Search results for: Aperiodic autocorrelation

Authors: Ibrahim M. Hussain

Abstract:

Three new algorithms based on minimization of autocorrelation of transmitted symbols and the SLM approach which are computationally less demanding have been proposed. In the first algorithm, autocorrelation of complex data sequence is minimized to a value of 1 that results in reduction of PAPR. Second algorithm generates multiple random sequences from the sequence generated in the first algorithm with same value of autocorrelation i.e. 1. Out of these, the sequence with minimum PAPR is transmitted. Third algorithm is an extension of the second algorithm and requires minimum side information to be transmitted. Multiple sequences are generated by modifying a fixed number of complex numbers in an OFDM data sequence using only one factor. The multiple sequences represent the same data sequence and the one giving minimum PAPR is transmitted. Simulation results for a 256 subcarrier OFDM system show that significant reduction in PAPR is achieved using the proposed algorithms.

Keywords: Aperiodic autocorrelation, OFDM, PAPR, SLM, wireless communication.

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Authors: Shinpei Kato, Nobuyuki Yamasaki

Abstract:

This paper proposes a scheduling scheme using feedback control to reduce the response time of aperiodic tasks with soft real-time constraints. We design an algorithm based on the proposed scheduling scheme and Total Bandwidth Server (TBS) that is a conventional server technique for scheduling aperiodic tasks. We then describe the feedback controller of the algorithm and give the control parameter tuning methods. The simulation study demonstrates that the algorithm can reduce the mean response time up to 26% compared to TBS in exchange for slight deadline misses.

Keywords: Real-Time Systems, Aperiodic Task Scheduling, Feedback-Control Scheduling, Total Bandwidth Server.

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Authors: V. Anil Kumar, Abhijit Mitra, S. R. Mahadeva Prasanna

Abstract:

We analyze the effectivity of different pseudo noise (PN) and orthogonal sequences for encrypting speech signals in terms of perceptual intelligence. Speech signal can be viewed as sequence of correlated samples and each sample as sequence of bits. The residual intelligibility of the speech signal can be reduced by removing the correlation among the speech samples. PN sequences have random like properties that help in reducing the correlation among speech samples. The mean square aperiodic auto-correlation (MSAAC) and the mean square aperiodic cross-correlation (MSACC) measures are used to test the randomness of the PN sequences. Results of the investigation show the effectivity of large Kasami sequences for this purpose among many PN sequences.

Keywords: Speech encryption, pseudo-noise codes, maximallength, Gold, Barker, Kasami, Walsh-Hadamard, autocorrelation, crosscorrelation, figure of merit.

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Authors: Youssef Khmou, Said Safi, Miloud Frikel

Abstract:

Diffraction grating is periodic module used in many engineering fields, its geometrical conception gives interesting properties of diffraction and interferences, a uniform and periodic diffraction grating consists of a number of identical apertures that are equally spaced, in this case, the amplitude of intensity distribution in the far field region is generally modulated by diffraction pattern of single aperture. In this paper, we study the case of aperiodic diffraction grating with identical rectangular apertures where theirs coordinates are modeled by square root function, we elaborate a computer simulation comparatively to the periodic array with same length and we discuss the numerical results.

Keywords: Diffraction grating, interferences, amplitude modulation, laser.

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Authors: Watcharin Sangma, Onsiri Chanmuang, Pitsanu Tongkhow

Abstract:

 A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.

Keywords: Forecasting model, Steel demand uncertainty, Hierarchical Bayesian framework, Exponential smoothing method.

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Authors: Yacine Ait Ali Yahia, Abderazak Guessoum

Abstract:

In this paper, a new method of image edge-detection and characterization is presented. “Parametric Filtering method" uses a judicious defined filter, which preserves the signal correlation structure as input in the autocorrelation of the output. This leads, showing the evolution of the image correlation structure as well as various distortion measures which quantify the deviation between two zones of the signal (the two Hamming signals) for the protection of an image edge.

Keywords: Edge detection, parametrable recursive filter, autocorrelation structure, distortion measurements.

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Authors: Hongdeuk Kim, Youngpo Lee, Seokho Yoon

Abstract:

Due to side-peaks of autocorrelation function, the binary offset carrier (BOC) signal acquisition suffers from an ambiguity when one of the side-peaks is acquired. In this paper, we first analyze that the BOC autocorrelation is made up of the sum of subcorrelations, and then, remove the side-peaks causing the ambiguity by recombining the sub-correlations. The proposed scheme is shown to remove the side-peaks completely. From numerical results, it is confirmed that the proposed scheme outperforms the conventional schemes in terms of the receiver operating characteristic and mean acquisition time.

Keywords: Binary offset carrier (BOC), acquisition, ambiguity problem, side-peak.

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Authors: J. Arneric, E. Jurun, L. Kordic

Abstract:

This paper deals with econometric analysis of real retail trade turnover. It is a part of an extensive scientific research about modern trends in Croatian national economy. At the end of the period of transition economy, Croatia confronts with challenges and problems of high consumption society. In such environment as crucial economic variables: real retail trade turnover, average monthly real wages and household loans are chosen for consequence analysis. For the purpose of complete procedure of multiple econometric analysis data base adjustment has been provided. Namely, it has been necessary to deflate original national statistics data of retail trade turnover using consumer price indices, as well as provide process of seasonally adjustment of its contemporary behavior. In model establishment it has been necessary to involve the overcoming procedure for the autocorrelation and colinearity problems. Moreover, for case of time-series shift a specific appropriate econometric instrument has been applied. It would be emphasize that the whole methodology procedure is based on the real Croatian national economy time-series.

Keywords: Consumption society, multiple econometric model, real retail trade turnover, second order autocorrelation.

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Authors: Ru-Shuo Sheu, Han-Hsin Chou, Te-Shyang Tan

Abstract:

Considering a reservoir with periodic states and different cost functions with penalty, its release rules can be modeled as a periodic Markov decision process (PMDP). First, we prove that policy- iteration algorithm also works for the PMDP. Then, with policy- iteration algorithm, we obtain the optimal policies for a special aperiodic reservoir model with two cost functions under large penalty and give a discussion when the penalty is small.

Keywords: periodic Markov decision process, periodic state, policy-iteration algorithm.

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Authors: Urvee B. Trivedi, U. D. Dalal

Abstract:

As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.

Keywords: Cognitive radio (CR), probability of detection (PD), probability of false alarm (PF), primary User (PU), secondary user (SU), Fast Fourier transform (FFT), signal to noise ratio (SNR).

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Authors: F. Bendahma, S. Bentata, S. Cherid, A. Zitouni, S. Terkhi, T. Lantri, Y. Sefir, Z. F. Meghoufel

Abstract:

We examine the electronic transport properties in Al_xGa_1-xAs/GaAs superlattices. Using the transfer-matrix technique and the exact Airy function formalism, we investigate theoretically the effect of structural parameters on the electronic energy spectra of trimer thickness barrier (TTB). Our numerical calculations showed that the localization length of the states becomes more extended when the disorder is correlated (trimer case). We have also found that the resonant tunneling time (RTT) is of the order of several femtoseconds.

Keywords: Electronic transport properties, structural parameters, superlattice, transfer-matrix technique.

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Authors: Jana Kaděrová, Miroslav Vořechovský

Abstract:

The presented paper copes with an experimental evaluation of a model based on modified Weibull size effect theory. Classical statistical Weibull theory was modified by introducing a new parameter (correlation length lp) representing the spatial autocorrelation of a random mechanical properties of material. This size effect modification was observed on two different materials used in civil engineering: unreinforced (plain) concrete and multi-filament yarns made of alkaliresistant (AR) glass which are used for textile-reinforced concrete. The behavior under flexural, resp. tensile loading was investigated by laboratory experiments. A high number of specimens of different sizes was tested to obtain statistically significant data which were subsequently corrected and statistically processed. Due to a distortion of the measured displacements caused by the unstiff experiment device, only the maximal load values were statistically evaluated. Results of the experiments showed a decreasing strength with an increasing sample length. Size effect curves were obtained and the correlation length was fitted according to measured data. Results did not exclude the existence of the proposed new parameter lp.

Keywords: Statistical size effect, concrete, multi filaments yarns, experiment, autocorrelation length.

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Authors: Hamed Alipour-Banaei, Farhad Mehdizadeh

Abstract:

We report the design and characterization of ultra high quality factor filter based on one-dimensional photonic-crystal Thue- Morse sequence structure. The behavior of aperiodic array of photonic crystal structure is numerically investigated and we show that by changing the angle of incident wave, desired wavelengths could be tuned and a tunable filter is realized. Also it is shown that high quality factor filter be achieved in the telecommunication window around 1550 nm, with a device based on Thue-Morse structure. Simulation results show that the proposed structure has a quality factor more than 100000 and it is suitable for DWDM communication applications.

Keywords: Thue-Morse, filter, quality factor.

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Authors: M. A. Beisenbi, N. M. Kissikova, S. E. Beisembina, S. T. Suleimenova, S. A. Kaliyeva

Abstract:

The paper proposes a method for constructing a self-organizing control system for unstable and deterministic chaotic processes in the class of catastrophe “hyperbolic umbilic” for objects with m-inputs and n-outputs. The self-organizing control system is investigated by the universal gradient-velocity method of Lyapunov vector-functions. The conditions for self-organization of the control system in the class of catastrophes “hyperbolic umbilic” are shown in the form of a system of algebraic inequalities that characterize the aperiodic robust stability in the stationary states of the system.

Keywords: Gradient-velocity method of Lyapunov vector-functions, hyperbolic umbilic, self-organizing control system, stability.

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Authors: Muhammad Aqil, Ichiro Kita, Moses Macalinao

Abstract:

Reliable water level forecasts are particularly important for warning against dangerous flood and inundation. The current study aims at investigating the suitability of the adaptive network based fuzzy inference system for continuous water level modeling. A hybrid learning algorithm, which combines the least square method and the back propagation algorithm, is used to identify the parameters of the network. For this study, water levels data are available for a hydrological year of 2002 with a sampling interval of 1-hour. The number of antecedent water level that should be included in the input variables is determined by two statistical methods, i.e. autocorrelation function and partial autocorrelation function between the variables. Forecasting was done for 1-hour until 12-hour ahead in order to compare the models generalization at higher horizons. The results demonstrate that the adaptive networkbased fuzzy inference system model can be applied successfully and provide high accuracy and reliability for river water level estimation. In general, the adaptive network-based fuzzy inference system provides accurate and reliable water level prediction for 1-hour ahead where the MAPE=1.15% and correlation=0.98 was achieved. Up to 12-hour ahead prediction, the model still shows relatively good performance where the error of prediction resulted was less than 9.65%. The information gathered from the preliminary results provide a useful guidance or reference for flood early warning system design in which the magnitude and the timing of a potential extreme flood are indicated.

Keywords: Neural Network, Fuzzy, River, Forecasting

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Authors: Fabrice Poirion, Benedicte Puig

Abstract:

Mathematical justifications are given for a simulation technique of multivariate nonGaussian random processes and fields based on Rosenblatt-s transformation of Gaussian processes. Different types of convergences are given for the approaching sequence. Moreover an original numerical method is proposed in order to solve the functional equation yielding the underlying Gaussian process autocorrelation function.

Keywords: Simulation, nonGaussian, random field, multivariate, stochastic process.

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Authors: M. Beisenbi, A. Sagymbay, S. Beisembina, A. Satpayeva

Abstract:

In this paper, the problem of unstable and deterministic chaotic processes in control systems is considered. The synthesis of a control system in the class of two-parameter structurally stable mappings is demonstrated. This is realized via the gradient-velocity method of Lyapunov vector functions. It is shown that the gradient-velocity method of Lyapunov vector functions allows generating an aperiodic robust stable system with the desired characteristics. A simple solution to the problem of synthesis of control systems for unstable and deterministic chaotic processes is obtained. Moreover, it is applicable for complex systems.

Keywords: Control system synthesis, deterministic chaotic processes, Lyapunov vector function, robust stability, structurally stable mappings.

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Authors: Meghna Maiti, Utpal Roy

Abstract:

In India, till today, malaria is considered to be one of the significant infectious diseases. Most of the cases regional geographical factors are the principal elements to let the places a unique identity. The incidence and intensity of infectious diseases are quite common and affect different places differently across the nation. The present study aims to identify spatial clusters of hot spots and cold spots of malaria incidence and their seasonal variation during the three periods of 2012-2014, 2015-2017 and 2018-20 in the state of West Bengal in India. As malaria is a vector-borne disease, numbers of positive test results are to be reported by the laboratories to the Department of Health, West Bengal (through the National Vector Borne Disease Control Programme). Data on block-wise monthly malaria positive cases are collected from Health Management Information System (HMIS), Ministry of Health and Family Welfare, Government of India. Moran’s I statistic is performed to assess the spatial autocorrelation of malaria incidence. The spatial statistical analysis mainly Local Indicators of Spatial Autocorrelation (LISA) cluster and Local Geary Cluster are applied to find the spatial clusters of hot spots and cold spots and seasonal variability of malaria incidence over the three periods. The result indicates that the spatial distribution of malaria is clustered during each of the three periods of 2012-2014, 2015-2017 and 2018-20. The analysis shows that in all the cases, high-high clusters are primarily concentrated in the western (Purulia, Paschim Medinipur districts), central (Maldah, Murshidabad districts) and the northern parts (Jalpaiguri, Kochbihar districts) and low-low clusters are found in the lower Gangetic plain (central-south) mainly and northern parts of West Bengal during the stipulated period. Apart from this seasonal variability inter-year variation is also visible. The results from different methods of this study indicate significant variation in the spatial distribution of malaria incidence in West Bengal and high incidence clusters are primarily persistently concentrated over the western part during 2012-2020 along with a strong seasonal pattern with a peak in rainy and autumn. By applying the different techniques in identifying the different degrees of incidence zones of malaria across West Bengal, some specific pockets or malaria hotspots are marked and identified where the incidence rates are quite harmonious over the different periods. From this analysis, it is clear that malaria is not a disease that is distributed uniformly across the state; some specific pockets are more prone to be affected in particular seasons of each year. Disease ecology and spatial patterns must be the factors in explaining the real factors for the higher incidence of this issue within those affected districts. The further study mainly by applying empirical approach is needed for discerning the strong relationship between communicable disease and other associated affecting factors.

Keywords: Malaria, infectious diseases, spatial statistics, spatial autocorrelation, LISA.

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Authors: Shazia Javed, Noor Atinah Ahmad

Abstract:

In this paper an efficient incomplete factorization preconditioner is proposed for the Least Mean Squares (LMS) adaptive filter. The proposed preconditioner is approximated from a priori knowledge of the factors of input correlation matrix with an incomplete strategy, motivated by the sparsity patter of the upper triangular factor in the QRD-RLS algorithm. The convergence properties of IPLMS algorithm are comparable with those of transform domain LMS(TDLMS) algorithm. Simulation results show efficiency and robustness of the proposed algorithm with reduced computational complexity.

Keywords: Autocorrelation matrix, Cholesky's factor, eigenvalue spread, Markov input.

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Authors: Youngpo Lee, Seokho Yoon

Abstract:

In this paper, we propose a side-peak cancellation scheme for code acquisition of composite binary offset carrier (CBOC) signals. We first model the family of CBOC signals in a generic form, and then, propose a side-peak cancellation scheme by combining correlation functions between the divided sub-carrier and received signals. From numerical results, it is shown that the proposed scheme removes the side-peak completely, and moreover, the resulting correlation function demonstrates the better power ratio performance than the CBOC autocorrelation.

Keywords: CBOC, side-peak, ambiguity problem, synchronization

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Authors: J. P. Dubois

Abstract:

This paper reports the feasibility of the ARMA model to describe a bursty video source transmitting over a AAL5 ATM link (VBR traffic). The traffic represents the activity of the action movie "Lethal Weapon 3" transmitted over the ATM network using the Fore System AVA-200 ATM video codec with a peak rate of 100 Mbps and a frame rate of 25. The model parameters were estimated for a single video source and independently multiplexed video sources. It was found that the model ARMA (2, 4) is well-suited for the real data in terms of average rate traffic profile, probability density function, autocorrelation function, burstiness measure, and the pole-zero distribution of the filter model.

Keywords: ARMA, ATM networks, burstiness, multimediatraffic, VBR video.

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Authors: Tarun Kumar Rawat, Abhirup Lahiri, Ashish Gupta

Abstract:

In this paper, we analyze the effect of noise in a single- ended input differential amplifier working at high frequencies. Both extrinsic and intrinsic noise are analyzed using time domain method employing techniques from stochastic calculus. Stochastic differential equations are used to obtain autocorrelation functions of the output noise voltage and other solution statistics like mean and variance. The analysis leads to important design implications and suggests changes in the device parameters for improved noise characteristics of the differential amplifier.

Keywords: Single-ended input differential amplifier, Noise, stochastic differential equation, mean and variance.

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Authors: Abhijit Mitra

Abstract:

Maximal length sequences (m-sequences) are also known as pseudo random sequences or pseudo noise sequences for closely following Golomb-s popular randomness properties: (P1) balance, (P2) run, and (P3) ideal autocorrelation. Apart from these, there also exist certain other less known properties of such sequences all of which are discussed in this tutorial paper. Comprehensive proofs to each of these properties are provided towards better understanding of such sequences. A simple test is also proposed at the end of the paper in order to distinguish pseudo noise sequences from truly random sequences such as Bernoulli sequences.

Keywords: Maximal length sequence, pseudo noise sequence, punctured de Bruijn sequence, auto-correlation, Bernoulli sequence, randomness tests.

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Authors: Dia I. Abu-Al-Nadi, M. J. Mismar, T. H. Ismail

Abstract:

A technique for estimating the direction-of-arrival (DOA) of unknown number of source signals is presented using the eigen-approach. The eigenvector corresponding to the minimum eigenvalue of the autocorrelation matrix yields the minimum output power of the array. Also, the array polynomial with this eigenvector possesses roots on the unit circle. Therefore, the pseudo-spectrum is found by perturbing the phases of the roots one by one and calculating the corresponding array output power. The results indicate that the DOAs and the number of source signals are estimated accurately in the presence of a wide range of input noise levels.

Keywords: Array signal processing, direction-of-arrival, antenna arrays, eigenvalues, eigenvectors.

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Authors: Rana Khalid Naeem, Asif Mansoor

Abstract:

Periodicities in the environmetric time series can be idyllically assessed by utilizing periodic models. In this communication fugitive emission of gases from open sewer channel Lyari which follows periodic behaviour are approximated by employing periodic autoregressive model of order p. The orders of periodic model for each season are selected through the examination of periodic partial autocorrelation or information criteria. The parameters for the selected order of season are estimated individually for each emitted air toxin. Subsequently, adequacies of fitted models are established by examining the properties of the residual for each season. These models are beneficial for schemer and administrative bodies for the improvement of implemented policies to surmount future environmental problems.

Keywords: Exchange of Gases, Goodness of Fit, Open Sewer Channel, PAR(p) Models, Periodicities, Season Wise Models.

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Authors: Abiodun M. Aibinu, Momoh J. E. Salami, Amir A. Shafie, Athaur Rahman Najeeb

Abstract:

Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.

Keywords: Autoregressive moving average, coefficients, back propagation, model parameters, neural network, weight.

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Authors: S. Wongkoon, M. Pollar, M. Jaroensutasinee, K. Jaroensutasinee

Abstract:

This study aimed at developing a forecasting model on the number of Dengue Haemorrhagic Fever (DHF) incidence in Northern Thailand using time series analysis. We developed Seasonal Autoregressive Integrated Moving Average (SARIMA) models on the data collected between 2003-2006 and then validated the models using the data collected between January-September 2007. The results showed that the regressive forecast curves were consistent with the pattern of actual values. The most suitable model was the SARIMA(2,0,1)(0,2,0)12 model with a Akaike Information Criterion (AIC) of 12.2931 and a Mean Absolute Percent Error (MAPE) of 8.91713. The SARIMA(2,0,1)(0,2,0)12 model fitting was adequate for the data with the Portmanteau statistic Q20 = 8.98644 ( x20,95= 27.5871, P>0.05). This indicated that there was no significant autocorrelation between residuals at different lag times in the SARIMA(2,0,1)(0,2,0)12 model.

Keywords: Dengue, SARIMA, Time Series Analysis, Northern Thailand.

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Authors: Abdel-Samad M. Ali

Abstract:

Given the motivation of maps impact in enhancing the perception of the quality of life in a region, this work examines the use of spatial analytical techniques in exploring the role of space in shaping human development patterns in Assiut governorate. Variations of human development index (HDI) of the governorate-s villages, districts and cities are mapped using geographic information systems (GIS). Global and local spatial autocorrelation measures are employed to assess the levels of spatial dependency in the data and to map clusters of human development. Results show prominent disparities in HDI between regions of Assiut. Strong patterns of spatial association were found proving the presence of clusters on the distribution of HDI. Finally, the study indicates several "hot-spots" in the governorate to be area of more investigations to explore the attributes of such levels of human development. This is very important for accomplishing the development plan of poorest regions currently adopted in Egypt.

Keywords: Human development, Egypt, GIS, Spatial analysis.

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Authors: Amel Abdoullah Ahmed Dghais, Mohd Tahir Ismail

Abstract:

In this paper the core objective is to apply discrete wavelet transform and maximal overlap discrete wavelet transform functions namely Haar, Daubechies2, Symmlet4, Coiflet2 and discrete approximation of the Meyer wavelets in non stationary financial time series data from Dow Jones index (DJIA30) of US stock market. The data consists of 2048 daily data of closing index from December 17, 2004 to October 23, 2012. Unit root test affirms that the data is non stationary in the level. A comparison between the results to transform non stationary data to stationary data using aforesaid transforms is given which clearly shows that the decomposition stock market index by discrete wavelet transform is better than maximal overlap discrete wavelet transform for original data.

Keywords: Discrete wavelet transform, maximal overlap discrete wavelet transform, stationarity, autocorrelation function.

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Authors: Abhijit Mitra

Abstract:

Different pseudo-random or pseudo-noise (PN) as well as orthogonal sequences that can be used as spreading codes for code division multiple access (CDMA) cellular networks or can be used for encrypting speech signals to reduce the residual intelligence are investigated. We briefly review the theoretical background for direct sequence CDMA systems and describe the main characteristics of the maximal length, Gold, Barker, and Kasami sequences. We also discuss about variable- and fixed-length orthogonal codes like Walsh- Hadamard codes. The equivalence of PN and orthogonal codes are also derived. Finally, a new PN sequence is proposed which is shown to have certain better properties than the existing codes.

Keywords: Code division multiple access, pseudo-noise codes, maximal length, Gold, Barker, Kasami, Walsh-Hadamard, autocorrelation, crosscorrelation, figure of merit.

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