Search results for: Fault Detection and Diagnosis (FDD)
1590 ICF Neutron Detection Techniques Based on Doped ZnO Crystal
Authors: L. Chen, X. P. Ouyang, Z. B. Zhang, J. F. Zhang, J. L. Liu
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Ultrafast doped zinc oxide crystal promised us a good opportunity to build new instruments for ICF fusion neutron measurement. Two pulsed neutron detectors based on ZnO crystal wafer have been conceptually designed, the superfast ZnO timing detector and the scintillation recoil proton neutron detection system. The structure of these detectors was presented, and some characters were studied as well. The new detectors could be much faster than existing systems, and would be more competent for ICF neutron diagnostics.Keywords: ICF fusion neutron detection, proton recoil telescope, superfast timing, ZnO crystal
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20281589 Object Detection in Digital Images under Non-Standardized Conditions Using Illumination and Shadow Filtering
Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause
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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.Keywords: Image processing, Illumination equalization, Shadow filtering, Object detection, Colour models, Image segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10071588 Neural Network Based Approach for Face Detection cum Face Recognition
Authors: Kesari Verma, Aniruddha S. Thoke, Pritam Singh
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Automatic face detection is a complex problem in image processing. Many methods exist to solve this problem such as template matching, Fisher Linear Discriminate, Neural Networks, SVM, and MRC. Success has been achieved with each method to varying degrees and complexities. In proposed algorithm we used upright, frontal faces for single gray scale images with decent resolution and under good lighting condition. In the field of face recognition technique the single face is matched with single face from the training dataset. The author proposed a neural network based face detection algorithm from the photographs as well as if any test data appears it check from the online scanned training dataset. Experimental result shows that the algorithm detected up to 95% accuracy for any image.Keywords: Face Detection, Face Recognition, NN Approach, PCA Algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22861587 Design of FIR Filter for Water Level Detection
Authors: Sakol Udomsiri, Masahiro Iwahashi
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This paper proposes a new design of spatial FIR filter to automatically detect water level from a video signal of various river surroundings. A new approach in this report applies "addition" of frames and a "horizontal" edge detector to distinguish water region and land region. Variance of each line of a filtered video frame is used as a feature value. The water level is recognized as a boundary line between the land region and the water region. Edge detection filter essentially demarcates between two distinctly different regions. However, the conventional filters are not automatically adaptive to detect water level in various lighting conditions of river scenery. An optimized filter is purposed so that the system becomes robust to changes of lighting condition. More reliability of the proposed system with the optimized filter is confirmed by accuracy of water level detection.Keywords: water level, video, filter, detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22001586 A Novel Estimation Method for Integer Frequency Offset in Wireless OFDM Systems
Authors: Taeung Yoon, Youngpo Lee, Chonghan Song, Na Young Ha, Seokho Yoon
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Ren et al. presented an efficient carrier frequency offset (CFO) estimation method for orthogonal frequency division multiplexing (OFDM), which has an estimation range as large as the bandwidth of the OFDM signal and achieves high accuracy without any constraint on the structure of the training sequence. However, its detection probability of the integer frequency offset (IFO) rapidly varies according to the fractional frequency offset (FFO) change. In this paper, we first analyze the Ren-s method and define two criteria suitable for detection of IFO. Then, we propose a novel method for the IFO estimation based on the maximum-likelihood (ML) principle and the detection criteria defined in this paper. The simulation results demonstrate that the proposed method outperforms the Ren-s method in terms of the IFO detection probability irrespective of a value of the FFO.Keywords: Orthogonal frequency division multiplexing, integer frequency offset, estimation, training symbol
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24431585 Investigation of Utilizing L-Band Horn Antenna in Landmine Detection
Authors: Ahmad H. Abdelgwad, Ahmed A. Nashat
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Landmine detection is an important and yet challenging problem remains to be solved. Ground Penetrating Radar (GPR) is a powerful and rapidly maturing technology for subsurface threat identification. The detection methodology of GPR depends mainly on the contrast of the dielectric properties of the searched target and its surrounding soil. This contrast produces a partial reflection of the electromagnetic pulses that are being transmitted into the soil and then being collected by the GPR. One of the most critical hardware components for the performance of GPR is the antenna system. The current paper explores the design and simulation of a pyramidal horn antenna operating at L-band frequencies (1- 2 GHz) to detect a landmine. A prototype model of the GPR system setup is developed to simulate full wave analysis of the electromagnetic fields in different soil types. The contrast in the dielectric permittivity of the landmine and the sandy soil is the most important parameter to be considered for detecting the presence of landmine. L-band horn antenna is proved to be well-versed in the investigation of landmine detection.
Keywords: Full wave analysis, ground penetrating radar, horn antenna design, landmine detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9911584 Liver Tumor Detection by Classification through FD Enhancement of CT Image
Authors: N. Ghatwary, A. Ahmed, H. Jalab
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In this paper, an approach for the liver tumor detection in computed tomography (CT) images is represented. The detection process is based on classifying the features of target liver cell to either tumor or non-tumor. Fractional differential (FD) is applied for enhancement of Liver CT images, with the aim of enhancing texture and edge features. Later on, a fusion method is applied to merge between the various enhanced images and produce a variety of feature improvement, which will increase the accuracy of classification. Each image is divided into NxN non-overlapping blocks, to extract the desired features. Support vector machines (SVM) classifier is trained later on a supplied dataset different from the tested one. Finally, the block cells are identified whether they are classified as tumor or not. Our approach is validated on a group of patients’ CT liver tumor datasets. The experiment results demonstrated the efficiency of detection in the proposed technique.Keywords: Fractional differential (FD), Computed Tomography (CT), fusion.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16731583 Shot Detection Using Modified Dugad Model
Authors: Lenka Krulikovská, Jaroslav Polec
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In this paper we present a modification to existed model of threshold for shot cut detection, which is able to adapt itself to the sequence statistics and operate in real time, because it use for calculation only previously evaluated frames. The efficiency of proposed modified adaptive threshold scheme was verified through extensive test experiment with several similarity metrics and achieved results were compared to the results reached by the original model. According to results proposed threshold scheme reached higher accuracy than existed original model.
Keywords: Abrupt cut, shot cut detection, adaptive threshold.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15221582 Fast Accurate Detection of Frequency Jumps Using Kalman Filter with Non Linear Improvements
Authors: Mahmoud E. Mohamed, Ahmed F. Shalash, Hanan A. Kamal
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In communication systems, frequency jump is a serious problem caused by the oscillators used. Kalman filters are used to detect that jump, despite the tradeoff between the noise level and the speed of the detection. In this paper, an improvement is introduced in the Kalman filter, through a nonlinear change in the bandwidth of the filter. Simulation results show a considerable improvement in the filter speed with a very low noise level. Additionally, the effect on the response to false alarms is also presented and false alarm rate show improvement.
Keywords: Kalman Filter, Innovation, False Detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22161581 Tomographic Images Reconstruction Simulation for Defects Detection in Specimen
Authors: Kedit J.
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This paper is the tomographic images reconstruction simulation for defects detection in specimen. The specimen is the thin cylindrical steel contained with low density materials. The defects in material are simulated in three shapes.The specimen image function will be transformed to projection data. Radon transform and its inverse provide the mathematical for reconstructing tomographic images from projection data. The result of the simulation show that the reconstruction images is complete for defect detection.Keywords: Tomography, Tomography Reconstruction, Radon Transform
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14161580 Combine a Population-based Incremental Learning with Artificial Immune System for Intrusion Detection System
Authors: Jheng-Long Wu, Pei-Chann Chang, Hsuan-Ming Chen
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This research focus on the intrusion detection system (IDS) development which using artificial immune system (AIS) with population based incremental learning (PBIL). AIS have powerful distinguished capability to extirpate antigen when the antigen intrude into human body. The PBIL is based on past learning experience to adjust new learning. Therefore we propose an intrusion detection system call PBIL-AIS which combine two approaches of PBIL and AIS to evolution computing. In AIS part we design three mechanisms such as clonal selection, negative selection and antibody level to intensify AIS performance. In experimental result, our PBIL-AIS IDS can capture high accuracy when an intrusion connection attacks.
Keywords: Artificial immune system, intrusion detection, population-based incremental learning, evolution computing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19141579 Fully Automated Methods for the Detection and Segmentation of Mitochondria in Microscopy Images
Authors: Blessing Ojeme, Frederick Quinn, Russell Karls, Shannon Quinn
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The detection and segmentation of mitochondria from fluorescence microscopy is crucial for understanding the complex structure of the nervous system. However, the constant fission and fusion of mitochondria and image distortion in the background make the task of detection and segmentation challenging. Although there exists a number of open-source software tools and artificial intelligence (AI) methods designed for analyzing mitochondrial images, the availability of only a few combined expertise in the medical field and AI required to utilize these tools poses a challenge to its full adoption and use in clinical settings. Motivated by the advantages of automated methods in terms of good performance, minimum detection time, ease of implementation, and cross-platform compactibility, this study proposes a fully automated framework for the detection and segmentation of mitochondria using both image shape information and descriptive statistics. Using the low-cost, open-source Python and OpenCV library, the algorithms are implemented in three stages: pre-processing; image binarization; and coarse-to-fine segmentation. The proposed model is validated using the fluorescence mitochondrial dataset. Ground truth labels generated using Labkit were also used to evaluate the performance of our detection and segmentation model using precision, recall and rand index. The study produces good detection and segmentation results and reports the challenges encountered during the image analysis of mitochondrial morphology from the fluorescence mitochondrial dataset. A discussion on the methods and future perspectives of fully automated frameworks concludes the paper.
Keywords: 2D, Binarization, CLAHE, detection, fluorescence microscopy, mitochondria, segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4291578 ML Detection with Symbol Estimation for Nonlinear Distortion of OFDM Signal
Authors: Somkiat Lerkvaranyu, Yoshikazu Miyanaga
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In this paper, a new technique of signal detection has been proposed for detecting the orthogonal frequency-division multiplexing (OFDM) signal in the presence of nonlinear distortion.There are several advantages of OFDM communications system.However, one of the existing problems is remain considered as the nonlinear distortion generated by high-power-amplifier at the transmitter end due to the large dynamic range of an OFDM signal. The proposed method is the maximum likelihood detection with the symbol estimation. When the training data are available, the neural network has been used to learn the characteristic of received signal and to estimate the new positions of the transmitted symbol which are provided to the maximum likelihood detector. Resulting in the system performance, the nonlinear distortions of a traveling wave tube amplifier with OFDM signal are considered in this paper.Simulation results of the bit-error-rate performance are obtained with 16-QAM OFDM systems.
Keywords: OFDM, TWTA, nonlinear distortion, detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16671577 Fused Structure and Texture (FST) Features for Improved Pedestrian Detection
Authors: Hussin K. Ragb, Vijayan K. Asari
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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.Keywords: Pedestrian detection, phase congruency, local phase, LBP features, CSLBP features, FST descriptor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14791576 An FPGA Implementation of Intelligent Visual Based Fall Detection
Authors: Peng Shen Ong, Yoong Choon Chang, Chee Pun Ooi, Ettikan K. Karuppiah, Shahirina Mohd Tahir
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Falling has been one of the major concerns and threats to the independence of the elderly in their daily lives. With the worldwide significant growth of the aging population, it is essential to have a promising solution of fall detection which is able to operate at high accuracy in real-time and supports large scale implementation using multiple cameras. Field Programmable Gate Array (FPGA) is a highly promising tool to be used as a hardware accelerator in many emerging embedded vision based system. Thus, it is the main objective of this paper to present an FPGA-based solution of visual based fall detection to meet stringent real-time requirements with high accuracy. The hardware architecture of visual based fall detection which utilizes the pixel locality to reduce memory accesses is proposed. By exploiting the parallel and pipeline architecture of FPGA, our hardware implementation of visual based fall detection using FGPA is able to achieve a performance of 60fps for a series of video analytical functions at VGA resolutions (640x480). The results of this work show that FPGA has great potentials and impacts in enabling large scale vision system in the future healthcare industry due to its flexibility and scalability.Keywords: Fall detection, FPGA, hardware implementation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24561575 High Level Synthesis of Canny Edge Detection Algorithm on Zynq Platform
Authors: Hanaa M. Abdelgawad, Mona Safar, Ayman M. Wahba
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Real time image and video processing is a demand in many computer vision applications, e.g. video surveillance, traffic management and medical imaging. The processing of those video applications requires high computational power. Thus, the optimal solution is the collaboration of CPU and hardware accelerators. In this paper, a Canny edge detection hardware accelerator is proposed. Edge detection is one of the basic building blocks of video and image processing applications. It is a common block in the pre-processing phase of image and video processing pipeline. Our presented approach targets offloading the Canny edge detection algorithm from processing system (PS) to programmable logic (PL) taking the advantage of High Level Synthesis (HLS) tool flow to accelerate the implementation on Zynq platform. The resulting implementation enables up to a 100x performance improvement through hardware acceleration. The CPU utilization drops down and the frame rate jumps to 60 fps of 1080p full HD input video stream.
Keywords: High Level Synthesis, Canny edge detection, Hardware accelerators, and Computer Vision.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 54171574 Optical Flow Based Moving Object Detection and Tracking for Traffic Surveillance
Authors: Sepehr Aslani, Homayoun Mahdavi-Nasab
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Automated motion detection and tracking is a challenging task in traffic surveillance. In this paper, a system is developed to gather useful information from stationary cameras for detecting moving objects in digital videos. The moving detection and tracking system is developed based on optical flow estimation together with application and combination of various relevant computer vision and image processing techniques to enhance the process. To remove noises, median filter is used and the unwanted objects are removed by applying thresholding algorithms in morphological operations. Also the object type restrictions are set using blob analysis. The results show that the proposed system successfully detects and tracks moving objects in urban videos.
Keywords: Optical flow estimation, moving object detection, tracking, morphological operation, blob analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 101371573 A Numerical Investigation of Lamb Wave Damage Diagnosis for Composite Delamination Using Instantaneous Phase
Authors: Haode Huo, Jingjing He, Rui Kang, Xuefei Guan
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This paper presents a study of Lamb wave damage diagnosis of composite delamination using instantaneous phase data. Numerical experiments are performed using the finite element method. Different sizes of delamination damages are modeled using finite element package ABAQUS. Lamb wave excitation and responses data are obtained using a pitch-catch configuration. Empirical mode decomposition is employed to extract the intrinsic mode functions (IMF). Hilbert–Huang Transform is applied to each of the resulting IMFs to obtain the instantaneous phase information. The baseline data for healthy plates are also generated using the same procedure. The size of delamination is correlated with the instantaneous phase change for damage diagnosis. It is observed that the unwrapped instantaneous phase of shows a consistent behavior with the increasing delamination size.Keywords: Delamination, lamb wave, finite element method, EMD, instantaneous phase.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 7081572 Comparative Analysis of Two Approaches to Joint Signal Detection, ToA and AoA Estimation in Multi-Element Antenna Arrays
Authors: Olesya Bolkhovskaya, Alexey Davydov, Alexander Maltsev
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In this paper two approaches to joint signal detection, time of arrival (ToA) and angle of arrival (AoA) estimation in multi-element antenna array are investigated. Two scenarios were considered: first one, when the waveform of the useful signal is known a priori and, second one, when the waveform of the desired signal is unknown. For first scenario, the antenna array signal processing based on multi-element matched filtering (MF) with the following non-coherent detection scheme and maximum likelihood (ML) parameter estimation blocks is exploited. For second scenario, the signal processing based on the antenna array elements covariance matrix estimation with the following eigenvector analysis and ML parameter estimation blocks is applied. The performance characteristics of both signal processing schemes are thoroughly investigated and compared for different useful signals and noise parameters.
Keywords: Antenna array, signal detection, ToA, AoA estimation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20381571 Frame and Burst Acquisition in TDMA Satellite Communication Networks with Transponder Hopping
Authors: Vitalice K. Oduol, C. Ardil
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The paper presents frame and burst acquisition in a satellite communication network based on time division multiple access (TDMA) in which the transmissions may be carried on different transponders. A unique word pattern is used for the acquisition process. The search for the frame is aided by soft-decision of QPSK modulated signals in an additive white Gaussian channel. Results show that when the false alarm rate is low the probability of detection is also low, and the acquisition time is long. Conversely when the false alarm rate is high, the probability of detection is also high and the acquisition time is short. Thus the system operators can trade high false alarm rates for high detection probabilities and shorter acquisition times.
Keywords: burst acquisition, burst time plan, frame acquisition, satellite access, satellite TDMA, unique word detection
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 91421570 Semi-Supervised Outlier Detection Using a Generative and Adversary Framework
Authors: Jindong Gu, Matthias Schubert, Volker Tresp
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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.Keywords: Outlier detection, generative adversary networks, semi-supervised learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10601569 A New Voting Approach to Texture Defect Detection Based on Multiresolutional Decomposition
Authors: B. B. M. Moasheri, S. Azadinia
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Wavelets have provided the researchers with significant positive results, by entering the texture defect detection domain. The weak point of wavelets is that they are one-dimensional by nature so they are not efficient enough to describe and analyze two-dimensional functions. In this paper we present a new method to detect the defect of texture images by using curvelet transform. Simulation results of the proposed method on a set of standard texture images confirm its correctness. Comparing the obtained results indicates the ability of curvelet transform in describing discontinuity in two-dimensional functions compared to wavelet transformKeywords: Curvelet, Defect detection, Wavelet.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15631568 Evolving Digital Circuits for Early Stage Breast Cancer Detection Using Cartesian Genetic Programming
Authors: Zahra Khalid, Gul Muhammad Khan, Arbab Masood Ahmad
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Cartesian Genetic Programming (CGP) is explored to design an optimal circuit capable of early stage breast cancer detection. CGP is used to evolve simple multiplexer circuits for detection of malignancy in the Fine Needle Aspiration (FNA) samples of breast. The data set used is extracted from Wisconsins Breast Cancer Database (WBCD). A range of experiments were performed, each with different set of network parameters. The best evolved network detected malignancy with an accuracy of 99.14%, which is higher than that produced with most of the contemporary non-linear techniques that are computational expensive than the proposed system. The evolved network comprises of simple multiplexers and can be implemented easily in hardware without any further complications or inaccuracy, being the digital circuit.Keywords: Breast cancer detection, cartesian genetic programming, evolvable hardware, fine needle aspiration (FNA).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8021567 Patients’ Perceptions of Receiving a Diagnosis of a Hematological Malignancy, Following the SPIKES Protocol
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Objective: Sharing devastating news with patients is often considered the most difficult task of doctors. This study aimed to explore patients’ perceptions of receiving bad news including which features improve the experience and which areas need refining. Methods: A questionnaire was written based on the steps of the SPIKES model for breaking bad new. 20 patients receiving treatment for a hematological malignancy completed the questionnaire. Results: Overall, the results are promising as most patients praised their consultation. ‘Poor’ was more commonly rated by women and participants aged 45-64. The main differences between the ‘excellent’ and ‘poor’ consultations include the doctor’s sensitivity and checking the patients’ understanding. Only 35% of patients were asked their existing knowledge and 85% of consultations failed to discuss the impact of the diagnosis on daily life. Conclusion: This study agreed with the consensus of existing literature. The commended aspects include consultation set-up and information given. Areas patients felt needed improvement include doctors determining the patient’s existing knowledge and checking new information has been understood. Doctors should also explore how the diagnosis will affect the patient’s life. With a poorer prognosis, doctors should work on conveying appropriate hope. The study was limited by a small sample size and potential recall bias.Keywords: Communication, diagnosis, hematology, patients.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19331566 Dislocation Modelling of the 1997-2009 High-Precision Global Positioning System Displacements in Darjiling- Sikkim Himalaya, India
Authors: Kutubuddin Ansari, Malay Mukul, Sridevi Jade
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We used high-precision Global Positioning System (GPS) to geodetically constrain the motion of stations in the Darjiling-Sikkim Himalayan (DSH) wedge and examine the deformation at the Indian-Tibetan plate boundary using IGS (International GPS Service) fiducial stations. High-precision GPS based displacement and velocity field was measured in the DSH between 1997 and 2009. To obtain additional insight north of the Indo-Tibetan border and in the Darjiling-Sikkim-Tibet (DaSiT) wedge, published velocities from four stations J037, XIGA, J029 and YADO were also included in the analysis. India-fixed velocities or the back-slip was computed relative to the pole of rotation of the Indian Plate (Latitude 52.97 ± 0.22º, Longitude - 0.30 ± 3.76º, and Angular Velocity 0.500 ± 0.008º/ Myr) in the DaSiT wedge. Dislocation modelling was carried out with the back-slip to model the best possible solution of a finite rectangular dislocation or the causative fault based on dislocation theory that produced the observed back-slip using a forward modelling approach. To find the best possible solution, three different models were attempted. First, slip along a single thrust fault, then two thrust faults and in finally, three thrust faults were modelled to simulate the back-slip in the DaSiT wedge. The three-fault case bests the measured displacements and is taken as the best possible solution.
Keywords: Global Positioning System, Darjiling-Sikkim Himalaya, Dislocation modelling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20931565 Automatic Detection of Mass Type Breast Cancer using Texture Analysis in Korean Digital Mammography
Authors: E. B. Jo, J. H. Lee, J. Y. Park, S. M. Kim
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In this study, we present an advanced detection technique for mass type breast cancer based on texture information of organs. The proposed method detects the cancer areas in three stages. In the first stage, the midpoints of mass area are determined based on AHE (Adaptive Histogram Equalization). In the second stage, we set the threshold coefficient of homogeneity by using MLE (Maximum Likelihood Estimation) to compute the uniformity of texture. Finally, mass type cancer tissues are extracted from the original image. As a result, it was observed that the proposed method shows an improved detection performance on dense breast tissues of Korean women compared with the existing methods. It is expected that the proposed method may provide additional diagnostic information for detection of mass-type breast cancer.Keywords: Mass Type Breast Cancer, Mammography, Maximum Likelihood Estimation (MLE), Ranklets, SVM
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19741564 An Efficient Method of Shot Cut Detection
Authors: Lenka Krulikovská, Jaroslav Polec
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In this paper we present a method of abrupt cut detection with a novel logic of frames- comparison. Actual frame is compared with its motion estimated prediction instead of comparison with successive frame. Four different similarity metrics were employed to estimate the resemblance of compared frames. Obtained results were evaluated by standard used measures of test accuracy and compared with existing approach. Based on the results, we claim the proposed method is more effective and Pearson correlation coefficient obtained the best results among chosen similarity metrics.
Keywords: Abrupt cut, mutual information, shot cut detection, Pearson correlation coefficient.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19221563 Evaluation of Graph-based Analysis for Forest Fire Detections
Authors: Young Gi Byun, Yong Huh, Kiyun Yu, Yong Il Kim
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Spatial outliers in remotely sensed imageries represent observed quantities showing unusual values compared to their neighbor pixel values. There have been various methods to detect the spatial outliers based on spatial autocorrelations in statistics and data mining. These methods may be applied in detecting forest fire pixels in the MODIS imageries from NASA-s AQUA satellite. This is because the forest fire detection can be referred to as finding spatial outliers using spatial variation of brightness temperature. This point is what distinguishes our approach from the traditional fire detection methods. In this paper, we propose a graph-based forest fire detection algorithm which is based on spatial outlier detection methods, and test the proposed algorithm to evaluate its applicability. For this the ordinary scatter plot and Moran-s scatter plot were used. In order to evaluate the proposed algorithm, the results were compared with the MODIS fire product provided by the NASA MODIS Science Team, which showed the possibility of the proposed algorithm in detecting the fire pixels.Keywords: Spatial Outlier Detection, MODIS, Forest Fire
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22151562 A Discriminatory Rewarding Mechanism for Sybil Detection with Applications to Tor
Authors: Asim Kumar Pal, Debabrata Nath, Sumit Chakraborty
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This paper presents an economic game for sybil detection in a distributed computing environment. Cost parameters reflecting impacts of different sybil attacks are introduced in the sybil detection game. The optimal strategies for this game in which both sybil and non-sybil identities are expected to participate are devised. A cost sharing economic mechanism called Discriminatory Rewarding Mechanism for Sybil Detection is proposed based on this game. A detective accepts a security deposit from each active agent, negotiates with the agents and offers rewards to the sybils if the latter disclose their identity. The basic objective of the detective is to determine the optimum reward amount for each sybil which will encourage the maximum possible number of sybils to reveal themselves. Maintaining privacy is an important issue for the mechanism since the participants involved in the negotiation are generally reluctant to share their private information. The mechanism has been applied to Tor by introducing a reputation scoring function.Keywords: Game theory, Incentive mechanism, Reputation, Sybil Attack
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17091561 An Empirical Mode Decomposition Based Method for Action Potential Detection in Neural Raw Data
Authors: Sajjad Farashi, Mohammadjavad Abolhassani, Mostafa Taghavi Kani
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Information in the nervous system is coded as firing patterns of electrical signals called action potential or spike so an essential step in analysis of neural mechanism is detection of action potentials embedded in the neural data. There are several methods proposed in the literature for such a purpose. In this paper a novel method based on empirical mode decomposition (EMD) has been developed. EMD is a decomposition method that extracts oscillations with different frequency range in a waveform. The method is adaptive and no a-priori knowledge about data or parameter adjusting is needed in it. The results for simulated data indicate that proposed method is comparable with wavelet based methods for spike detection. For neural signals with signal-to-noise ratio near 3 proposed methods is capable to detect more than 95% of action potentials accurately.
Keywords: EMD, neural data processing, spike detection, wavelet decomposition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2364