Search results for: Linear quadratic Gaussian (LQG)

Authors: Mohammad Shams Esfand Abadi, John Hakon Husøy

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Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a general framework in nonstationary environment. This approach is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. This general performance analysis can be used to evaluate the mean square performance of the Least Mean Square (LMS) algorithm, its normalized version (NLMS), the family of Affine Projection Algorithms (APA), the Recursive Least Squares (RLS), the Data-Reusing LMS (DR-LMS), its normalized version (NDR-LMS), the Block Least Mean Squares (BLMS), the Block Normalized LMS (BNLMS), the Transform Domain Adaptive Filters (TDAF) and the Subband Adaptive Filters (SAF) in nonstationary environment. Also, we establish the general expressions for the steady-state excess mean square in this environment for all these adaptive algorithms. Finally, we demonstrate through simulations that these results are useful in predicting the adaptive filter performance.

Keywords: Adaptive filter, general framework, energy conservation, mean-square performance, nonstationary environment.

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Authors: S. Boorboor, S. A. H. Feghhi, H. Jafari

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The ionizing radiations cause different kinds of damages in electronic components. MOSFETs, most common transistors in today’s digital and analog circuits, are severely sensitive to TID damage. In this work, the threshold voltage shift of CD4007 device, which is an integrated circuit including P-channel and N-channel MOS transistors, was investigated for low dose gamma irradiation under different gate bias voltages. We used linear extrapolation method to extract threshold voltage from I_D-V_G characteristic curve. The results showed that the threshold voltage shift was approximately 27.5 mV/Gy for N-channel and 3.5 mV/Gy for P-channel transistors at the gate bias of |9 V| after irradiation by Co-60 gamma ray source. Although the sensitivity of the devices under test were strongly dependent to biasing condition and transistor type, the threshold voltage shifted linearly versus accumulated dose in all cases. The overall results show that the application of CD4007 as an electronic buffer in a radiation therapy system is limited by TID damage. However, this integrated circuit can be used as a cheap and sensitive radiation dosimeter for accumulated dose measurement in radiation therapy systems.

Keywords: Threshold voltage shift, MOS transistor, linear extrapolation, gamma irradiation.

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: Moving object detection, histogram of oriented gradient histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine.

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Authors: Osama A. Terfas, Abdusalam A. Alaktiwi

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Pressure vessels are usually operating at temperatures where the conditions of linear elastic fracture mechanics are no longer met because massive plasticity precedes crack propagation. In this work the development of a surface crack in a pressure vessel subject to bending and tension under elastic-plastic fracture mechanics conditions was investigated. Finite element analysis was used to evaluate the hydrostatic stress, the J-integral and crack growth for semi-elliptical surface-breaking cracks. The results showed non-uniform stress triaxiality and crack driving force around the crack front at large deformation levels. Different ductile crack extensions were observed which emphasis the dependent of ductile tearing on crack geometry and type of loading. In bending the crack grew only beneath the surface, and growth was suppressed at the deepest segment. This contrasts to tension where the crack breaks through the thickness with uniform growth along the entire crack front except at the free surface. Current investigations showed that the crack growth developed under linear elastic fracture mechanics conditions will no longer be applicable under ductile tearing scenarios.

Keywords: Bending, ductile tearing, fracture toughness, stress triaxiality, tension.

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Authors: Mohammed Zidane, Said Safi, Mohamed Sabri, Ahmed Boumezzough

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In this paper we propose an algorithm based on higher order cumulants, for blind impulse response identification of frequency radio channels and downlink (MC−CDMA) system Equalization. In order to test its efficiency, we have compared with another algorithm proposed in the literature, for that we considered on theoretical channel as the Proakis’s ‘B’ channel and practical frequency selective fading channel, called Broadband Radio Access Network (BRAN C), normalized for (MC−CDMA) systems, excited by non-Gaussian sequences. In the part of (MC−CDMA), we use the Minimum Mean Square Error (MMSE) equalizer after the channel identification to correct the channel’s distortion. The simulation results, in noisy environment and for different signal to noise ratio (SNR), are presented to illustrate the accuracy of the proposed algorithm.

Keywords: Blind identification and equalization, Higher Order Cumulants, (MC−CDMA) system, MMSE equalizer.

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Authors: Adel Aloraini

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Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.

Keywords: Causal interactions , banks, feature selection, regularizere,

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Authors: Ghobad Gorji, Hasan Golabi

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The goal of this paper is to describe the design of an ultra-wideband (UWB) system that is optimized for the low-rate wireless personal area network application. To this aim, we propose a system based on direct chaotic communication (DCC) technology. Based on this system, a 2-GHz wide chaotic signal is produced into the UWB spectrum lower band, i.e., 3.1–5.1 GHz. For this system, two simple modulation schemes, namely chaotic on-off keying (COOK) and differential chaos shift keying  (DCSK) are evaluated first. We propose a modulation scheme, namely Double DCSK, to improve the performance of UWB DCC. Different characteristics of these systems, with Monte Carlo simulations based on the Additive White Gaussian Noise (AWGN) and the IEEE 802.15.4a standard channel models, are compared.

Keywords: Ultra-wideband, UWB, Direct Chaotic Communication, DCC, IEEE 802.15.4a, COOK, DCSK.

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Authors: Prasan Pitiranggon, Nunthika Benjathepanun, Somsri Banditvilai, Veera Boonjing

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Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IFTHEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.

Keywords: Fuzzy Rule Base, Rule Extraction, Rule Generation, Support Vector Machine.

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Authors: M. A. M. El-Bendary, A. E. Abu El-Azm, N. A. El-Fishawy, F. Shawky, F. E. El-Samie

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With increasing utilization of the wireless devices in different fields such as medical devices and industrial fields, the paper presents a method for simplify the Bluetooth packets with throughput enhancing. The paper studies a vital issue in wireless communications, which is the throughput of data over wireless networks. In fact, the Bluetooth and ZigBee are a Wireless Personal Area Network (WPAN). With taking these two systems competition consideration, the paper proposes different schemes for improve the throughput of Bluetooth network over a reliable channel. The proposition depends on the Channel Quality Driven Data Rate (CQDDR) rules, which determines the suitable packet in the transmission process according to the channel conditions. The proposed packet is studied over additive White Gaussian Noise (AWGN) and fading channels. The Experimental results reveal the capability of extension of the PL length by 8, 16, 24 bytes for classic and EDR packets, respectively. Also, the proposed method is suitable for the low throughput Bluetooth.

Keywords: Bluetooth, throughput, adaptive packets, EDRpackets, CQDDR, low latency. Channel condition

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Authors: T. H. Lee, J. H. Park, S. M. Lee, S. C. Lee

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In this paper, we consider Wiener nonlinear model for solid oxide fuel cell (SOFC). The Wiener model of the SOFC consists of a linear dynamic block and a static output non-linearity followed by the block, in which linear part is approximated by state-space model and the nonlinear part is identified by a polynomial form. To control the SOFC system, we have to consider various view points such as operating conditions, another constraint conditions, change of load current and so on. A change of load current is the significant one of these for good performance of the SOFC system. In order to keep the constant stack terminal voltage by changing load current, the nonlinear model predictive control (MPC) is proposed in this paper. After primary control method is designed to guarantee the fuel utilization as a proper constant, a nonlinear model predictive control based on the Wiener model is developed to control the stack terminal voltage of the SOFC system. Simulation results verify the possibility of the proposed Wiener model and MPC method to control of SOFC system.

Keywords: SOFC, model predictive control, Wiener model.

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Authors: Vahid Nademi

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In response to widely used wearable medical devices equipped with a continuous glucose monitor (CGM) and insulin pump, the advanced control methods are still demanding to get the full benefit of these devices. Unlike costly clinical trials, implementing effective insulin-glucose control strategies can provide significant contributions to the patients suffering from chronic diseases such as diabetes. This study deals with a key role of two-layer insulin-glucose regulator based on model-predictive-control (MPC) scheme so that the patient’s predicted glucose profile is in compliance with the insulin level injected through insulin pump automatically. It is achieved by iterative optimization algorithm which is called an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) solver for handling uncertainties due to unexpected variations in glucose-insulin values and body’s characteristics. The feasibility evaluation of the discussed control approach is also studied by means of numerical simulations of two case scenarios via measured data. The obtained results are presented to verify the superior and reliable performance of the proposed control scheme with no negative impact on patient safety.

Keywords: Blood glucose monitoring, insulin pump, optimization, predictive control, diabetes disease.

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125 Hz to 8000 Hz. The data contain patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R2 values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R2 values for the best models for KNN ranges from .89 to .95. The best imputation models received R2 between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our imputation models versus constant imputations by a two percent increase.

Keywords: Machine Learning, audiograms, data imputations, single imputations.

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Authors: Udeme O. Ini, Edikan E. Akpanibah

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This paper studies the optimal investment strategies for a plan member (PM) in a defined contribution (DC) pension scheme with transaction cost, taxes on invested funds and couple risky assets (stocks) under the Ornstein-Uhlenbeck (O-U) process. The PM’s portfolio is assumed to consist of a risk-free asset and two risky assets where the two risky assets are driven by the O-U process. The Legendre transformation and dual theory is use to transform the resultant optimal control problem which is a nonlinear partial differential equation (PDE) into linear PDE and the resultant linear PDE is then solved for the explicit solutions of the optimal investment strategies for PM exhibiting constant absolute risk aversion (CARA) using change of variable technique. Furthermore, theoretical analysis is used to study the influences of some sensitive parameters on the optimal investment strategies with observations that the optimal investment strategies for the two risky assets increase with increase in the dividend and decreases with increase in tax on the invested funds, risk averse coefficient, initial fund size and the transaction cost.

Keywords: Ornstein-Uhlenbeck process, portfolio management, Legendre transforms, CARA utility.

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Authors: Randhir Singh Baghel, Joydip Dhar, Renu Jain

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In this paper, for the understanding of the phytoplankton dynamics in marine ecosystem, a susceptible and an infected class of phytoplankton population is considered in spatiotemporal domain. Here, the susceptible phytoplankton is growing logistically and the growth of infected phytoplankton is due to the instantaneous Holling type-II infection response function. The dynamics are studied in terms of the local and global stabilities for the system and further explore the possibility of Hopf -bifurcation, taking the half saturation period as (i.e., ) the bifurcation parameter in temporal domain. It is also observe that the reaction diffusion system exhibits spatiotemporal chaos and pattern formation in phytoplankton dynamics, which is particularly important role play for the spatially extended phytoplankton system. Also the effect of the diffusion coefficient on the spatial system for both one and two dimensional case is obtained. Furthermore, we explore the higher-order stability analysis of the spatial phytoplankton system for both linear and no-linear system. Finally, few numerical simulations are carried out for pattern formation.

Keywords: Phytoplankton dynamics, Reaction-diffusion system, Local stability, Hopf-bifurcation, Global stability, Chaos, Pattern Formation, Higher-order stability analysis.

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Authors: Ahmed Aljudaya, Derek Ingham, Lin Ma, Kevin Hughes, Mohammed Pourkashanian

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Researchers, power companies, and state politicians have given concentrated solar power (CSP) much attention due to its capacity to generate large amounts of electricity whereas overcoming the intermittent nature of solar resources. The Linear Fresnel Reflector (LFR) is a well-known CSP technology type for being inexpensive, having a low land use factor, and suffering from low optical efficiency. The LFR was considered a cost-effective alternative option to the Parabolic Trough Collector (PTC) because of its simplistic design, and this often outweighs its lower efficiency. The LFR power plants commercially generate steam directly and indirectly in order to produce electricity with high technical efficiency and lower its costs. The purpose of this important analysis is to compare the annual performance of the Direct Steam Generation (DSG) and Indirect Steam Generation (ISG) of LFR power plants using molten salt and other different Heat Transfer Fluids (HTF) to investigate their technical and economic effects. A 50 MWe solar-only system is examined as a case study for both steam production methods in extreme weather conditions. In addition, a parametric analysis is carried out to determine the optimal solar field size that provides the lowest Levelized Cost of Electricity (LCOE) while achieving the highest technical performance. As a result of optimizing the optimum solar field size, the solar multiple (SM) is found to be between 1.2 – 1.5 in order to achieve as low as 9 Cent/KWh for the DSG of the LFR. In addition, the power plant is capable of producing around 141 GWh annually and up to 36% of the capacity factor, whereas the ISG produces less energy at a higher cost. The optimization results show that the DSG’s performance overcomes the ISG in producing around 3% more annual energy, 2% lower LCOE, and 28% less capital cost.

Keywords: Concentrated Solar Power, Levelized cost of electricity, Linear Fresnel reflectors, Steam generation.

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Authors: Nisa Özge Önal, Kamil Karaçuha, Göksu Hazar Erdinç, Banu Bahar Karaçuha, Ertuğrul Karaçuha

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The study aims to use a mathematical approach with the fractional calculus which is developed to have the ability to continuously analyze the factors related to the children’s height development. Until now, tracking the development of the child is getting more important and meaningful. Knowing and determining the factors related to the physical development of the child any desired time would provide better, reliable and accurate results for childcare. In this frame, 7 groups for height percentile curve (3th, 10th, 25th, 50th, 75th, 90th, and 97th) of Turkey are used. By using discrete height data of 0-18 years old children and the least squares method, a continuous curve is developed valid for any time interval. By doing so, in any desired instant, it is possible to find the percentage and location of the child in Percentage Chart. Here, with the help of the fractional calculus theory, a mathematical model is developed. The outcomes of the proposed approach are quite promising compared to the linear and the polynomial method. The approach also yields to predict the expected values of children in the sense of height.

Keywords: Children growth percentile, children physical development, fractional calculus, linear and polynomial model.

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

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Vector quantization is a powerful tool for speech coding applications. This paper deals with LPC Coding of speech signals which uses a new technique called Multi Switched Split Vector Quantization, This is a hybrid of two product code vector quantization techniques namely the Multi stage vector quantization technique, and Switched split vector quantization technique,. Multi Switched Split Vector Quantization technique quantizes the linear predictive coefficients in terms of line spectral frequencies. From results it is proved that Multi Switched Split Vector Quantization provides better trade off between bitrate and spectral distortion performance, computational complexity and memory requirements when compared to Switched Split Vector Quantization, Multi stage vector quantization, and Split Vector Quantization techniques. By employing the switching technique at each stage of the vector quantizer the spectral distortion, computational complexity and memory requirements were greatly reduced. Spectral distortion was measured in dB, Computational complexity was measured in floating point operations (flops), and memory requirements was measured in (floats).

Keywords: Unconstrained vector quantization, Linear predictiveCoding, Split vector quantization, Multi stage vector quantization, Switched Split vector quantization, Line Spectral Frequencies.

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Authors: Othman Belgassim, Krzysztof Jemielniak

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The analysis of Acoustic Emission (AE) signal generated from metal cutting processes has often approached statistically. This is due to the stochastic nature of the emission signal as a result of factors effecting the signal from its generation through transmission and sensing. Different techniques are applied in this manner, each of which is suitable for certain processes. In metal cutting where the emission generated by the deformation process is rather continuous, an appropriate method for analysing the AE signal based on the root mean square (RMS) of the signal is often used and is suitable for use with the conventional signal processing systems. The aim of this paper is to set a strategy in tool failure detection in turning processes via the statistic analysis of the AE generated from the cutting zone. The strategy is based on the investigation of the distribution moments of the AE signal at predetermined sampling. The skews and kurtosis of these distributions are the key elements in the detection. A normal (Gaussian) distribution has first been suggested then this was eliminated due to insufficiency. The so called Beta distribution was then considered, this has been used with an assumed β density function and has given promising results with regard to chipping and tool breakage detection.

Keywords: AE signal, skew, kurtosis, tool failure

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Authors: Mariyam Arif, Ye Liu, Israr Ul Haq, Ahsan Ashfaq

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High cost of fossil fuels and intensifying installations of alternate energy generation sources are intimidating main challenges in power systems. Making accurate load forecasting an important and challenging task for optimal energy planning and management at both distribution and generation side. There are many techniques to forecast load but each technique comes with its own limitation and requires data to accurately predict the forecast load. Artificial Neural Network (ANN) is one such technique to efficiently forecast the load. Comparison between two different ranges of input datasets has been applied to dynamic ANN technique using MATLAB Neural Network Toolbox. It has been observed that selection of input data on training of a network has significant effects on forecasted results. Day-wise input data forecasted the load accurately as compared to year-wise input data. The forecasted load is then distributed among the six generators by using the linear programming to get the optimal point of generation. The algorithm is then verified by comparing the results of each generator with their respective generation limits.

Keywords: Artificial neural networks, demand-side management, economic dispatch, linear programming, power generation dispatch.

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Authors: Jaime Martín-de-Nicolás, David Mata-Moya, Nerea del-Rey-Maestre, Pedro Gómez-del-Hoyo, María-Pilar Jarabo-Amores

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Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions and sea state can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. A fast CFAR ship detector based on a robust statistical modeling of sea clutter with respect to sea states in SAR images is used. In this paper, the minimum SNR required to obtain a given detection probability with a given false alarm rate for any sea state is determined. A Gaussian target model using real SAR data is considered. Results show that SNR does not depend heavily on the class considered. Provided there is some variation in the backscattering of targets in SAR imagery, the detection probability is limited and a post-processing stage based on morphology would be suitable.

Keywords: SAR, generalized gamma distribution, detection curves, radar detection.

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Authors: Ji–Wook Mauk, Yu–Suk Kim, Hyung–Joon Kim

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This study carried out comparative seismic performance of reinforced concrete frames infilled by masonry walls with different heights. Partial and fully infilled reinforced concrete frames were modeled for the research objectives and the analysis model for a bare reinforced concrete frame was also established for comparison. Non–linear static analyses for the studied frames were performed to investigate their structural behavior under extreme seismic loads and to find out their collapse mechanism. It was observed from analysis results that the strengths of the partial infilled reinforced concrete frames are increased and their ductilities are reduced, as infilled masonry walls are higher. Especially, reinforced concrete frames with higher partial infilled masonry walls would experience shear failures. Non–linear dynamic analyses using 10 earthquake records show that the bare and fully infilled reinforced concrete frame present stable collapse mechanism while the reinforced concrete frames with partially infilled masonry walls collapse in more brittle manner due to short-column effects.

Keywords: Fully infilled RC frame, partially infilled RC frame, masonry wall, short–column effects.

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Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

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Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: Bootstrap, Edgeworth approximation, independent and Identical distributed, quantile.

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Authors: Cheng Zhang, James Marco, Walid Allafi, Truong Q. Dinh, W. D. Widanage

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Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The presented method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the presented CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the presented method for online SOC estimation is also verified on test data.

Keywords: Equivalent circuit model, continuous time domain estimation, linear integral filter method, parameter and SOC estimation, recursive least square.

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Authors: Marina Velikova, Hennie Daniels, Ad Feelders

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In many data mining applications, it is a priori known that the target function should satisfy certain constraints imposed by, for example, economic theory or a human-decision maker. In this paper we consider partially monotone prediction problems, where the target variable depends monotonically on some of the input variables but not on all. We propose a novel method to construct prediction models, where monotone dependences with respect to some of the input variables are preserved by virtue of construction. Our method belongs to the class of mixture models. The basic idea is to convolute monotone neural networks with weight (kernel) functions to make predictions. By using simulation and real case studies, we demonstrate the application of our method. To obtain sound assessment for the performance of our approach, we use standard neural networks with weight decay and partially monotone linear models as benchmark methods for comparison. The results show that our approach outperforms partially monotone linear models in terms of accuracy. Furthermore, the incorporation of partial monotonicity constraints not only leads to models that are in accordance with the decision maker's expertise, but also reduces considerably the model variance in comparison to standard neural networks with weight decay.

Keywords: mixture models, monotone neural networks, partially monotone models, partially monotone problems.

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Authors: A. I. A. Rahman, Sh-Hussain Salleh, K. Ahmad, K. Anuar

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Analysis of vocal fold vibration is essential for understanding the mechanism of voice production and for improving clinical assessment of voice disorders. This paper presents a Dynamic Time Warping (DTW) based approach to analyze and objectively classify vocal fold vibration patterns. The proposed technique was designed and implemented on a Glottal Area Waveform (GAW) extracted from high-speed laryngeal images by delineating the glottal edges for each image frame. Feature extraction from the GAW was performed using Linear Predictive Coding (LPC). Several types of voice reference templates from simulations of clear, breathy, fry, pressed and hyperfunctional voice productions were used. The patterns of the reference templates were first verified using the analytical signal generated through Hilbert transformation of the GAW. Samples from normal speakers’ voice recordings were then used to evaluate and test the effectiveness of this approach. The classification of the voice patterns using the technique of LPC and DTW gave the accuracy of 81%.

Keywords: Dynamic Time Warping, Glottal Area Waveform, Linear Predictive Coding, High-Speed Laryngeal Images, Hilbert Transform.

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Authors: Basem Mustafa Abd. Amer , Syed Abdul Rahman Al-Attas

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The aim of this research is to develop a fast and reliable surveillance system based on a personal digital assistant (PDA) device. This is to extend the capability of the device to detect moving objects which is already available in personal computers. Secondly, to compare the performance between Background subtraction (BS) and Temporal Frame Differencing (TFD) techniques for PDA platform as to which is more suitable. In order to reduce noise and to prepare frames for the moving object detection part, each frame is first converted to a gray-scale representation and then smoothed using a Gaussian low pass filter. Two moving object detection schemes i.e., BS and TFD have been analyzed. The background frame is updated by using Infinite Impulse Response (IIR) filter so that the background frame is adapted to the varying illuminate conditions and geometry settings. In order to reduce the effect of noise pixels resulting from frame differencing morphological filters erosion and dilation are applied. In this research, it has been found that TFD technique is more suitable for motion detection purpose than the BS in term of speed. On average TFD is approximately 170 ms faster than the BS technique

Keywords: Surveillance, PDA, Motion Detection, ImageProcessing , Background Subtraction.

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Authors: Kamel Hashem, Ibrahim Arman

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Information and Communication Technologies (ICT) in mathematical education is a very active field of research and innovation, where learning is understood to be meaningful and grasping multiple linked representation rather than rote memorization, a great amount of literature offering a wide range of theories, learning approaches, methodologies and interpretations, are generally stressing the potentialities for teaching and learning using ICT. Despite the utilization of new learning approaches with ICT, students experience difficulties in learning concepts relevant to understanding mathematics, much remains unclear about the relationship between the computer environment, the activities it might support, and the knowledge that might emerge from such activities. Many questions that might arise in this regard: to what extent does the use of ICT help students in the process of understanding and solving tasks or problems? Is it possible to identify what aspects or features of students' mathematical learning can be enhanced by the use of technology? This paper will highlight the interest of the integration of information and communication technologies (ICT) into the teaching and learning of mathematics (quadratic functions), it aims to investigate the effect of four instructional methods on students- mathematical understanding and problem solving. Quantitative and qualitative methods are used to report about 43 students in middle school. Results showed that mathematical thinking and problem solving evolves as students engage with ICT activities and learn cooperatively.

Keywords: Dynamic Geometry Software, Information and Communication Technologies, Visualization, Mathematical Education.

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Authors: Hassan Ezzaidi, Jean Rouat

Abstract:

Recently many research has been conducted to retrieve pertinent parameters and adequate models for automatic music genre classification. In this paper, two measures based upon information theory concepts are investigated for mapping the features space to decision space. A Gaussian Mixture Model (GMM) is used as a baseline and reference system. Various strategies are proposed for training and testing sessions with matched or mismatched conditions, long training and long testing, long training and short testing. For all experiments, the file sections used for testing are never been used during training. With matched conditions all examined measures yield the best and similar scores (almost 100%). With mismatched conditions, the proposed measures yield better scores than the GMM baseline system, especially for the short testing case. It is also observed that the average discrimination information measure is most appropriate for music category classifications and on the other hand the divergence measure is more suitable for music subcategory classifications.

Keywords: Audio feature, information measures, music genre.

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Authors: V. Tawiwat, T. Amornthep, P. Pnop

Abstract:

Both the minimum energy consumption and smoothness, which is quantified as a function of jerk, are generally needed in many dynamic systems such as the automobile and the pick-and-place robot manipulator that handles fragile equipments. Nevertheless, many researchers come up with either solely concerning on the minimum energy consumption or minimum jerk trajectory. This research paper considers the indirect minimum Jerk method for higher order differential equation in dynamics optimization proposes a simple yet very interesting indirect jerks approaches in designing the time-dependent system yielding an alternative optimal solution. Extremal solutions for the cost functions of indirect jerks are found using the dynamic optimization methods together with the numerical approximation. This case considers the linear equation of a simple system, for instance, mass, spring and damping. The simple system uses two mass connected together by springs. The boundary initial is defined the fix end time and end point. The higher differential order is solved by Galerkin-s methods weight residual. As the result, the 6th higher differential order shows the faster solving time.

Keywords: Optimization, Dynamic, Linear Systems, Jerks.

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Authors: Badarch Tuyatsetseg

Abstract:

This paper presents parametric probability density models for call holding times (CHTs) into emergency call center based on the actual data collected for over a week in the public Emergency Information Network (EIN) in Mongolia. When the set of chosen candidates of Gamma distribution family is fitted to the call holding time data, it is observed that the whole area in the CHT empirical histogram is underestimated due to spikes of higher probability and long tails of lower probability in the histogram. Therefore, we provide the Gaussian parametric model of a mixture of lognormal distributions with explicit analytical expressions for the modeling of CHTs of PSNs. Finally, we show that the CHTs for PSNs are fitted reasonably by a mixture of lognormal distributions via the simulation of expectation maximization algorithm. This result is significant as it expresses a useful mathematical tool in an explicit manner of a mixture of lognormal distributions.

Keywords: A mixture of lognormal distributions, modeling call holding times, public safety network.

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