Search results for: facial feature detection

Authors: Kangkang Wu, Kaizhi Wang, Zhijun Yuan

Abstract:

This paper deals with a mini radar system aimed at detecting small targets at the low latitude. The radar operates at Ku-band in the frequency modulated continuous wave (FMCW) mode with two receiving channels. The radar system has the characteristics of compactness, mobility, and low power consumption. This paper focuses on the implementation of the radar system, and the Block least mean square (Block LMS) algorithm is applied to minimize the fortuitous distortion. It is validated from a series of experiments that the track of the unmanned aerial vehicle (UAV) can be easily distinguished with the radar system.

Keywords: Unmanned aerial vehicle, interference, block least mean square, frequency modulated continuous wave.

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Authors: K. Nagaraju, S. Sivanagaraju, T. Ramana, V. Ganesh

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This paper presents a simple method for estimation of additional load as a factor of the existing load that may be drawn before reaching the point of line maximum loadability of radial distribution system (RDS) with different realistic load models at different substation voltages. The proposed method involves a simple line loadability index (LLI) that gives a measure of the proximity of the present state of a line in the distribution system. The LLI can use to assess voltage instability and the line loading margin. The proposed method also compares with the existing method of maximum loadability index [10]. The simulation results show that the LLI can identify not only the weakest line/branch causing system instability but also the system voltage collapse point when it is near one. This feature enables us to set an index threshold to monitor and predict system stability on-line so that a proper action can be taken to prevent the system from collapse. To demonstrate the validity of the proposed algorithm, computer simulations are carried out on two bus and 69 bus RDS.

Keywords: line loadability index, line loading margin, maximum line loadability, system stability, radial distribution system

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Authors: Muhammad Faisal Zafar, Dzulkifli Mohamad, Razib M. Othman

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On-line handwritten scripts are usually dealt with pen tip traces from pen-down to pen-up positions. Time evaluation of the pen coordinates is also considered along with trajectory information. However, the data obtained needs a lot of preprocessing including filtering, smoothing, slant removing and size normalization before recognition process. Instead of doing such lengthy preprocessing, this paper presents a simple approach to extract the useful character information. This work evaluates the use of the counter- propagation neural network (CPN) and presents feature extraction mechanism in full detail to work with on-line handwriting recognition. The obtained recognition rates were 60% to 94% using the CPN for different sets of character samples. This paper also describes a performance study in which a recognition mechanism with multiple thresholds is evaluated for counter-propagation architecture. The results indicate that the application of multiple thresholds has significant effect on recognition mechanism. The method is applicable for off-line character recognition as well. The technique is tested for upper-case English alphabets for a number of different styles from different peoples.

Keywords: On-line character recognition, character digitization, counter-propagation neural networks, extreme coordinates.

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Authors: Yunyong Guo, Man Wang, Bryan Guo, Nathan Guo

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This paper presents an advanced security and privacy model tailored for Telehealth systems, emphasizing end-to-end protection across IoT, Fog, and Cloud components. The proposed model integrates encryption, key management, intrusion detection, and privacy-preserving measures to safeguard patient data. A comprehensive simulation study evaluates the model's effectiveness in scenarios such as unauthorized access, physical breaches, and insider threats. Results indicate notable success in detecting and mitigating threats yet underscore areas for refinement. The study contributes insights into the intricate balance between security and usability in Telehealth environments, setting the stage for continued advancements.

Keywords: Cloud, enhancing security, Fog, IoT, telehealth.

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Authors: Roza Dastres, Mohsen Soori

Abstract:

Networks are vulnerable devices due to their basic feature of facilitating remote access and data communication. The information in the networks needs to be kept secured and safe in order to provide an effective communication and sharing device in the web of data. Due to challenges and threats of the data in networks, the network security is one of the most important considerations in information technology infrastructures. As a result, the security measures are considered in the network in order to decrease the probability of accessing the secured data by the hackers. The purpose of network security is to protect the network and its components from unauthorized access and abuse in order to provide a safe and secured communication device for the users. In the present research work a review in recent development of network threats and security measures is presented and future research works are also suggested. Different attacks to the networks and security measured against them are discussed in order to increase security in the web of data. So, new ideas in the network security systems can be presented by analyzing the published papers in order to move forward the research field.

Keywords: Network threats, network security, security measures, firewalls.

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Authors: Liping Jing, Michael K. Ng, Xinhua Yang, Joshua Zhexue Huang

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This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For understanding and interpretation of clustering results, a few keywords that can best represent the semantic topic are extracted from each cluster. Two methods are used to extract the representative words. The candidate words are first selected according to their weights calculated by our new algorithm. Then, the candidates are fed to the WordNet to identify the set of noun words and consolidate the synonymy and hyponymy words. Experimental results have shown that the clustering algorithm is superior to the other subspace clustering algorithms, such as PROCLUS and HARP and kmeans type algorithm, e.g., Bisecting-KMeans. Furthermore, the word extraction method is effective in selection of the words to represent the topics of the clusters.

Keywords: Subspace Clustering, Text Mining, Feature Weighting, Cluster Interpretation, Ontology

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Authors: S. M. Anowarul Haque, Md. Asiful Islam

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Electrical load forecasting is considered to be one of the most indispensable parts of a modern-day electrical power system. To ensure a reliable and efficient supply of electric energy, special emphasis should have been put on the predictive feature of electricity supply. Artificial Neural Network-based approaches have emerged to be a significant area of interest for electric load forecasting research. This paper proposed an Artificial Neural Network model based on the particle swarm optimization algorithm for improved electric load forecasting for Mymensingh, Bangladesh. The forecasting model is developed and simulated on the MATLAB environment with a large number of training datasets. The model is trained based on eight input parameters including historical load and weather data. The predicted load data are then compared with an available dataset for validation. The proposed neural network model is proved to be more reliable in terms of day-wise load forecasting for Mymensingh, Bangladesh.

Keywords: Load forecasting, artificial neural network, particle swarm optimization.

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Authors: Rameswar Debnath, Haruhisa Takahashi

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An evolutionary method whose selection and recombination operations are based on generalization error-bounds of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently [7]. In this paper, we will use the derivative of error-bound (first-order criteria) to select and recombine gene features in the evolutionary process, and compare the performance of the derivative of error-bound with the error-bound itself (zero-order) in the evolutionary process. We also investigate several error-bounds and their derivatives to compare the performance, and find the best criteria for gene selection and classification. We use 7 cancer-related human gene expression datasets to evaluate the performance of the zero-order and first-order criteria of error-bounds. Though both criteria have the same strategy in theoretically, experimental results demonstrate the best criterion for microarray gene expression data.

Keywords: support vector machine, generalization error-bound, feature selection, evolutionary algorithm, microarray data

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Authors: Hyunsup Yoon, Youngjoon Han, Hernsoo Hahn

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In order to enhance the contrast in the regions where the pixels have similar intensities, this paper presents a new histogram equalization scheme. Conventional global equalization schemes over-equalizes these regions so that too bright or dark pixels are resulted and local equalization schemes produce unexpected discontinuities at the boundaries of the blocks. The proposed algorithm segments the original histogram into sub-histograms with reference to brightness level and equalizes each sub-histogram with the limited extents of equalization considering its mean and variance. The final image is determined as the weighted sum of the equalized images obtained by using the sub-histogram equalizations. By limiting the maximum and minimum ranges of equalization operations on individual sub-histograms, the over-equalization effect is eliminated. Also the result image does not miss feature information in low density histogram region since the remaining these area is applied separating equalization. This paper includes how to determine the segmentation points in the histogram. The proposed algorithm has been tested with more than 100 images having various contrasts in the images and the results are compared to the conventional approaches to show its superiority.

Keywords: Contrast Enhancement, Histogram Equalization, Histogram Region Equalization, Equalization Noise

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Authors: Jamila Bhar, Ridha Ouni, Kholdoun Torki, Salem Nasri

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To support user mobility for a wireless network new mechanisms are needed and are fundamental, such as paging, location updating, routing, and handover. Also an important key feature is mobile QoS offered by the WATM. Several ATM network protocols should be updated to implement mobility management and to maintain the already ATM QoS over wireless ATM networks. A survey of the various schemes and types of handover is provided. Handover procedure allows guarantee the terminal connection reestablishment when it moves between areas covered by different base stations. It is useful to satisfy user radio link transfer without interrupting a connection. However, failure to offer efficient solutions will result in handover important packet loss, severe delays and degradation of QoS offered to the applications. This paper reviews the requirements, characteristics and open issues of wireless ATM, particularly with regard to handover. It introduces key aspects of WATM and mobility extensions, which are added in the fixed ATM network. We propose a flexible approach for handover management that will minimize the QoS deterioration. Functional entities of this flexible approach are discussed in order to achieve minimum impact on the connection quality when a MT crosses the BS.

Keywords: Handover, HDL synthesis, QoS, Wireless ATM.

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Authors: Zerrin Erginkaya, Gözde Konuray

Abstract:

Microbial degradation of foods is defined as a decrease of food safety due to microorganism activity. Organic acids, sulfur dioxide, sulfide, nitrate, nitrite, dimethyl dicarbonate and several preservative gases have been used as chemical preservatives in foods as well as natural preservatives which are indigenous in foods. It is determined that usage of herbal preservatives such as blueberry, dried grape, prune, garlic, mustard, spices inhibited several microorganisms. Moreover, it is determined that animal origin preservatives such as whey, honey, lysosomes of duck egg and chicken egg, chitosan have antimicrobial effect. Other than indigenous antimicrobials in foods, antimicrobial agents produced by microorganisms could be used as natural preservatives. The antimicrobial feature of preservatives depends on the antimicrobial spectrum, chemical and physical features of material, concentration, mode of action, components of food, process conditions, and pH and storage temperature. In this review, studies about antimicrobial components which are indigenous in food (such as herbal and animal origin antimicrobial agents), antimicrobial materials synthesized by microorganisms, and their usage as an antimicrobial agent to preserve foods are discussed.

Keywords: Animal origin preservatives, antimicrobial, chemical preservatives, herbal preservatives.

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Authors: Mingtao Ding, Zheng Tian, Haixia Xu

Abstract:

The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard KPCA.This formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.

Keywords: adaptive method, kernel principal component analysis, online extraction, recursive algorithm

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: Bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks.

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Authors: Dev Ras Pandey, Bharat Mishra, S. K. Tripathi

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Cloud computing is ready to transform the structure of businesses and learning through supplying the real-time applications and provide an immediate help for small to medium sized businesses. The ability to run a hypervisor inside a virtual machine is important feature of virtualization and it is called nested virtualization. In today’s growing field of information technology, many of the virtualization models are available, that provide a convenient approach to implement, but decision for a single model selection is difficult. This paper explains the applications of operating system based virtualization in cloud computing with an appropriate/suitable model with their different specifications and user’s requirements. In the present paper, most popular models are selected, and the selection was based on container and hypervisor based virtualization. Selected models were compared with a wide range of user’s requirements as number of CPUs, memory size, nested virtualization supports, live migration and commercial supports, etc. and we identified a most suitable model of virtualization.

Keywords: Virtualization, OS based virtualization, container and hypervisor based virtualization.

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Authors: S. K. Aruna, S. Chitra

Abstract:

This research paper presents a framework for classifying Magnetic Resonance Imaging (MRI) images for Dementia. Dementia, an age-related cognitive decline is indicated by degeneration of cortical and sub-cortical structures. Characterizing morphological changes helps understand disease development and contributes to early prediction and prevention of the disease. Modelling, that captures the brain’s structural variability and which is valid in disease classification and interpretation is very challenging. Features are extracted using Gabor filter with 0, 30, 60, 90 orientations and Gray Level Co-occurrence Matrix (GLCM). It is proposed to normalize and fuse the features. Independent Component Analysis (ICA) selects features. Support Vector Machine (SVM) classifier with different kernels is evaluated, for efficiency to classify dementia. This study evaluates the presented framework using MRI images from OASIS dataset for identifying dementia. Results showed that the proposed feature fusion classifier achieves higher classification accuracy.

Keywords: Magnetic resonance imaging, dementia, Gabor filter, gray level co-occurrence matrix, support vector machine.

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Authors: S.Jerald Jeba Kumar, M.Madheswaran

Abstract:

The structure of retinal vessels is a prominent feature, that reveals information on the state of disease that are reflected in the form of measurable abnormalities in thickness and colour. Vascular structures of retina, for implementation of clinical diabetic retinopathy decision making system is presented in this paper. Retinal Vascular structure is with thin blood vessel, whose accuracy is highly dependent upon the vessel segmentation. In this paper the blood vessel thickness is automatically detected using preprocessing techniques and vessel segmentation algorithm. First the capture image is binarized to get the blood vessel structure clearly, then it is skeletonised to get the overall structure of all the terminal and branching nodes of the blood vessels. By identifying the terminal node and the branching points automatically, the main and branching blood vessel thickness is estimated. Results are presented and compared with those provided by clinical classification on 50 vessels collected from Bejan Singh Eye hospital..

Keywords: Diabetic retinopathy, Binarization, SegmentationClinical Decision Support Systems.

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Authors: Sher Muhammad, Mirza Muhammad Waqar

Abstract:

It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79⁰ to 24.85⁰ in latitude and 66.91⁰ to 66.97⁰ in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image pre processing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end member extraction. Well distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF) and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (White Mangroves) and Avicennia germinans (Black Mangroves) have been observed throughout the study area.

Keywords: Mangrove, Hyperspectral, SAM, SFF, SID.

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Authors: Doo-chul Choi, Yong-Ju Jeon, Jong Pil Yun, Sung Wook Yun, Sang Woo Kim

Abstract:

In this study, we developed an algorithm for detecting seam cracks in a steel plate. Seam cracks are generated in the edge region of a steel plate. We used the Gabor filter and an adaptive double threshold method to detect them. To reduce the number of pseudo defects, features based on the shape of seam cracks were used. To evaluate the performance of the proposed algorithm, we tested 989 images with seam cracks and 9470 defect-free images. Experimental results show that the proposed algorithm is suitable for detecting seam cracks. However, it should be improved to increase the true positive rate.

Keywords: Defect detection, Gabor filter, machine vision, surface inspection.

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Authors: Zarita Zainuddin, Mohammed Mahmod Shuaib, Ibtesam M. Abu-Sulyman

Abstract:

The social force model which belongs to the microscopic pedestrian studies has been considered as the supremacy by many researchers and due to the main feature of reproducing the self-organized phenomena resulted from pedestrian dynamic. The Preferred Force which is a measurement of pedestrian-s motivation to adapt his actual velocity to his desired velocity is an essential term on which the model was set up. This Force has gone through stages of development: first of all, Helbing and Molnar (1995) have modeled the original force for the normal situation. Second, Helbing and his co-workers (2000) have incorporated the panic situation into this force by incorporating the panic parameter to account for the panic situations. Third, Lakoba and Kaup (2005) have provided the pedestrians some kind of intelligence by incorporating aspects of the decision-making capability. In this paper, the authors analyze the most important incorporations into the model regarding the preferred force. They make comparisons between the different factors of these incorporations. Furthermore, to enhance the decision-making ability of the pedestrians, they introduce additional features such as the familiarity factor to the preferred force to let it appear more representative of what actually happens in reality.

Keywords: Pedestrian movement, social force model, preferredforce, familiarity.

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Authors: Mahmoud Z. Iskandarani, Nidal F. Shilbayeh

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The voltage/current characteristics and the effect of NO2 gas on the electrical conductivity of a PbPc gas sensor array is investigated. The gas sensor is manufactured using vacuum deposition of gold electrodes on sapphire substrate with the leadphathalocyanine vacuum sublimed on the top of the gold electrodes. Two versions of the PbPc gas sensor array are investigated. The tested types differ in the gap sizes between the deposited gold electrodes. The sensors are tested at different temperatures to account for conductivity changes as the molecular adsorption/desorption rate is affected by heat. The obtained results found to be encouraging as the sensors shoed stability and sensitivity towards low concentration of applied NO2 gas.

Keywords: Intelligent System, PbPc, Gas Sensor, Hardware, Software, Neural.

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Authors: Rajamani Doraiswami, Lahouari Cheded

Abstract:

Fault diagnosis of Linear Parameter-Varying (LPV) system using an adaptive Kalman filter is proposed. The LPV model is comprised of scheduling parameters, and the emulator parameters. The scheduling parameters are chosen such that they are capable of tracking variations in the system model as a result of changes in the operating regimes. The emulator parameters, on the other hand, simulate variations in the subsystems during the identification phase and have negligible effect during the operational phase. The nominal model and the influence vectors, which are the gradient of the feature vector respect to the emulator parameters, are identified off-line from a number of emulator parameter perturbed experiments. A Kalman filter is designed using the identified nominal model. As the system varies, the Kalman filter model is adapted using the scheduling variables. The residual is employed for fault diagnosis. The proposed scheme is successfully evaluated on simulated system as well as on a physical process control system.

Keywords: Keywords—Identification, linear parameter-varying systems, least-squares estimation, fault diagnosis, Kalman filter, emulators

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Authors: Ayaz Keerio, Bhargav Kumar Mitra, Philip Birch, Rupert Young, Chris Chatwin

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Preprocessing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. In this paper, we present some popular statistical outlier-detection based strategies to segregate the silence/unvoiced part of the speech signal from the voiced portion. The proposed methods are based on the utilization of the 3 σ edit rule, and the Hampel Identifier which are compared with the conventional techniques: (i) short-time energy (STE) based methods, and (ii) distribution based methods. The results obtained after applying the proposed strategies on some test voice signals are encouraging.

Keywords: STE based methods, Mahalanobis distance, 3 edit σ rule, Hampel Identifier.

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Authors: M. J. Kao, S.F. Chang, C.C. Chen, C.G. Kuo

Abstract:

This paper aims to fabricated high quality anodic aluminum oxide (AAO) film by anodization method. AAO pore size, pore density, and film thickness can be controlled in 10~500 nm, 108~1011 pore．cm-2, and 1~100 μm. AAO volume and surface area can be computed based on structural parameters such as thickness, pore size, pore density, and sample size. Base on the thetorical calculation, AAO has 100 μm thickness with 15 nm, 60 nm, and 500 nm pore diameters AAO surface areas are 1225.2 cm2, 3204.4 cm2, and 549.7 cm2, respectively. The large unit surface area which is useful for adsorption application. When AAO adsorbed pH indictor of bromphenol blue presented a sensitive pH detection of solution change. This testing method can further be used for the precise measurement of biotechnology, convenience measurement of industrial engineering.

Keywords: AAO, Pore, Surface area, Adsorption, Indicator

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Authors: Sanjay Kanade, Danielle Camara, Dijana Petrovska-Delacretaz, Bernadette Dorizzi

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In this paper, a two factor scheme is proposed to generate cryptographic keys directly from biometric data, which unlike passwords, are strongly bound to the user. Hash value of the reference iris code is used as a cryptographic key and its length depends only on the hash function, being independent of any other parameter. The entropy of such keys is 94 bits, which is much higher than any other comparable system. The most important and distinct feature of this scheme is that it regenerates the reference iris code by providing a genuine iris sample and the correct user password. Since iris codes obtained from two images of the same eye are not exactly the same, error correcting codes (Hadamard code and Reed-Solomon code) are used to deal with the variability. The scheme proposed here can be used to provide keys for a cryptographic system and/or for user authentication. The performance of this system is evaluated on two publicly available databases for iris biometrics namely CBS and ICE databases. The operating point of the system (values of False Acceptance Rate (FAR) and False Rejection Rate (FRR)) can be set by properly selecting the error correction capacity (ts) of the Reed- Solomon codes, e.g., on the ICE database, at ts = 15, FAR is 0.096% and FRR is 0.76%.

Keywords:

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Authors: S. El Safty, A. El-Zonkoly

Abstract:

The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropy of such decompositions is analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.

Keywords: Fault classification, wavelet transform, waveletentropy.

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Authors: Yong-JuJeon, Doo-chul Choi, Jong Pil Yun, Changhyun Park, Homoon Bae, Sang Woo Kim

Abstract:

This paper presents an automated inspection algorithm for a thick plate. Thick plates typically have various types of surface defects, such as scabs, scratches, and roller marks. These defects have individual characteristics including brightness and shape. Therefore, it is not simple to detect all the defects. In order to solve these problems and to detect defects more effectively, we propose a dual light switching lighting method and a defect detection algorithm based on Gabor filters.

Keywords: Thick plate, Defect, Inspection, Gabor filter, Dual Light Switching.

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Authors: Chvalkovska, J., Jansky, P., Mejstrik, M.

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The objective of this article is to discuss the potential of economic analysis as a tool for identification and evaluation of corruption in legislative acts. We propose that corruption be perceived as a risk variable within the legislative process. Therefore we find it appropriate to employ risk analysis methods, used in various fields of economics, for the evaluation of corruption in legislation. Furthermore we propose the incorporation of these methods into the so called corruption impact assessment (CIA), the general framework for detection of corruption in legislative acts. The applications of the risk analysis methods are demonstrated on examples of implementation of proposed CIA in the Czech Republic.

Keywords: corruption; corruption impact assessment (CIA); legislative; legislative process; risk analysis; Czech Republic

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Authors: K. Kajsanthia, J. Wittayakun, S. Prayoonpokarach

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An optical chemical sensing film based on immobilizing of 1,1′- diethyl 2,2′-cyanine (pseudocyanine iodide) in nafion film was developed for the determination of Fe(III). The sensing film was homogeneous, transparent, and mechanically stable. Decrease of the absorbance measured at 518 nm was observed when the sensing film was immersed in a solution of Fe(III). The optimum response of the sensing film to Fe(III) was obtained in a solution with pH 4.0. Linear calibration curve over an Fe(III) concentration range of 1-30 ppm with a limit of detection of 0.71 ppm was obtained. Cd(II) is the major interference. The sensing film exhibited good stability for 2 months and high reproducibility. The proposed method was applied for the determination of Fe(III) in water samples with satisfactory results.

Keywords: iron(III), _nafion, optical sensing film, pseudocyanine iodide

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Authors: Su Hyung Jo, Ki Young Kim, Sang Ho Lee

Abstract:

Security management has changed from the management of security equipments and useful interface to manager. It analyzes the whole security conditions of network and preserves the network services from attacks. Secure router technology has security functions, such as intrusion detection, IPsec(IP Security) and access control, are applied to legacy router for secure networking. It controls an unauthorized router access and detects an illegal network intrusion. This paper relates to a security engine management of router based on a security policy, which is the definition of security function against a network intrusion. This paper explains the security policy and designs the structure of security engine management framework.

Keywords: Policy server, security engine, security management, security policy

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Authors: Javed Khan, Aamir Saeed Malik, Nidal Kamel, Sarat Chandra Dass, Azura Mohd Affandi

Abstract:

Low contrast images can result from the wrong setting of image acquisition or poor illumination conditions. Such images may not be visually appealing and can be difficult for feature extraction. Contrast enhancement of color images can be useful in medical area for visual inspection. In this paper, a new technique is proposed to improve the contrast of color images. The RGB (red, green, blue) color image is transformed into normalized RGB color space. Adaptive histogram equalization technique is applied to each of the three channels of normalized RGB color space. The corresponding channels in the original image (low contrast) and that of contrast enhanced image with adaptive histogram equalization (AHE) are morphed together in proper proportions. The proposed technique is tested on seventy color images of acne patients. The results of the proposed technique are analyzed using cumulative variance and contrast improvement factor measures. The results are also compared with decorrelation stretch. Both subjective and quantitative analysis demonstrates that the proposed techniques outperform the other techniques.

Keywords: Contrast enhancement, normalized RGB, adaptive histogram equalization, cumulative variance.

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