Search results for: initial detection

Authors: Chun-Wei Huang, Jyh-Cherng Gu, Ming-Ta Yang

Abstract:

Non-synchronous breakage or line failure in power systems with light or no loads can lead to core saturation in transformers or potential transformers. This can cause component and capacitance matching resulting in the formation of resonant circuits, which trigger ferroresonance. This study employed a wavelet transform for the detection of ferroresonance. Simulation results demonstrate the efficacy of the proposed method.

Keywords: Ferroresonance, Wavelet Transform, Intelligent Electronic Device, Transformer.

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Authors: Lokesh Tharani, R.P.Yadav

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This paper presents the results related to the interference reduction technique in multistage multiuser detector for asynchronous DS-CDMA system. To meet the real-time requirements for asynchronous multiuser detection, a bit streaming, cascade architecture is used. An asynchronous multiuser detection involves block-based computations and matrix inversions. The paper covers iterative-based suboptimal schemes that have been studied to decrease the computational complexity, eliminate the need for matrix inversions, decreases the execution time, reduces the memory requirements and uses joint estimation and detection process that gives better performance than the independent parameter estimation method. The stages of the iteration use cascaded and bits processed in a streaming fashion. The simulation has been carried out for asynchronous DS-CDMA system by varying one parameter, i.e., number of users. The simulation result exhibits that system gives optimum bit error rate (BER) at 3rd stage for 15-users.

Keywords: Multi-user detection (MUD), multiple accessinterference (MAI), near-far effect, decision feedback detector, successive interference cancellation detector (SIC) and parallelinterference cancellation (PIC) detector.

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Authors: Kandpal M. , Gundampati R. K , Debnath M.

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Microbial contamination, most of which are fecal born in drinking water and food industry is a serious threat to humans. Escherichia coli is one of the most common and prevalent among them. We have developed a sensor for rapid and an early detection of contaminants, taking E.coli as a threat indicator organism. The sensor is based on co-polymerizations of aniline and formaldehyde in form of thin film over glass surface using the vacuum deposition technique. The particular doping combination of thin film with Fe-Al and Fe-Cu in different concentrations changes its non conducting properties to p- type semi conductor. This property is exploited to detect the different contaminants, believed to have the different surface charge. It was found through experiments that different microbes at same OD (0.600 at 600 nm) have different conductivity in solution. Also the doping concentration is found to be specific for attracting microbes on the basis of surface charge. This is a simple, cost effective and quick detection method which not only decreases the measurement time but also gives early warnings for highly contaminated samples.

Keywords: Sensor, Vacuum deposition technique, thin film, E.coli detection, doping concentration.

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: Image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization.

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Authors: Hatem Hajri, Mohamed-Cherif Rahal

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Both Lidars and Radars are sensors for obstacle detection. While Lidars are very accurate on obstacles positions and less accurate on their velocities, Radars are more precise on obstacles velocities and less precise on their positions. Sensor fusion between Lidar and Radar aims at improving obstacle detection using advantages of the two sensors. The present paper proposes a real-time Lidar/Radar data fusion algorithm for obstacle detection and tracking based on the global nearest neighbour standard filter (GNN). This algorithm is implemented and embedded in an automative vehicle as a component generated by a real-time multisensor software. The benefits of data fusion comparing with the use of a single sensor are illustrated through several tracking scenarios (on a highway and on a bend) and using real-time kinematic sensors mounted on the ego and tracked vehicles as a ground truth.

Keywords: Ground truth, Hungarian algorithm, lidar Radar data fusion, global nearest neighbor filter.

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Authors: Feifei Lee, Qiu Chen, Koji Kotani, Tadahiro Ohmi

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In this paper, we present an improved fast and robust search algorithm for copy detection using histogram-based features for short MPEG video clips from large video database. There are two types of histogram features used to generate more robust features. The first one is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Another one is ordinal histogram feature which is robust to color distortion. Furthermore, by Combining with a temporal division method, the spatial and temporal features of the video sequence are integrated to realize fast and robust video search for copy detection. Experimental results show the proposed algorithm can detect the similar video clip more accurately and robust than conventional fast video search algorithm.

Keywords: Fast search, Copy detection, Adjacent pixel intensity difference quantization (APIDQ), DC image, Histogram feature.

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: Ring recognition, edge detection, X-ray computed tomography, dendrochronology.

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Authors: Hazem M. El-Bakry, Qiangfu Zhao

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Recently, neural networks have shown good results for detection of a certain pattern in a given image. In our previous papers [1-5], a fast algorithm for pattern detection using neural networks was presented. Such algorithm was designed based on cross correlation in the frequency domain between the input image and the weights of neural networks. Image conversion into symmetric shape was established so that fast neural networks can give the same results as conventional neural networks. Another configuration of symmetry was suggested in [3,4] to improve the speed up ratio. In this paper, our previous algorithm for fast neural networks is developed. The frequency domain cross correlation is modified in order to compensate for the symmetric condition which is required by the input image. Two new ideas are introduced to modify the cross correlation algorithm. Both methods accelerate the speed of the fast neural networks as there is no need for converting the input image into symmetric one as previous. Theoretical and practical results show that both approaches provide faster speed up ratio than the previous algorithm.

Keywords: Fast Pattern Detection, Neural Networks, Modified Cross Correlation

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Authors: Chau Nguyen Viet, Ian Marshall

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This paper presents a new classification algorithm using colour and texture for obstacle detection. Colour information is computationally cheap to learn and process. However in many cases, colour alone does not provide enough information for classification. Texture information can improve classification performance but usually comes at an expensive cost. Our algorithm uses both colour and texture features but texture is only needed when colour is unreliable. During the training stage, texture features are learned specifically to improve the performance of a colour classifier. The algorithm learns a set of simple texture features and only the most effective features are used in the classification stage. Therefore our algorithm has a very good classification rate while is still fast enough to run on a limited computer platform. The proposed algorithm was tested with a challenging outdoor image set. Test result shows the algorithm achieves a much better trade-off between classification performance and efficiency than a typical colour classifier.

Keywords: Colour, texture, classification, obstacle detection.

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Authors: Bharti B. Parmar, Vivek J. Pandya

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The issue of unintentional islanding detection of grid connected synchronous distributed generation (SDG) remains the most challenging task faced by the distributed generation (DG) industry as SDG is highly capable of prolonging an island. This paper gives an insight of anti-islanding detection techniques mainly applied for SDG. Different techniques conclude that it is challenging to point out a generic method for a distinct purpose as the application of particular practice depends on nature of the end use and system dependent elements. Also, the setup and operational cost affect the selection of anti-islanding technique to achieve minimal compromising between cost and system quality. A test bench is created in the MATLAB/Simulink^® to demonstrate the results of a 33 kV system. The results are highly satisfactory and they are according to the current practices.

Keywords: Synchronous distributed generation, islanding, point of common coupling, loss of grid.

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Authors: Lei Wang, Sizong Guo

Abstract:

In this paper, we study the numerical method for solving second-order fuzzy differential equations using Adomian method under strongly generalized differentiability. And, we present an example with initial condition having four different solutions to illustrate the efficiency of the proposed method under strongly generalized differentiability.

Keywords: Fuzzy-valued function, fuzzy initial value problem, strongly generalized differentiability, adomian decomposition method.

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Authors: Vigen Arakelian

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This paper deals with the study of devices for displacement of the mould base of blow-molding machines. The displacement of the mould in the studied case is carried out by a linear actuator, which ensures the descent of the mould base and by extension springs, which return the letter in the initial position. The aim of this paper is to study the inverse dynamics of the device for displacement of the mould base of blow-molding machines and to determine its optimum parameters for higher rate of production. In the other words, it is necessary to solve the inverse dynamic problem to find the equation of motion linking applied forces with displacements. This makes it possible to determine the stiffness coefficient of the spring to turn the mold base back to the initial position for a given time. The obtained results are illustrated by a numerical example. It is shown that applying a spring with stiffness returns the mould base of the blow molding machine into the initial position in 0.1 sec.

Keywords: Design, blow-molding machines, dynamics.

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Authors: Jaipong Kasemsuwan

Abstract:

A numerical solution of the initial boundary value problem of the suspended string vibrating equation with the particular nonlinear damping term based on the finite difference scheme is presented in this paper. The investigation of how the second and third power terms of the nonlinear term affect the vibration characteristic. We compare the vibration amplitude as a result of the third power nonlinear damping with the second power obtained from previous report provided that the same initial shape and initial velocities are assumed. The comparison results show that the vibration amplitude is inversely proportional to the coefficient of the damping term for the third power nonlinear damping case, while the vibration amplitude is proportional to the coefficient of the damping term in the second power nonlinear damping case.

Keywords: Finite-difference method, the nonlinear damped equation, the numerical simulation, the suspended string equation

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Authors: Wei Yee Teoh, Chee Wei Tan

Abstract:

The issue of unintentional islanding in PV grid interconnection still remains as a challenge in grid-connected photovoltaic (PV) systems. This paper discusses the overview of popularly used anti-islanding detection methods, practically applied in PV grid-connected systems. Anti-islanding methods generally can be classified into four major groups, which include passive methods, active methods, hybrid methods and communication base methods. Active methods have been the preferred detection technique over the years due to very small non-detected zone (NDZ) in small scale distribution generation. Passive method is comparatively simpler than active method in terms of circuitry and operations. However, it suffers from large NDZ that significantly reduces its performance. Communication base methods inherit the advantages of active and passive methods with reduced drawbacks. Hybrid method which evolved from the combination of both active and passive methods has been proven to achieve accurate anti-islanding detection by many researchers. For each of the studied anti-islanding methods, the operation analysis is described while the advantages and disadvantages are compared and discussed. It is difficult to pinpoint a generic method for a specific application, because most of the methods discussed are governed by the nature of application and system dependent elements. This study concludes that the setup and operation cost is the vital factor for anti-islanding method selection in order to achieve minimal compromising between cost and system quality.

Keywords: Active method, hybrid method, islanding detection, passive method, photovoltaic (PV), utility method

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Authors: Sunaitan Al Mutairi

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A perfect start is a key factor for project completion on time. The study examined the effects of delayed mobilization of resources during the initial phases of the project. This paper mainly highlights the identification and categorization of all delays during the initial construction phase and their root cause analysis with corrective/control measures for the Kuwait Oil Company oil and gas projects. A relatively good percentage of the delays identified during the project execution (Contract award to end of defects liability period) attributed to mobilization/preliminary activity delays. Data analysis demonstrated significant increase in average project delay during the last five years compared to the previous period. Contractors had delays/issues during the initial phase, which resulted in slippages and progressively increased, resulting in time and cost overrun. Delays/issues not mitigated on time during the initial phase had very high impact on project completion. Data analysis of the delays for the past five years was carried out using trend chart, scatter plot, process map, box plot, relative importance index and Pareto chart. Construction of any project inside the Gathering Centers involves complex management skills related to work force, materials, plant, machineries, new technologies etc. Delay affects completion of projects and compromises quality, schedule and budget of project deliverables. Works executed as per plan during the initial phase and start-up duration of the project construction activities resulted in minor slippages/delays in project completion. In addition, there was a good working environment between client and contractor resulting in better project execution and management. Mainly, the contractor was on the front foot in the execution of projects, which had minimum/no delays during the initial and construction period. Hence, having a perfect start during the initial construction phase shall have a positive influence on the project success. Our research paper studies each type of delay with some real example supported by statistic results and suggests mitigation measures. Detailed analysis carried out with all stakeholders based on impact and occurrence of delays to have a practical and effective outcome to mitigate the delays. The key to improvement is to have proper control measures and periodic evaluation/audit to ensure implementation of the mitigation measures. The focus of this research is to reduce the delays encountered during the initial construction phase of the project life cycle.

Keywords: Construction activities delays, delay analysis for construction projects, mobilization delays, oil and gas projects delays.

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Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

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In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: Agricultural object detection, Deep learning, machine vision, YOLO family.

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Authors: Stefania Arguelles Reyes, Octavio José Salcedo Parra, Alberto Acosta López

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This paper presents a web application for the improvement of images through recognition. The web application is based on the analysis of picture-based recognition methods that allow an improvement on the physical appearance of people posting in social networks. The basis relies on the study of tools that can correct or improve some features of the face, with the help of a wide collection of user images taken as reference to build a facial profile. Automatic facial profiling can be achieved with a deeper study of the Object Detection Library. It was possible to improve the initial images with the help of MATLAB and its filtering functions. The user can have a direct interaction with the program and manually adjust his preferences.

Keywords: Application, MATLAB, make up, model, recognition.

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Authors: H. Nemmour, Y. Chibani

Abstract:

Decision fusion is one of hot research topics in classification area, which aims to achieve the best possible performance for the task at hand. In this paper, we investigate the usefulness of this concept to improve change detection accuracy in remote sensing. Thereby, outputs of two fuzzy change detectors based respectively on simultaneous and comparative analysis of multitemporal data are fused by using fuzzy integral operators. This method fuses the objective evidences produced by the change detectors with respect to fuzzy measures that express the difference of performance between them. The proposed fusion framework is evaluated in comparison with some ordinary fuzzy aggregation operators. Experiments carried out on two SPOT images showed that the fuzzy integral was the best performing. It improves the change detection accuracy while attempting to equalize the accuracy rate in both change and no change classes.

Keywords: change detection, decision fusion, fuzzy logic, remote sensing.

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Authors: Kapil Kumar, M. G. Dastidar, T. R. Sreekrishnan

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In the present study, an attempt was made to examine the potential of aerobic mixed culture for decolourization of Remazol Black B dye in batch reactors. The effect of pH, temperature, inoculum, initial concentration of dye and initial concentration of glucose was studied with an aim to determine the optimal conditions required for maximum decolourization and degradation. The culture exhibited maximum decolourization ability at pH between 7-8 and at 30°C. A 10% (v/v) inoculum and 1% (w/v) glucose concentration were found to be the optimum for decolourization. A maximum of 98% decolourization was observed at 25 ppm initial concentration of dye after 18 hours of incubation period. At higher dye concentration of 300 ppm, the removal in colour was found to be 75% in 48 hours of incubation period. The results show that the enriched mixed culture from activated sludge has good potential in removal of Remazol Black B dye from wastewater under aerobic conditions.

Keywords: Aerobic conditions, Decolourization, Mixed culture, Remazol Black B.

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

Abstract:

Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: Remote monitoring, non-destructive testing, embedded linux system, image processing.

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Authors: M. Derakhshanfar, B. Minaei-Bidgoli

Abstract:

Spatial trends are one of the valuable patterns in geo databases. They play an important role in data analysis and knowledge discovery from spatial data. A spatial trend is a regular change of one or more non spatial attributes when spatially moving away from a start object. Spatial trend detection is a graph search problem therefore heuristic methods can be good solution. Artificial immune system (AIS) is a special method for searching and optimizing. AIS is a novel evolutionary paradigm inspired by the biological immune system. The models based on immune system principles, such as the clonal selection theory, the immune network model or the negative selection algorithm, have been finding increasing applications in fields of science and engineering. In this paper, we develop a novel immunological algorithm based on clonal selection algorithm (CSA) for spatial trend detection. We are created neighborhood graph and neighborhood path, then select spatial trends that their affinity is high for antibody. In an evolutionary process with artificial immune algorithm, affinity of low trends is increased with mutation until stop condition is satisfied.

Keywords: Spatial Data Mining, Spatial Trend Detection, Heuristic Methods, Artificial Immune System, Clonal Selection Algorithm (CSA)

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Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

Abstract:

With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes.

Keywords: Android, information security, intrusion detection systems, malware, mobile devices.

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Authors: Mousa Mojarrad, Amir Masoud Rahmani, Mehrab Mohebi

Abstract:

Detection, feature extraction and pose estimation of people in images and video is made challenging by the variability of human appearance, the complexity of natural scenes and the high dimensionality of articulated body models and also the important field in Image, Signal and Vision Computing in recent years. In this paper, four types of people in 2D dimension image will be tested and proposed. The system will extract the size and the advantage of them (such as: tall fat, short fat, tall thin and short thin) from image. Fat and thin, according to their result from the human body that has been extract from image, will be obtained. Also the system extract every size of human body such as length, width and shown them in output.

Keywords: Analysis of Image Processing, Canny Edge Detection, Human Body Recognition, Measurement, Pose Estimation, 2D Human Dimension.

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Authors: S. Liepina, A. Jemeljanovs

Abstract:

Tasks of the work were study the possible E.coli contamination in red deer meat, identify pathogenic strains from isolated E.coli, determine their incidence in red deer meat and determine the presence of VT1, VT2 and eaeA genes for the pathogenic E.coli. 8 (10%) samples were randomly selected from 80 analysed isolates of E.coli and PCR reaction was performed on them. PCR was done both on initial materials – samples of red deer meat - and for already isolated liqueurs. Two of analysed venison samples contain verotoxin-producing strains of E. coli. It means that this meat is not safe to consumer. It was proven by the sequestration reaction of E. coli and by comparison of the obtained results with the database of microorganism genome available on the internet that the isolated culture corresponds to region 16S rDNS of E. coli thus presenting correctness of the microbiological methods.

Keywords: Deer meat, pathogenic Escherichia coli

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Authors: J. P. Dubois, O. M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: J. Costa, M. Ortigueira, A. Batista, T. Paiva

Abstract:

Sleep spindles are the most interesting hallmark of stage 2 sleep EEG. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Sleep Spindles are also promising objective indicators for neurodegenerative disorders. Visual spindle scoring however is a tedious workload. In this paper three different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform, Wavelet Transform and Wave Morphology for Spindle Detection. In order to improve the results, a combination of the three detectors is presented and comparison with human expert scorers is performed. The best performance is obtained with a combination of the three algorithms which resulted in a sensitivity and specificity of 94% when compared to human expert scorers.

Keywords: EEG, Short Time Fourier Transform, Sleep Spindles, Wave Morphology for Spindle Detection, Wavelet Transform.

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Authors: J. P. Dubois, O. M. Abdul-Latif

Abstract:

Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of SAR (synthetic aperture radar) images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to real SAR images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected SAR images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (the detection hypotheses) in the original images.

Keywords: Least Square-Support Vector Machine, SyntheticAperture Radar. Partially Developed Speckle, Multi-Look Model.

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

Abstract:

The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: Degree, initial cluster center, k-means, minimum spanning tree.

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Authors: M. Azadbakht, N. Tajari, R. Alimoradzade

Abstract:

Knowing pea pods mechanical resistance against dynamic forces are important for design of combine harvester. In pea combine harvesters, threshing is accomplished by two mechanical actions of impact and friction forces. In this research, the effects of initial moisture content and needed impact and friction energy on threshing of pea pods were studied. An impact device was built based on pendulum mechanism. The experiments were done at three initial moisture content levels of 12.1, 23.5 and 39.5 (%w.b.) for both impact and friction methods. Three energy levels of 0.088, 0.126 and 0.202 J were used for impact method and for friction method three energy levels of 0.784, 0.930 and 1.351 J. The threshing percentage was measured in each method. By using a frictional device, kinetic friction coefficients at above moisture contents were measured 0.257, 0.303 and 0.336, respectively. The results of variance analysis of the two methods showed that moisture content and energy have significant effects on the threshing percentage.

Keywords: Pea pod, Energy, Friction, Impact, Initial moisture content, Threshing.

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Authors: Sallem F., Dahhou B., Kamoun A.

Abstract:

In this work, the main problem considered is the  detection and the isolation of the actuator fault. A new formulation of  the linear system is generated to obtain the conditions of the actuator  fault diagnosis. The proposed method is based on the representation  of the actuator as a subsystem connected with the process system in  cascade manner. The designed formulation is generated to obtain the  conditions of the actuator fault detection and isolation. Detectability  conditions are expressed in terms of the invertibility notions. An  example and a comparative analysis with the classic formulation  illustrate the performances of such approach for simple actuator fault  diagnosis by using the linear model of nuclear reactor.

 

Keywords: Actuator fault, Fault detection, left invertibility, nuclear reactor, observability, parameter intervals, system inversion.

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