Search results for: Gesture detection
931 Fault Detection and Identification of COSMED K4b2 Based On PCA and Neural Network
Authors: Jing Zhou, Steven Su, Aihuang Guo
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COSMED K4b2 is a portable electrical device designed to test pulmonary functions. It is ideal for many applications that need the measurement of the cardio-respiratory response either in the field or in the lab is capable with the capability to delivery real time data to a sink node or a PC base station with storing data in the memory at the same time. But the actual sensor outputs and data received may contain some errors, such as impulsive noise which can be related to sensors, low batteries, environment or disturbance in data acquisition process. These abnormal outputs might cause misinterpretations of exercise or living activities to persons being monitored. In our paper we propose an effective and feasible method to detect and identify errors in applications by principal component analysis (PCA) and a back propagation (BP) neural network.
Keywords: BP Neural Network, Exercising Testing, Fault Detection and Identification, Principal Component Analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3077930 The Comparison Study of Current Control Techniques for Active Power Filters
Authors: T. Narongrit, K-L. Areerak, K-N. Areerak
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This paper presents the comparison study of current control techniques for shunt active power filter. The hysteresis current control, the delta modulation control and the carrier-based PWM control are considered in the paper. The synchronous detection method is used to calculate the reference currents for shunt active power filter. The simulation results show that the carrier-based PWM control technique provides the minimum %THD value of the source currents compared with other comparable techniques after compensation. However, the %THD values of all three techniques can follow the IEEE std.519-1992.
Keywords: hysteresis current control, delta modulation current control, pulse width modulation control, shunt active power filter, synchronous detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2491929 Indian License Plate Detection and Recognition Using Morphological Operation and Template Matching
Authors: W. Devapriya, C. Nelson Kennedy Babu, T. Srihari
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Automatic License plate recognition (ALPR) is a technology which recognizes the registration plate or number plate or License plate of a vehicle. In this paper, an Indian vehicle number plate is mined and the characters are predicted in efficient manner. ALPR involves four major technique i) Pre-processing ii) License Plate Location Identification iii) Individual Character Segmentation iv) Character Recognition. The opening phase, named pre-processing helps to remove noises and enhances the quality of the image using the conception of Morphological Operation and Image subtraction. The second phase, the most puzzling stage ascertain the location of license plate using the protocol Canny Edge detection, dilation and erosion. In the third phase, each characters characterized by Connected Component Approach (CCA) and in the ending phase, each segmented characters are conceptualized using cross correlation template matching- a scheme specifically appropriate for fixed format. Major application of ALPR is Tolling collection, Border Control, Parking, Stolen cars, Enforcement, Access Control, Traffic control. The database consists of 500 car images taken under dissimilar lighting condition is used. The efficiency of the system is 97%. Our future focus is Indian Vehicle License Plate Validation (Whether License plate of a vehicle is as per Road transport and highway standard).
Keywords: Automatic License plate recognition, Character recognition, Number plate Recognition, Template matching, morphological operation, canny edge detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2406928 Analysis of Acoustic Emission Signal for the Detection of Defective Manufactures in Press Process
Authors: Dong Hun Kim, Won Kyu Lee, Sok Won Kim
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Small cracks or chips of a product appear very frequently in the course of continuous production of an automatic press process system. These phenomena become the cause of not only defective product but also damage of a press mold. In order to solve this problem AE system was introduced. AE system was expected to be very effective to real time detection of the defective product and to prevention of the damage of the press molds. In this study, for pick and analysis of AE signals generated from the press process, AE sensors/pre-amplifier/analysis and processing board were used as frequently found in the other similar cases. For analysis and processing the AE signals picked in real time from the good or bad products, specialized software called cdm8 was used. As a result of this work it was conformed that intensity and shape of the various AE signals differ depending on the weight and thickness of metal sheet and process type.Keywords: press, acoustic emission, signal processing
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1631927 The Use of Music Therapy to Improve Non-Verbal Communication Skills for Children with Autism
Authors: Maria Vinca Novenia
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The number of school-aged children with autism in Indonesia has been increasing each year. Autism is a developmental disorder which can be diagnosed in childhood. One of the symptoms is the lack of communication skills. Music therapy is known as an effective treatment for children with autism. Music elements and structures create a good space for children with autism to express their feelings and communicate their thoughts. School-aged children are expected to be able to communicate non-verbally very well, but children with autism experience the difficulties of communicating non-verbally. The aim of this research is to analyze the significance of music therapy treatment to improve non-verbal communication tools for children with autism. This research informs teachers and parents on how music can be used as a media to communicate with children with autism. The qualitative method is used to analyze this research, while the result is described with the microanalysis technique. The result is measured specifically from the whole experiment, hours of every week, minutes of every session, and second of every moment. The samples taken are four school-aged children with autism in the age range of six to 11 years old. This research is conducted within four months started with observation, interview, literature research, and direct experiment. The result demonstrates that music therapy could be effectively used as a non-verbal communication tool for children with autism, such as changes of body gesture, eye contact, and facial expression.Keywords: Autism, non-verbal communication, microanalysis, music therapy, school-aged children.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2118926 Study of Anti-Symmetric Flexural Mode Propagation along Wedge Tip with a Crack
Authors: Manikanta Prasad Banda, Che Hua Yang
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Anti-symmetric wave propagation along the particle motion of the wedge waves is known as anti-symmetric flexural (ASF) modes which travel along the wedge tips of the mid-plane apex with a small truncation. This paper investigates the characteristics of the ASF modes propagation with the wedge tip crack. The simulation and experimental results obtained by a three-dimensional (3-D) finite element model explained the contact acoustic non-linear (CAN) behavior in explicit dynamics in ABAQUS and the ultrasonic non-destructive testing (NDT) method is used for defect detection. The effect of various parameters on its high and low-level conversion modes are known for complex reflections and transmissions involved with direct reflections and transmissions. The results are used to predict the location of crack through complex transmission and reflection coefficients.
Keywords: ASF mode, crack detection, finite elements method, laser ultrasound technique, wedge waves.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 533925 Current Status and Future Trends of Mechanized Fruit Thinning Devices and Sensor Technology
Authors: Marco Lopes, Pedro D. Gaspar, Maria P. Simões
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This paper reviews the different concepts that have been investigated concerning the mechanization of fruit thinning as well as multiple working principles and solutions that have been developed for feature extraction of horticultural products, both in the field and industrial environments. The research should be committed towards selective methods, which inevitably need to incorporate some kinds of sensor technology. Computer vision often comes out as an obvious solution for unstructured detection problems, although leaves despite the chosen point of view frequently occlude fruits. Further research on non-traditional sensors that are capable of object differentiation is needed. Ultrasonic and Near Infrared (NIR) technologies have been investigated for applications related to horticultural produce and show a potential to satisfy this need while simultaneously providing spatial information as time of flight sensors. Light Detection and Ranging (LIDAR) technology also shows a huge potential but it implies much greater costs and the related equipment is usually much larger, making it less suitable for portable devices, which may serve a purpose on smaller unstructured orchards. Portable devices may serve a purpose on these types of orchards. In what concerns sensor methods, on-tree fruit detection, major challenge is to overcome the problem of fruits’ occlusion by leaves and branches. Hence, nontraditional sensors capable of providing some type of differentiation should be investigated.
Keywords: Fruit thinning, horticultural field, portable devices, sensor technologies.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 983924 Guided Wave Sensitivity for De-Bond Defects in Aluminum Skin-Honeycomb Core
Authors: A. Satour, F. Boubenider, R. Halimi, A. Badidibouda
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Sandwich plates are finding an increasing range of application in the aircraft industry. The inspection of honeycomb composite structure by conventional ultrasonic technique is complex and very time consuming. The present study demonstrates a technique using guided Lamb waves at low frequencies to predict de-bond defects in aluminum skin-honeycomb core sandwich structure used in aeronautics. The numerical method was investigated for drawing the dispersion and displacement curves of ultrasonic Lamb wave propagated in Aluminum plate. An experimental study was carried out to check the theoretical prediction. The detection of unsticking between the skin and the core was tested by the two first modes for a low frequency. It was found that A0 mode is more sensitive to delamination defect compared to S0 mode.
Keywords: Damage detection, delamination, guided waves, Sandwich structure.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2324923 Decoder Design for a New Single Error Correcting/Double Error Detecting Code
Authors: M. T. Anwar, P. K. Lala, P. Thenappan
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This paper presents the decoder design for the single error correcting and double error detecting code proposed by the authors in an earlier paper. The speed of error detection and correction of a code is largely dependent upon the associated encoder and decoder circuits. The complexity and the speed of such circuits are determined by the number of 1?s in the parity check matrix (PCM). The number of 1?s in the parity check matrix for the code proposed by the authors are fewer than in any currently known single error correcting/double error detecting code. This results in simplified encoding and decoding circuitry for error detection and correction.Keywords: Decoder, Hsiao code, Parity Check Matrix, Syndrome Pattern.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2080922 A Fast Sign Localization System Using Discriminative Color Invariant Segmentation
Authors: G.P. Nguyen, H.J. Andersen
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Building intelligent traffic guide systems has been an interesting subject recently. A good system should be able to observe all important visual information to be able to analyze the context of the scene. To do so, signs in general, and traffic signs in particular, are usually taken into account as they contain rich information to these systems. Therefore, many researchers have put an effort on sign recognition field. Sign localization or sign detection is the most important step in the sign recognition process. This step filters out non informative area in the scene, and locates candidates in later steps. In this paper, we apply a new approach in detecting sign locations using a new color invariant model. Experiments are carried out with different datasets introduced in other works where authors claimed the difficulty in detecting signs under unfavorable imaging conditions. Our method is simple, fast and most importantly it gives a high detection rate in locating signs.Keywords: Sign localization, color-based segmentation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1295921 Autonomously Determining the Parameters for SVDD with RBF Kernel from a One-Class Training Set
Authors: Andreas Theissler, Ian Dear
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The one-class support vector machine “support vector data description” (SVDD) is an ideal approach for anomaly or outlier detection. However, for the applicability of SVDD in real-world applications, the ease of use is crucial. The results of SVDD are massively determined by the choice of the regularisation parameter C and the kernel parameter of the widely used RBF kernel. While for two-class SVMs the parameters can be tuned using cross-validation based on the confusion matrix, for a one-class SVM this is not possible, because only true positives and false negatives can occur during training. This paper proposes an approach to find the optimal set of parameters for SVDD solely based on a training set from one class and without any user parameterisation. Results on artificial and real data sets are presented, underpinning the usefulness of the approach.
Keywords: Support vector data description, anomaly detection, one-class classification, parameter tuning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2935920 Structural Damage Detection via Incomplete Modal Data Using Output Data Only
Authors: Ahmed Noor Al-Qayyim, Barlas Ozden Caglayan
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Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on to obtain very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. The study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using ‘Two Points Condensation (TPC) technique’. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices obtain from optimization the equation of motion using the measured test data. The current stiffness matrices compare with original (undamaged) stiffness matrices. The large percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element, where two cases consider. The method detects the damage and determines its location accurately in both cases. In addition, the results illustrate these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can be used also for big structures.Keywords: Damage detection, two points–condensation, structural health monitoring, signals processing, optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2699919 Structural Health Monitoring of Offshore Structures Using Wireless Sensor Networking under Operational and Environmental Variability
Authors: Srinivasan Chandrasekaran, Thailammai Chithambaram, Shihas A. Khader
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The early-stage damage detection in offshore structures requires continuous structural health monitoring and for the large area the position of sensors will also plays an important role in the efficient damage detection. Determining the dynamic behavior of offshore structures requires dense deployment of sensors. The wired Structural Health Monitoring (SHM) systems are highly expensive and always needs larger installation space to deploy. Wireless sensor networks can enhance the SHM system by deployment of scalable sensor network, which consumes lesser space. This paper presents the results of wireless sensor network based Structural Health Monitoring method applied to a scaled experimental model of offshore structure that underwent wave loading. This method determines the serviceability of the offshore structure which is subjected to various environment loads. Wired and wireless sensors were installed in the model and the response of the scaled BLSRP model under wave loading was recorded. The wireless system discussed in this study is the Raspberry pi board with Arm V6 processor which is programmed to transmit the data acquired by the sensor to the server using Wi-Fi adapter, the data is then hosted in the webpage. The data acquired from the wireless and wired SHM systems were compared and the design of the wireless system is verified.Keywords: Condition assessment, damage detection, structural health monitoring, structural response, wireless sensor network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2970918 Global Security Using Human Face Understanding under Vision Ubiquitous Architecture System
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Different methods containing biometric algorithms are presented for the representation of eigenfaces detection including face recognition, are identification and verification. Our theme of this research is to manage the critical processing stages (accuracy, speed, security and monitoring) of face activities with the flexibility of searching and edit the secure authorized database. In this paper we implement different techniques such as eigenfaces vector reduction by using texture and shape vector phenomenon for complexity removal, while density matching score with Face Boundary Fixation (FBF) extracted the most likelihood characteristics in this media processing contents. We examine the development and performance efficiency of the database by applying our creative algorithms in both recognition and detection phenomenon. Our results show the performance accuracy and security gain with better achievement than a number of previous approaches in all the above processes in an encouraging mode.Keywords: Ubiquitous architecture, verification, Identification, recognition
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1336917 Wavelet Entropy Based Algorithm for Fault Detection and Classification in FACTS Compensated Transmission Line
Authors: Amany M. El-Zonkoly, Hussein Desouki
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Distance protection of transmission lines including advanced flexible AC transmission system (FACTS) devices has been a very challenging task. FACTS devices of interest in this paper are static synchronous series compensators (SSSC) and unified power flow controller (UPFC). In this paper, a new algorithm is proposed to detect and classify the fault and identify the fault position in a transmission line with respect to a FACTS device placed in the midpoint of the transmission line. Discrete wavelet transformation and wavelet entropy calculations are used to analyze during fault current and voltage signals of the compensated transmission line. The proposed algorithm is very simple and accurate in fault detection and classification. A variety of fault cases and simulation results are introduced to show the effectiveness of such algorithm.
Keywords: Entropy calculation, FACTS, SSSC, UPFC, wavelet transform.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2075916 An Advanced Stereo Vision Based Obstacle Detection with a Robust Shadow Removal Technique
Authors: Saeid Fazli, Hajar Mohammadi D., Payman Moallem
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This paper presents a robust method to detect obstacles in stereo images using shadow removal technique and color information. Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle depth using stereo matching and disparity map. The proposed advanced method is divided into three phases, the first phase is detecting obstacles and removing shadows, the second one is matching and the last phase is depth computing. We propose a robust method for detecting obstacles in stereo images using a shadow removal technique based on color information in HIS space, at the first phase. In this paper we use Normalized Cross Correlation (NCC) function matching with a 5 × 5 window and prepare an empty matching table τ and start growing disparity components by drawing a seed s from S which is computed using canny edge detector, and adding it to τ. In this way we achieve higher performance than the previous works [2,17]. A fast stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. The obstacle identified in phase one which appears in the disparity map of phase two enters to the third phase of depth computing. Finally, experimental results are presented to show the effectiveness of the proposed method.
Keywords: obstacle detection, stereo vision, shadowremoval, color, stereo matching
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2073915 High Performance Liquid Chromatography Determination of Urinary Hippuric Acid and Benzoic Acid as Indices for Glue Sniffer Urine
Authors: Abdul Rahim Yacob, Mohamad Raizul Zinalibdin
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A simple method for the simultaneous determination of hippuric acid and benzoic acid in urine using reversed-phase high performance liquid chromatography was described. Chromatography was performed on a Nova-Pak C18 (3.9 x 150 mm) column with a mobile phase of mixed solution methanol: water: acetic acid (20:80:0.2) and UV detection at 254 nm. The calibration curve was linear within concentration range at 0.125 to 6.0 mg/ml of hippuric acid and benzoic acid. The recovery, accuracy and coefficient variance of hippuric acid were 104.54%, 0.2% and 0.2% respectively and for benzoic acid were 98.48%, 1.25% and 0.60% respectively. The detection limit of this method was 0.01ng/l for hippuric acid and 0.06ng/l for benzoic acid. This method has been applied to the analysis of urine samples from the suspected of toluene abuser or glue sniffer among secondary school students at Johor Bahru.Keywords: Glue sniffer, High Performance LiquidChromatography, Hippuric Acid, Toluene, Urine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3372914 Real Time Detection, Tracking and Recognition of Medication Intake
Authors: H. H. Huynh, J. Meunier, J.Sequeira, M.Daniel
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In this paper, the detection and tracking of face, mouth, hands and medication bottles in the context of medication intake monitoring with a camera is presented. This is aimed at recognizing medication intake for elderly in their home setting to avoid an inappropriate use. Background subtraction is used to isolate moving objects, and then, skin and bottle segmentations are done in the RGB normalized color space. We use a minimum displacement distance criterion to track skin color regions and the R/G ratio to detect the mouth. The color-labeled medication bottles are simply tracked based on the color space distance to their mean color vector. For the recognition of medication intake, we propose a three-level hierarchal approach, which uses activity-patterns to recognize the normal medication intake activity. The proposed method was tested with three persons, with different medication intake scenarios, and gave an overall precision of over 98%.
Keywords: Activity recognition, background subtraction, tracking, medication intake, video surveillance
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1986913 Fault Detection of Broken Rotor Bars Using Stator Current Spectrum for the Direct Torque Control Induction Motor
Authors: Ridha Kechida, Arezki Menacer, Abdelhamid Benakcha
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The numerous qualities of squirrel cage induction machines enhance their use in industry. However, various faults can occur, such as stator short-circuits and rotor failures. In this paper, we use a technique based on the spectral analysis of stator current in order to detect the fault in the machine: broken rotor bars. Thus, the number effect of the breaks has been highlighted. The effect is highlighted by considering the machine controlled by the Direct Torque Control (DTC). The key to fault detection is the development of a simplified dynamic model of a squirrel cage induction motor taking account the broken bars fault and the stator current spectrum analysis (FFT).Keywords: Rotor faults, diagnosis, induction motor, DTC, statorcurrent spectrum.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3122912 Segmental and Subsegmental Lung Vessel Segmentation in CTA Images
Authors: H. Özkan
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In this paper, a novel and fast algorithm for segmental and subsegmental lung vessel segmentation is introduced using Computed Tomography Angiography images. This process is quite important especially at the detection of pulmonary embolism, lung nodule, and interstitial lung disease. The applied method has been realized at five steps. At the first step, lung segmentation is achieved. At the second one, images are threshold and differences between the images are detected. At the third one, left and right lungs are gathered with the differences which are attained in the second step and Exact Lung Image (ELI) is achieved. At the fourth one, image, which is threshold for vessel, is gathered with the ELI. Lastly, identifying and segmentation of segmental and subsegmental lung vessel have been carried out thanks to image which is obtained in the fourth step. The performance of the applied method is found quite well for radiologists and it gives enough results to the surgeries medically.Keywords: Computed tomography angiography (CTA), Computer aided detection (CAD), Lung segmentation, Lung vessel segmentation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2179911 Delay and Packet Loss Analysis for Handovers between MANETs and NEMO Networks
Authors: Jirawat Thaenthong, Steven Gordon
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MANEMO is the integration of Network Mobility (NEMO) and Mobile Ad Hoc Network (MANET). A MANEMO node has an interface to both a MANET and NEMO network, and therefore should choose the optimal interface for packet delivery, however such a handover between interfaces will introduce packet loss. We define the steps necessary for a MANEMO handover, using Mobile IP and NEMO to signal the new binding to the relevant Home Agent(s). The handover steps aim to minimize the packet loss by avoiding waiting for Duplicate Address Detection and Neighbour Unreachability Detection. We present expressions for handover delay and packet loss, and then use numerical examples to evaluate a MANEMO handover. The analysis shows how the packet loss depends on level of nesting within NEMO, the delay between Home Agents and the load on the MANET, and hence can be used to developing optimal MANEMO handover algorithms.Keywords: IP mobility, handover, MANET, network mobility
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2083910 Image Ranking to Assist Object Labeling for Training Detection Models
Authors: Tonislav Ivanov, Oleksii Nedashkivskyi, Denis Babeshko, Vadim Pinskiy, Matthew Putman
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Training a machine learning model for object detection that generalizes well is known to benefit from a training dataset with diverse examples. However, training datasets usually contain many repeats of common examples of a class and lack rarely seen examples. This is due to the process commonly used during human annotation where a person would proceed sequentially through a list of images labeling a sufficiently high total number of examples. Instead, the method presented involves an active process where, after the initial labeling of several images is completed, the next subset of images for labeling is selected by an algorithm. This process of algorithmic image selection and manual labeling continues in an iterative fashion. The algorithm used for the image selection is a deep learning algorithm, based on the U-shaped architecture, which quantifies the presence of unseen data in each image in order to find images that contain the most novel examples. Moreover, the location of the unseen data in each image is highlighted, aiding the labeler in spotting these examples. Experiments performed using semiconductor wafer data show that labeling a subset of the data, curated by this algorithm, resulted in a model with a better performance than a model produced from sequentially labeling the same amount of data. Also, similar performance is achieved compared to a model trained on exhaustive labeling of the whole dataset. Overall, the proposed approach results in a dataset that has a diverse set of examples per class as well as more balanced classes, which proves beneficial when training a deep learning model.Keywords: Computer vision, deep learning, object detection, semiconductor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 830909 Face Recognition with Image Rotation Detection, Correction and Reinforced Decision using ANN
Authors: Hemashree Bordoloi, Kandarpa Kumar Sarma
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Rotation or tilt present in an image capture by digital means can be detected and corrected using Artificial Neural Network (ANN) for application with a Face Recognition System (FRS). Principal Component Analysis (PCA) features of faces at different angles are used to train an ANN which detects the rotation for an input image and corrected using a set of operations implemented using another system based on ANN. The work also deals with the recognition of human faces with features from the foreheads, eyes, nose and mouths as decision support entities of the system configured using a Generalized Feed Forward Artificial Neural Network (GFFANN). These features are combined to provide a reinforced decision for verification of a person-s identity despite illumination variations. The complete system performing facial image rotation detection, correction and recognition using re-enforced decision support provides a success rate in the higher 90s.Keywords: Rotation, Face, Recognition, ANN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2062908 Analysis of Linear Equalizers for Cooperative Multi-User MIMO Based Reporting System
Authors: S. Hariharan, P. Muthuchidambaranathan
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In this paper, we consider a multi user multiple input multiple output (MU-MIMO) based cooperative reporting system for cognitive radio network. In the reporting network, the secondary users forward the primary user data to the common fusion center (FC). The FC is equipped with linear equalizers and an energy detector to make the decision about the spectrum. The primary user data are considered to be a digital video broadcasting - terrestrial (DVB-T) signal. The sensing channel and the reporting channel are assumed to be an additive white Gaussian noise and an independent identically distributed Raleigh fading respectively. We analyzed the detection probability of MU-MIMO system with linear equalizers and arrived at the closed form expression for average detection probability. Also the system performance is investigated under various MIMO scenarios through Monte Carlo simulations.
Keywords: Cooperative MU-MIMO, DVB-T, Linear Equalizers.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2023907 Diagnostic Evaluation of Urinary Angiogenin (ANG) and Clusterin (CLU) as Biomarker for Bladder Cancer
Authors: Marwa I. Shabayek, Ola A. Said, Hanan A. Attaia, Heba A. Awida
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Bladder carcinoma is an important worldwide health problem. Both cystoscopy and urine cytology used in detecting bladder cancer suffer from drawbacks where cystoscopy is an invasive method and urine cytology shows low sensitivity in low grade tumors. This study validates easier and less time-consuming techniques to evaluate the value of combined use of angiogenin and clusterin in comparison and combination with voided urine cytology in the detection of bladder cancer patients. This study includes malignant (bladder cancer patients, n= 50), benign (n=20) and healthy (n=20) groups. The studied groups were subjected to cystoscopic examination, detection of bilharzial antibodies, urine cytology, and estimation of urinary angiogenin and clusterin by ELISA. The overall sensitivity and specificity were 66% and 75% for angiogenin, 70% and 82.5% for clusterin and 46% and 80% for voided urine cytology. Combined sensitivity of angiogenin and clusterin with urine cytology increased from 82 to 88%.
Keywords: Angiogenin, Bladder Cancer, Clusterin, Cytology.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1835906 Infrastructure Change Monitoring Using Multitemporal Multispectral Satellite Images
Authors: U. Datta
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The main objective of this study is to find a suitable approach to monitor the land infrastructure growth over a period of time using multispectral satellite images. Bi-temporal change detection method is unable to indicate the continuous change occurring over a long period of time. To achieve this objective, the approach used here estimates a statistical model from series of multispectral image data over a long period of time, assuming there is no considerable change during that time period and then compare it with the multispectral image data obtained at a later time. The change is estimated pixel-wise. Statistical composite hypothesis technique is used for estimating pixel based change detection in a defined region. The generalized likelihood ratio test (GLRT) is used to detect the changed pixel from probabilistic estimated model of the corresponding pixel. The changed pixel is detected assuming that the images have been co-registered prior to estimation. To minimize error due to co-registration, 8-neighborhood pixels around the pixel under test are also considered. The multispectral images from Sentinel-2 and Landsat-8 from 2015 to 2018 are used for this purpose. There are different challenges in this method. First and foremost challenge is to get quite a large number of datasets for multivariate distribution modelling. A large number of images are always discarded due to cloud coverage. Due to imperfect modelling there will be high probability of false alarm. Overall conclusion that can be drawn from this work is that the probabilistic method described in this paper has given some promising results, which need to be pursued further.
Keywords: Co-registration, GLRT, infrastructure growth, multispectral, multitemporal, pixel-based change detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 731905 Sub-Image Detection Using Fast Neural Processors and Image Decomposition
Authors: Hazem M. El-Bakry, Qiangfu Zhao
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In this paper, an approach to reduce the computation steps required by fast neural networksfor the searching process is presented. The principle ofdivide and conquer strategy is applied through imagedecomposition. Each image is divided into small in sizesub-images and then each one is tested separately usinga fast neural network. The operation of fast neuralnetworks based on applying cross correlation in thefrequency domain between the input image and theweights of the hidden neurons. Compared toconventional and fast neural networks, experimentalresults show that a speed up ratio is achieved whenapplying this technique to locate human facesautomatically in cluttered scenes. Furthermore, fasterface detection is obtained by using parallel processingtechniques to test the resulting sub-images at the sametime using the same number of fast neural networks. Incontrast to using only fast neural networks, the speed upratio is increased with the size of the input image whenusing fast neural networks and image decomposition.
Keywords: Fast Neural Networks, 2D-FFT, CrossCorrelation, Image decomposition, Parallel Processing.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2179904 Assessment of Urban Heat Island through Remote Sensing in Nagpur Urban Area Using Landsat 7 ETM+ Satellite Images
Authors: Meenal Surawar, Rajashree Kotharkar
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Urban Heat Island (UHI) is found more pronounced as a prominent urban environmental concern in developing cities. To study the UHI effect in the Indian context, the Nagpur urban area has been explored in this paper using Landsat 7 ETM+ satellite images through Remote Sensing and GIS techniques. This paper intends to study the effect of LU/LC pattern on daytime Land Surface Temperature (LST) variation, contributing UHI formation within the Nagpur Urban area. Supervised LU/LC area classification was carried to study urban Change detection using ENVI 5. Change detection has been studied by carrying Normalized Difference Vegetation Index (NDVI) to understand the proportion of vegetative cover with respect to built-up ratio. Detection of spectral radiance from the thermal band of satellite images was processed to calibrate LST. Specific representative areas on the basis of urban built-up and vegetation classification were selected for observation of point LST. The entire Nagpur urban area shows that, as building density increases with decrease in vegetation cover, LST increases, thereby causing the UHI effect. UHI intensity has gradually increased by 0.7°C from 2000 to 2006; however, a drastic increase has been observed with difference of 1.8°C during the period 2006 to 2013. Within the Nagpur urban area, the UHI effect was formed due to increase in building density and decrease in vegetative cover.
Keywords: Land use, land cover, land surface temperature, remote sensing, urban heat island.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2610903 A Novel Method for Non-Invasive Diagnosis of Hepatitis C Virus Using Electromagnetic Signal Detection: A Multicenter International Study
Authors: Gamal Shiha, Waleed Samir, Zahid Azam, Premashis Kar, Saeed Hamid, Shiv Sarin
Abstract:
A simple, rapid and non-invasive electromagnetic sensor (C-FAST device) was- patented; for diagnosis of HCV RNA. Aim: To test the validity of the device compared to standard HCV PCR. Subjects and Methods: The first phase was done as pilot in Egypt on 79 participants; the second phase was done in five centers: one center from Egypt, two centers from Pakistan and two centers from India (800, 92 and 113 subjects respectively). The third phase was done nationally as multicenter study on (1600) participants for ensuring its representativeness. Results: When compared to PCR technique, C-FAST device revealed sensitivity 95% to 100%, specificity 95.5% to 100%, PPV 89.5% to 100%, NPV 95% to 100% and positive likelihood ratios 21.8% to 38.5%. Conclusion: It is practical evidence that HCV nucleotides emit electromagnetic signals that can be used for its identification. As compared to PCR, C-FAST is an accurate, valid and non-invasive device.
Keywords: C-FAST- a valid and reliable device, Distant cellular interaction, Electromagnetic signal detection, Non-invasive diagnosis of HCV.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18052902 Performance of Hybrid-MIMO Receiver Scheme in Cognitive Radio Network
Authors: Tanapong Khomyat, Peerapong Uthansakul, Monthippa Uthansakul
Abstract:
In this paper, we evaluate the performance of the Hybrid-MIMO Receiver Scheme (HMRS) in Cognitive Radio network (CR-network). We investigate the efficiency of the proposed scheme which the energy level and user number of primary user are varied according to the characteristic of CR-network. HMRS can allow users to transmit either Space-Time Block Code (STBC) or Spatial-Multiplexing (SM) streams simultaneously by using Successive Interference Cancellation (SIC) and Maximum Likelihood Detection (MLD). From simulation, the results indicate that the interference level effects to the performance of HMRS. Moreover, the exact closed-form capacity of the proposed scheme is derived and compared with STBC scheme.Keywords: Hybrid-MIMO, Cognitive radio network (CRnetwork), Symbol Error Rate (SER), Successive interference cancellation (SIC), Maximum likelihood detection (MLD).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1637