Search results for: filtering and estimation.

Authors: Young-Seok Choi

Abstract:

This paper presents a normalized subband adaptive filtering (NSAF) algorithm to cope with the sparsity condition of an underlying system in the context of compressive sensing. By regularizing a weighted l1-norm of the filter taps estimate onto the cost function of the NSAF and utilizing a subgradient analysis, the update recursion of the l1-norm constraint NSAF is derived. Considering two distinct weighted l1-norm regularization cases, two versions of the l1-norm constraint NSAF are presented. Simulation results clearly indicate the superior performance of the proposed l1-norm constraint NSAFs comparing with the classical NSAF.

Keywords: Subband adaptive filtering, sparsity constraint, weighted l1-norm.

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Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

Abstract:

Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: Airborne laser scanning, digital terrain models, filtering, forested areas.

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Authors: Irena Markovska, Nikolai Zaicev, Bogdan Bogdanov, Dimitar Georgiev, Yancho Hristov

Abstract:

The present paper deals with problems related to the possibilities to use fractal systems to solve some important scientific and practical problems connected with filtering and separation of aqueous phases from organic ones. For this purpose a special separator have been designed. The reactor was filled with a porous material with fractal dimension, which is an integral part of the set for filtration and separation of emulsions. As a model emulsion hexadecan mixture with water in equal quantities (1:1) was used. We examined the hydrodynamics of the separation of the emulsion at different rates of submission of the entrance of the reactor.

Keywords: pilot reactor, fractal systems, separation, emulsions

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Authors: Halil Ersin Soken, Chingiz Hajiyev

Abstract:

In the normal operation conditions of a pico satellite, conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study, introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and the estimations are corrected without affecting the characteristic of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.

Keywords: attitude algorithms, Kalman filters, robustestimation.

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Authors: Maria C. Mariani, Md Al Masum Bhuiyan, Osei K. Tweneboah, Hector G. Huizar

Abstract:

This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.

Keywords: Augmented Dickey Fuller Test, geophysical time series, maximum likelihood estimation, stochastic volatility model.

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Authors: F. H. Abanda, B. Kamsu-Foguem, J. H. M. Tah

Abstract:

Construction cost estimation is one of the most important aspects of construction project design. For generations, the process of cost estimating has been manual, time-consuming and error-prone. This has partly led to most cost estimates to be unclear and riddled with inaccuracies that at times lead to over- or underestimation of construction cost. The development of standard set of measurement rules that are understandable by all those involved in a construction project, have not totally solved the challenges. Emerging Building Information Modelling (BIM) technologies can exploit standard measurement methods to automate cost estimation process and improve accuracies. This requires standard measurement methods to be structured in ontological and machine readable format; so that BIM software packages can easily read them. Most standard measurement methods are still text-based in textbooks and require manual editing into tables or Spreadsheet during cost estimation. The aim of this study is to explore the development of an ontology based on New Rules of Measurement (NRM) commonly used in the UK for cost estimation. The methodology adopted is Methontology, one of the most widely used ontology engineering methodologies. The challenges in this exploratory study are also reported and recommendations for future studies proposed.

Keywords: BIM, Construction projects, Cost estimation, NRM, Ontology.

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Authors: Lalita Gupta, R. P. Singh

Abstract:

Array signal processing involves signal enumeration and source localization. Array signal processing is centered on the ability to fuse temporal and spatial information captured via sampling signals emitted from a number of sources at the sensors of an array in order to carry out a specific estimation task: source characteristics (mainly localization of the sources) and/or array characteristics (mainly array geometry) estimation. Array signal processing is a part of signal processing that uses sensors organized in patterns or arrays, to detect signals and to determine information about them. Beamforming is a general signal processing technique used to control the directionality of the reception or transmission of a signal. Using Beamforming we can direct the majority of signal energy we receive from a group of array. Multiple signal classification (MUSIC) is a highly popular eigenstructure-based estimation method of direction of arrival (DOA) with high resolution. This Paper enumerates the effect of missing sensors in DOA estimation. The accuracy of the MUSIC-based DOA estimation is degraded significantly both by the effects of the missing sensors among the receiving array elements and the unequal channel gain and phase errors of the receiver.

Keywords: Array Signal Processing, Beamforming, ULA, Direction of Arrival, MUSIC

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Authors: S. K. Satpathy, S. Panda, K. K. Nagwanshi, S. K. Nayak, C. Ardil

Abstract:

Removing noise from the any processed images is very important. Noise should be removed in such a way that important information of image should be preserved. A decisionbased nonlinear algorithm for elimination of band lines, drop lines, mark, band lost and impulses in images is presented in this paper. The algorithm performs two simultaneous operations, namely, detection of corrupted pixels and evaluation of new pixels for replacing the corrupted pixels. Removal of these artifacts is achieved without damaging edges and details. However, the restricted window size renders median operation less effective whenever noise is excessive in that case the proposed algorithm automatically switches to mean filtering. The performance of the algorithm is analyzed in terms of Mean Square Error [MSE], Peak-Signal-to-Noise Ratio [PSNR], Signal-to-Noise Ratio Improved [SNRI], Percentage Of Noise Attenuated [PONA], and Percentage Of Spoiled Pixels [POSP]. This is compared with standard algorithms already in use and improved performance of the proposed algorithm is presented. The advantage of the proposed algorithm is that a single algorithm can replace several independent algorithms which are required for removal of different artifacts.

Keywords: Filtering, Decision Based Algorithm, noise, imagerestoration.

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Authors: Nermin A. Mohamed, Mohamed F. Madkour

Abstract:

We address the problem of joint beamforming and multipath channel parameters estimation in Wideband Code Division Multiple Access (WCDMA) communication systems that employ Multiple-Access Interference (MAI) suppression techniques in the uplink (from mobile to base station). Most of the existing schemes rely on time multiplex a training sequence with the user data. In WCDMA, the channel parameters can also be estimated from a code multiplexed common pilot channel (CPICH) that could be corrupted by strong interference resulting in a bad estimate. In this paper, we present new methods to combine interference suppression together with channel estimation when using multiple receiving antennas by using adaptive signal processing techniques. Computer simulation is used to compare between the proposed methods and the existing conventional estimation techniques.

Keywords: Adaptive arrays, channel estimation, interferencecancellation, wideband code division multiple access (WCDMA).

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Authors: Nurbanu Guzey, S. Jagannathan

Abstract:

Locating Radio Controlled (RC) devices using their unintended emissions has a great interest considering security concerns. Weak nature of these emissions requires near field localization approach since it is hard to detect these signals in far field region of array. Instead of only angle estimation, near field localization also requires range estimation of the source which makes this method more complicated than far field models. Challenges of locating such devices in a near field region and real time environment are analyzed in this paper. An ESPRIT like near field localization scheme is utilized for both angle and range estimation. 1-D search with symmetric subarrays is provided. Two 7 element uniform linear antenna arrays (ULA) are employed for locating RC source. Experiment results of location estimation for one unintended emitting walkie-talkie for different positions are given.

Keywords: Localization, angle of arrival (AoA), range estimation, array signal processing, ESPRIT, uniform linear array (ULA).

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Authors: Kriangkrai Maneerat, Chutima Prommak

Abstract:

Indoor wireless localization systems have played an important role to enhance context-aware services. Determining the position of mobile objects in complex indoor environments, such as those in multi-floor buildings, is very challenging problems. This paper presents an effective floor estimation algorithm, which can accurately determine the floor where mobile objects located. The proposed algorithm is based on the confidence interval of the summation of online Received Signal Strength (RSS) obtained from the IEEE 802.15.4 Wireless Sensor Networks (WSN).We compare the performance of the proposed algorithm with those of other floor estimation algorithms in literature by conducting a real implementation of WSN in our facility. The experimental results and analysis showed that the proposed floor estimation algorithm outperformed the other algorithms and provided highest percentage of floor accuracy up to 100% with 95-percent confidence interval.

Keywords: Floor estimation algorithm, floor determination, multi-floor building, indoor wireless systems.

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Authors: Shuimiao Wan, Chao Yuan, Baoguang Tian

Abstract:

In statistics parameter theory, usually the parameter estimations have two kinds, one is the least-square estimation (LSE), and the other is the best linear unbiased estimation (BLUE). Due to the determining theorem of minimum variance unbiased estimator (MVUE), the parameter estimation of BLUE in linear model is most ideal. But since the calculations are complicated or the covariance is not given, people are hardly to get the solution. Therefore, people prefer to use LSE rather than BLUE. And this substitution will take some losses. To quantize the losses, many scholars have presented many kinds of different relative efficiencies in different views. For the linear weighted regression model, this paper discusses the relative efficiencies of LSE of β to BLUE of β. It also defines two new relative efficiencies and gives their lower bounds.

Keywords: Linear weighted regression, Relative efficiency, Lower bound, Parameter estimation.

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Authors: Anastasiia Yu. Timofeeva

Abstract:

Two new algorithms for nonparametric estimation of errors-in-variables models are proposed. The first algorithm is based on penalized regression spline. The spline is represented as a piecewise-linear function and for each linear portion orthogonal regression is estimated. This algorithm is iterative. The second algorithm involves locally weighted regression estimation. When the independent variable is measured with error such estimation is a complex nonlinear optimization problem. The simulation results have shown the advantage of the second algorithm under the assumption that true smoothing parameters values are known. Nevertheless the use of some indexes of fit to smoothing parameters selection gives the similar results and has an oversmoothing effect.

Keywords: Grade point average, orthogonal regression, penalized regression spline, locally weighted regression.

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Authors: Dipankaj G Medhi, Juri Dakua

Abstract:

Recommender Systems act as personalized decision guides, aiding users in decisions on matters related to personal taste. Most previous research on Recommender Systems has focused on the statistical accuracy of the algorithms driving the systems, with no emphasis on the trustworthiness of the user. RS depends on information provided by different users to gather its knowledge. We believe, if a large group of users provide wrong information it will not be possible for the RS to arrive in an accurate conclusion. The system described in this paper introduce the concept of Testing the knowledge of user to filter out these “bad users". This paper emphasizes on the mechanism used to provide robust and effective recommendation.

Keywords: Collaborative Filtering, Content Based Filtering, Intelligent Agent, Level of Interest, Recommendation System.

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Authors: Hazem Al-Mofleh, John Daniels, Joseph McKean

Abstract:

Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares.

Keywords: Non-Linear Wilcoxon, robust estimation, Variogram estimation.

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Authors: N. Ben Si Ali, N. Benalia, N. Zarzouri

Abstract:

Speed sensorless systems are intensively studied during recent years; this is mainly due to their economical benefit and fragility of mechanical sensors and also the difficulty of installing this type of sensor in many applications. These systems suffer from instability problems and sensitivity to parameter mismatch at low speed operation. In this paper an analysis of adaptive observer stability with stator resistance estimation is given.

Keywords: Motor drive, sensorless control, adaptive observer, stator resistance estimation.

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Authors: T. Messikh, S. Mekhilef, N. A. Rahim

Abstract:

This paper presents an effective technique for harmonic current mitigation using an adaptive notch filter (ANF) to estimate current harmonics. The proposed filter consists of multiple units of ANF connected in parallel structure; each unit is governed by two ordinary differential equations. The frequency estimation is carried out based on the output of these units. The simulation and experimental results show the ability of the proposed tracking scheme to accurately estimate harmonics. The proposed filter was implemented digitally in TMS320F2808 and used in the control of hybrid active power filter (HAPF). The theoretical expectations are verified and demonstrated experimentally.

Keywords: Adaptive notch filter, Active power filter, harmonic filtering, Time varying frequency.

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Authors: V. R. Asghari, M. Ardebilipour

Abstract:

In the context of spectrum surveillance, a method to recover the code of spread spectrum signal is presented, whereas the receiver has no knowledge of the transmitter-s spreading sequence. The approach is based on a genetic algorithm (GA), which is forced to model the received signal. Genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems. Experimental results show that the method provides a good estimation, even when the signal power is below the noise power.

Keywords: Code estimation, genetic algorithms, spread spectrum.

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Authors: Beata Czarnacka-Chrobot

Abstract:

Execution of Business Software Systems (BSS) Development and Enhancement Projects (D&EP) is characterized by the exceptionally low effectiveness, leading to considerable financial losses. The general reason for low effectiveness of such projects is that they are inappropriately managed. One of the factors of proper BSS D&EP management is suitable (reliable and objective) method of project work effort estimation since this is what determines correct estimation of its major attributes: project cost and duration. BSS D&EP is usually considered to be accomplished effectively if product of a planned functionality is delivered without cost and time overrun. The goal of this paper is to prove that choosing approach to the BSS D&EP work effort estimation has a considerable influence on the effectiveness of such projects execution.

Keywords: Business software systems, development and enhancement projects, effectiveness, work effort estimation methods, software product size, software product functionality, project duration, project cost.

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Authors: Mamoun F. Abdel-Hafez

Abstract:

In this paper, the detection of a fault in the Global Positioning System (GPS) measurement is addressed. The class of faults considered is a bias in the GPS pseudorange measurements. This bias is modeled as an unknown constant. The fault could be the result of a receiver fault or signal fault such as multipath error. A bias bank is constructed based on set of possible fault hypotheses. Initially, there is equal probability of occurrence for any of the biases in the bank. Subsequently, as the measurements are processed, the probability of occurrence for each of the biases is sequentially updated. The fault with a probability approaching unity will be declared as the current fault in the GPS measurement. The residual formed from the GPS and Inertial Measurement Unit (IMU) measurements is used to update the probability of each fault. Results will be presented to show the performance of the presented algorithm.

Keywords: Estimation and filtering, Statistical data analysis, Faultdetection and identification.

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Authors: Won Sup Kim, Xue-Mei Cui, Seung Kee Han

Abstract:

We present on the method of inverse coherence matrix for the estimation of network connectivity from multivariate time series of a complex system. In a model system of coupled chaotic oscillators, it is shown that the inverse coherence matrix defined as the inverse of cross coherence matrix is proportional to the network connectivity. Therefore the inverse coherence matrix could be used for the distinction between the directly connected links from indirectly connected links in a complex network. We compare the result of network estimation using the method of the inverse coherence matrix with the results obtained from the coherence matrix and the partial coherence matrix.

Keywords: Chaotic oscillator, complex network, inverse coherence matrix, network estimation.

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Authors: B. S. Saini, Dilbag Singh, Moin Uddin, Vinod Kumar

Abstract:

The RR interval series is non-stationary and unevenly spaced in time. For estimating its power spectral density (PSD) using traditional techniques like FFT, require resampling at uniform intervals. The researchers have used different interpolation techniques as resampling methods. All these resampling methods introduce the low pass filtering effect in the power spectrum. The lomb transform is a means of obtaining PSD estimates directly from irregularly sampled RR interval series, thus avoiding resampling. In this work, the superiority of Lomb transform method has been established over FFT based approach, after applying linear and cubicspline interpolation as resampling methods, in terms of reproduction of exact frequency locations as well as the relative magnitudes of each spectral component.

Keywords: HRV, Lomb Transform, Resampling, RR-intervals.

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Authors: Jian Shi, Baoguo Yu, Haonan Jia, Meng Liu, Ping Huang

Abstract:

Adapting to the flexibility of war, a large number of guided weapons launch from aircraft. Therefore, the inertial navigation system loaded in the weapon needs to undergo an alignment process in the air. This article proposes the following methods to the problem of inaccurate modeling of the system under large misalignment angles, the accuracy reduction of filtering caused by outliers, and the noise changes in GPS signals: first, considering the large misalignment errors of Strapdown Inertial Navigation System (SINS)/GPS, a more accurate model is made rather than to make a small-angle approximation, and the Unscented Kalman Filter (UKF) algorithms are used to estimate the state; then, taking into account the impact of GPS noise changes on the fine alignment algorithm, the innovation adaptive filtering algorithm is introduced to estimate the GPS’s noise in real-time; at the same time, in order to improve the anti-interference ability of the air fine alignment algorithm, a robust filtering algorithm based on outlier detection is combined with the air fine alignment algorithm to improve the robustness of the algorithm. The algorithm can improve the alignment accuracy and robustness under interference conditions, which is verified by simulation.

Keywords: Air alignment, fine alignment, inertial navigation system, integrated navigation system, UKF.

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Authors: M. Ravindra, R. Srinivasa Rao

Abstract:

This paper presents a Generalized Binary Integer Linear Programming (GBILP) method for optimal allocation of Phasor Measurement Units (PMUs) and to generate Dynamic State Estimation (DSE) solution with complete observability. The GBILP method is formulated with Zero Injection Bus (ZIB) constraints to reduce the number of locations for placement of PMUs in the case of normal and single line contingency. The integration of PMU and conventional measurements is modeled in DSE process to estimate accurate states of the system. To estimate the dynamic behavior of the power system with proposed method, load change up to 40% considered at a bus in the power system network. The proposed DSE method is compared with traditional Weighted Least Squares (WLS) state estimation method in presence of load changes to show the impact of PMU measurements. MATLAB simulations are carried out on IEEE 14, 30, 57, and 118 bus systems to prove the validity of the proposed approach.

Keywords: Observability, phasor measurement units, PMU, state estimation, dynamic state estimation, SCADA measurements, zero injection bus.

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Authors: Baoguang Tian, Nan Chen

Abstract:

A new relative efficiency in linear model in reference is instructed into the linear weighted regression, and its upper and lower bound are proposed. In the linear weighted regression model, for the best linear unbiased estimation of mean matrix respect to the least-squares estimation, two new relative efficiencies are given, and their upper and lower bounds are also studied.

Keywords: Linear weighted regression, Relative efficiency, Mean matrix, Trace.

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Authors: Akash Rathee, Harish Parthasarathy

Abstract:

In this paper, a frequency-variation based method has been proposed for transistor parameter estimation in a commonemitter transistor amplifier circuit. We design an algorithm to estimate the transistor parameters, based on noisy measurements of the output voltage when the input voltage is a sine wave of variable frequency and constant amplitude. The common emitter amplifier circuit has been modelled using the transistor Ebers-Moll equations and the perturbation technique has been used for separating the linear and nonlinear parts of the Ebers-Moll equations. This model of the amplifier has been used to determine the amplitude of the output sinusoid as a function of the frequency and the parameter vector. Then, applying the proposed method to the frequency components, the transistor parameters have been estimated. As compared to the conventional time-domain least squares method, the proposed method requires much less data storage and it results in more accurate parameter estimation, as it exploits the information in the time and frequency domain, simultaneously. The proposed method can be utilized for parameter estimation of an analog device in its operating range of frequencies, as it uses data collected from different frequencies output signals for parameter estimation.

Keywords: Perturbation Technique, Parameter estimation, frequency-variation based method.

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Authors: S. K. Pillai, M. K. Jeyakumar

Abstract:

Estimation of model parameters is necessary to predict the behavior of a system. Model parameters are estimated using optimization criteria. Most algorithms use historical data to estimate model parameters. The known target values (actual) and the output produced by the model are compared. The differences between the two form the basis to estimate the parameters. In order to compare different models developed using the same data different criteria are used. The data obtained for short scale projects are used here. We consider software effort estimation problem using radial basis function network. The accuracy comparison is made using various existing criteria for one and two predictors. Then, we propose a new criterion based on linear least squares for evaluation and compared the results of one and two predictors. We have considered another data set and evaluated prediction accuracy using the new criterion. The new criterion is easy to comprehend compared to single statistic. Although software effort estimation is considered, this method is applicable for any modeling and prediction.

Keywords: Software effort estimation, accuracy, Radial Basis Function, linear least squares.

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Authors: Biao Qin, Jin Huang

Abstract:

In this paper, together with some improved Lyapunov-Krasovskii functional and effective mathematical techniques, several sufficient conditions are derived to guarantee the error system is globally asymptotically stable with H∞ performance, in which both the time-delay and its time variation can be fully considered. In order to get less conservative results of the state estimation condition, zero equalities and reciprocally convex approach are employed. The estimator gain matrix can be obtained in terms of the solution to linear matrix inequalities. A numerical example is provided to illustrate the usefulness and effectiveness of the obtained results.

Keywords: H∞ performance, Neural networks, State estimation.

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Authors: Do Hyeung Kim, Dae Hee Seo, Byung Doo Kim, Byung Gil Lee

Abstract:

As many recent researches being implemented in aviation and maritime aspects, strong doubts have been raised concerning the reliability of the estimation of collision risk. It is shown that using position and velocity of objects can lead to imprecise results. In this paper, therefore, a new approach to the estimation of collision risks using pose and shape of objects is proposed. Simulation results are presented validating the accuracy of the new criterion to adapt to collision risk algorithm based on fuzzy logic.

Keywords: Collision risk, Pose and shape, Fuzzy logic.

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Authors: Khaled M. EL-Naggar

Abstract:

This paper presents a fast and efficient on-line technique for estimating impedance of unbalanced loads in power systems. The proposed technique is an application of a discrete timedynamic filter based on stochastic estimation theory which is suitable for estimating parameters in noisy environment. The algorithm uses sets of digital samples of the distorted voltage and current waveforms of the non-linear load to estimate the harmonic contents of these two signal. The non-linear load impedance is then calculated from these contents. The method is tested using practical data. Results are reported and compared with those obtained using the conventional least error squares technique. In addition to the very accurate results obtained, the method can detect and reject bad measurements. This can be considered as a very important advantage over the conventional static estimation methods such as the least error square method.

Keywords: Estimation, identification, Harmonics, Dynamic Filter.

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