algorithm,Network Coding with Relay Assistance (NCRA),Transmission Request (TR),and the Direct Transmission (DT) without relay algorithm by adopting IEEE 802.11b.The simulation results demonstrate that our algorithm that improves the coding opportunity by the cooperation of the relay nodes leads to the transmission time decrease of up to 17% over the traditional relay algorithms.

  14. Honeypot based Secure Network System

    Directory of Open Access Journals (Sweden)

    Yogendra Kumar Jain

    2011-02-01

    Full Text Available A honeypot is a non-production system, design to interact with cyber-attackers to collect intelligence on attack techniques and behaviors. There has been great amount of work done in the field of networkintrusion detection over the past three decades. With networks getting faster and with the increasing dependence on the Internet both at the personal and commercial level, intrusion detection becomes a challenging process. The challenge here is not only to be able to actively monitor large numbers of systems, but also to be able to react quickly to different events. Before deploying a honeypot it is advisable to have a clear idea of what the honeypot should and should not do. There should be clear understandingof the operating systems to be used and services (like a web server, ftp server etc a honeypot will run. The risks involved should be taken into consideration and methods to tackle or reduce these risks should be understood. It is also advisable to have a plan on what to do should the honeypot be compromised. In case of production honeypots, a honeypot policy addressing security issues should be documented. Any legal issues with respect to the honeypots or their functioning should also be taken into consideration. In this paper we explain the relatively new concept of “honeypot.” Honeypots are a computer specifically designed to help learn the motives, skills and techniques of the hacker community and also describes in depth the concepts of honeypots and their contribution to the field of network security. The paper then proposes and designs an intrusion detection tool based on some of the existing intrusion detection techniques and the concept of honeypots.

  15. Network Medicine: A Network-based Approach to Human Diseases

    Science.gov (United States)

    Ghiassian, Susan Dina

    With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics. Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology. Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the

  16. Distribution network planning algorithm based on Hopfield neural network

    Institute of Scientific and Technical Information of China (English)

    GAO Wei-xin; LUO Xian-jue

    2005-01-01

    This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn't need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.

  17. Towards an Automatic and Application-Based EigensolverSelection

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Yeliang; Li, Xiaoye S.; Marques, Osni

    2005-09-09

    The computation of eigenvalues and eigenvectors is an important and often time-consuming phase in computer simulations. Recent efforts in the development of eigensolver libraries have given users good algorithms without the need for users to spend much time in programming. Yet, given the variety of numerical algorithms that are available to domain scientists, choosing the ''best'' algorithm suited for a particular application is a daunting task. As simulations become increasingly sophisticated and larger, it becomes infeasible for a user to try out every reasonable algorithm configuration in a timely fashion. Therefore, there is a need for an intelligent engine that can guide the user through the maze of various solvers with various configurations. In this paper, we present a methodology and a software architecture aiming at determining the best solver based on the application type and the matrix properties. We combine a decision tree and an intelligent engine to select a solver and a preconditioner combination for the application submitted by the user. We also discuss how our system interface is implemented with third party numerical libraries. In the case study, we demonstrate the feasibility and usefulness of our system with a simplified linear solving system. Our experiments show that our proposed intelligent engine is quite adept in choosing a suitable algorithm for different applications.

  18. Automatic software fault localization based on ar tificial bee colony

    Institute of Scientific and Technical Information of China (English)

    Linzhi Huang∗; Jun Ai

    2015-01-01

    Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help au-tomate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony (ABC) algorithm is proposed to be integrated with other related techniques. In this process, the source program is initial y instru-mented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iter-ative process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.

  19. A random network based, node attraction facilitated network evolution method

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-03-01

    Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.

  20. 3-D Storm Automatic Identification Based on Mathematical Morphology

    Institute of Scientific and Technical Information of China (English)

    HAN Lei; ZHENG Yongguang; WANG Hongqing; LIN Yinjing

    2009-01-01

    The strom identification, tracking, and forecasting method is one of the important nowcasting techniques. Accurate storm identification is a prerequisite for successful storm tracking and forecasting. Storm identi-fication faces two difficulties: one is false merger and the other is failure to isolate adjacent storms within a cluster of storms. The TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) algo-rithm is apt to identify adjacent storm cells as one storm because it uses a single refiectivity threshold. The SCIT (Storm Cell Identification and Tracking) algorithm uses seven reflectivity thresholds and therefore is capable of isolating adjacent storm cells, but it discards the results identified by the lower threshold, leading to the loss of the internal structure information of storms. Both TITAN and SCIT have the problem of failing to satisfactorily identify false merger. To overcome these shortcomings, this paper proposes a novel approach based on mathematical morphology. The approach first applies the single threshold identification followed by implementing an erosion process to mitigate the false merger problem. During multi-threshold identification stages, dilation operation is performed against the storm cells which are just obtained by the higher threshold identification, until the storm edges touch each other or touch the edges of the previous storms identified by the lower threshold. The results of experiment show that by combining the strengths of the dilation and erosion operations, this approach is able to mitigate the false merger problem as well as maintain the internal structure of sub-storms when isolating storms within a cluster of storms.

  1. Community Based Networks and 5G

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2016-01-01

    The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument...... is hinged on a research aimed at understanding how and why Community Based Networks deploy telecom and Broadband infrastructure. The study was a qualitative study carried out inductively using Grounded Theory. Six cases were investigated.Two Community Based Network Mobilization models were identified....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

  2. Automatic detection of the intima-media thickness in ultrasound images of the common carotid artery using neural networks.

    Science.gov (United States)

    Menchón-Lara, Rosa-María; Bastida-Jumilla, María-Consuelo; Morales-Sánchez, Juan; Sancho-Gómez, José-Luis

    2014-02-01

    Atherosclerosis is the leading underlying pathologic process that results in cardiovascular diseases, which represents the main cause of death and disability in the world. The atherosclerotic process is a complex degenerative condition mainly affecting the medium- and large-size arteries, which begins in childhood and may remain unnoticed during decades. The intima-media thickness (IMT) of the common carotid artery (CCA) has emerged as one of the most powerful tool for the evaluation of preclinical atherosclerosis. IMT is measured by means of B-mode ultrasound images, which is a non-invasive and relatively low-cost technique. This paper proposes an effective image segmentation method for the IMT measurement in an automatic way. With this purpose, segmentation is posed as a pattern recognition problem, and a combination of artificial neural networks has been trained to solve this task. In particular, multi-layer perceptrons trained under the scaled conjugate gradient algorithm have been used. The suggested approach is tested on a set of 60 longitudinal ultrasound images of the CCA by comparing the automatic segmentation with four manual tracings. Moreover, the intra- and inter-observer errors have also been assessed. Despite of the simplicity of our approach, several quantitative statistical evaluations have shown its accuracy and robustness.

  3. A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis.

    Science.gov (United States)

    Sánchez, Clara I; Hornero, Roberto; López, María I; Aboy, Mateo; Poza, Jesús; Abásolo, Daniel

    2008-04-01

    We present an automatic image processing algorithm to detect hard exudates. Automatic detection of hard exudates from retinal images is an important problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Fisher's linear discriminant analysis and makes use of colour information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 58 retinal images with variable colour, brightness, and quality. Our proposed algorithm obtained a sensitivity of 88% with a mean number of 4.83+/-4.64 false positives per image using the lesion-based performance evaluation criterion, and achieved an image-based classification accuracy of 100% (sensitivity of 100% and specificity of 100%).

  4. An Exercise in Invariant-based Programming with Interactive and Automatic Theorem Prover Support

    CERN Document Server

    Back, Ralph-Johan; 10.4204/EPTCS.79.2

    2012-01-01

    Invariant-Based Programming (IBP) is a diagram-based correct-by-construction programming methodology in which the program is structured around the invariants, which are additionally formulated before the actual code. Socos is a program construction and verification environment built specifically to support IBP. The front-end to Socos is a graphical diagram editor, allowing the programmer to construct invariant-based programs and check their correctness. The back-end component of Socos, the program checker, computes the verification conditions of the program and tries to prove them automatically. It uses the theorem prover PVS and the SMT solver Yices to discharge as many of the verification conditions as possible without user interaction. In this paper, we first describe the Socos environment from a user and systems level perspective; we then exemplify the IBP workflow by building a verified implementation of heapsort in Socos. The case study highlights the role of both automatic and interactive theorem provi...

  5. Automatic Publication of a MIS Product to GeoNetwork: Case of the AIS Indexer

    Science.gov (United States)

    2012-11-01

    problem, then the MEF file is available for manual import to GeoNetwork. The output of this operation is the absolute path of the created MEF file. 2.3...computation. Since the AIS reception index application is implemented in Java, a Java interface was required to launch the GNP. The Publisher constructor ...product on GeoNetwork. This would reduce the overhead associated to the production of metadata at each manual publication. The following sections

  6. An automatic synthesis method of compact models of integrated circuit devices based on equivalent circuits

    Science.gov (United States)

    Abramov, I. I.

    2006-05-01

    An automatic synthesis method of equivalent circuits of integrated circuit devices is described in the paper. This method is based on a physical approach to construction of finite-difference approximation to basic equations of semiconductor device physics. It allows to synthesize compact equivalent circuits of different devices automatically as alternative to, for example, sufficiently formal BSIM2 and BSIM3 models used in circuit simulation programs of SPICE type. The method is one of possible variants of general methodology for automatic synthesis of compact equivalent circuits of almost arbitrary devices and circuit-type structures of micro- and nanoelecronics [1]. The method is easily extended in the case of necessity to account thermal effects in integrated circuits. It was shown that its application would be especially perspective for analysis of integrated circuit fragments as a whole and for identification of significant collective physical effects, including parasitic effects in VLSI and ULSI. In the paper the examples illustrating possibilities of the method for automatic synthesis of compact equivalent circuits of some of semiconductor devices and integrated circuit devices are considered. Special attention is given to examples of integrated circuit devices for coarse grids of spatial discretization (less than 10 nodes).

  7. Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach.

    Science.gov (United States)

    Song, Jiangdian; Yang, Caiyun; Fan, Li; Wang, Kun; Yang, Feng; Liu, Shiyuan; Tian, Jie

    2016-01-01

    The accurate segmentation of lung lesions from computed tomography (CT) scans is important for lung cancer research and can offer valuable information for clinical diagnosis and treatment. However, it is challenging to achieve a fully automatic lesion detection and segmentation with acceptable accuracy due to the heterogeneity of lung lesions. Here, we propose a novel toboggan based growing automatic segmentation approach (TBGA) with a three-step framework, which are automatic initial seed point selection, multi-constraints 3D lesion extraction and the final lesion refinement. The new approach does not require any human interaction or training dataset for lesion detection, yet it can provide a high lesion detection sensitivity (96.35%) and a comparable segmentation accuracy with manual segmentation (P > 0.05), which was proved by a series assessments using the LIDC-IDRI dataset (850 lesions) and in-house clinical dataset (121 lesions). We also compared TBGA with commonly used level set and skeleton graph cut methods, respectively. The results indicated a significant improvement of segmentation accuracy . Furthermore, the average time consumption for one lesion segmentation was under 8 s using our new method. In conclusion, we believe that the novel TBGA can achieve robust, efficient and accurate lung lesion segmentation in CT images automatically.

  8. 基于边界的Markov网的发现%Learning Markov Network Based on the Boundary

    Institute of Scientific and Technical Information of China (English)

    何盈捷; 刘惟一

    2001-01-01

    Markov network ts an another powerful tool besides Bayesian network which can be used to do uncertain inference. A method of learning Markov network automaticly from mass data based on boundary has been discussed in this paper. Taking advantage of an important conclusion in information theory ,we present an efficient boundary based Markov network learning algorithm. This algorithm only demands O(N2) times CI (conditional independence) test. We prove if the joint probability is strictly positive,then the found Markov network must be the minimal I_map of the sample.

  9. Strategic Sensor Placement for Intrusion Detection in Network-Based IDS

    Directory of Open Access Journals (Sweden)

    Longe Olumide Babatope

    2014-01-01

    Full Text Available Network Intrusion Detection Systems (NIDSs can be composed of a potentially large number of sensors, which monitor the traffic flowing in the network. Deciding where sensors should be placed and what information they need in order to detect the desired attacks can be a demanding task for network administrators, one that should be made as automatic as possible. Some few works have been done on positioning sensors using attack graph analysis, formal logic-based approach and Network Simulator NS2 which were studied to determine a strategy for sensors placement on the network. This paper analysed the major considerations for sensors placements, typical sensors deployments in NIDS, and established an extended model for sensors deployment to further strengthen the network for intrusion detection which was based on the escape of some malicious activities through the firewall.

  10. Automatic Verification of Biochemical Network Using Model Checking Method%基于模型校核的生化网络自动辨别方法

    Institute of Scientific and Technical Information of China (English)

    Jinkyung Kim; Younghee Lee; Il Moon

    2008-01-01

    This study focuses on automatic searching and verifying methods for the reachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic search and verification for alternative paths within complex and large networks in biological process can provide a consid-erable amount of solutions, which is difficult to handle manually. Model checking is an automatic method for veri-fying if a circuit or a condition, expressed as a concurrent transition system, satisfies a set of properties expressed ina temporal logic, such as computational tree logic (CTL). This article represents that model checking is feasible in biochemical network verification and it shows certain advantages over simulation for querying and searching of special behavioral properties in biochemical processes.

  11. Optical OFDM-based Data Center Networks

    Directory of Open Access Journals (Sweden)

    Christoforos Kachris

    2013-07-01

    Full Text Available Cloud computing and web emerging application has created the need for more powerful data centers with high performance interconnection networks.Current data center networks,based on electronic packet switches,will not be able to satisfy the required communication bandwidth of emerging applications without consuming excessive power.Optical interconnercts have gained attention recently as a promising solution offering high throughput,low latency and reduced energy cosumption compared to current networks based in commidity switches.This paper presents a novel architecture for data center networks based on optical OFDM using Wavelength Selective Swithces(WSS. The OFDM-based solution provides high throughput,reduced latency and fine grain bandwidth allocation. A heuristic algorithm for the bandwidth allocation is presented and evaluated in terms of utilization. The power analysis shows that the proposed scheme is almost 60% more energy efficient compared to the current networks based on eommodity switches.

  12. Automatic Clustering of Flow Cytometry Data with Density-Based Merging

    Directory of Open Access Journals (Sweden)

    Guenther Walther

    2009-01-01

    made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.

  13. Automatic Hardware Implementation Tool for a Discrete Adaboost-Based Decision Algorithm

    Directory of Open Access Journals (Sweden)

    J. Dubois

    2005-05-01

    Full Text Available We propose a method and a tool for automatic generation of hardware implementation of a decision rule based on the Adaboost algorithm. We review the principles of the classification method and we evaluate its hardware implementation cost in terms of FPGA's slice, using different weak classifiers based on the general concept of hyperrectangle. The main novelty of our approach is that the tool allows the user to find automatically an appropriate tradeoff between classification performances and hardware implementation cost, and that the generated architecture is optimized for each training process. We present results obtained using Gaussian distributions and examples from UCI databases. Finally, we present an example of industrial application of real-time textured image segmentation.

  14. Automatic face detection and tracking based on Adaboost with camshift algorithm

    Science.gov (United States)

    Lin, Hui; Long, JianFeng

    2011-10-01

    With the development of information technology, video surveillance is widely used in security monitoring and identity recognition. For most of pure face tracking algorithms are hard to specify the initial location and scale of face automatically, this paper proposes a fast and robust method to detect and track face by combining adaboost with camshift algorithm. At first, the location and scale of face is specified by adaboost algorithm based on Haar-like features and it will be conveyed to the initial search window automatically. Then, we apply camshift algorithm to track face. The experimental results based on OpenCV software yield good results, even in some special circumstances, such as light changing and face rapid movement. Besides, by drawing out the tracking trajectory of face movement, some abnormal behavior events can be analyzed.

  15. Speedup for quantum optimal control from automatic differentiation based on graphics processing units

    Science.gov (United States)

    Leung, Nelson; Abdelhafez, Mohamed; Koch, Jens; Schuster, David

    2017-04-01

    We implement a quantum optimal control algorithm based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them in the optimization process with ease. We show that the use of GPUs can speedup calculations by more than an order of magnitude. Our strategy facilitates efficient numerical simulations on affordable desktop computers and exploration of a host of optimization constraints and system parameters relevant to real-life experiments. We demonstrate optimization of quantum evolution based on fine-grained evaluation of performance at each intermediate time step, thus enabling more intricate control on the evolution path, suppression of departures from the truncated model subspace, as well as minimization of the physical time needed to perform high-fidelity state preparation and unitary gates.

  16. Automatic diet monitoring: a review of computer vision and wearable sensor-based methods.

    Science.gov (United States)

    Hassannejad, Hamid; Matrella, Guido; Ciampolini, Paolo; De Munari, Ilaria; Mordonini, Monica; Cagnoni, Stefano

    2017-01-31

    Food intake and eating habits have a significant impact on people's health. Widespread diseases, such as diabetes and obesity, are directly related to eating habits. Therefore, monitoring diet can be a substantial base for developing methods and services to promote healthy lifestyle and improve personal and national health economy. Studies have demonstrated that manual reporting of food intake is inaccurate and often impractical. Thus, several methods have been proposed to automate the process. This article reviews the most relevant and recent researches on automatic diet monitoring, discussing their strengths and weaknesses. In particular, the article reviews two approaches to this problem, accounting for most of the work in the area. The first approach is based on image analysis and aims at extracting information about food content automatically from food images. The second one relies on wearable sensors and has the detection of eating behaviours as its main goal.

  17. The Modelling Of Basing Holes Machining Of Automatically Replaceable Cubical Units For Reconfigurable Manufacturing Systems With Low-Waste Production

    Science.gov (United States)

    Bobrovskij, N. M.; Levashkin, D. G.; Bobrovskij, I. N.; Melnikov, P. A.; Lukyanov, A. A.

    2017-01-01

    Article is devoted the decision of basing holes machining accuracy problems of automatically replaceable cubical units (carriers) for reconfigurable manufacturing systems with low-waste production (RMS). Results of automatically replaceable units basing holes machining modeling on the basis of the dimensional chains analysis are presented. Influence of machining parameters processing on accuracy spacings on centers between basing apertures is shown. The mathematical model of carriers basing holes machining accuracy is offered.

  18. Automatic Recognition of Chinese Personal Name Using Conditional Random Fields and Knowledge Base

    Directory of Open Access Journals (Sweden)

    Chuan Gu

    2015-01-01

    Full Text Available According to the features of Chinese personal name, we present an approach for Chinese personal name recognition based on conditional random fields (CRF and knowledge base in this paper. The method builds multiple features of CRF model by adopting Chinese character as processing unit, selects useful features based on selection algorithm of knowledge base and incremental feature template, and finally implements the automatic recognition of Chinese personal name from Chinese document. The experimental results on open real corpus demonstrated the effectiveness of our method and obtained high accuracy rate and high recall rate of recognition.

  19. Robustness in semantic networks based on cliques

    Science.gov (United States)

    Grilo, M.; Fadigas, I. S.; Miranda, J. G. V.; Cunha, M. V.; Monteiro, R. L. S.; Pereira, H. B. B.

    2017-04-01

    Here, we present a study on how the structure of semantic networks based on cliques (specifically, article titles) behaves when vertex removal strategies (i.e., random and uniform vertex removal - RUR, highest degree vertex removal - HDR, and highest intermediation centrality vertex removal - HICR) are applied to this type of network. We propose a method for calculation of the average size of the small components and we identify the existence of a fraction (fp) where the topological structure of the network changes. Semantic networks based on cliques maintain the small-world phenomenon when subjected to RUR, HDR and HICR for fractions of removed vertices less than or equal to fp.

  20. Community Based Networks and 5G

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2016-01-01

    The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

  1. Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based on Fuzzy State Description

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A new framework of knowledge representation of fuzzy language field and fuzzy language value structure is shown. Then the generalized cell automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model are put forward. On this basis, the new logic indeterminacy causal inductive automatic reasoning mechanism which is based on fuzzy state description is presented. At the end of this paper its application in the development of intelligent controller is discussed.

  2. Automatic Carbon Dioxide-Methane Gas Sensor Based on the Solubility of Gases in Water

    OpenAIRE

    Cadena-Pereda, Raúl O.; Anaya-Rivera, Ely K.; Gilberto Herrera-Ruiz; Eric M. Rivera-Muñoz; Gomez-Melendez, Domingo J.

    2012-01-01

    Biogas methane content is a relevant variable in anaerobic digestion processing where knowledge of process kinetics or an early indicator of digester failure is needed. The contribution of this work is the development of a novel, simple and low cost automatic carbon dioxide-methane gas sensor based on the solubility of gases in water as the precursor of a sensor for biogas quality monitoring. The device described in this work was used for determining the composition of binary mixtures, such a...

  3. An Automatic Evaluation Method for Conversational Agents Based on Affect-as-Information Theory

    OpenAIRE

    Ptaszynski, Michal; Dybala, Pawel; Rzepka, Rafal; Araki, Kenji

    2010-01-01

    This paper presents a method for automatic evaluation of conversational agents. The method consists of several steps. First, an affect analysis system is used to detect users' general emotional engagement in the conversation and classify their specific emotional states. Next, we interpret this data with the use of reasoning based on Affect-as-Information Theory to obtain information about users' general attitudes to the conversational agent and its performance. The affect analysis system was ...

  4. Advances in Fully-Automatic and Interactive Phrase-Based Statistical Machine Translation

    OpenAIRE

    Ortiz Martínez, Daniel

    2011-01-01

    This thesis presents different contributions in the fields of fully-automatic statistical machine translation and interactive statistical machine translation. In the field of statistical machine translation there are three problems that are to be addressed, namely, the modelling problem, the training problem and the search problem. In this thesis we present contributions regarding these three problems. Regarding the modelling problem, an alternative derivation of phrase-based s...

  5. Depfix, a Tool for Automatic Rule-based Post-editing of SMT

    Directory of Open Access Journals (Sweden)

    Rudolf Rosa

    2014-09-01

    Full Text Available We present Depfix, an open-source system for automatic post-editing of phrase-based machine translation outputs. Depfix employs a range of natural language processing tools to obtain analyses of the input sentences, and uses a set of rules to correct common or serious errors in machine translation outputs. Depfix is currently implemented only for English-to-Czech translation direction, but extending it to other languages is planned.

  6. Automatic illumination compensation device based on a photoelectrochemical biofuel cell driven by visible light

    Science.gov (United States)

    Yu, You; Han, Yanchao; Xu, Miao; Zhang, Lingling; Dong, Shaojun

    2016-04-01

    Inverted illumination compensation is important in energy-saving projects, artificial photosynthesis and some forms of agriculture, such as hydroponics. However, only a few illumination adjustments based on self-powered biodetectors that quantitatively detect the intensity of visible light have been reported. We constructed an automatic illumination compensation device based on a photoelectrochemical biofuel cell (PBFC) driven by visible light. The PBFC consisted of a glucose dehydrogenase modified bioanode and a p-type semiconductor cuprous oxide photocathode. The PBFC had a high power output of 161.4 μW cm-2 and an open circuit potential that responded rapidly to visible light. It adjusted the amount of illumination inversely irrespective of how the external illumination was changed. This rational design of utilizing PBFCs provides new insights into automatic light adjustable devices and may be of benefit to intelligent applications.Inverted illumination compensation is important in energy-saving projects, artificial photosynthesis and some forms of agriculture, such as hydroponics. However, only a few illumination adjustments based on self-powered biodetectors that quantitatively detect the intensity of visible light have been reported. We constructed an automatic illumination compensation device based on a photoelectrochemical biofuel cell (PBFC) driven by visible light. The PBFC consisted of a glucose dehydrogenase modified bioanode and a p-type semiconductor cuprous oxide photocathode. The PBFC had a high power output of 161.4 μW cm-2 and an open circuit potential that responded rapidly to visible light. It adjusted the amount of illumination inversely irrespective of how the external illumination was changed. This rational design of utilizing PBFCs provides new insights into automatic light adjustable devices and may be of benefit to intelligent applications. Electronic supplementary information (ESI) available. See DOI: 10.1039/c6nr00759g

  7. Route Optimization of Stacker in Automatic Warehouse Based on Genetic Algorithm

    OpenAIRE

    Cui Changqing; Wang Yiqiang

    2013-01-01

    Today, automatic warehouse system gradually replaced manual labor, and played an important role in the production work, especially in the cargo handling work. It was important to research the time-consuming and efficiency of stacker in the automated warehouse system. This paper researched the path of stacker in automated warehouse and calculated the operation time of stacker working path according to actual working condition, and then put forward a route optimization method of stacker based o...

  8. Automatic classification of bengali sentences based on sense definitions present in bengali wordnet

    OpenAIRE

    Pal, Alok Ranjan; Saha, Diganta; Dash, Niladri Sekhar

    2015-01-01

    Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the Bengali sentences automatically into different groups in accordance with their underlying senses. The input sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL project of the Govt. of India, while information about the different senses of particular ambiguous lexical item is collected from Bengali WordNet. In an experimental basis we hav...

  9. Feature-Based Classification of Networks

    CERN Document Server

    Barnett, Ian; Kuijjer, Marieke L; Mucha, Peter J; Onnela, Jukka-Pekka

    2016-01-01

    Network representations of systems from various scientific and societal domains are neither completely random nor fully regular, but instead appear to contain recurring structural building blocks. These features tend to be shared by networks belonging to the same broad class, such as the class of social networks or the class of biological networks. At a finer scale of classification within each such class, networks describing more similar systems tend to have more similar features. This occurs presumably because networks representing similar purposes or constructions would be expected to be generated by a shared set of domain specific mechanisms, and it should therefore be possible to classify these networks into categories based on their features at various structural levels. Here we describe and demonstrate a new, hybrid approach that combines manual selection of features of potential interest with existing automated classification methods. In particular, selecting well-known and well-studied features that ...

  10. Inference of Gene Regulatory Network Based on Local Bayesian Networks.

    Directory of Open Access Journals (Sweden)

    Fei Liu

    2016-08-01

    Full Text Available The inference of gene regulatory networks (GRNs from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN, to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only

  11. Combination of automatic non-rigid and landmark based registration: the best of both worlds

    Science.gov (United States)

    Fischer, Bernd; Modersitzki, Jan

    2003-05-01

    Automatic, parameter-free, and non-rigid registration schemes are known to be valuable tools in various (medical) image processing applications. Typically, these approaches aim to match intensity patterns in each scan by minimizing an appropriate distance measure. The outcome of an automatic registration procedure in general matches the target image quite good on the average. However, it may be inaccurate for specific, important locations as for example anatomical landmarks. On the other hand, landmark based registration techniques are designed to accurately match user specified landmarks. A drawback of landmark based registration is that the intensities of the images are completely neglected. Consequently, the registration result away from the landmarks may be very poor. Here we propose a framework for novel registration techniques which are capable to combine automatic and landmark driven approaches in order to benefit from the advantages of both strategies. We also propose a general, mathematical treatment of this framework and a particular implementation. The procedure computes a displacement field which is guaranteed to produce a one-to-one match between given landmarks and at the smae time minimizes an intensity based measure for the remaining parts of the images. The properties of the new scheme are demonstrated for a variety of numerical example. It is worthwhile noticing, that we not only present a new approach. Instead, we propose a general framework for a variety of different approaches. The choice of the main building blocks, the distance measure and the smoothness constraint, is essentially free.

  12. Automatic Segmentation of Nature Object Using Salient Edge Points Based Active Contour

    Directory of Open Access Journals (Sweden)

    Shangbing Gao

    2015-01-01

    Full Text Available Natural image segmentation is often a crucial first step for high-level image understanding, significantly reducing the complexity of content analysis of images. LRAC may have some disadvantages. (1 Segmentation results heavily depend on the initial contour selection which is a very skillful task. (2 In some situations, manual interactions are infeasible. To overcome these shortcomings, we propose a novel model for unsupervised segmentation of viewer’s attention object from natural images based on localizing region-based active model (LRAC. With aid of the color boosting Harris detector and the core saliency map, we get the salient object edge points. Then, these points are employed as the seeds of initial convex hull. Finally, this convex hull is improved by the edge-preserving filter to generate the initial contour for our automatic object segmentation system. In contrast with localizing region-based active contours that require considerable user interaction, the proposed method does not require it; that is, the segmentation task is fulfilled in a fully automatic manner. Extensive experiments results on a large variety of natural images demonstrate that our algorithm consistently outperforms the popular existing salient object segmentation methods, yielding higher precision and better recall rates. Our framework can reliably and automatically extract the object contour from the complex background.

  13. An immunity based network security risk estimation

    Institute of Scientific and Technical Information of China (English)

    LI Tao

    2005-01-01

    According to the relationship between the antibody concentration and the pathogen intrusion intensity, here we present an immunity-based model for the network security risk estimation (Insre). In Insre, the concepts and formal definitions of self,nonself, antibody, antigen and lymphocyte in the network security domain are given. Then the mathematical models of the self-tolerance, the clonal selection, the lifecycle of mature lymphocyte, immune memory and immune surveillance are established. Building upon the above models, a quantitative computation model for network security risk estimation,which is based on the calculation of antibody concentration, is thus presented. By using Insre, the types and intensity of network attacks, as well as the risk level of network security, can be calculated quantitatively and in real-time. Our theoretical analysis and experimental results show that Insre is a good solution to real-time risk evaluation for the network security.

  14. Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

    Science.gov (United States)

    Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

    2012-01-01

    Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified…

  15. Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm

    Science.gov (United States)

    Zhu, Maohu; Jie, Nanfeng; Jiang, Tianzi

    2014-03-01

    A reliable and precise classification of schizophrenia is significant for its diagnosis and treatment of schizophrenia. Functional magnetic resonance imaging (fMRI) is a novel tool increasingly used in schizophrenia research. Recent advances in statistical learning theory have led to applying pattern classification algorithms to access the diagnostic value of functional brain networks, discovered from resting state fMRI data. The aim of this study was to propose an adaptive learning algorithm to distinguish schizophrenia patients from normal controls using resting-state functional language network. Furthermore, here the classification of schizophrenia was regarded as a sample selection problem where a sparse subset of samples was chosen from the labeled training set. Using these selected samples, which we call informative vectors, a classifier for the clinic diagnosis of schizophrenia was established. We experimentally demonstrated that the proposed algorithm incorporating resting-state functional language network achieved 83.6% leaveone- out accuracy on resting-state fMRI data of 27 schizophrenia patients and 28 normal controls. In contrast with KNearest- Neighbor (KNN), Support Vector Machine (SVM) and l1-norm, our method yielded better classification performance. Moreover, our results suggested that a dysfunction of resting-state functional language network plays an important role in the clinic diagnosis of schizophrenia.

  16. A modular neural network classifier for the recognition of occluded characters in automatic license plate reading

    NARCIS (Netherlands)

    Nijhuis, JAG; Broersma, A; Spaanenburg, L; Ruan, D; Dhondt, P; Kerre, EE

    2002-01-01

    Occlusion is the most common reason for lowered recognition yield in free-flow license-plate reading systems. (Non-)occluded characters can readily be learned in separate neural networks but not together. Even a small proportion of occluded characters in the training set will already significantly r

  17. Using Virtualization and Automatic Evaluation: Adapting Network Services Management Courses to the EHEA

    Science.gov (United States)

    Ros, S.; Robles-Gomez, A.; Hernandez, R.; Caminero, A. C.; Pastor, R.

    2012-01-01

    This paper outlines the adaptation of a course on the management of network services in operating systems, called NetServicesOS, to the context of the new European Higher Education Area (EHEA). NetServicesOS is a mandatory course in one of the official graduate programs in the Faculty of Computer Science at the Universidad Nacional de Educacion a…

  18. Using Virtualization and Automatic Evaluation: Adapting Network Services Management Courses to the EHEA

    Science.gov (United States)

    Ros, S.; Robles-Gomez, A.; Hernandez, R.; Caminero, A. C.; Pastor, R.

    2012-01-01

    This paper outlines the adaptation of a course on the management of network services in operating systems, called NetServicesOS, to the context of the new European Higher Education Area (EHEA). NetServicesOS is a mandatory course in one of the official graduate programs in the Faculty of Computer Science at the Universidad Nacional de Educacion a…

  19. Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

    Science.gov (United States)

    Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

    2012-01-01

    Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified…

  20. Automatic Recognition of Human Parasite Cysts on Microscopic Stools Images using Principal Component Analysis and Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Beaudelaire Saha Tchinda

    2015-09-01

    Full Text Available Parasites live in a host and get its food from or at the expensive of that host. Cysts represent a form of resistance and spread of parasites. The manual diagnosis of microscopic stools images is time-consuming and depends on the human expert. In this paper, we propose an automatic recognition system that can be used to identify various intestinal parasite cysts from their microscopic digital images. We employ image pixel feature to train the probabilistic neural networks (PNN. Probabilistic neural networks are suitable for classification problems. The main novelty is the use of features vectors extracted directly from the image pixel. For this goal, microscopic images are previously segmented to separate the parasite image from the background. The extracted parasite is then resized to 12x12 image features vector. For dimensionality reduction, the principal component analysis basis projection has been used. 12x12 extracted features were orthogonalized into two principal components variables that consist the input vector of the PNN. The PNN is trained using 540 microscopic images of the parasite. The proposed approach was tested successfully on 540 samples of protozoan cysts obtained from 9 kinds of intestinal parasites.

  1. Automatic Mitigation of Sensor Variations for Signal Strength Based Location Systems

    DEFF Research Database (Denmark)

    Kjærgaard, Mikkel Baun

    2006-01-01

    n the area of pervasive computing a key concept is context-awareness. One type of context information is location information of wireless network clients. Research in indoor localization of wireless network clients based on signal strength is receiving a lot of attention. However, not much...

  2. Automatic mapping of monitoring data

    DEFF Research Database (Denmark)

    Lophaven, Søren; Nielsen, Hans Bruun; Søndergaard, Jacob

    2005-01-01

    This paper presents an approach, based on universal kriging, for automatic mapping of monitoring data. The performance of the mapping approach is tested on two data-sets containing daily mean gamma dose rates in Germany reported by means of the national automatic monitoring network (IMIS......). In the second dataset an accidental release of radioactivity in the environment was simulated in the South-Western corner of the monitored area. The approach has a tendency to smooth the actual data values, and therefore it underestimates extreme values, as seen in the second dataset. However, it is capable...

  3. A Framework for Automatic Web Service Discovery Based on Semantics and NLP Techniques

    Directory of Open Access Journals (Sweden)

    Asma Adala

    2011-01-01

    Full Text Available As a greater number of Web Services are made available today, automatic discovery is recognized as an important task. To promote the automation of service discovery, different semantic languages have been created that allow describing the functionality of services in a machine interpretable form using Semantic Web technologies. The problem is that users do not have intimate knowledge about semantic Web service languages and related toolkits. In this paper, we propose a discovery framework that enables semantic Web service discovery based on keywords written in natural language. We describe a novel approach for automatic discovery of semantic Web services which employs Natural Language Processing techniques to match a user request, expressed in natural language, with a semantic Web service description. Additionally, we present an efficient semantic matching technique to compute the semantic distance between ontological concepts.

  4. Automatic stress-relieving music recommendation system based on photoplethysmography-derived heart rate variability analysis.

    Science.gov (United States)

    Shin, Il-Hyung; Cha, Jaepyeong; Cheon, Gyeong Woo; Lee, Choonghee; Lee, Seung Yup; Yoon, Hyung-Jin; Kim, Hee Chan

    2014-01-01

    This paper presents an automatic stress-relieving music recommendation system (ASMRS) for individual music listeners. The ASMRS uses a portable, wireless photoplethysmography module with a finger-type sensor, and a program that translates heartbeat signals from the sensor to the stress index. The sympathovagal balance index (SVI) was calculated from heart rate variability to assess the user's stress levels while listening to music. Twenty-two healthy volunteers participated in the experiment. The results have shown that the participants' SVI values are highly correlated with their prespecified music preferences. The sensitivity and specificity of the favorable music classification also improved as the number of music repetitions increased to 20 times. Based on the SVI values, the system automatically recommends favorable music lists to relieve stress for individuals.

  5. Automatic multi-resolution image registration based on genetic algorithm and Hausdorff distance

    Institute of Scientific and Technical Information of China (English)

    Famao Ye; Lin Su; Shukai Li

    2006-01-01

    @@ Image registration is a crucial step in all image analysis tasks in which the final information is gained from the combination of various data sources, and it is difficult to automatically register due to the complexity of image. An approach based on genetic algorithm and Hausdorff distance to automatic image registration is presented. We use a multi-resolution edge tracker to find out the fine-quality edges and utilize the Hausdorff distance between the input image and the reference image as similarity measure. We use wavelet decomposition and genetic algorithm, which combine local search methods with global ones balancing exploration and exploitation, to speed up the search of the best transformation parameters.Experimental results show that the proposed approach is a promising method for registration of image.

  6. Evaluation of Model Recognition for Grammar-Based Automatic 3d Building Model Reconstruction

    Science.gov (United States)

    Yu, Qian; Helmholz, Petra; Belton, David

    2016-06-01

    In recent years, 3D city models are in high demand by many public and private organisations, and the steadily growing capacity in both quality and quantity are increasing demand. The quality evaluation of these 3D models is a relevant issue both from the scientific and practical points of view. In this paper, we present a method for the quality evaluation of 3D building models which are reconstructed automatically from terrestrial laser scanning (TLS) data based on an attributed building grammar. The entire evaluation process has been performed in all the three dimensions in terms of completeness and correctness of the reconstruction. Six quality measures are introduced to apply on four datasets of reconstructed building models in order to describe the quality of the automatic reconstruction, and also are assessed on their validity from the evaluation point of view.

  7. The Research of ECG Signal Automatic Segmentation Algorithm Based on Fractal Dimension Trajectory

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    <正>In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain characteristics.Through Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.

  8. Automatic identification of bullet signatures based on consecutive matching striae (CMS) criteria.

    Science.gov (United States)

    Chu, Wei; Thompson, Robert M; Song, John; Vorburger, Theodore V

    2013-09-10

    The consecutive matching striae (CMS) numeric criteria for firearm and toolmark identifications have been widely accepted by forensic examiners, although there have been questions concerning its observer subjectivity and limited statistical support. In this paper, based on signal processing and extraction, a model for the automatic and objective counting of CMS is proposed. The position and shape information of the striae on the bullet land is represented by a feature profile, which is used for determining the CMS number automatically. Rapid counting of CMS number provides a basis for ballistics correlations with large databases and further statistical and probability analysis. Experimental results in this report using bullets fired from ten consecutively manufactured barrels support this developed model.

  9. An atlas-based fuzzy connectedness method for automatic tissue classification in brain MRI

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yongxin; BAI Jing

    2006-01-01

    A framework incorporating a subject-registered atlas into the fuzzy connectedness (FC) method is proposed for the automatic tissue classification of 3D images of brain MRI. The pre-labeled atlas is first registered onto the subject to provide an initial approximate segmentation. The initial segmentation is used to estimate the intensity histograms of gray matter and white matter. Based on the estimated intensity histograms, multiple seed voxels are assigned to each tissue automatically. The normalized intensity histograms are utilized in the FC method as the intensity probability density function (PDF) directly. Relative fuzzy connectedness technique is adopted in the final classification of gray matter and white matter. Experimental results based on the 20 data sets from IBSR are included, as well as comparisons of the performance of our method with that of other published methods. This method is fully automatic and operator-independent. Therefore, it is expected to find wide applications, such as 3D visualization, radiation therapy planning, and medical database construction.

  10. Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification

    Directory of Open Access Journals (Sweden)

    Behnam Torabi

    2014-10-01

    Full Text Available Automatic speech segmentation as an important part of speech recognition system (ASR is highly noise dependent. Noise is made by changes in the communication channel, background, level of speaking etc. In recent years, many researchers have proposed noise cancelation techniques and have added visual features from speaker’s face to reduce the effect of noise on ASR systems. Removing noise from audio signals depends on the type of the noise; so it cannot be used as a general solution. Adding visual features improve this lack of efficiency, but advanced methods of this type need manual extraction of visual features. In this paper we propose a completely automatic system which uses optical flow vectors from speaker’s image sequence to obtain visual features. Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow. The developed segmentation system based on such method acts totally automatic and become more robust to noise.

  11. Design and implementation of microcontroller-based automatic sequence counting and switching system

    Directory of Open Access Journals (Sweden)

    Joshua ABOLARINWA

    2015-05-01

    Full Text Available Technological advancement and its influence on human being have been on the increase in recent time. Major areas of such influence, include monitoring and control activities. In order to keep track of human movement in and out of a particular building, there is the need for an automatic counting system. Therefore, in this paper, we present the design and implementation of a microcontroller-based automatic sequence counting and switching system. This system was designed and developed to save cost, time, energy, and to achieve seamless control in the event of switching on or off of electrical appliances within a building. Top-down modular design approach was used in conjunction with the versatility of microcontroller. The system is able to monitor, sequentially count the number of entry and exit of people through an entrance, afterwards, automatically control any electrical device connected to it. From various tests and measurements obtained, there are comparative benefits derived from the deployment of this system in terms of simplicity and accuracy over similar system that is not microcontroller-based. Therefore, this system can be deployed at commercial quantity with wide range of applications in homes, offices and other public places.

  12. IP Network Management Model Based on NGOSS

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jin-yu; LI Hong-hui; LIU Feng

    2004-01-01

    This paper addresses a management model for IP network based on Next Generation Operation Support System (NGOSS). It makes the network management on the base of all the operation actions of ISP, It provides QoS to user service through the whole path by providing end-to-end Service Level Agreements (SLA) management through whole path. Based on web and coordination technology, this paper gives an implement architecture of this model.

  13. Fabric Defect Detection Technique Based on Two-double Neural Network

    Institute of Scientific and Technical Information of China (English)

    XIE Chun-ping; XU Bo-jun; CHEN Jun-jie

    2008-01-01

    This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimensional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.

  14. Automatic detection of cell divisions (mitosis) in live-imaging microscopy images using Convolutional Neural Networks.

    Science.gov (United States)

    Shkolyar, Anat; Gefen, Amit; Benayahu, Dafna; Greenspan, Hayit

    2015-08-01

    We propose a semi-automated pipeline for the detection of possible cell divisions in live-imaging microscopy and the classification of these mitosis candidates using a Convolutional Neural Network (CNN). We use time-lapse images of NIH3T3 scratch assay cultures, extract patches around bright candidate regions that then undergo segmentation and binarization, followed by a classification of the binary patches into either containing or not containing cell division. The classification is performed by training a Convolutional Neural Network on a specially constructed database. We show strong results of AUC = 0.91 and F-score = 0.89, competitive with state-of-the-art methods in this field.

  15. Automatic system for 3D reconstruction of the chick eye based on digital photographs.

    Science.gov (United States)

    Wong, Alexander; Genest, Reno; Chandrashekar, Naveen; Choh, Vivian; Irving, Elizabeth L

    2012-01-01

    The geometry of anatomical specimens is very complex and accurate 3D reconstruction is important for morphological studies, finite element analysis (FEA) and rapid prototyping. Although magnetic resonance imaging, computed tomography and laser scanners can be used for reconstructing biological structures, the cost of the equipment is fairly high and specialised technicians are required to operate the equipment, making such approaches limiting in terms of accessibility. In this paper, a novel automatic system for 3D surface reconstruction of the chick eye from digital photographs of a serially sectioned specimen is presented as a potential cost-effective and practical alternative. The system is designed to allow for automatic detection of the external surface of the chick eye. Automatic alignment of the photographs is performed using a combination of coloured markers and an algorithm based on complex phase order likelihood that is robust to noise and illumination variations. Automatic segmentation of the external boundaries of the eye from the aligned photographs is performed using a novel level-set segmentation approach based on a complex phase order energy functional. The extracted boundaries are sampled to construct a 3D point cloud, and a combination of Delaunay triangulation and subdivision surfaces is employed to construct the final triangular mesh. Experimental results using digital photographs of the chick eye show that the proposed system is capable of producing accurate 3D reconstructions of the external surface of the eye. The 3D model geometry is similar to a real chick eye and could be used for morphological studies and FEA.

  16. Automated management of life cycle for future network experiment based on description language

    Science.gov (United States)

    Niu, Hongxia; Liang, Junxue; Lin, Zhaowen; Ma, Yan

    2016-12-01

    Future network is a complex resources pool including multiple physical resources and virtual resources. Establishing experiment on future network is complicate and tedious. That achieving the automated management of future network experiments is so important. This paper brings forward the way for researching and managing the life cycle of experiment based on the description language. The description language uses the framework, which couples with a low hierarchical structure and a complete description of the network experiment. In this way, the experiment description template can be generated by this description framework accurately and completely. In reality, we can also customize and reuse network experiment by modifying the description template. The results show that this method can achieve the aim for managing the life cycle of network experiment effectively and automatically, which greatly saves time, reduces the difficulty, and implements the reusability of services.

  17. Entropy-based generation of supervised neural networks for classification of structured patterns.

    Science.gov (United States)

    Tsai, Hsien-Leing; Lee, Shie-Jue

    2004-03-01

    Sperduti and Starita proposed a new type of neural network which consists of generalized recursive neurons for classification of structures. In this paper, we propose an entropy-based approach for constructing such neural networks for classification of acyclic structured patterns. Given a classification problem, the architecture, i.e., the number of hidden layers and the number of neurons in each hidden layer, and all the values of the link weights associated with the corresponding neural network are automatically determined. Experimental results have shown that the networks constructed by our method can have a better performance, with respect to network size, learning speed, or recognition accuracy, than the networks obtained by other methods.

  18. Automatic volcanic ash detection from MODIS observations using a back-propagation neural network

    Directory of Open Access Journals (Sweden)

    T. M. Gray

    2015-12-01

    Full Text Available Due to the climate effects and aviation threats of volcanic eruptions, it is important to accurately locate ash in the atmosphere. This study aims to explore the accuracy and reliability of training a neural network to identify cases of ash using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS. Satellite images were obtained for the following eruptions: Kasatochi, Aleutian Islands, 2008; Okmok, Aleutian Islands, 2008; Grímsvötn, northeastern Iceland, 2011; Chaitén, southern Chile, 2008; Puyehue-Cordón Caulle, central Chile, 2011; Sangeang Api, Indonesia, 2014; and Kelut, Indonesia, 2014. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT model was used to obtain ash concentrations for the same archived eruptions. Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12–11, 11–8.6, 11–7.3, and 11 μm. Using the ash concentrations determined via HYSPLIT, flags were created to differentiate between ash (1 and no ash (0 and SO2-rich ash (1 and no SO2-rich ash (0 and used as output. When neural network output was compared to the test data set, 93 % of pixels containing ash were correctly identified and 7 % were missed. Nearly 100 % of pixels containing SO2-rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO2-rich ash retrieval were 0.48 and 0.47, respectively. The networks show significantly less accuracy in the presence of high water vapor, liquid water, ice, or dust concentrations. Significant errors are also observed at the edge of the MODIS swath.

  19. Automatic volcanic ash detection from MODIS observations using a back-propagation neural network

    Directory of Open Access Journals (Sweden)

    T. M. Gray

    2015-08-01

    Full Text Available Due to the climate effects and aviation threats of volcanic eruptions, it is important to accurately locate ash in the atmosphere. This study aims to explore the accuracy and reliability of training a neural network to identify cases of ash using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS. Satellite images were obtained for the following eruptions: Kasatochi, Aleutian Islands, 2008; Okmok, Aleutian Islands, 2008; Grímsvötn, northeastern Iceland, 2011; Chaiteìn, southern Chile, 2008; Puyehue-Cordoìn Caulle, central Chile, 2011; Sangeang Api, Indonesia, 2014; and Kelut, Indonesia, 2014. The Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT was used to obtain ash concentrations for the same archived eruptions. Two back-propagation neural networks were then trained using brightness temperature differences as inputs obtained via the following band combinations: 12-11, 11-8.6, 11-7.3, and 11 μm. Using the ash concentrations determined via HYSPLIT, flags were created to differentiate between ash (1 and no ash (0 and SO2-rich ash (1 and no SO2-rich ash (0 and used as output. When neural network output was compared to the test dataset, 93 % of pixels containing ash were correctly identified and 7 % were missed. Nearly 100 % of pixels containing SO2-rich ash were correctly identified. The optimal thresholds, determined using Heidke skill scores, for ash retrieval and SO2-rich ash retrieval were 0.48 and 0.47, respectively. The networks show significantly less accuracy in the presence of high water vapor, liquid water, ice, or dust concentrations. Significant errors are also observed at the edge of the MODIS swath.

  20. Automatic identification of terpenoid skeletons by feed-forward neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Emerenciano, Vicente P. [Instituto de Quimica, Universidade de Sao Paulo, Caixa Postal 26077, 05513-970 Sao Paulo, SP (Brazil)]. E-mail: vdpemere@iq.usp.br; Alvarenga, Sandra A.V. [Faculdade de Engenharia de Guaratingueta, UNESP, CEP 12516-410, Guaratingueta, Sao Paulo (Brazil); Scotti, Marcus Tullius [Instituto de Quimica, Universidade de Sao Paulo, Caixa Postal 26077, 05513-970 Sao Paulo, SP (Brazil); Ferreira, Marcelo J.P. [Instituto de Quimica, Universidade de Sao Paulo, Caixa Postal 26077, 05513-970 Sao Paulo, SP (Brazil); Stefani, Ricardo [Departamento de Quimica, FFCLRP, USP, Av. Bandeirantes 3900, CEP 14040-905, Ribeirao Preto, Sao Paulo (Brazil); Nuzillard, Jean-Marc [FRE 2715, University of Reims, Moulin de la Housse, BP 1039, 51687 REIMS Cedex 2 (France)

    2006-10-10

    Feed-forward neural networks (FFNNs) were used to predict the skeletal type of molecules belonging to six classes of terpenoids. A database that contains the {sup 13}C NMR spectra of about 5000 compounds was used to train the FFNNs. An efficient representation of the spectra was designed and the constitution of the best FFNN input vector format resorted from an heuristic approach. The latter was derived from general considerations on terpenoid structures.

  1. Prototyping Web Services based Network Monitoring

    NARCIS (Netherlands)

    Drevers, Thomas; van de Meent, R.; Pras, Aiko; Harjo, J.; Moltchanov, D.; Silverajan, B.

    Web services is one of the emerging approaches in network management. This paper describes the design and implementation of four Web services based network monitoring prototypes. Each prototype follows a speci��?c approach to retrieve management data, ranging from retrieving a single management

  2. Development of an automatic measuring device for total sugar content in chlortetracycline fermenter based on STM32

    Science.gov (United States)

    Liu, Ruochen; Chen, Xiangguang; Yao, Minpu; Huang, Suyi; Ma, Deshou; Zhou, Biao

    2017-01-01

    Because fermented liquid in chlortetracycline fermenter has high viscosity and complex composition, conventional instruments can't directly measure its total sugar content of fermented liquid. At present, offline artificial sampling measurement is usually the way to measuring total sugar content in chlortetracycline Fermenter. it will take too much time and manpower to finish the measurement., and the results will bring the lag of control process. To realize automatic measurement of total sugar content in chlortetracycline fermenter, we developed an automatic measuring device for total sugar content based on STM32 microcomputer. It can not only realize the function of automatic sampling, filtering, measuring of fermented liquid and automatic washing of the device, but also can make the measuring results display in the field and finish data communication. The experiment results show that the automatic measuring device of total sugar content in chlortetracycline fermenter can meet the demand of practical application.

  3. Modeling the interdependent network based on two-mode networks

    Science.gov (United States)

    An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

    2017-10-01

    Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

  4. Trust Based Routing in Ad Hoc Network

    Science.gov (United States)

    Talati, Mikita V.; Valiveti, Sharada; Kotecha, K.

    Ad Hoc network often termed as an infrastructure-less, self- organized or spontaneous network.The execution and survival of an ad-hoc network is solely dependent upon the cooperative and trusting nature of its nodes. However, this naive dependency on intermediate nodes makes the ad-hoc network vulnerable to passive and active attacks by malicious nodes and cause inflict severe damage. A number of protocols have been developed to secure ad-hoc networks using cryptographic schemes, but all rely on the presence of trust authority. Due to mobility of nodes and limitation of resources in wireless network one interesting research area in MANET is routing. This paper offers various trust models and trust based routing protocols to improve the trustworthiness of the neighborhood.Thus it helps in selecting the most secure and trustworthy route from the available ones for the data transfer.

  5. Model-based control of networked systems

    CERN Document Server

    Garcia, Eloy; Montestruque, Luis A

    2014-01-01

    This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled.   The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control.   Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...

  6. An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records.

    Science.gov (United States)

    Garces Correa, Agustina; Laciar Leber, Eric

    2010-01-01

    An algorithm to detect automatically drowsiness episodes has been developed. It uses only one EEG channel to differentiate the stages of alertness and drowsiness. In this work the vectors features are building combining Power Spectral Density (PDS) and Wavelet Transform (WT). The feature extracted from the PSD of EEG signal are: Central frequency, the First Quartile Frequency, the Maximum Frequency, the Total Energy of the Spectrum, the Power of Theta and Alpha bands. In the Wavelet Domain, it was computed the number of Zero Crossing and the integrated from the scale 3, 4 and 5 of Daubechies 2 order WT. The classifying of epochs is being done with neural networks. The detection results obtained with this technique are 86.5 % for drowsiness stages and 81.7% for alertness segment. Those results show that the features extracted and the classifier are able to identify drowsiness EEG segments.

  7. Poster Abstract: Automatic Calibration of Device Attitude in Inertial Measurement Unit Based Traffic Probe Vehicles

    KAUST Repository

    Mousa, Mustafa

    2016-04-28

    Probe vehicles consist in mobile traffic sensor networks that evolve with the flow of vehicles, transmitting velocity and position measurements along their path, generated using GPSs. To address the urban positioning issues of GPSs, we propose to replace them with inertial measurement units onboard vehicles, to estimate vehicle location and attitude using inertial data only. While promising, this technology requires one to carefully calibrate the orientation of the device inside the vehicle to be able to process the acceleration and rate gyro data. In this article, we propose a scheme that can perform this calibration automatically by leveraging the kinematic constraints of ground vehicles, and that can be implemented on low-end computational platforms. Preliminary testing shows that the proposed scheme enables one to accurately estimate the actual accelerations and rotation rates in the vehicle coordinates. © 2016 IEEE.

  8. Evaluation of a Meta-1-based automatic indexing method for medical documents.

    Science.gov (United States)

    Wagner, M M; Cooper, G F

    1992-08-01

    This paper describes MetaIndex, an automatic indexing program that creates symbolic representations of documents for the purpose of document retrieval. MetaIndex uses a simple transition network parser to recognize a language that is derived from the set of main concepts in the Unified Medical Language System Metathesaurus (Meta-1). MetaIndex uses a hierarchy of medical concepts, also derived from Meta-1, to represent the content of documents. The goal of this approach is to improve document retrieval performance by better representation of documents. An evaluation method is described, and the performance of MetaIndex on the task of indexing the Slice of Life medical image collection is reported.

  9. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    Science.gov (United States)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  10. A new automatic alignment technology for single mode fiber-waveguide based on improved genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yu; CHEN Zhuang-zhuang; LI Ya-juan; DUAN Jian

    2009-01-01

    A novel automatic alignment algorithm of single mode fiber-waveguide based on improved genetic algorithm is proposed. The genetic searching is based on the dynamic crossover operator and the adaptive mutation operator to solve the premature convergence of simple genetic algorithm The improved genetic algorithm combines with hill-climbing method and pattern searching algorithm, to solve low precision of simple genetic algorithm in later searching. The simulation results indicate that the improved genetic algorithm can rise the alignment precision and reach the coupling loss of 0.01 dB when platform moves near 207 space points averagely.

  11. Arabic Language Learning Assisted by Computer, based on Automatic Speech Recognition

    CERN Document Server

    Terbeh, Naim

    2012-01-01

    This work consists of creating a system of the Computer Assisted Language Learning (CALL) based on a system of Automatic Speech Recognition (ASR) for the Arabic language using the tool CMU Sphinx3 [1], based on the approach of HMM. To this work, we have constructed a corpus of six hours of speech recordings with a number of nine speakers. we find in the robustness to noise a grounds for the choice of the HMM approach [2]. the results achieved are encouraging since our corpus is made by only nine speakers, but they are always reasons that open the door for other improvement works.

  12. 基于受限领域自动问答系统设计%The Design of Automatic Question-Answering System Based on the Restricted Domain

    Institute of Scientific and Technical Information of China (English)

    庄永新; 武鹏; 朱峰; 黄振宇

    2014-01-01

    The design of automatic question-answering system has been a research focus in the field of Natural Language Process⁃ing. Especially in the restricted domain, automatic question-answering system based on the problem base has advantages of accu⁃racy, shortcut and efficiency. The paper describes the design of an automatic question-answering system based on"computer net⁃work" course, which integrates the social networking technology. VSM model is used to construct the problem base. Experi⁃ments show that this system has a higher precision, which has certain significance of promoting.%自动问答系统的设计一直是自然语言处理领域的研究热点。尤其是在受限领域,基于问题库的问答系统具有准确、快捷和高效等优点。该文设计了一种融合社交网络技术的基于《计算机网络》课程的自动问答系统,其问答库的构建采用了VSM模型。实验证明,该系统具有较高的准确率,有一定的推广意义。

  13. Image Based Hair Segmentation Algorithm for the Application of Automatic Facial Caricature Synthesis

    Directory of Open Access Journals (Sweden)

    Yehu Shen

    2014-01-01

    Full Text Available Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.

  14. [Optimization of genomic DNA extraction with magnetic bead- based semi-automatic system].

    Science.gov (United States)

    Ling, Jie; Wang, Hao; Zhang, Shuai; Zhang, Dan-dan; Lai, Mao-de; Zhu, Yi-min

    2012-05-01

    To develop a rapid and effective method for genomic DNA extraction with magnetic bead-based semi-automatic system. DNA was extracted from whole blood samples semi-automatically with nucleic acid automatic extraction system.The concentration and purity of samples was determined by UV-spectrophotometer. Orthogonal design was used to analyze the main effect of lysis time, blood volume, magnetic bead quantity and ethanol concentration on the DNA yield; also the 2-way interaction of these factors. Lysis time, blood volume, magnetic bead quantity and ethanol concentration were associated with DNA yield (PDNA yield was higher under the condition with 15 min of lysis time, 100 μl of blood volume, 80 μl of magnetic beads and 80 % of ethanol. A significant association was found between the magnetic bead quantity and DNA purity OD260/OD280 (P=0.008). Interaction of blood volume and lysis time also existed (P=0.013). DNA purity was better when the extracting condition was 40 μl of magnetic beads, 15 min of lysis time and 100 μl of blood volume. Magnetic beads and ethanol concentration were associated with DNA purity OD260/OD230 (P=0.017 and Pgenomic DNA from the whole blood samples.

  15. Fully automatic vertebra detection in x-ray images based on multi-class SVM

    Science.gov (United States)

    Lecron, Fabian; Benjelloun, Mohammed; Mahmoudi, Saïd

    2012-02-01

    Automatically detecting vertebral bodies in X-Ray images is a very complex task, especially because of the noise and the low contrast resulting in that kind of medical imagery modality. Therefore, the contributions in the literature are mainly interested in only 2 medical imagery modalities: Computed Tomography (CT) and Magnetic Resonance (MR). Few works are dedicated to the conventional X-Ray radiography and propose mostly semi-automatic methods. However, vertebra detection is a key step in many medical applications such as vertebra segmentation, vertebral morphometry, etc. In this work, we develop a fully automatic approach for the vertebra detection, based on a learning method. The idea is to detect a vertebra by its anterior corners without human intervention. To this end, the points of interest in the radiograph are firstly detected by an edge polygonal approximation. Then, a SIFT descriptor is used to train an SVM-model. Therefore, each point of interest can be classified in order to detect if it belongs to a vertebra or not. Our approach has been assessed by the detection of 250 cervical vertebræ on radiographs. The results show a very high precision with a corner detection rate of 90.4% and a vertebra detection rate from 81.6% to 86.5%.

  16. Grammar-based Automatic 3D Model Reconstruction from Terrestrial Laser Scanning Data

    Science.gov (United States)

    Yu, Q.; Helmholz, P.; Belton, D.; West, G.

    2014-04-01

    The automatic reconstruction of 3D buildings has been an important research topic during the last years. In this paper, a novel method is proposed to automatically reconstruct the 3D building models from segmented data based on pre-defined formal grammar and rules. Such segmented data can be extracted e.g. from terrestrial or mobile laser scanning devices. Two steps are considered in detail. The first step is to transform the segmented data into 3D shapes, for instance using the DXF (Drawing Exchange Format) format which is a CAD data file format used for data interchange between AutoCAD and other program. Second, we develop a formal grammar to describe the building model structure and integrate the pre-defined grammars into the reconstruction process. Depending on the different segmented data, the selected grammar and rules are applied to drive the reconstruction process in an automatic manner. Compared with other existing approaches, our proposed method allows the model reconstruction directly from 3D shapes and takes the whole building into account.

  17. Dynamics-based centrality for directed networks

    Science.gov (United States)

    Masuda, Naoki; Kori, Hiroshi

    2010-11-01

    Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.

  18. Means-end chains as associative networks: Do they exhibit automatic spreading activation

    DEFF Research Database (Denmark)

    Scholderer, Joachim; Grunert, Klaus G.

    networks with a three-layered structure, consisting of attributes (A), consequences (C) and values (V) that are hierarchically linked. This yields two predictions when operationalized in a lexical decision task: means-end chains should display spreading activation (direct as well as mediated priming...... participant's interview. Largely, the pattern of results was in line with the predictions. In Experiment 2, direct priming (AC and CV pairs) as well as mediated priming (AV pairs) could be observed, consistent with the spreading-activation assumption. Furthermore, results in Experiment 2 were obtained under...

  19. Clustering in mobile ad hoc network based on neural network

    Institute of Scientific and Technical Information of China (English)

    CHEN Ai-bin; CAI Zi-xing; HU De-wen

    2006-01-01

    An on-demand distributed clustering algorithm based on neural network was proposed. The system parameters and the combined weight for each node were computed, and cluster-heads were chosen using the weighted clustering algorithm, then a training set was created and a neural network was trained. In this algorithm, several system parameters were taken into account, such as the ideal node-degree, the transmission power, the mobility and the battery power of the nodes. The algorithm can be used directly to test whether a node is a cluster-head or not. Moreover, the clusters recreation can be speeded up.

  20. Standard Cell-Based Implementation of a Digital Optoelectronic Neural-Network Hardware

    Science.gov (United States)

    Maier, Klaus D.; Beckstein, Clemens; Blickhan, Reinhard; Erhard, Werner

    2001-03-01

    A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.