Search results for: Gaussian filter

Authors: B. Elshafei, X. Mao

Abstract:

The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: data fusion, Gaussian process regression, signal denoise, temporal extrapolation

Procedia PDF Downloads 136

Authors: Wataru Nakamura, Tomoaki Hashimoto, Liang-Kuang Chen

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This paper provides a state estimation method for automatic control systems of nonlinear vehicle dynamics. A nonlinear tire model is employed to represent the realistic behavior of a vehicle. In general, all the state variables of control systems are not precisedly known, because those variables are observed through output sensors and limited parts of them might be only measurable. Hence, automatic control systems must incorporate some type of state estimation. It is needed to establish a state estimation method for nonlinear vehicle dynamics with restricted measurable state variables. For this purpose, unscented Kalman filter method is applied in this study for estimating the state variables of nonlinear vehicle dynamics. The objective of this paper is to propose a state estimation method using unscented Kalman filter for nonlinear vehicle dynamics. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: state estimation, control systems, observer systems, nonlinear systems

Procedia PDF Downloads 138

Authors: Keisuke Takahata, Hiroshi Suetsugu

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Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation.

Keywords: digital terrain model, satellite LiDAR, gaussian processes, uncertainty quantification

Procedia PDF Downloads 184

Authors: Takuro Kida, Yuichi Kida

Abstract:

In this paper, it is shown a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detail algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output-signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory, and it is indicated that introducing conversations with feedback do not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: matrix filterbank, optimum signal approximation, category theory, simultaneous minimization

Procedia PDF Downloads 145

Authors: Kul Pun, F. A. Hamad, T. Ahmed, J. O. Ugwu, J. Eyers, G. Lawson, P. A. Russell

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Determining droplet size and distribution is essential when determining the separation efficiency of a two/three-phase separator. This paper investigates the effect of liquid flow and oil pad thickness on the droplet size at the lab scale. The findings show that increasing the inlet flow rates of the oil and water results in size reduction of the droplets and increasing the thickness of the oil pad increases the size of the droplets. The data were fitted with a simple Gaussian model, and the parameters of mean, standard deviation, and amplitude were determined. Trends have been obtained for the fitted parameters as a function of the Reynolds number, which suggest a way forward to better predict the starting parameters for population models when simulating separation using CFD packages. The key parameter to predict to fix the position of the Gaussian distribution was found to be the mean droplet size.

Keywords: two-phase separator, average bubble droplet, bubble size distribution, liquid-liquid phase

Procedia PDF Downloads 205

Authors: Justas Dilys

Abstract:

ThispaperaddressestheimplementationandoptimizationofanUnscentedKalmanFilter(UKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex- M3 microcontroller. A various optimization levels based on arithmetic calculation reduction was implemented in ARM Cortex-M3 microcontroller. The execution time of UKF estimator was up to 90µs without loss of accuracy. Moreover, simulation studies on the Unscented Kalman filters are carried out using Matlab to explore the usability of the UKF in a sensorless PMSMdrive.

Keywords: unscented kalman filter, ARM, PMSM, implementation

Procedia PDF Downloads 169

Authors: Ju-Hong Lee, Ding-Chen Chung

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This paper deals with the optimal design of recursive doubly complementary (DC) digital filter design using a metaheuristic based optimization technique. Based on the theory of DC digital filters using two recursive digital all-pass filters (DAFs), the design problem is appropriately formulated to result in an objective function which is a weighted sum of the phase response errors of the designed DAFs. To deal with the stability of the recursive DC filters during the design process, we can either impose some necessary constraints on the phases of the recursive DAFs. Through a frequency sampling and a weighted least squares approach, the optimization problem of the objective function can be solved by utilizing a population based stochastic optimization approach. The resulting DC digital filters can possess satisfactory frequency response. Simulation results are presented for illustration and comparison.

Keywords: doubly complementary, digital all-pass filter, weighted least squares algorithm, particle swarm optimization

Procedia PDF Downloads 691

Authors: Wesam Ali

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This paper provides useful guidelines to the circuit designers on the magnitude of fabrication errors in multilayer millimeter-wave components that are acceptable and presents data not previously reported in the literature. A particularly significant error that was quantified was that of skew between conductors on different layers, where it was found that a skew angle of only 0.1° resulted in very significant changes in bandwidth and insertion loss. The work was supported by a detailed investigation on a 35GHz, multilayer edge-coupled band-pass filter, which was fabricated on alumina substrates using photoimageable thick film process.

Keywords: fabrication errors, multilayer, high frequency band, photoimagable technology

Procedia PDF Downloads 474

Authors: Rahmat Purwoko, Bambang Riyanto Trilaksono

Abstract:

This paper gives comparison of INS/GNSS architecture namely Loosely Coupled and Tightly Coupled using Hardware in the Loop Simulation in Guided Missile RKX-200 rocket model. INS/GNSS Tightly Coupled architecture requires pseudo-range, pseudo-range rate, and position and velocity of each satellite in constellation from GPS (Global Positioning System) measurement. The Loosely Coupled architecture use estimated position and velocity from GNSS receiver. INS/GNSS architecture also requires angular rate and specific force measurement from IMU (Inertial Measurement Unit). Loosely Coupled arhitecture designed using 15 states Kalman Filter and Tightly Coupled designed using 17 states Kalman Filter. Integration algorithm calculation using ECEF frame. Navigation System implemented Zedboard All Programmable SoC.

Keywords: kalman filter, loosely coupled, navigation system, tightly coupled

Procedia PDF Downloads 310

Authors: Takashi Shimizu, Tomoaki Hashimoto

Abstract:

Controlling the flow of fluids is a challenging problem that arises in many fields. Burgers’ equation is a fundamental equation for several flow phenomena such as traffic, shock waves, and turbulence. The optimal feedback control method, so-called model predictive control, has been proposed for Burgers’ equation. However, the model predictive control method is inapplicable to systems whose all state variables are not exactly known. In practical point of view, it is unusual that all the state variables of systems are exactly known, because the state variables of systems are measured through output sensors and limited parts of them can be only available. In fact, it is usual that flow velocities of fluid systems cannot be measured for all spatial domains. Hence, any practical feedback controller for fluid systems must incorporate some type of state estimator. To apply the model predictive control to the fluid systems described by Burgers’ equation, it is needed to establish a state estimation method for Burgers’ equation with limited measurable state variables. To this purpose, we apply unscented Kalman filter for estimating the state variables of fluid systems described by Burgers’ equation. The objective of this study is to establish a state estimation method based on unscented Kalman filter for Burgers’ equation. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: observer systems, unscented Kalman filter, nonlinear systems, Burgers' equation

Procedia PDF Downloads 153

Authors: Cemil Turan, Mohammad Shukri Salman

Abstract:

The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms.

Keywords: adaptive filtering, sparse system identification, TD-LMS algorithm, VSSLMS algorithm

Procedia PDF Downloads 361

Authors: Yousif Mohamed Y. Abdallah, Eltayeb Wagallah

Abstract:

Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. Image enhancement is the transformation or mapping of one image to another. The enhancement of certain features in images is accompanied by undesirable effects. To achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian scale mixture model and median filter were presented, which accomplishes nonlinearities from scattering a new nonlinear approach for contrast enhancement of bones in bone scan images using both gamma correction and negative transform methods. The usual assumption of a distribution of gamma and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity but to underestimation in regions of high intensity and therefore to non-optional results. The contrast enhancement results were obtained and evaluated using MatLab program in nuclear medicine images of the bones. The optimal number of bins, in particular the number of gray-levels, is chosen automatically using entropy and average distance between the histogram of the original gray-level distribution and the contrast enhancement function’s curve.

Keywords: bone scan, nuclear medicine, Matlab, image processing technique

Procedia PDF Downloads 511

Authors: Nimisha Elsa Koshy, Varun P. Gopi, V. I. Thajudin Ahamed

Abstract:

The entire visualization of GastroIntestinal (GI) tract is not possible with conventional endoscopic exams. Wireless Capsule Endoscopy (WCE) is a low risk, painless, noninvasive procedure for diagnosing diseases such as bleeding, polyps, ulcers, and Crohns disease within the human digestive tract, especially the small intestine that was unreachable using the traditional endoscopic methods. However, analysis of massive images of WCE detection is tedious and time consuming to physicians. Hence, researchers have developed software methods to detect these diseases automatically. Thus, the effectiveness of WCE can be improved. In this paper, a novel textural feature extraction method is proposed based on Contourlet transform and Log Gabor filter to distinguish ulcer regions from normal regions. The results show that the proposed method performs well with a high accuracy rate of 94.16% using Support Vector Machine (SVM) classifier in HSV colour space.

Keywords: contourlet transform, log gabor filter, ulcer, wireless capsule endoscopy

Procedia PDF Downloads 540

Authors: Dehini Rachid, Ferdi Brahim

Abstract:

The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.

Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion

Procedia PDF Downloads 387

Authors: Takuro Kida, Yuichi Kida

Abstract:

We show a base of the new trend of algorithm mathematically that treats a historical reason of continuous discrimination in the world as well as its solution by introducing new concepts of parallel world that includes an invisible set of errors as its companion. With respect to a matrix operator-filter bank that the matrix operator-analysis-filter bank H and the matrix operator-sampling-filter bank S are given, firstly, we introduce the detailed algorithm to derive the optimum matrix operator-synthesis-filter bank Z that minimizes all the worst-case measures of the matrix operator-error-signals E(ω) = F(ω) − Y(ω) between the matrix operator-input-signals F(ω) and the matrix operator-output signals Y(ω) of the matrix operator-filter bank at the same time. Further, feedback is introduced to the above approximation theory and it is indicated that introducing conversations with feedback does not superior automatically to the accumulation of existing knowledge of signal prediction. Secondly, the concept of category in the field of mathematics is applied to the above optimum signal approximation and is indicated that the category-based approximation theory is applied to the set-theoretic consideration of the recognition of humans. Based on this discussion, it is shown naturally why the narrow perception that tends to create isolation shows an apparent advantage in the short term and, often, why such narrow thinking becomes intimate with discriminatory action in a human group. Throughout these considerations, it is presented that, in order to abolish easy and intimate discriminatory behavior, it is important to create a parallel world of conception where we share the set of invisible error signals, including the words and the consciousness of both worlds.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, conditional optimization

Procedia PDF Downloads 158

Authors: Neziha Jaouedi, Noureddine Boujnah, Mohamed Salim Bouhlel

Abstract:

In the framework of human machine interaction systems enhancement, we focus throw this paper on human behavior analysis and action recognition. Human behavior is characterized by actions and reactions duality (movements, psychological modification, verbal and emotional expression). It’s worth noting that many information is hidden behind gesture, sudden motion points trajectories and speeds, many research works reconstructed an information retrieval issues. In our work we will focus on motion extraction, tracking and action recognition using wavelet network approaches. Our contribution uses an analysis of human subtraction by Gaussian Mixture Model (GMM) and body movement through trajectory models of motion constructed from kalman filter. These models allow to remove the noise using the extraction of the main motion features and constitute a stable base to identify the evolutions of human activity. Each modality is used to recognize a human action using wavelets of derived beta distributions approach. The proposed approach has been validated successfully on a subset of KTH and UCF sports database.

Keywords: feautures extraction, human action classifier, wavelet neural network, beta wavelet

Procedia PDF Downloads 411

Authors: Yi Huang, Clemens Guehmann

Abstract:

In order to monitor the thermal behavior of an asynchronous machine with squirrel cage rotor, a 9th-order extended Kalman filter (EKF) algorithm is implemented to estimate the temperatures of the stator windings, the rotor cage and the stator core. The state-space equations of EKF are established based on the electrical, mechanical and the simplified thermal models of an asynchronous machine. The asynchronous machine with simplified thermal model in Dymola is compiled as DymolaBlock, a physical model in MATLAB/Simulink. The coolant air temperature, three-phase voltages and currents are exported from the physical model and are processed by EKF estimator as inputs. Compared to the temperatures exported from the physical model of the machine, three parts of temperatures can be estimated quite accurately by the EKF estimator. The online EKF estimator is independent from the machine control algorithm and can work under any speed and load condition if the stator current is nonzero current system.

Keywords: asynchronous machine, extended Kalman filter, resistance, simulation, temperature estimation, thermal model

Procedia PDF Downloads 285

Authors: Jongwoo Lee, Dae-Eun Kang, Sang-Young Park

Abstract:

This study presents a precise relative navigational method for satellites flying in formation using laser-based intermittent measurement data. The measurement data for the relative navigation between two satellites consist of a relative distance measured by a laser instrument and relative attitude angles measured by attitude determination. The relative navigation solutions are estimated by both the Extended Kalman filter (EKF) and unscented Kalman filter (UKF). The solutions estimated by the EKF may become inaccurate or even diverge as measurement outage time gets longer because the EKF utilizes a linearization approach. However, this study shows that the UKF with the appropriate scaling parameters provides a stable and accurate relative navigation solutions despite the long measurement outage time and large initial error as compared to the relative navigation solutions of the EKF. Various navigation results have been analyzed by adjusting the scaling parameters of the UKF.

Keywords: satellite relative navigation, laser-based measurement, intermittent measurement, unscented Kalman filter

Procedia PDF Downloads 357

Authors: Analise Borg, Paul Micallef

Abstract:

Over the past few years, the online multimedia collection has grown at a fast pace. Several companies showed interest to study the different ways to organize the amount of audio information without the need of human intervention to generate metadata. In the past few years, many applications have emerged on the market which are capable of identifying a piece of music in a short time. Different audio effects and degradation make it much harder to identify the unknown piece. In this paper, an audio fingerprinting system which makes use of a non-parametric based algorithm is presented. Parametric analysis is also performed using Gaussian Mixture Models (GMMs). The feature extraction methods employed are the Mel Spectrum Coefficients and the MPEG-7 basic descriptors. Bin numbers replaced the extracted feature coefficients during the non-parametric modelling. The results show that non-parametric analysis offer potential results as the ones mentioned in the literature.

Keywords: audio fingerprinting, mapping algorithm, Gaussian Mixture Models, MFCC, MPEG-7

Procedia PDF Downloads 422

Authors: Girmaye Abebe, Brook Lemma

Abstract:

Adsorption of phosphorus (P as PO43-) in filter cake was studied to assess the media's capability in removing phosphorous from wastewaters. The composition of the filter cake that was generated from alum manufacturing process as waste residue has high amount of silicate from the complete silicate analysis of the experiment. Series of batches adsorption experiments were carried out to evaluate parameters that influence the adsorption capacity of PO43-. The factors studied include the effect of contact time, adsorbent dose, thermal pretreatment of the adsorbent, neutralization of the adsorbent, initial PO43- concentration, pH of the solution and effect of co-existing anions. Results showed that adsorption of PO43- is fairly rapid in first 5 min and after that it increases slowly to reach the equilibrium in about 1 h. The treatment efficiency of PO43- was increased with adsorbent extent. About 90% removal efficiency was increased within 1 h at an optimum adsorbent dose of 10 g/L for initial PO43- concentration of 10 mg/L. The amount of PO43- adsorbed increased with increasing initial PO43- concentration. Heat treatment and surface neutralization of the adsorbent did not improve the PO43- removal capacity and efficiency. The percentage of PO43- removal remains nearly constant within the pH range of 3-8. The adsorption data at ambient pH were well fitted to the Langmuir Isotherm and Dubinin–Radushkevick (D–R) isotherm model with a capacity of 25.84 and 157.55 mg/g of the adsorbent respectively. The adsorption kinetic was found to follow a pseudo-second-order rate equation with an average rate constant of 3.76 g.min−1.mg−1. The presence of bicarbonate or carbonate at higher concentrations (10–1000 mg/L) decreased the PO43- removal efficiency slightly while other anions (Cl-, SO42-, and NO3-) have no significant effect within the concentration range tested. The overall result shows that the filter cake is an efficient PO43- removing adsorbent against many parameters.

Keywords: wastewater, filter cake, adsorption capacity, phosphate (PO43-)

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Authors: Jyh Sheen, Yang-Hung Cheng

Abstract:

This paper presents a design of a microstrip bandpass filter with a compact size and wide stopband by using 0.15-μm GaAs pHEMT process. The wide stop band is achieved by suppressing the first and second harmonic resonance frequencies. The slow-wave coupling stepped impedance resonator with cross coupled structure is adopted to design the bandpass filter. A two-resonator filter was fabricated with 13.5GHz center frequency and 11% bandwidth was achieved. The devices are simulated using the ADS design software. This device has shown a compact size and very low insertion loss of 2.6 dB. Microstrip planar bandpass filters have been widely adopted in various communication applications due to the attractive features of compact size and ease of fabricating. Various planar resonator structures have been suggested. In order to reach a wide stopband to reduce the interference outside the passing band, various designs of planar resonators have also been submitted to suppress the higher order harmonic frequencies of the designed center frequency. Various modifications to the traditional hairpin structure have been introduced to reduce large design area of hairpin designs. The stepped-impedance, slow-wave open-loop, and cross-coupled resonator structures have been studied to miniaturize the hairpin resonators. In this study, to suppress the spurious harmonic bands and further reduce the filter size, a modified hairpin-line bandpass filter with cross coupled structure is suggested by introducing the stepped impedance resonator design as well as the slow-wave open-loop resonator structure. In this way, very compact circuit size as well as very wide upper stopband can be achieved and realized in a Roger 4003C substrate. On the other hand, filters constructed with integrated circuit technology become more attractive for enabling the integration of the microwave system on a single chip (SOC). To examine the performance of this design structure at the integrated circuit, the filter is fabricated by the 0.15 μm pHEMT GaAs integrated circuit process. This pHEMT process can also provide a much better circuit performance for high frequency designs than those made on a PCB board. The design example was implemented in GaAs with center frequency at 13.5 GHz to examine the performance in higher frequency in detail. The occupied area is only about 1.09×0.97 mm2. The ADS software is used to design those modified filters to suppress the first and second harmonics.

Keywords: microstrip resonator, bandpass filter, harmonic suppression, GaAs

Procedia PDF Downloads 326

Authors: Hyojin Lim, Cuong Nguyen Khac, Ho-Youl Jung

Abstract:

In this paper, we propose moving object detection method which is helpful for driver to safely take his/her car out of parking lot. When moving objects such as motorbikes, pedestrians, the other cars and some obstacles are detected at the rear-side of host vehicle, the proposed algorithm can provide to driver warning. We assume that the host vehicle is just before departure. Gaussian Mixture Model (GMM) based background subtraction is basically applied. Pre-processing such as smoothing and post-processing as morphological filtering are added.We examine “which color space has better performance for detection of moving objects?” Three color spaces including RGB, YCbCr, and Y are applied and compared, in terms of detection rate. Through simulation, we prove that RGB space is more suitable for moving object detection based on background subtraction.

Keywords: gaussian mixture model, background subtraction, moving object detection, color space, morphological filtering

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Authors: Zikiou Nadia, Lahdir Mourad, Ameur Soltane

Abstract:

In this paper, we propose an effective method for image compression based on SVM Regression (SVR), with three different kernels, and biorthogonal 2D Discrete Wavelet Transform. SVM regression could learn dependency from training data and compressed using fewer training points (support vectors) to represent the original data and eliminate the redundancy. Biorthogonal wavelet has been used to transform the image and the coefficients acquired are then trained with different kernels SVM (Gaussian, Polynomial, and Linear). Run-length and Arithmetic coders are used to encode the support vectors and its corresponding weights, obtained from the SVM regression. The peak signal noise ratio (PSNR) and their compression ratios of several test images, compressed with our algorithm, with different kernels are presented. Compared with other kernels, Gaussian kernel achieves better image quality. Experimental results show that the compression performance of our method gains much improvement.

Keywords: image compression, 2D discrete wavelet transform (DWT-2D), support vector regression (SVR), SVM Kernels, run-length, arithmetic coding

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Authors: Nihad Karim Chowdhury, Mohammad Sanaullah Chowdhury, Muhammed Jamshed Alam Patwary, Rubel Biswas

Abstract:

In content-based 3D shape retrieval system, achieving high search performance has become an important research problem. A challenging aspect of this problem is to find an effective shape descriptor which can discriminate similar shapes adequately. To address this problem, we propose a new shape descriptor for 3D shape models by combining multi-resolution with multi-block center-symmetric local binary pattern operator. Given an arbitrary 3D shape, we first apply pose normalization, and generate a set of multi-viewed 2D rendered images. Second, we apply Gaussian multi-resolution filter to generate several levels of images from each of 2D rendered image. Then, overlapped sub-images are computed for each image level of a multi-resolution image. Our unique multi-block CS-LBP comes next. It allows the center to be composed of m-by-n rectangular pixels, instead of a single pixel. This process is repeated for all the 2D rendered images, derived from both ‘depth-buffer’ and ‘silhouette’ rendering. Finally, we concatenate all the features vectors into one dimensional histogram as our proposed 3D shape descriptor. Through several experiments, we demonstrate that our proposed 3D shape descriptor outperform the previous methods by using a benchmark dataset.

Keywords: 3D shape retrieval, 3D shape descriptor, CS-LBP, overlapped sub-images

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Authors: Youtao Gao, Bingyu Jin, Tanran Zhao, Bo Xu

Abstract:

The history of satellite navigation can be dated back to the 1960s. From the U.S. Transit system and the Russian Tsikada system to the modern Global Positioning System (GPS) and the Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS), performance of satellite navigation has been greatly improved. Nowadays, the navigation accuracy and coverage of these existing systems have already fully fulfilled the requirement of near-Earth users, but these systems are still beyond the reach of deep space targets. Due to the renewed interest in space exploration, a novel high-precision satellite navigation system is becoming even more important. The increasing demand for such a deep space navigation system has contributed to the emergence of a variety of new constellation architectures, such as the Lunar Global Positioning System. Apart from a Walker constellation which is similar to the one adopted by GPS on Earth, a novel constellation architecture which consists of libration point satellites in the Earth-Moon system is also available to construct the lunar navigation system, which can be called accordingly, the libration point satellite navigation system. The concept of using Earth-Moon libration point satellites for lunar navigation was first proposed by Farquhar and then followed by many other researchers. Moreover, due to the special characteristics of Libration point orbits, an autonomous orbit determination technique, which is called ‘Liaison navigation’, can be adopted by the libration point satellites. Using only scalar satellite-to-satellite tracking data, both the orbits of the user and libration point satellites can be determined autonomously. In this way, the extensive Earth-based tracking measurement can be eliminated, and an autonomous satellite navigation system can be developed for future space exploration missions. The method of state estimate is an unnegligible factor which impacts on the orbit determination accuracy besides type of orbit, initial state accuracy and measurement accuracy. We apply the extended Kalman filter(EKF) and the unscented Kalman filter(UKF) to determinate the orbits of Lagrangian navigation satellites. The autonomous orbit determination errors are compared. The simulation results illustrate that UKF can improve the accuracy and z-axis convergence to some extent.

Keywords: extended Kalman filter, autonomous orbit determination, unscented Kalman filter, navigation constellation

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Authors: S. Bouhouche, R. Drai, J. Bast

Abstract:

This paper is concerned with a method for uncertainty evaluation of steel sample content using X-Ray Fluorescence method. The considered method of analysis is a comparative technique based on the X-Ray Fluorescence; the calibration step assumes the adequate chemical composition of metallic analyzed sample. It is proposed in this work a new combined approach using the Kalman Filter and Markov Chain Monte Carlo (MCMC) for uncertainty estimation of steel content analysis. The Kalman filter algorithm is extended to the model identification of the chemical analysis process using the main factors affecting the analysis results; in this case, the estimated states are reduced to the model parameters. The MCMC is a stochastic method that computes the statistical properties of the considered states such as the probability distribution function (PDF) according to the initial state and the target distribution using Monte Carlo simulation algorithm. Conventional approach is based on the linear correlation, the uncertainty budget is established for steel Mn(wt%), Cr(wt%), Ni(wt%) and Mo(wt%) content respectively. A comparative study between the conventional procedure and the proposed method is given. This kind of approaches is applied for constructing an accurate computing procedure of uncertainty measurement.

Keywords: Kalman filter, Markov chain Monte Carlo, x-ray fluorescence calibration and testing, steel content measurement, uncertainty measurement

Procedia PDF Downloads 286

Authors: Hilal Naimi, Amelbahahouda Adamou-Mitiche, Lahcene Mitiche

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The main problem in the area of medical imaging has been image denoising. The most defying for image denoising is to secure data carrying structures like surfaces and edges in order to achieve good visual quality. Different algorithms with different denoising performances have been proposed in previous decades. More recently, models focused on deep learning have shown a great promise to outperform all traditional approaches. However, these techniques are limited to the necessity of large sample size training and high computational costs. This research proposes a denoising approach basing on LDTCWT (Lifting Dual Tree Complex Wavelet Transform) using Hybrid Thresholding with Wiener filter to enhance the quality image. This research describes the LDTCWT as a type of lifting wavelets remodeling that produce complex coefficients by employing a dual tree of lifting wavelets filters to get its real part and imaginary part. Permits the remodel to produce approximate shift invariance, directionally selective filters and reduces the computation time (properties lacking within the classical wavelets transform). To develop this approach, a hybrid thresholding function is modeled by integrating the Wiener filter into the thresholding function.

Keywords: lifting wavelet transform, image denoising, dual tree complex wavelet transform, wavelet shrinkage, wiener filter

Procedia PDF Downloads 163

Authors: Alexander Machikhin, Olga Polschikova, Vitold Pozhar, Alina Ramazanova

Abstract:

White-light quantitative phase imaging is an effective technique for achieving sub-nanometer phase sensitivity. Highly stable interferometers based on common-path geometry have been developed in recent years to solve this task. Some of these methods also apply multispectral approach. The purpose of this research is to suggest a simple and effective interferometer for such systems. We developed a three-lens common-path interferometer, which can be used for quantitative phase imaging with or without multispectral modality. The lens system consists of two components, the first one of which is a compound lens, consisting of two lenses. A pinhole is placed between the components. The lens-in-lens approach enables effective light transmission and high stability of the interferometer. The multispectrality is easily implemented by placing a tunable filter in front of the interferometer. In our work, we used an acousto-optical tunable filter. Some design considerations are discussed and multispectral quantitative phase retrieval is demonstrated.

Keywords: acousto-optical tunable filter, common-path interferometry, digital holography, multispectral quantitative phase imaging

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Authors: Ming Su, Ziqiang Mu

Abstract:

This paper proposes a synthesis method for nonuniform linear antenna arrays that combine Gaussian process regression (GPR) and genetic algorithm (GA). In this method, the GPR model can be used to calculate the array radiation pattern in the presence of mutual coupling effects, and then the GA is used to optimize the excitations and locations of the elements so as to generate the desired radiation pattern. In this paper, taking a 9-element nonuniform linear array as an example and the desired radiation pattern corresponding to a Chebyshev distribution as the optimization objective, optimize the excitations and locations of the elements. Finally, the optimization results are verified by electromagnetic simulation software CST, which shows that the method is effective.

Keywords: nonuniform linear antenna arrays, GPR, GA, mutual coupling effects, active element pattern

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Authors: Kayode Fatokun, Nozipho N. Motsa

Abstract:

Field experiments were carried out during the summer/autumn (first trial) and winter/spring (second trial) seasons of 2019 and 2021 inDlangubo, Ngwelezane, and Mtubatubaareas of KwaZulu-Natal Province of South Africa to study the drought amelioration effects and impact of 2 locally available organic wastes [filter cake (FC) and macadamia husk compost (MHC)] on the productivity, and physiological responses of 4 orange-fleshed sweet potato cultivars (Buregard cv., Impilo, W-119 and 199062.1). The effects of FC and MHC were compared with that of inorganic fertilizer (IF) [2:3:2 (30)], FC+IF, MHC+IF, and control. The soil amendments were applied in the first trials only. Climatic data such as humidity, temperature, and rainfall were taken via remote sensing. The results of the first trial indicated that filter cake and IF significantly performed better than MHC. While the strength of filter cake may be attributable to its rich array of mineral nutrients such as calcium, magnesium, potassium, sodium, zinc, copper, manganese, iron, and phosphorus. The little performance from MHC may be attributable to its water holding capacity. Also, a positive correction occurred between the yield of the test OFSP cultivars and climatic factors such as rainfall, NDVI, and NDWI values. Whereas the inorganic fertilizer did not have any significant effect on the growth and productivity of any of the tested sweet potato cultivars in the second trial; FC, and MHC largely maintained their significant performances. In conclusion, the use of FC is highly recommended in the production of the test orange-fleshed sweet potato cultivars. Also, the study indicated that both FC and MHC may not only supply the needed plant nutrients but has the capacity to reduce the impact of drought on the growth of the test cultivars. These findings are of great value to farmers, especially the resource-poorones.

Keywords: amendments, drought, filter cake, macadamia husk compost, sweet potato

Procedia PDF Downloads 98