Search results for: Coding region prediction

Authors: Hamza Nejib, Okba Taouali

Abstract:

This paper presents two of the most knowing kernel adaptive filtering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing filters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to figure the performance of the approaches presented and finally to result which of them is the adapted one.

Keywords: KLMS, online prediction, KAF, signal processing, RKHS, Kernel methods, KRLS, KLMS.

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Authors: M. F. V. Montezuma, E. P. Deus, M. C. Carvalho

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Steel bridges are normally subjected to random loads with different traffic frequencies. They are structures with dynamic behavior and are subject to fatigue failure process, where the nucleation of a crack, growth and failure can occur. After locating and determining the size of an existing fault, it is important to predict the crack propagation and the convenient time for repair. Therefore, fracture mechanics and fatigue concepts are essential to the right approach to the problem. To study the fatigue crack growth, a computational code was developed by using the root mean square (RMS) and the cycle-by-cycle models. One observes the variable amplitude loading influence on the life structural prediction. Different loads histories and initial crack length were considered as input variables. Thus, it was evaluated the dispersion of results of the expected structural life choosing different initial parameters.

Keywords: Fatigue crack propagation, life prediction, variable loadings, steel bridges.

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Authors: Jahar Sarkar

Abstract:

In the present study, a steady-state simulation model has been developed to evaluate the system performance of a transcritical carbon dioxide heat pump system for simultaneous water cooling and heating. Both the evaporator (including both two-phase and superheated zone) and gas cooler models consider the highly variable heat transfer characteristics of CO2 and pressure drop. The numerical simulation model of transcritical CO2 heat pump has been validated by test data obtained from experiments on the heat pump prototype. Comparison between the test results and the model prediction for system COP variation with compressor discharge pressure shows a modest agreement with a maximum deviation of 15% and the trends are fairly similar. Comparison for other operating parameters also shows fairly similar deviation between the test results and the model prediction. Finally, the simulation results are presented to study the effects of operating parameters such as, temperature of heat exchanger fluid at the inlet, discharge pressure, compressor speed on system performance of CO2 heat pump, suitable in a dairy plant where simultaneous cooling at 4oC and heating at 73oC are required. Results show that good heat transfer properties of CO2 for both two-phase and supercritical region and efficient compression process contribute a lot for high system COPs.

Keywords: CO2 heat pump, dairy system, experiment, simulation model, validation.

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

Abstract:

In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: Electrocardiogram, dictionary learning, sparse coding, classification.

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Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

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This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model, where document topics are extracted using LDA. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: Regression model, social mood, stock market prediction, Twitter.

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Authors: E. Ardil, Erdem Uçar, Parvinder S. Sandhu

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done on time especially for the critical applications. In this paper, we have explored the different predictor models to NASA-s public domain defect dataset coded in Perl programming language. Different machine learning algorithms belonging to the different learner categories of the WEKA project including Mamdani Based Fuzzy Inference System and Neuro-fuzzy based system have been evaluated for the modeling of maintenance severity or impact of fault severity. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results show that Neuro-fuzzy based model provides relatively better prediction accuracy as compared to other models and hence, can be used for the maintenance severity prediction of the software.

Keywords: Software Metrics, Fuzzy, Neuro-Fuzzy, SoftwareFaults, Accuracy, MAE, RMSE.

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Authors: Chang S. Woo, Hyun S. Park, Wan D. Kim

Abstract:

Fatigue life prediction and evaluation are the key technologies to assure the safety and reliability of automotive rubber components. The objective of this study is to develop the fatigue analysis process for vulcanized rubber components, which is applicable to predict fatigue life at initial product design step. Fatigue life prediction methodology of vulcanized natural rubber was proposed by incorporating the finite element analysis and fatigue damage parameter of maximum strain appearing at the critical location determined from fatigue test. In order to develop an appropriate fatigue damage parameter of the rubber material, a series of displacement controlled fatigue test was conducted using threedimensional dumbbell specimen with different levels of mean displacement. It was shown that the maximum strain was a proper damage parameter, taking the mean displacement effects into account. Nonlinear finite element analyses of three-dimensional dumbbell specimens were performed based on a hyper-elastic material model determined from the uni-axial tension, equi-biaxial tension and planar test. Fatigue analysis procedure employed in this study could be used approximately for the fatigue design.

Keywords: Rubber, Material test, Finite element analysis, Strain, Fatigue test, Fatigue life prediction.

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Authors: Chia-Ta Tsai, Wen-Lin Huang, Shinn-Jang Ho, Li-Sun Shu, Shinn-Ying Ho

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Prediction of bacterial virulent protein sequences can give assistance to identification and characterization of novel virulence-associated factors and discover drug/vaccine targets against proteins indispensable to pathogenicity. Gene Ontology (GO) annotation which describes functions of genes and gene products as a controlled vocabulary of terms has been shown effectively for a variety of tasks such as gene expression study, GO annotation prediction, protein subcellular localization, etc. In this study, we propose a sequence-based method Virulent-GO by mining informative GO terms as features for predicting bacterial virulent proteins. Each protein in the datasets used by the existing method VirulentPred is annotated by using BLAST to obtain its homologies with known accession numbers for retrieving GO terms. After investigating various popular classifiers using the same five-fold cross-validation scheme, Virulent-GO using the single kind of GO term features with an accuracy of 82.5% is slightly better than VirulentPred with 81.8% using five kinds of sequence-based features. For the evaluation of independent test, Virulent-GO also yields better results (82.0%) than VirulentPred (80.7%). When evaluating single kind of feature with SVM, the GO term feature performs much well, compared with each of the five kinds of features.

Keywords: Bacterial virulence factors, GO terms, prediction, protein sequence.

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Authors: Amarunnishad T.M., Govindan V.K., Abraham T. Mathew

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An image compression method has been developed using fuzzy edge image utilizing the basic Block Truncation Coding (BTC) algorithm. The fuzzy edge image has been validated with classical edge detectors on the basis of the results of the well-known Canny edge detector prior to applying to the proposed method. The bit plane generated by the conventional BTC method is replaced with the fuzzy bit plane generated by the logical OR operation between the fuzzy edge image and the corresponding conventional BTC bit plane. The input image is encoded with the block mean and standard deviation and the fuzzy bit plane. The proposed method has been tested with test images of 8 bits/pixel and size 512×512 and found to be superior with better Peak Signal to Noise Ratio (PSNR) when compared to the conventional BTC, and adaptive bit plane selection BTC (ABTC) methods. The raggedness and jagged appearance, and the ringing artifacts at sharp edges are greatly reduced in reconstructed images by the proposed method with the fuzzy bit plane.

Keywords: Image compression, Edge detection, Ground truth image, Peak signal to noise ratio

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Authors: Chuan-Ching Sue, Shi-Zhou Chen, Ting-Yu Huang

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Dynamic bandwidth allocation in EPONs can be generally separated into inter-ONU scheduling and intra-ONU scheduling. In our previous work, the active intra-ONU scheduling (AS) utilizes multiple queue reports (QRs) in each report message to cooperate with the inter-ONU scheduling and makes the granted bandwidth fully utilized without leaving unused slot remainder (USR). This scheme successfully solves the USR problem originating from the inseparability of Ethernet frame. However, without proper setting of threshold value in AS, the number of QRs constrained by the IEEE 802.3ah standard is not enough, especially in the unbalanced traffic environment. This limitation may be solved by enlarging the threshold value. The large threshold implies the large gap between the adjacent QRs, thus resulting in the large difference between the best granted bandwidth and the real granted bandwidth. In this paper, we integrate AS with a cooperative prediction mechanism and distribute multiple QRs to reduce the penalty brought by the prediction error. Furthermore, to improve the QoS and save the usage of queue reports, the highest priority (EF) traffic which comes during the waiting time is granted automatically by OLT and is not considered in the requested bandwidth of ONU. The simulation results show that the proposed scheme has better performance metrics in terms of bandwidth utilization and average delay for different classes of packets.

Keywords: EPON, Inter-ONU and Intra-ONU scheduling, Prediction, Unused slot remainder

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Authors: Sofien Chtourou, Mohamed Chtourou, Omar Hammami

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Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.

Keywords: Address, data set, memory, prediction, recurrentneural network.

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Authors: Mahdi Yaghobi, Mohammad Haeri

Abstract:

The current methods of predictive controllers are utilized for those processes in which the rate of output variations is not high. For such processes, therefore, stability can be achieved by implementing the constrained predictive controller or applying infinite prediction horizon. When the rate of the output growth is high (e.g. for unstable nonminimum phase process) the stabilization seems to be problematic. In order to avoid this, it is suggested to change the method in the way that: first, the prediction error growth should be decreased at the early stage of the prediction horizon, and second, the rate of the error variation should be penalized. The growth of the error is decreased through adjusting its weighting coefficients in the cost function. Reduction in the error variation is possible by adding the first order derivate of the error into the cost function. By studying different examples it is shown that using these two remedies together, the closed-loop stability of unstable nonminimum phase process can be achieved.

Keywords: GPC, Stability, Varying Weighting Coefficients.

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Authors: Yu Zhao, Min Zhang, Yuanqian Wang, Qiuyu Yu

Abstract:

The prediction of aesthetics of apparel is helpful for the development of a new type of apparel. This study is to build the quantitative relationship between the aesthetics and its design parameters. In particular, women’s pants have been preliminarily studied. This aforementioned relationship has been carried out by statistical analysis. The contributions of this study include the development of a more personalized apparel design mechanism and the provision of some empirical knowledge for the development of other products in the aspect of aesthetics.

Keywords: Aesthetics, crease line, cropped straight leg pants, knee width.

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Authors: M. Satya Sai Ram, P. Siddaiah, M. Madhavi Latha

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This paper investigates the performance of a speech recognizer in an interactive voice response system for various coded speech signals, coded by using a vector quantization technique namely Multi Switched Split Vector Quantization Technique. The process of recognizing the coded output can be used in Voice banking application. The recognition technique used for the recognition of the coded speech signals is the Hidden Markov Model technique. The spectral distortion performance, computational complexity, and memory requirements of Multi Switched Split Vector Quantization Technique and the performance of the speech recognizer at various bit rates have been computed. From results it is found that the speech recognizer is showing better performance at 24 bits/frame and it is found that the percentage of recognition is being varied from 100% to 93.33% for various bit rates.

Keywords: Linear predictive coding, Speech Recognition, Voice banking, Multi Switched Split Vector Quantization, Hidden Markov Model, Linear Predictive Coefficients.

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Authors: Raza Abdulla Saeed

Abstract:

In this study, the three-dimensional cavitating turbulent flow in a complete Francis turbine is simulated using mixture model for cavity/liquid two-phase flows. Numerical analysis is carried out using ANSYS CFX software release 12, and standard k-ε turbulence model is adopted for this analysis. The computational fluid domain consist of spiral casing, stay vanes, guide vanes, runner and draft tube. The computational domain is discretized with a threedimensional mesh system of unstructured tetrahedron mesh. The finite volume method (FVM) is used to solve the governing equations of the mixture model. Results of cavitation on the runner’s blades under three different boundary conditions are presented and discussed. From the numerical results it has been found that the numerical method was successfully applied to simulate the cavitating two-phase turbulent flow through a Francis turbine, and also cavitation is clearly predicted in the form of water vapor formation inside the turbine. By comparison the numerical prediction results with a real runner; it’s shown that the region of higher volume fraction obtained by simulation is consistent with the region of runner cavitation damage.

Keywords: Computational Fluid Dynamics, Hydraulic Francis Turbine, Numerical Simulation, Two-Phase Mixture Cavitation Model.

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Authors: Amandeep Singh, Mandeep Kaur

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Error correcting codes are used for detection and correction of errors in digital communication system. Error correcting coding is based on appending of redundancy to the information message according to a prescribed algorithm. Reed Solomon codes are part of channel coding and withstand the effect of noise, interference and fading. Galois field arithmetic is used for encoding and decoding reed Solomon codes. Galois field multipliers and linear feedback shift registers are used for encoding the information data block. The design of Reed Solomon encoder is complex because of use of LFSR and Galois field arithmetic. The purpose of this paper is to design and implement Reed Solomon (255, 239) encoder with optimized and lesser number of Galois Field multipliers. Symmetric generator polynomial is used to reduce the number of GF multipliers. To increase the capability toward error correction, convolution interleaving will be used with RS encoder. The Design will be implemented on Xilinx FPGA Spartan II.

Keywords: Galois Field, Generator polynomial, LFSR, Reed Solomon.

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Authors: Mohammad Ali Tabarzad, Caro Lucas, Ali Hamzeh

Abstract:

Generalization is one of the most challenging issues of Learning Classifier Systems. This feature depends on the representation method which the system used. Considering the proposed representation schemes for Learning Classifier System, it can be concluded that many of them are designed to describe the shape of the region which the environmental states belong and the other relations of the environmental state with that region was ignored. In this paper, we propose a new representation scheme which is designed to show various relationships between the environmental state and the region that is specified with a particular classifier.

Keywords: Classifier Systems, Reinforcement Learning, Relational Representation, XCSF.

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Authors: Sagir M. Yusuf, Chris Baber

Abstract:

In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Lèvy flight, situation awareness, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: V. G. Rifert, V. V. Gorin, V. V. Sereda, V. V. Treputnev

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The analysis of heat transfer design methods in condensing inside plain tubes under existing influence of shear stress is presented in this paper. The existing discrepancy in more than 30-50% between rating heat transfer coefficients and experimental data has been noted. The analysis of existing theoretical and semi-empirical methods of heat transfer prediction is given. The influence of a precise definition concerning boundaries of phase flow (it is especially important in condensing inside horizontal tubes), shear stress (friction coefficient) and heat flux on design of heat transfer is shown. The substantiation of boundary conditions of the values of parameters, influencing accuracy of rated relationships, is given. More correct relationships for heat transfer prediction, which showed good convergence with experiments made by different authors, are substantiated in this work.

Keywords: Film condensation, heat transfer, plain tube, shear stress.

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Authors: Vakhtang Geladze, Nana Bolashvili, Tamazi Karalashvili, Nino Machavariani, Ana Karalashvili, George Geladze, Nana Kvirkvelia

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Fresh water deficit is one of the most important global problems today. It must be taken into consideration that in the nearest future fresh water crisis will become even more acute owing to the global climate warming and fast desertification processes in the world. Georgia is rich in water resources, but there are disbalance between the eastern and western parts of the country. The goal of the study is to integrate the recent mechanisms compatible with European standards into Georgian water resources management system on the basis of GIS. Moreover, to draw up water economy balance for the purpose of proper determination of water consumption priorities that will be an exchange ratio of water resources and water consumption of the concrete territory. For study region was choose south-eastern part of country, Kvemo kartli Region. This is typical agrarian region, tends to the desertification. The water supply of the region was assessed on the basis of water economy balance, which was first time calculated for this region.

Keywords: GIS, water economy balance, water resources.

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

Abstract:

Sea level rise threatens to increase the impact of future  storms and hurricanes on coastal communities. Accurate sea level  change prediction and supplement is an important task in determining  constructions and human activities in coastal and oceanic areas. In  this study, support vector machines (SVM) is proposed to predict  daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal  parameter values of kernel function are determined using a genetic  algorithm. The SVM results are compared with the field data and  with back propagation (BP). Among the models, the SVM is superior  to BPNN and has better generalization performance.

 

Keywords: Tides, Prediction, Support Vector Machines, Genetic Algorithm, Back-Propagation Neural Network, Risk, Hazards.

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Authors: Kamaldeep Kaur, Arvinder Kaur, Ruchika Malhotra

Abstract:

The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction modeling using Artificial Neural Networks(ANNs). Past studies on empirical validation of object oriented metrics as fault predictors using ANNs have focused on the predictive quality of neural networks versus standard statistical techniques. In this empirical study we turn our attention to the capability of ANNs in ranking the impact of these explanatory metrics on fault proneness. In ANNs data analysis approach, there is no clear method of ranking the impact of individual metrics. Five ANN based techniques are studied which rank object oriented metrics in predicting fault proneness of classes. These techniques are i) overall connection weights method ii) Garson-s method iii) The partial derivatives methods iv) The Input Perturb method v) the classical stepwise methods. We develop and evaluate different prediction models based on the ranking of the metrics by the individual techniques. The models based on overall connection weights and partial derivatives methods have been found to be most accurate.

Keywords: Artificial Neural Networks (ANNS), Backpropagation, Fault Prediction Modeling.

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

Abstract:

Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: ANN, DWT, GLCM, KNN, ROI, artificial neural networks, discrete wavelet transform, gray-level co-occurrence matrix, k-nearest neighbor, region of interest.

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Authors: R. K. Z. Sahbudin, M. K. Abdullah, M. Mokhtar, S. B. A. Anas, S. Hitam

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A subcarrier - spectral amplitude coding optical code division multiple access system using the Khazani-Syed code with Complementary subtraction detection technique is proposed. The proposed system has been analyzed by taking into account the effects of phase-induced intensity noise, shot noise, thermal noise and intermodulation distortion noise. The performance of the system has been compared with the spectral amplitude coding optical code division multiple access system using the Hadamard code and the Modified Quadratic Congruence code. The analysis shows that the proposed system can eliminate the multiple access interference using the Complementary subtraction detection technique, and hence improve the overall system performance.

Keywords: Complementary subtraction, Khazani-Syed code, multiple access interference, phase-induced intensity noise

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Authors: T.A. Adegbola, IEA Aghachi, E.R. Sadiku

Abstract:

To investigates the effect of fiberglass clamping process improvement on drape simulation prediction. This has great effect on the mould and the fiber during manufacturing process. This also, improves the fiber strain, the quality of the fiber orientation in the area of folding and wrinkles formation during the press-forming process. Drape simulation software tool was used to digitalize the process, noting the formation problems on the contour sensitive part. This was compared with the real life clamping processes using single and double frame set-ups to observe the effects. Also, restrains are introduced by using clips, and the G-clamps with predetermine revolution to; restrain the fabric deformation during the forming process.The incorporation of clamping and fabric restrain deformation improved on the prediction of the simulation tool. Therefore, for effective forming process, incorporation of clamping process into the drape simulation process will assist in the development of fiberglass application in manufacturing process.

Keywords: clamping, fiberglass, drape simulation, pressforming.

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Authors: Sulata Maharana, B Biswas, Adity Ganguly, Ashok Kumar

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In this paper, the requirement for Coke quality prediction, its role in Blast furnaces, and the model output is explained. By applying method of Artificial Neural Networking (ANN) using back propagation (BP) algorithm, prediction model has been developed to predict CSR. Important blast furnace functions such as permeability, heat exchanging, melting, and reducing capacity are mostly connected to coke quality. Coke quality is further dependent upon coal characterization and coke making process parameters. The ANN model developed is a useful tool for process experts to adjust the control parameters in case of coke quality deviations. The model also makes it possible to predict CSR for new coal blends which are yet to be used in Coke Plant. Input data to the model was structured into 3 modules, for tenure of past 2 years and the incremental models thus developed assists in identifying the group causing the deviation of CSR.

Keywords: Artificial Neural Networks, backpropagation, CokeStrength after Reaction, Multilayer Perceptron.

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Authors: Revanth Sai Kosaraju, Michael Ramscar, Melody Dye

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Accounts of language acquisition differ significantly in their treatment of the role of prediction in language learning. In particular, nativist accounts posit that probabilistic learning about words and word sequences has little to do with how children come to use language. The accuracy of this claim was examined by testing whether distributional probabilities and frequency contributed to how well 3-4 year olds repeat simple word chunks. Corresponding chunks were the same length, expressed similar content, and were all grammatically acceptable, yet the results of the study showed marked differences in performance when overall distributional frequency varied. It was found that a distributional model of language predicted the empirical findings better than a number of other models, replicating earlier findings and showing that children attend to distributional probabilities in an adult corpus. This suggested that language is more prediction-and-error based, rather than on abstract rules which nativist camps suggest.

Keywords: Abstractness, child psychology, language acquisition, prediction and error.

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Authors: S. Sowmyayani, P. Arockia Jansi Rani

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This paper integrates Octagon and Square Search pattern (OCTSS) motion estimation algorithm into H.264/AVC (Advanced Video Coding) video codec in Adaptive Group of Pictures (AGOP) mode. AGOP structure is computed based on scene change in the video sequence. Octagon and square search pattern block-based motion estimation method is implemented in inter-prediction process of H.264/AVC. Both these methods reduce bit rate and computational complexity while maintaining the quality of the video sequence respectively. Experiments are conducted for different types of video sequence. The results substantially proved that the bit rate, computation time and PSNR gain achieved by the proposed method is better than the existing H.264/AVC with fixed GOP and AGOP. With a marginal gain in quality of 0.28dB and average gain in bitrate of 132.87kbps, the proposed method reduces the average computation time by 27.31 minutes when compared to the existing state-of-art H.264/AVC video codec.

Keywords: Block Distortion Measure, Block Matching Algorithms, H.264/AVC, Motion estimation, Search patterns, Shot cut detection.

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Authors: Tawfig Eltaif, Hesham A. Bakarman, N. Alsowaidi, M. R. Mokhtar, Malek Harbawi

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Multiple User Interference (MUI) considers the primary problem in Optical Code-Division Multiple Access (OCDMA), which resulting from the overlapping among the users. In this article we aim to mitigate this problem by studying an interference cancellation scheme called successive interference cancellation (SIC) scheme. This scheme will be tested on two different detection schemes, spectral amplitude coding (SAC) and direct detection systems (DS), using partial modified prime (PMP) as the signature codes. It was found that SIC scheme based on both SAC and DS methods had a potential to suppress the intensity noise, that is to say it can mitigate MUI noise. Furthermore, SIC/DS scheme showed much lower bit error rate (BER) performance relative to SIC/SAC scheme for different magnitude of effective power. Hence, many more users can be supported by SIC/DS receiver system.

Keywords: Multiple User Interference (MUI), Optical Code-Division Multiple Access (OCDMA), Partial Modified Prime Code (PMP), Spectral Amplitude Coding (SAC), Successive Interference Cancellation (SIC).

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Authors: Prasit Wonglersak, Prakarnkiat Youngkong, Ittipon Cheowanish

Abstract:

This paper aims to improve a fine lapping process of hard disk drive (HDD) lapping machines by removing materials from each slider together with controlling the strip height (SH) variation to minimum value. The standard deviation is the key parameter to evaluate the strip height variation, hence it is minimized. In this paper, a design of experiment (DOE) with factorial analysis by twoway analysis of variance (ANOVA) is adopted to obtain a statistically information. The statistics results reveal that initial stripe height patterns affect the final SH variation. Therefore, initial SH classification using a radial basis function neural network is implemented to achieve the proportional gain prediction.

Keywords: Stripe height variation, Two-way analysis ofvariance (ANOVA), Radial basis function neural network, Proportional gain prediction.

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