Search results for: Kernel Matching

Authors: O. Abiodun Adeyinka, B. Adeyemo Adesesan

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The forensic use of handwriting depends on the analysis, comparison, and evaluation decisions made by forensic document examiners. When using biometric technology in forensic applications, it is necessary to compute Likelihood Ratio (LR) for quantifying strength of evidence under two competing hypotheses, namely the prosecution and the defense hypotheses wherein a set of assumptions and methods for a given data set will be made. It is therefore important to know how repeatable and reproducible our estimated LR is. This paper evaluated the accuracy and reproducibility of examiners' decisions. Confidence interval for the estimated LR were presented so as not get an incorrect estimate that will be used to deliver wrong judgment in the court of Law. The estimate of LR is fundamentally a Bayesian concept and we used two LR estimators, namely Logistic Regression (LoR) and Kernel Density Estimator (KDE) for this paper. The repeatability evaluation was carried out by retesting the initial experiment after an interval of six months to observe whether examiners would repeat their decisions for the estimated LR. The experimental results, which are based on handwriting dataset, show that LR has different confidence intervals which therefore implies that LR cannot be estimated with the same certainty everywhere. Though the LoR performed better than the KDE when tested using the same dataset, the two LR estimators investigated showed a consistent region in which LR value can be estimated confidently. These two findings advance our understanding of LR when used in computing the strength of evidence in handwriting using forensics.

Keywords: Logistic Regression LoR, Kernel Density Estimator KDE, Handwriting, Confidence Interval, Repeatability, Reproducibility.

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Authors: R. Santhosh, T. Ravichandran

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This paper evaluates the accrual based scheduling for cloud in single and multi-resource system. Numerous organizations benefit from Cloud computing by hosting their applications. The cloud model provides needed access to computing with potentially unlimited resources. Scheduling is tasks and resources mapping to a certain optimal goal principle. Scheduling, schedules tasks to virtual machines in accordance with adaptable time, in sequence under transaction logic constraints. A good scheduling algorithm improves CPU use, turnaround time, and throughput. In this paper, three realtime cloud services scheduling algorithm for single resources and multiple resources are investigated. Experimental results show Resource matching algorithm performance to be superior for both single and multi-resource scheduling when compared to benefit first scheduling, Migration, Checkpoint algorithms.

Keywords: Cloud computing, Scheduling, Migration, Checkpoint, Resource Matching.

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Authors: Elmi Abu Bakar, Tetsuo Miyake, Zhong Zhang, Takashi Imamura

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Knowing the geometrical object pose of products in manufacturing line before robot manipulation is required and less time consuming for overall shape measurement. In order to perform it, the information of shape representation and matching of objects is become required. Objects are compared with its descriptor that conceptually subtracted from each other to form scalar metric. When the metric value is smaller, the object is considered closed to each other. Rotating the object from static pose in some direction introduce the change of value in scalar metric value of boundary information after feature extraction of related object. In this paper, a proposal method for indexing technique for retrieval of 3D geometrical models based on similarity between boundaries shapes in order to measure 3D CAD object pose using object shape feature matching for Computer Aided Testing (CAT) system in production line is proposed. In experimental results shows the effectiveness of proposed method.

Keywords: CAD, rendering, feature extraction, feature classification.

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Authors: Ghulam Rasool, Ilka Philippow

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Modernizing legacy applications is the key issue facing IT managers today because there's enormous pressure on organizations to change the way they run their business to meet the new requirements. The importance of software maintenance and reengineering is forever increasing. Understanding the architecture of existing legacy applications is the most critical issue for maintenance and reengineering. The artifacts recovery can be facilitated with different recovery approaches, methods and tools. The existing methods provide static and dynamic set of techniques for extracting architectural information, but are not suitable for all users in different domains. This paper presents a simple and lightweight pattern extraction technique to extract different artifacts from legacy systems using regular expression pattern specifications with multiple language support. We used our custom-built tool DRT to recover artifacts from existing system at different levels of abstractions. In order to evaluate our approach a case study is conducted.

Keywords: Artifacts recovery, Pattern matching, Reverseengineering, Program understanding, Regular expressions, Sourcecode analysis.

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Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: Video tracking, particle filter, greedy snake, neural network.

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Authors: B.R. Potgieter, G. Venter

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Numerical design optimization is a powerful tool that can be used by engineers during any stage of the design process. There are many different applications for structural optimization. A specific application that will be discussed in the following paper is experimental data matching. Data obtained through tests on a physical structure will be matched with data from a numerical model of that same structure. The data of interest will be the dynamic characteristics of an antenna structure focusing on the mode shapes and modal frequencies. The structure used was a scaled and simplified model of the Karoo Array Telescope-7 (KAT-7) antenna structure. This kind of data matching is a complex and difficult task. This paper discusses how optimization can assist an engineer during the process of correlating a finite element model with vibration test data.

Keywords: Finite Element Model (FEM), Karoo Array Telescope(KAT-7), modal frequencies, mode shapes, optimization, shape optimization, size optimization, vibration tests

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Authors: Dorine M. Mattar, Rim M. El Khoury

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This paper examined the influence of matching students- learning preferences with the teaching methodology adopted, on their academic performance in an accounting course in two types of learning environment in one university in Lebanon: classes with PowerPoint (PPT) vs. conventional classes. Learning preferences were either for PPT or for Conventional methodology. A statistically significant increase in academic achievement is found in the conventionally instructed group as compared to the group taught with PPT. This low effectiveness of PPT might be attributed to the learning preferences of Lebanese students. In the PPT group, better academic performance was found among students with learning/teaching match as compared with students with learning/teaching mismatch. Since the majority of students display a preference for the conventional methodology, the result might suggest that Lebanese students- performance is not optimized by PPT in the accounting classrooms, not because of PPT itself, but because it is not matching the Lebanese students- learning preferences in such a quantitative course.

Keywords: Accounting education, learning preferences, learning/teaching match, Lebanon, Student performance.

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Authors: A. Elmarhomy, S. Karungaru, K. Terada

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Understanding the number of people and the flow of the persons is useful for efficient promotion of the institution managements and company-s sales improvements. This paper introduces an automated method for counting passerby using virtualvertical measurement lines. The process of recognizing a passerby is carried out using an image sequence obtained from the USB camera. Space-time image is representing the human regions which are treated using the segmentation process. To handle the problem of mismatching, different color space are used to perform the template matching which chose automatically the best matching to determine passerby direction and speed. A relation between passerby speed and the human-pixel area is used to distinguish one or two passersby. In the experiment, the camera is fixed at the entrance door of the hall in a side viewing position. Finally, experimental results verify the effectiveness of the presented method by correctly detecting and successfully counting them in order to direction with accuracy of 97%.

Keywords: counting passersby, virtual-vertical measurement line, passerby speed, space-time image

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: Computational social science, movie preference, machine learning, SVM.

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Authors: N. B. Mahesh Kumar, K. Premalatha

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The inherent skin patterns created at the joints in the finger exterior are referred as finger knuckle-print. It is exploited to identify a person in a unique manner because the finger knuckle print is greatly affluent in textures. In biometric system, the region of interest is utilized for the feature extraction algorithm. In this paper, local and global features are extracted separately. Fast Discrete Orthonormal Stockwell Transform is exploited to extract the local features. Global feature is attained by escalating the size of Fast Discrete Orthonormal Stockwell Transform to infinity. Two features are fused to increase the recognition accuracy. A matching distance is calculated for both the features individually. Then two distances are merged mutually to acquire the final matching distance. The proposed scheme gives the better performance in terms of equal error rate and correct recognition rate.

Keywords: Hamming distance, Instantaneous phase, Region of Interest, Recognition accuracy.

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Authors: Afzel Noore

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A new digital watermarking technique for images that are sensitive to blocking artifacts is presented. Experimental results show that the proposed MDCT based approach produces highly imperceptible watermarked images and is robust to attacks such as compression, noise, filtering and geometric transformations. The proposed MDCT watermarking technique is applied to fingerprints for ensuring security. The face image and demographic text data of an individual are used as multiple watermarks. An AFIS system was used to quantitatively evaluate the matching performance of the MDCT-based watermarked fingerprint. The high fingerprint matching scores show that the MDCT approach is resilient to blocking artifacts. The quality of the extracted face and extracted text images was computed using two human visual system metrics and the results show that the image quality was high.

Keywords: Digital watermarking, data hiding, modified discretecosine transformation (MDCT).

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Authors: Wipassorn Vinicchayakul, Pichaya Supanakoon, Sathaporn Promwong

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A human’s hand localization is revised by using radar cross section (RCS) measurements with a minimum root mean square (RMS) error matching algorithm on a touchless keypad mock-up model. RCS and frequency transfer function measurements are carried out in an indoor environment on the frequency ranged from 3.0 to 11.0 GHz to cover federal communications commission (FCC) standards. The touchless keypad model is tested in two different distances between the hand and the keypad. The initial distance of 19.50 cm is identical to the heights of transmitting (Tx) and receiving (Rx) antennas, while the second distance is 29.50 cm from the keypad. Moreover, the effects of Rx angles relative to the hand of human factor are considered. The RCS input parameters are compared with power loss parameters at each frequency. From the results, the performance of the RCS input parameters with the second distance, 29.50 cm at 3 GHz is better than the others.

Keywords: Radar cross section (RCS), fingerprint-based localization, minimum root mean square (RMS) error matching algorithm, touchless keypad model.

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Authors: Serkan Ozbay, Ergun Ercelebi

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Automatic Vehicle Identification (AVI) has many applications in traffic systems (highway electronic toll collection, red light violation enforcement, border and customs checkpoints, etc.). License Plate Recognition is an effective form of AVI systems. In this study, a smart and simple algorithm is presented for vehicle-s license plate recognition system. The proposed algorithm consists of three major parts: Extraction of plate region, segmentation of characters and recognition of plate characters. For extracting the plate region, edge detection algorithms and smearing algorithms are used. In segmentation part, smearing algorithms, filtering and some morphological algorithms are used. And finally statistical based template matching is used for recognition of plate characters. The performance of the proposed algorithm has been tested on real images. Based on the experimental results, we noted that our algorithm shows superior performance in car license plate recognition.

Keywords: Character recognizer, license plate recognition, plate region extraction, segmentation, smearing, template matching.

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Authors: M. C. Akay, A. Aybakan, H. Temeltas

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Aerial and ground robots have various advantages of usage in different missions. Aerial robots can move quickly and get a different sight of view of the area, but those vehicles cannot carry heavy payloads. On the other hand, unmanned ground vehicles (UGVs) are slow moving vehicles, since those can carry heavier payloads than unmanned aerial vehicles (UAVs). In this context, we investigate the performances of various Similarity Metrics to provide a common map for Heterogeneous Robot Team (HRT) in complex environments. Within the usage of Lidar Odometry and Octree Mapping technique, the local 3D maps of the environment are gathered.  In order to obtain a common map for HRT, informative theoretic similarity metrics are exploited. All types of these similarity metrics gave adequate as allowable simulation time and accurate results that can be used in different types of applications. For the heterogeneous multi robot team, those methods can be used to match different types of maps.

Keywords: Common maps, heterogeneous robot team, map matching, informative theoretic similarity metrics.

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Authors: Suguru Ochiai, Toru Yabuki, Yoshiki Yamaguchi, Yuetsu Kodama

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In this paper, we propose a Connect6 solver which adopts a hybrid approach based on a tree-search algorithm and image processing techniques. The solver must deal with the complicated computation and provide high performance in order to make real-time decisions. The proposed approach enables the solver to be implemented on a single Spartan-6 XC6SLX45 FPGA produced by XILINX without using any external devices. The compact implementation is achieved through image processing techniques to optimize a tree-search algorithm of the Connect6 game. The tree search is widely used in computer games and the optimal search brings the best move in every turn of a computer game. Thus, many tree-search algorithms such as Minimax algorithm and artificial intelligence approaches have been widely proposed in this field. However, there is one fundamental problem in this area; the computation time increases rapidly in response to the growth of the game tree. It means the larger the game tree is, the bigger the circuit size is because of their highly parallel computation characteristics. Here, this paper aims to reduce the size of a Connect6 game tree using image processing techniques and its position symmetric property. The proposed solver is composed of four computational modules: a two-dimensional checkmate strategy checker, a template matching module, a skilful-line predictor, and a next-move selector. These modules work well together in selecting next moves from some candidates and the total amount of their circuits is small. The details of the hardware design for an FPGA implementation are described and the performance of this design is also shown in this paper.

Keywords: Connect6, pattern matching, game-tree reduction, hardware direct computation

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Authors: H. M. Al-Qassem

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We obtain appropriate sharp estimates for rough oscillatory integrals. Our results represent significant improvements as well as natural extensions of what was known previously.

Keywords: Oscillatory singular integral, Rough kernel, Singular integral, L^{p} boundedness.

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Authors: Ahmad Fadel Klaib, Zurinahni Zainol, Nurul Hashimah Ahamed, Rosma Ahmad, Wahidah Hussin

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Bioinformatics and Cheminformatics use computer as disciplines providing tools for acquisition, storage, processing, analysis, integrate data and for the development of potential applications of biological and chemical data. A chemical database is one of the databases that exclusively designed to store chemical information. NMRShiftDB is one of the main databases that used to represent the chemical structures in 2D or 3D structures. SMILES format is one of many ways to write a chemical structure in a linear format. In this study we extracted Antimicrobial Structures in SMILES format from NMRShiftDB and stored it in our Local Data Warehouse with its corresponding information. Additionally, we developed a searching tool that would response to user-s query using the JME Editor tool that allows user to draw or edit molecules and converts the drawn structure into SMILES format. We applied Quick Search algorithm to search for Antimicrobial Structures in our Local Data Ware House.

Keywords: Exact String-matching Algorithms, NMRShiftDB, SMILES Format, Antimicrobial Structures.

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Authors: Qianhua He, Weili Zhou, Aiwu Chen

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A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure.

Keywords: Speech denoising, sparse representation, K-singular value decomposition, orthogonal matching pursuit.

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Authors: C Hisham, K Komal, Amit K Mishra

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Full search block matching algorithm is widely used for hardware implementation of motion estimators in video compression algorithms. In this paper we are proposing a new architecture, which consists of a 2D parallel processing unit and a 1D unit both working in parallel. The proposed architecture reduces both data access power and computational power which are the main causes of power consumption in integer motion estimation. It also completes the operations with nearly the same number of clock cycles as compared to a 2D systolic array architecture. In this work sum of absolute difference (SAD)-the most repeated operation in block matching, is calculated in two steps. The first step is to calculate the SAD for alternate rows by a 2D parallel unit. If the SAD calculated by the parallel unit is less than the stored minimum SAD, the SAD of the remaining rows is calculated by the 1D unit. Early termination, which stops avoidable computations has been achieved with the help of alternate rows method proposed in this paper and by finding a low initial SAD value based on motion vector prediction. Data reuse has been applied to the reference blocks in the same search area which significantly reduced the memory access.

Keywords: Sum of absolute difference, high speed DSP.

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Authors: Bokyung Sung, Kwanghyo Koo, Jungsoo Kim, Myung-Bum Jung, Jinman Kwon, Ilju Ko

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In this paper, we propose effective system for digital music retrieval. We divided proposed system into Client and Server. Client part consists of pre-processing and Content-based feature extraction stages. In pre-processing stage, we minimized Time code Gap that is occurred among same music contents. As content-based feature, first-order differentiated MFCC were used. These presented approximately envelop of music feature sequences. Server part included Music Server and Music Matching stage. Extracted features from 1,000 digital music files were stored in Music Server. In Music Matching stage, we found retrieval result through similarity measure by DTW. In experiment, we used 450 queries. These were made by mixing different compression standards and sound qualities from 50 digital music files. Retrieval accurate indicated 97% and retrieval time was average 15ms in every single query. Out experiment proved that proposed system is effective in retrieve digital music and robust at various user environments of web.

Keywords: Music Retrieval, Content-based, Music Feature and Digital Music.

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Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: Color moments, visual thing recognition system, SIFT, color SIFT.

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Authors: Maohai Li, Bingrong Hong, Zesu Cai, Ronghua Luo

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This paper presents the novel Rao-Blackwellised particle filter (RBPF) for mobile robot simultaneous localization and mapping (SLAM) using monocular vision. The particle filter is combined with unscented Kalman filter (UKF) to extending the path posterior by sampling new poses that integrate the current observation which drastically reduces the uncertainty about the robot pose. The landmark position estimation and update is also implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which seriously reduces the particle depletion problem, and introducing the evolution strategies (ES) for avoiding particle impoverishment. The 3D natural point landmarks are structured with matching Scale Invariant Feature Transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-Tree in the time cost of O(log2 N). Experiment results on real robot in our indoor environment show the advantages of our methods over previous approaches.

Keywords: Mobile robot, simultaneous localization and mapping, Rao-Blackwellised particle filter, evolution strategies, scale invariant feature transform.

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Authors: Kittisak Kerdprasop, Nittaya Kerdprasop

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The problem of frequent pattern discovery is defined as the process of searching for patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a database. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages. Such paradigm is inefficient when set of patterns is large and the frequent pattern is long. We suggest a high-level declarative style of programming apply to the problem of frequent pattern discovery. We consider two languages: Haskell and Prolog. Our intuitive idea is that the problem of finding frequent patterns should be efficiently and concisely implemented via a declarative paradigm since pattern matching is a fundamental feature supported by most functional languages and Prolog. Our frequent pattern mining implementation using the Haskell and Prolog languages confirms our hypothesis about conciseness of the program. The comparative performance studies on line-of-code, speed and memory usage of declarative versus imperative programming have been reported in the paper.

Keywords: Frequent pattern mining, functional programming, pattern matching, logic programming.

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Authors: Siti Nor Hafizah bt Mohd Zaid, Mohamed Abdel Maguid, Abdel Hamid Soliman

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Road traffic accidents are a major cause of death worldwide. In an attempt to reduce accidents, some research efforts have focused on creating Advanced Driver Assistance Systems (ADAS) able to detect vehicle, driver and environmental conditions and to use this information to identify cues for potential accidents. This paper presents continued work on a novel Non-intrusive Intelligent Driver Assistance and Safety System (Ni-DASS) for assessing driver point of regard within vehicles. It uses an on-board CCD camera to observe the driver-s face. A template matching approach is used to compare the driver-s eye-gaze pattern with a set of eye-gesture templates of the driver looking at different focal points within the vehicle. The windscreen is divided into cells and comparison of the driver-s eye-gaze pattern with templates of a driver-s eyes looking at each cell is used to determine the driver-s point of regard on the windscreen. Results indicate that the proposed technique could be useful in situations where low resolution estimates of driver point of regard are adequate. For instance, To allow ADAS systems to alert the driver if he/she has positively failed to observe a hazard.

Keywords: Head rotation, Eye-gestures, Windscreen, Template matching.

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Authors: Le Linh, Truong Nguyen Luan Vu, Le Hieu Giang

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In this work, the IMC-PID controller cascaded filter based on Internal Model Control (IMC) scheme is systematically proposed for the simplified decoupling control system. The simplified decoupling is firstly introduced for multivariable processes by using coefficient matching to obtain a stable, proper, and causal simplified decoupler. Accordingly, transfer functions of decoupled apparent processes can be expressed as a set of n equivalent independent processes and then derived as a ratio of the original open-loop transfer function to the diagonal element of the dynamic relative gain array. The IMC-PID controller in series with filter is then directly employed to enhance the overall performance of the decoupling control system while avoiding difficulties arising from properties inherent to simplified decoupling. Some simulation studies are considered to demonstrate the simplicity and effectiveness of the proposed method. Simulations were conducted by tuning various controllers of the multivariate processes with multiple time delays. The results indicate that the proposed method consistently performs well with fast and well-balanced closed-loop time responses.

Keywords: Coefficient matching method, internal model control scheme, PID controller cascaded filter, simplified decoupler.

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Authors: J.H. Low, C.O. Wong, E.J. Han, K.R Kim K.C. Jung, H.K. Yang

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This paper proposes an Interactive Chinese Character Learning System (ICCLS) based on pictorial evolution as an edutainment concept in computer-based learning of language. The advantage of the language origination itself is taken as a learning platform due to the complexity in Chinese language as compared to other types of languages. Users especially children enjoy more by utilize this learning system because they are able to memories the Chinese Character easily and understand more of the origin of the Chinese character under pleasurable learning environment, compares to traditional approach which children need to rote learning Chinese Character under un-pleasurable environment. Skeletonization is used as the representation of Chinese character and object with an animated pictograph evolution to facilitate the learning of the language. Shortest skeleton path matching technique is employed for fast and accurate matching in our implementation. User is required to either write a word or draw a simple 2D object in the input panel and the matched word and object will be displayed as well as the pictograph evolution to instill learning. The target of computer-based learning system is for pre-school children between 4 to 6 years old to learn Chinese characters in a flexible and entertaining manner besides utilizing visual and mind mapping strategy as learning methodology.

Keywords: Computer-based learning, Chinese character, pictograph evolution, skeletonization.

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Authors: Chaouki Ghenai

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Computational fluid dynamics analysis of the burning of syngas fuels derived from biomass and plastic solid waste mixture through gasification process is presented in this paper. The syngas fuel is burned in gas turbine can combustor. Gas turbine can combustor with swirl is designed to burn the fuel efficiently and reduce the emissions. The main objective is to test the impact of the alternative syngas fuel compositions and lower heating value on the combustion performance and emissions. The syngas fuel is produced by blending palm kernel shell (PKS) with polyethylene (PE) waste via catalytic steam gasification (fluidized bed reactor). High hydrogen content syngas fuel was obtained by mixing 30% PE waste with PKS. The syngas composition obtained through the gasification process is 76.2% H2, 8.53% CO, 4.39% CO2 and 10.90% CH4. The lower heating value of the syngas fuel is LHV = 15.98 MJ/m3. Three fuels were tested in this study natural gas (100%CH4), syngas fuel and pure hydrogen (100% H2). The power from the combustor was kept constant for all the fuels tested in this study. The effect of syngas fuel composition and lower heating value on the flame shape, gas temperature, mass of carbon dioxide (CO2) and nitrogen oxides (NOX) per unit of energy generation is presented in this paper. The results show an increase of the peak flame temperature and NO mass fractions for the syngas and hydrogen fuels compared to natural gas fuel combustion. Lower average CO2 emissions at the exit of the combustor are obtained for the syngas compared to the natural gas fuel.

Keywords: CFD, Combustion, Emissions, Gas Turbine Combustor, Gasification, Solid Waste, Syngas and Waste to Energy.

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Authors: Ryuya Uda

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Nowadays, computer worms, viruses and Trojan horse become popular, and they are collectively called malware. Those malware just spoiled computers by deleting or rewriting important files a decade ago. However, recent malware seems to be born to earn money. Some of malware work for collecting personal information so that malicious people can find secret information such as password for online banking, evidence for a scandal or contact address which relates with the target. Moreover, relation between money and malware becomes more complex. Many kinds of malware bear bots to get springboards. Meanwhile, for ordinary internet users, countermeasures against malware come up against a blank wall. Pattern matching becomes too much waste of computer resources, since matching tools have to deal with a lot of patterns derived from subspecies. Virus making tools can automatically bear subspecies of malware. Moreover, metamorphic and polymorphic malware are no longer special. Recently there appears malware checking sites that check contents in place of users' PC. However, there appears a new type of malicious sites that avoids check by malware checking sites. In this paper, existing protocols and methods related with the web are reconsidered in terms of protection from current attacks, and new protocol and method are indicated for the purpose of security of the web.

Keywords: Information Security, Malware, Network Security, World Wide Web

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Authors: J. Daba, J. Dubois

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In this work, we improve a previously developed segmentation scheme aimed at extracting edge information from speckled images using a maximum likelihood edge detector. The scheme was based on finding a threshold for the probability density function of a new kernel defined as the arithmetic mean-to-geometric mean ratio field over a circular neighborhood set and, in a general context, is founded on a likelihood random field model (LRFM). The segmentation algorithm was applied to discriminated speckle areas obtained using simple elliptic discriminant functions based on measures of the signal-to-noise ratio with fractional order moments. A rigorous stochastic analysis was used to derive an exact expression for the cumulative density function of the probability density function of the random field. Based on this, an accurate probability of error was derived and the performance of the scheme was analysed. The improved segmentation scheme performed well for both simulated and real images and showed superior results to those previously obtained using the original LRFM scheme and standard edge detection methods. In particular, the false alarm probability was markedly lower than that of the original LRFM method with oversegmentation artifacts virtually eliminated. The importance of this work lies in the development of a stochastic-based segmentation, allowing an accurate quantification of the probability of false detection. Non visual quantification and misclassification in medical ultrasound speckled images is relatively new and is of interest to clinicians.

Keywords: Discriminant function, false alarm, segmentation, signal-to-noise ratio, skewness, speckle.

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Authors: D. Zare, H. Naderi, A. A. Jafari

Abstract:

Drying characteristics of rough rice (variety of lenjan) with an initial moisture content of 25% dry basis (db) was studied in a hot air dryer assisted by infrared heating. Three arrival air temperatures (30, 40 and 500C) and four infrared radiation intensities (0, 0.2 , 0.4 and 0.6 W/cm2) and three arrival air speeds (0.1, 0.15 and 0.2 m.s-1) were studied. Bending strength of brown rice kernel, percentage of cracked kernels and time of drying were measured and evaluated. The results showed that increasing the drying arrival air temperature and radiation intensity of infrared resulted decrease in drying time. High bending strength and low percentage of cracked kernel was obtained when paddy was dried by hot air assisted infrared dryer. Between this factors and their interactive effect were a significant difference (p<0.01). An intensity level of 0.2 W/cm2 was found to be optimum for radiation drying. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the moisture content during drying (output parameter for ANN modeling) was investigated. Infrared Radiation intensity, drying air temperature, arrival air speed and drying time were considered as input parameters for the model. An ANN model with two hidden layers with 8 and 14 neurons were selected for studying the influence of transfer functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent sigmoid) transfer function and trainlm (Levenberg-Marquardt) back propagation algorithm made the most accurate predictions for the paddy drying system. Mean square error (MSE) was calculated and found that the random errors were within and acceptable range of ±5% with coefficient of determination (R2) of 99%.

Keywords: Rough rice, Infrared-hot air, Artificial Neural Network

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