Search results for: most likelihood amplitude estimation (MLQAE)

Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

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In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

Procedia PDF Downloads 466

Authors: Jasbir Arora, Indu Talwar, Daisy Sahni, Vidya Rattan

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Introduction: Age estimation is a crucial step to build the identity of a person, both in case of deceased and alive. In adults, age can be estimated on the basis of six regressive (Attrition, Secondary dentine, Dentine transparency, Root resorption, Cementum apposition and Periodontal Disease) changes in teeth qualitatively using scoring system and quantitatively by micrometric method. The present research was designed to establish the reliability of qualitative (method 1) and quantitative (method 2) of age estimation among North Indians and to compare the efficacy of these two methods. Method: 250 single-rooted extracted teeth (18-75 yrs.) were collected from Department of Oral Health Sciences, PGIMER, Chandigarh. Before extraction, periodontal score of each tooth was noted. Labiolingual sections were prepared and examined under light microscope for regressive changes. Each parameter was scored using Gustafson’s 0-3 point score system (qualitative), and total score was calculated. For quantitative method, each regressive change was measured quantitatively in form of 18 micrometric parameters under microscope with the help of measuring eyepiece. Age was estimated using linear and multiple regression analysis in Gustafson’s method and Kedici’s method respectively. Estimated age was compared with actual age on the basis of absolute mean error. Results: In pooled data, by Gustafson’s method, significant correlation (r= 0.8) was observed between total score and actual age. Total score generated an absolute mean error of ±7.8 years. Whereas, for Kedici’s method, a value of correlation coefficient of r=0.5 (p<0.01) was observed between all the eighteen micrometric parameters and known age. Using multiple regression equation, age was estimated, and an absolute mean error of age was found to be ±12.18 years. Conclusion: Gustafson’s (qualitative) method was found to be a better predictor for age estimation among North Indians.

Keywords: forensic odontology, age estimation, North India, teeth

Procedia PDF Downloads 241

Authors: H. Ben Salah, A. Gannoun, C. de Peretti, A. Trabelsi

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The Downside Risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developped a new recurcive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents an appearance which is not very smooth. In order to overcome that, Athayde (2003) proposed a mean kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which we had continuous observations. In this paper, taking advantage on the the robustness of the median, we replace the mean estimator in Athayde's model by a nonparametric median estimator of the returns. Then, we give a new version of the former algorithm (of Athayde (2001, 2003)). We eventually analyse the properties of this improved portfolio frontier and apply this new method on real examples.

Keywords: Downside Risk, Kernel Method, Median, Nonparametric Estimation, Semivariance

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Authors: Muhammad Jabbar

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In this paper we propose two ratio and ratio type exponential estimator for the estimation of population coefficient of variation using the auxiliary information under two-stage sampling. The properties of these estimators are derived up to first order of approximation. The efficiency conditions under which suggested estimator are more efficient, are obtained. Numerical and simulated studies are conducted to support the superiority of the estimators. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimator.

Keywords: two-stage sampling, coefficient of variation, ratio type exponential estimator

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

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Performance of different filtering approaches depends on modeling of dynamical system and algorithm structure. For modeling and smoothing the data the evaluation of posterior distribution in different filtering approach should be chosen carefully. In this paper different filtering approaches like filter KALMAN, EKF, UKF, EKS and smoother RTS is simulated in some trajectory tracking of path and accuracy and limitation of these approaches are explained. Then probability of model with different filters is compered and finally the effect of the noise variance to estimation is described with simulations results.

Keywords: Gaussian approximation, Kalman smoother, parameter estimation, noise variance

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Authors: Ekin Nurbaş

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One of the essential challenges in array signal processing, which has drawn enormous research interest over the past several decades, is estimating the direction of arrival (DOA) of plane waves impinging on an array of sensors. In recent years, the Compressive Sensing based DoA estimation methods have been proposed by researchers, and it has been discovered that the Compressive Sensing (CS)-based algorithms achieved significant performances for DoA estimation even in scenarios where there are multiple coherent sources. On the other hand, the Genetic Algorithm, which is a method that provides a solution strategy inspired by natural selection, has been used in sparse representation problems in recent years and provides significant improvements in performance. With all of those in consideration, in this paper, a method that combines the Genetic Algorithm (GA) and the Multi-Criteria Decision Making (MCDM) approaches for Direction of Arrival (DoA) estimation in the Compressive Sensing (CS) framework is proposed. In this method, we generate a multi-objective optimization problem by splitting the norm minimization and reconstruction loss minimization parts of the Compressive Sensing algorithm. With the help of the Genetic Algorithm, multiple non-dominated solutions are achieved for the defined multi-objective optimization problem. Among the pareto-frontier solutions, the final solution is obtained with the multiple MCDM methods. Moreover, the performance of the proposed method is compared with the CS-based methods in the literature.

Keywords: genetic algorithm, direction of arrival esitmation, multi criteria decision making, compressive sensing

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Authors: Ghulam Mustafa Peerzada, Shagufta Rashid, Nadeem Bashir

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These the influence of initial reagent concentrations on the Belousov-Zhabotinsky (BZ) system with Mn2+/Mn3+ as redox catalyst, inorganic bromate as oxidant and pyrogallol as organic substrate was studied. The reactions were monitored by potentiometery in oxidation reduction potential (ORP) mode. The aforesaid reagents were mixed with varying concentrations to evolve the optimal concentrations at which the reaction system exhibited better oscillations. The various oscillatory parameters such as induction period (tin), time period (tp), frequency (v), amplitude (A) and number of oscillations (n) were derived and the dependence of concentration of the reacting species on these oscillatory parameters was interpreted on the basis of the Field-Koros-Noyes mechanism. Ferroin based BZ system with pyrogallol as organic substrate was optimized under CSTR condition at temperature of 30±0.1oC Effect of molecules like monomer and polymer as additives to the system was checked and their interaction with the system was also studied. It has been observed that the monomer affects the time period, while the polymer has its effect on the amplitude of oscillations because of monomer’s interaction with the bromine and polymer’s with that of the Ferroin.

Keywords: Belousov Zhabotinsky reaction, oscillatory parameters, polymer, pyrogallol

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Authors: Farshad Ghazalian, Laleh Hakemi, Lotfali Pourkazemi, Maryam Ameri, Seyed Hossein Alavi

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Background: The use of whole body vibration (WBV) as an exercise method has rapidly increased over the last decade. The aim of this study was to evaluate long term effects of different amplitudes of whole body vibration training with progressive frequencies on lipid profile of young healthy men. Materials and methods: Thirty three healthy male students were divided randomly in three groups: high amplitude vibration group (n=11), low amplitude vibration group (n=11), and control group (n=11). The vibration training consisted of 5 week whole-body vibration 3 times a week with amplitudes 4 and 2 mm and progressive frequencies from 25 Hz with increments of 5 Hz weekly. Concentrations TG, HDL, LDL, cholesterol, and VLDL before and after 5 weeks of training were measured in plasma samples. Statistical analysis was done using one way analysis of variance. P<0.05 was considered statistically significant. Results: The most important result of the present study is finding no favorable changes of 5-week vibration training with different amplitudes on blood lipid profiles. Discussion and conclusions: It was emphasized that in vibration training there should be a relationship between intensity and volume of exercise and lipid responses in order to improve blood lipoprotein profiles.

Keywords: long term, body, vibration training, lipid

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: Toufik Bensana, Slimane Mekhilef, Kamel Tadjine

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The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally non-stationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. the results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, local mean decomposition, rolling element bearing, vibration analysis

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Authors: Shi Yu Wang, Qian Wei Zhang, He Li, Hao Han He, Yun Bo Li

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The spatial controls of electromagnetic (EM) waves have always been a research hot spot in recent years. And the rapid development of metasurface-based technologies has provided more freedoms for manipulating the EM waves. Here we propose the design of reconfigurable and non-reciprocal metasurface with independent controls of transmission gain, attenuation and phase. The proposed meta-atom mainly consists of the cascaded textures including the receiving antenna, the middle layer in which the power amplifiers (PAs), programmable attenuator and phase shifter locate, and the transmitting antenna. The programmable attenuator and phase shifter can realize the dynamic controls of transmission amplitude and phase independently, and the PA devices in the meta-atom can actualize the performance of non-reciprocal transmission. The proposed meta-atom is analyzed applying field-circuit co-simulation and a sample of the meta-atom is fabricated and measured under using two standard waveguides. The measured results verify the ability of the independent manipulation for transmission amplitude and phase of the proposed the meta-atom and the design method has been verified very well correspondingly.

Keywords: active circuits, independent controls of multiple electromagnetic features, non-reciprocal electromagnetic transmission, reconfigurable and programmable

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

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Authors: Baghdasaryan Marinka, Ulikyan Azatuhi

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The efficient usage of the compensation abilities of the electrical drive synchronous motors used in production processes can essentially improve the technical and economic indices of the process.  Reducing the flows of the reactive electrical energy due to the compensation of reactive power allows to significantly reduce the load losses of power in the electrical networks. As a result of analyzing the scientific works devoted to the issues of regulating the excitation of the synchronous motors, the need for comprehensive investigation and estimation of the excitation mode has been substantiated. By means of the obtained transmission functions, in the Simulink environment of the software package MATLAB, the transition processes of the excitation mode have been studied. As a result of obtaining and estimating the graph of the Nyquist plot and the transient process, the necessity of developing the Proportional-Integral-Derivative (PID) regulator has been justified. The transient processes of the system of the PID regulator have been investigated, and the amplitude–phase characteristics of the system have been estimated. The analysis of the obtained results has shown that the regulation indices of the developed system have been improved. The developed system can be successfully applied for regulating the excitation voltage of different-power synchronous motors, operating with a changing load, ensuring a value of the power coefficient close to 1.

Keywords: transition process, synchronous motor, excitation mode, regulator, reactive power

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Authors: G. Punithavathy

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The research proposes a statistical model for the distributed acoustic sensor interrogation units that broadcast a laser pulse into the fiber optics, where interactions within the fiber determine the localized acoustic energy that causes light reflections known as backscatter. The backscattered signal's amplitude and phase can be calculated using explicit equations. The created model makes amplitude signal spectrum and autocorrelation predictions that are confirmed by experimental findings. Phase signal characteristics that are useful for researching optical time domain reflectometry (OTDR) system sensing applications are provided and examined, showing good agreement with the experiment. The experiment was successfully done with the use of Python coding. In this research, we can analyze the entire distributed acoustic sensing (DAS) component parts separately. This model assumes that the fiber is in a static condition, meaning that there is no external force or vibration applied to the cable, that means no external acoustic disturbances present. The backscattered signal consists of a random noise component, which is caused by the intrinsic imperfections of the fiber, and a coherent component, which is due to the laser pulse interacting with the fiber.

Keywords: distributed acoustic sensing, optical fiber devices, optical time domain reflectometry, Rayleigh scattering

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Authors: Kouzi Katia

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This paper proposes a Direct Torque Control (DTC) algorithm of dual Stator Induction Motor (DSIM) drive using two approach intelligent techniques: Artificial Neural Network (ANN) approach replaces the switching table selector block of conventional DTC and Mamdani Fuzzy Logic controller (FLC) is used for stator resistance estimation. The fuzzy estimation method is based on an online stator resistance correction through the variations of stator current estimation error and its variation. The fuzzy logic controller gives the future stator resistance increment at the output. The main advantage of suggested algorithm control is to reduce the hardware complexity of conventional selectors, to avoid the drive instability that may occur in certain situation and ensure the tracking of the actual of the stator resistance. The effectiveness of the technique and the improvement of the whole system performance are proved by results.

Keywords: artificial neural network, direct torque control, dual stator induction motor, fuzzy logic estimator, switching table

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Authors: Victoria Goodall, Paul Fatti, Norman Owen-Smith

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Hidden Markov Models have become popular for the analysis of animal tracking data. These models are being used to model the movements of a variety of species in many areas around the world. A common assumption of the model is that the observations need to have regular time steps. In many ecological studies, this will not be the case. The objective of the research is to modify the movement model to allow for irregularly spaced locations and investigate the effect on the inferences which can be made about the latent states. A modification of the likelihood function to allow for these irregular spaced locations is investigated, without using interpolation or averaging the movement rate. The suitability of the modification is investigated using GPS tracking data for lion (Panthera leo) in South Africa, with many observations obtained during the night, and few observations during the day. Many nocturnal predator tracking studies are set up in this way, to obtain many locations at night when the animal is most active and is difficult to observe. Few observations are obtained during the day, when the animal is expected to rest and is potentially easier to observe. Modifying the likelihood function allows the popular Hidden Markov Model framework to be used to model these irregular spaced locations, making use of all the observed data.

Keywords: hidden Markov Models, irregular observations, animal movement modelling, nocturnal predator

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Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa

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The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.

Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score

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Authors: Q. Ni, J. A. Serna, D. Holland, X. Wang

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The objective of this study is to develop Nuclear Magnetic Resonance (NMR) techniques to enhance bone related research applied on normal and disuse (Biglycan knockout) mice bone in vitro by using both low-field and high-field NMR simultaneously. It is known that the total amplitude of T₂ relaxation envelopes, measured by the Carr-Purcell-Meiboom-Gill NMR spin echo train (CPMG), is a representation of the liquid phase inside the pores. Therefore, the NMR CPMG magnetization amplitude can be transferred to the volume of water after calibration with the NMR signal amplitude of the known volume of the selected water. In this study, the distribution of mobile water, porosity that can be determined by using low-field (20 MHz) CPMG relaxation technique, and the pore size distributions can be determined by a computational inversion relaxation method. It is also known that the total proton intensity of magnetization from the NMR free induction decay (FID) signal is due to the water present inside the pores (mobile water), the water that has undergone hydration with the bone (bound water), and the protons in the collagen and mineral matter (solid-like protons). Therefore, the components of total mobile and bound water within bone that can be determined by low-field NMR free induction decay technique. Furthermore, the bound water in solid phase (mineral and organic constituents), especially, the dominated component of calcium hydroxyapatite (Ca₁₀(OH)₂(PO₄)₆) can be determined by using high-field (400 MHz) magic angle spinning (MAS) NMR. With MAS technique reducing NMR spectral linewidth inhomogeneous broadening and susceptibility broadening of liquid-solid mix, in particular, we can conduct further research into the ¹H and ³¹P elements and environments of bone materials to identify the locations of bound water such as OH- group within minerals and bone architecture. We hypothesize that with low-field and high-field magic angle spinning NMR can provide a more complete interpretation of water distribution, particularly, in bound water, and these data are important to access bone quality and predict the mechanical behavior of bone.

Keywords: bone, mice bone, NMR, water in bone

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Authors: Hanieh Mohammadi, Sarah Fraser, Anil Nigam, Frederic Lesage, Louis Bherer

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A chronically higher cerebral pulsatility is thought to damage cerebral microcirculation, leading to cognitive decline in older adults. Although it is widely known that regular physical activity is linked to improvement in some cognitive domains, including executive functions, the mediating role of cerebral pulsatility on this link remains to be elucidated. This study assessed the impact of 6 months of regular physical activity upon changes in an optical index of cerebral pulsatility and the role of physical activity for the improvement of executive functions. 27 older adults (aged 57-79, 66.7% women) with cardiovascular risk factors (CVRF) were enrolled in the study. The participants completed the behavioral Stroop test, which was extracted from the Delis-Kaplan executive functions system battery at baseline (T0) and after 6 months (T6) of physical activity. Near-infrared spectroscopy (NIRS) was applied for an innovative approach to indexing cerebral pulsatility in the brain microcirculation at T0 and T6. The participants were at standing rest while a NIRS device recorded hemodynamics data from frontal and motor cortex subregions at T0 and T6. The cerebral pulsatility index of interest was cerebral pulse amplitude, which was extracted from the pulsatile component of NIRS data. Our data indicated that 6 months of physical activity was associated with a reduction in the response time for the executive functions, including inhibition (T0: 56.33± 18.2 to T6: 53.33± 15.7,p= 0.038)and Switching(T0: 63.05± 5.68 to T6: 57.96 ±7.19,p< 0.001) conditions of the Stroop test. Also, physical activity was associated with a reduction in cerebral pulse amplitude (T0: 0.62± 0.05 to T6: 0.55± 0.08, p < 0.001). Notably, cerebral pulse amplitude was a significant mediator of the link between physical activity and response to the Stroop test for both inhibition (β=0.33 (0.61,0.23),p< 0.05)and switching (β=0.42 (0.69,0.11),p <0.01) conditions. This study suggests that regular physical activity may support cognitive functions through the improvement of cerebral pulsatility in older adults with CVRF.

Keywords: near-infrared spectroscopy, cerebral pulsatility, physical activity, cardiovascular risk factors, executive functions

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

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It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Mansha Kotwani

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The specific spatio-temporal calcium concentration patterns are required by the fibroblasts to maintain its structure and functions. Thus, calcium concentration is regulated in cell at different levels in various activities of the cell. The variations in cytosolic calcium concentration largely depend on the buffers present in cytosol and influx of calcium into cytosol from ER through IP3Rs or Raynodine receptors followed by reuptake of calcium into ER through sarcoplasmic/endoplasmic reticulum ATPs (SERCA) pump. In order to understand the mechanisms of wound repair, tissue remodeling and growth performed by fibroblasts, it is of crucial importance to understand the mechanisms of calcium concentration regulation in fibroblasts. In this paper, a model has been developed to study calcium distribution in NRK fibroblast in the presence of buffers and ER flux with SERCA pump. The model has been developed for two dimensional unsteady state case. Appropriate initial and boundary conditions have been framed along with physiology of the cell. Finite element technique has been employed to obtain the solution. The numerical results have been used to study the effect of buffers, ER flux and source amplitude on calcium distribution in fibroblast cell.

Keywords: buffers, IP3R, ER flux, SERCA pump, source amplitude

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Authors: Hugo Espinosa-Andrews

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Introduction: Yogurt is widely accepted worldwide due to its high nutritional value, consistency, and texture. Their rheological properties play a significant role in consumer acceptance and are related to the manufacturing process and formulation. Typically, the viscoelastic characteristics of yogurts are studied using the small amplitude oscillatory shear test; however, the initial stages of flow and oral processing are described in the nonlinear zone, in which a large amplitude oscillatory stress test is applied. The objective of this work was to analyze the nonlinear viscoelastic behavior of commercial yogurts using Lissajous curves. Methods: Two commercial yogurts were purchased in a local store in Guadalajara Jalisco Mexico: a natural Greek-style yogurt and a low-fat traditional yogurt. Viscoelastic properties were evaluated using a large amplitude oscillatory stress procedure (LAOS). A crosshatch geometry of 40 mm and a truncation of 1000 µm were used. Stress sweeps were performed at 6.28 rad/s from 1 to 250 Pa at 5°C. The nonlinear viscoelastic properties were analyzed using the Lissajous curves. Results: The yogurts showed strain-viscoelastic behavior related to deformation-dependent materials. In the low-strain region, the elastic modulus predominated over the viscous modulus, showing gel-elastic properties. The sol-gel transitions were observed at approximately 66.5 Pa for the Greek yogurt, double that detected for traditional yogurt. The viscoelastic behavior of the yogurts was characteristic of weak excess deformation: behavior indicating a stable molecular structure at rest, and moderate structure at medium shear-forces. The normalized Lissajous curves characterized viscoelastic transitions of the yogurt as the stress increased. Greater viscoelasticity deformation was observed in Greek yogurt than in traditional yogurt, which is related to the presence of a protein network with a greater degree of crosslinking. Conclusions: The yogurt composition influences the viscoelastic properties of the material. Yogurt with the higher percentage of protein has greater viscoelastic and viscous properties, which describe a product of greater consistency and creaminess.

Keywords: yogurt, viscoelastic properties, LAOS, elastic modulus

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Authors: David A. Padilla, Rodolfo Villamizar

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In recent years, closed loop control strategies have been developed in order to establish a healthy glucose profile in type 1 diabetic mellitus (T1DM) patients. However, the controller itself is unable to define a suitable reference trajectory for glucose. In this paper, a control strategy Is proposed where the shape of the reference trajectory is generated bases in the amount of carbohydrates present during the digestive process, due to the effect of carbohydrate intake. Since there no exists a sensor to measure the amount of carbohydrates consumed, an estimator is proposed. Thus this paper presents the entire process of designing a carbohydrate estimator, which allows estimate disturbance for a predictive controller (MPC) in a T1MD patient, the estimation will be used to establish a profile of reference and improve the response of the controller by providing the estimated information of ingested carbohydrates. The dynamics of the diabetic model used are due to the equations described by the UVA/Padova model of the T1DMS simulator, the system was developed and simulated in Simulink, taking into account the noise and limitations of the glucose control system actuators.

Keywords: estimation, glucose control, predictive controller, MPC, UVA/Padova

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Authors: Toufik Bensana, Medkour Mihoub, Slimane Mekhilef

Abstract:

The use of vibration analysis has been established as the most common and reliable method of analysis in the field of condition monitoring and diagnostics of rotating machinery. Rolling bearings cover a broad range of rotary machines and plays a crucial role in the modern manufacturing industry. Unfortunately, the vibration signals collected from a faulty bearing are generally nonstationary, nonlinear and with strong noise interference, so it is essential to obtain the fault features correctly. In this paper, a novel numerical analysis method based on local mean decomposition (LMD) is proposed. LMD decompose the signal into a series of product functions (PFs), each of which is the product of an envelope signal and a purely frequency modulated FM signal. The envelope of a PF is the instantaneous amplitude (IA), and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. After that, the fault characteristic frequency of the roller bearing can be extracted by performing spectrum analysis to the instantaneous amplitude of PF component containing dominant fault information. The results show the effectiveness of the proposed technique in fault detection and diagnosis of rolling element bearing.

Keywords: fault diagnosis, rolling element bearing, local mean decomposition, condition monitoring

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: A. B. Bolkhir, A. Elshafie, T. K. Yousif

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This paper highlights the methods of error estimation in finite element analysis (FEA) results. It indicates that the modeling error could be eliminated by performing finite element analysis with successively finer meshes or by extrapolating response predictions from an orderly sequence of relatively low degree of freedom analysis results. In addition, the paper eliminates the round-off error by running the code at a higher precision. The paper provides application in finite element analysis results. It draws a conclusion based on results of application of methods of error estimation.

Keywords: finite element analysis (FEA), discretization error, round-off error, mesh refinement, richardson extrapolation, monotonic convergence

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Authors: Peng Li, Luan Wang, Haifeng Fei, Renhong Xie, Yibin Rui, Shanhong Guo

Abstract:

The applications of satellite-based automatic identification system (S-AIS) pave the road for wide-range maritime traffic monitoring and management. But the coverage of satellite’s view includes multiple AIS self-organizing networks, which leads to the collision of AIS signals from different cells. The contribution of this work is to propose an improved multi-channel blind source separation algorithm based on Artificial Bee Colony (ABC) and advanced stochastic optimization to perform separation of the mixed AIS signals. The proposed approach adopts modified ABC algorithm to get an optimized initial separating matrix, which can expedite the initialization bias correction, and utilizes the Adaptive Moment Estimation (Adam) to update the separating matrix by adjusting the learning rate for each parameter dynamically. Simulation results show that the algorithm can speed up convergence and lead to better performance in separation accuracy.

Keywords: satellite-based automatic identification system, blind source separation, artificial bee colony, adaptive moment estimation

Procedia PDF Downloads 185

Authors: Mohamed Khalifa Zayet, Salma Belal Eiid, Mushira Mohamed Dahaba

Abstract:

Introduction: Malignant tumors may not be easily detected by traditional radiographic techniques especially in an anatomically complex area like maxillofacial region. At the same time, the advent of biological functional MRI was a significant footstep in the diagnostic imaging field. Objective: The purpose of this study was to define the malignant metabolic profile of maxillofacial lesions using diffusion MRI and magnetic resonance spectroscopy, as adjunctive aids for diagnosing of such lesions. Subjects and Methods: Twenty-one patients with twenty-two lesions were enrolled in this study. Both morphological and functional MRI scans were performed, where T1, T2 weighted images, diffusion-weighted MRI with four apparent diffusion coefficient (ADC) maps were constructed for analysis, and magnetic resonance spectroscopy with qualitative and semi-quantitative analyses of choline and lactate peaks were applied. Then, all patients underwent incisional or excisional biopsies within two weeks from MR scans. Results: Statistical analysis revealed that not all the parameters had the same diagnostic performance, where lactate had the highest areas under the curve (AUC) of 0.9 and choline was the lowest with insignificant diagnostic value. The best cut-off value suggested for lactate was 0.125, where any lesion above this value is supposed to be malignant with 90 % sensitivity and 83.3 % specificity. Despite that ADC maps had comparable AUCs still, the statistical measure that had the final say was the interpretation of likelihood ratio. As expected, lactate again showed the best combination of positive and negative likelihood ratios, whereas for the maps, ADC map with 500 and 1000 b-values showed the best realistic combination of likelihood ratios, however, with lower sensitivity and specificity than lactate. Conclusion: Diffusion weighted imaging and magnetic resonance spectroscopy are state-of-art in the diagnostic arena and they manifested themselves as key players in the differentiation process of orofacial tumors. The complete biological profile of malignancy can be decoded as low ADC values, high choline and/or high lactate, whereas that of benign entities can be translated as high ADC values, low choline and no lactate.

Keywords: diffusion magnetic resonance imaging, magnetic resonance spectroscopy, malignant tumors, maxillofacial

Procedia PDF Downloads 169

Authors: Serge B. Provost

Abstract:

Numerous statistical inference and modeling methodologies are based on sample moments rather than the actual observations. A result justifying the validity of this approach is introduced. More specifically, it will be established that given the first n moments of a sample of size n, one can recover the original n sample points. This implies that a sample of size n and its first associated n moments contain precisely the same amount of information. However, it is efficient to make use of a limited number of initial moments as most of the relevant distributional information is included in them. Two types of density estimation techniques that rely on such moments will be discussed. The first one expresses a density estimate as the product of a suitable base density and a polynomial adjustment whose coefficients are determined by equating the moments of the density estimate to the sample moments. The second one assumes that the derivative of the logarithm of a density function can be represented as a rational function. This gives rise to a system of linear equations involving sample moments, the density estimate is then obtained by solving a differential equation. Unlike kernel density estimation, these methodologies are ideally suited to model ‘big data’ as they only require a limited number of moments, irrespective of the sample size. What is more, they produce simple closed form expressions that are amenable to algebraic manipulations. They also turn out to be more accurate as will be shown in several illustrative examples.

Keywords: density estimation, log-density, polynomial adjustments, sample moments

Procedia PDF Downloads 164