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The estimate process of a moving target trajectory is a well-known problem, where the main objective is to improve the estimation of object position. During the tracking are presented errors or variations between the true position and the estimated. In this paper, we treat such variations as a Gauss-Markov colored measurement noise (CMN). The estimation process is performed in predict and update, where the prediction indicates the next position of the bounding box, and the update is a correction step, which includes the new measurement of the tracking model and helps to improve the estimation. Looking for this improvement we use Kalman and Unbiased Finite Impulse Response filters in the standard version and modified for CMN to demonstrate the filter with the best performance. To test the most robust filter we use a high coloredness factor. The tests were carried out with simulated data (ideal and no ideal conditions) and with benchmark data (no ideal conditions). The UFIR modified for the CMN algorithm showed favorable results with high precision and low RMSE in the object tracking process with benchmark data and under no ideal conditions. While KF CMN showed better results under ideal conditions.

Object tracking is an area of study of great interest to various researchers, where the main objective is to improve estimation of the trajectory of a moving object. This is due to the fact that in the object tracking process there are usually variations between the true position of the moving object and the estimated position, that is, the object is not exactly followed throughout its trajectory. These variations can be thought of as Colored Measurement Noise (CMN) caused by the object and the movement of the camera frame. In this paper, we treat such differences as Gauss-Markov colored measurement noise.We use Finite Impulse Response and Kalman Filters with a recursive strategy on the tracking: predict and update. To demonstrate the filter with the best performance, tests were carried out with simulated trajectories and with benchmarks from a database available online. The UFIR modified for CMN algorithm showed favorable results with high precision and accuracy in the object tracking process with benchamark data and under no ideal conditions.While KF CMN showed better results in tests with simulated data under ideal conditions.

In visual object tracking, the estimation of the trajectory of a moving object is a widely studied problem. In the object tracking process, there are usually variations between the real position of the objet in the scene and the estimated position, that is, the object is not exactly followed throughout its trajectory. These variations can be considered as color measurement noise (CMN) caused by the object and the camera frame movement. In this paper, we treat such differences as Gauss-Markov coloring measurement noise. We use Finite Impulse Response filters and Kalman filter with a recursive strategy in tracking: predict and update. To demonstrate the best performance, tests were carried out with simulated trajectories and with benchmarks from a database available online. The OUFIR and UFIR algorithms showed favorable results with high precision and accuracy in the object tracking task.

This work presents an object tracking process using Kalman and UFIR filters, standard and modified for Colored Measurement Noise (CMN). UFIR CMN showed favorable results with no ideal conditions, and KF CMN for ideal conditions.

The Finite Difference in Time Domain (FDTD) method is currently one of the most used electromagnetism methods in electromagnetic field simulation, however, in order to obtain reliable results from this simulation it is necessary to have optimal boundary conditions. Perfectly Coupled Layers (PML) is characterized by the decomposition of the transversal field to the direction of propagation in its rectangular projections, and the use of both electrical and magnetic conductivity in the numerical layers for the absorption of electromagnetic fields. The PML layers are characterized by values of permittivity, permeability and conductivity that are assigned to each of the equations according to their position in the region. Because it is the same set of equations there is no need to use connection conditions at the junction of the analysis region with the PML absorption region, which provides a natural transition throughout the region. This paper presents the study of the difference between the calculation region with and without PML layers.

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles, then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.

Visual object tracking is commonly accompanied with large variations in the image frame size and position, as caused by the object dynamics and low frame rate. In this paper, we treat such variations as a Gauss-Markov colored measurement noise (CMN), modify the Kalman filter and unbiased finite impulse response filter using the backward Euler method and employing measurement differencing, and apply to some simulated and benchmark data. It is shown that the modified filters can suppress efficiently both the slow frame variations associated with CMN and fast variations associated with white noise. Extensive experimental investigations conducted for the "Car4" benchmark database has demonstrated a high efficiency of the modified algorithms.

Air pollution continues to be a significant risk to public health. It is an important issue that affects everyone and should be studied to take actions that help mitigate the risk. This research’s main objective is to explore air quality from 2010 to 2018 in metropolitan zones of Guadalajara, Monterrey, and the Valley of Mexico. Air quality is analyzed based on the concentrations of criteria pollutants: carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), particulate matter less than or equal to 2.5 micrometers (PM 2.5 ), particulate matter less than or equal to 10 micrometers (PM 10 ), and sulfur dioxide (SO 2 ). In this paper, six stages are proposed:1) data collection; 2) data cleansing; 3) descriptive analysis. 4) design of cluster analysis models and main components; 5) application of machine learning models; 6) interpretation and analysis of results. This study has concluded that higher mean hourly concentrations for pollutants, CO, NO 2 , O 3 , PM 2.5 , and SO 2 were found in Guadalajara Metropolitan Zone. In contrast, the highest mean hourly concentration of PM 10 was found in the Monterrey Metropolitan Zone. Furthermore, the pollutant with the highest effect in the three critical zones was PM 2.5 , presenting high concentrations with hazardous air quality levels. Finally, the area with the worst air quality was Guadalajara Metropolitan Zone.

The mass-spring-damper system has the minimum complexity scenario which characterizes almost all the mechanical vibration phenomena. Also it is well known that a second-order differential equation can model its dynamics. However, if the damper has a magnetorheological fluid, then it shows viscoelastic properties in the presence of a magnetic field. Hence the mathematical model that best reflects the dynamics of this system is a fractional order differential equation. Naturally, here the Mittag–Leffler function appears in the analytical solution. Mathematical modeling of the mass-spring-magnetorheological damper mechanical system has been presented here. The main focus of the investigation is to show how the fractional order $$\gamma $$ changes by varying the viscosity damping coefficient $$\beta $$ . These observations have been made by varying current intensity in the range of 0.2–2 A. A Helmholtz coil has been used to produce the magnetic field. It is worth mentioning that, this work has a high pedagogical value in the connection of fractional calculus to mechanical vibrations as well as it can be used as a starting point for a more advanced treatment of fractional mechanical oscillations.

In this paper, we explored the application of the fractional calculus to digital watermarking. In order to reach this goal, we chose a set of watermarking systems, and then, we altered the watermark detection step of each system. The novelty of our proposal is that we deviated from the trend of proposing new embedding domains computed by using fractional calculus principles. Instead, we proposed to reformulate the detection equation set. Then, this paper contributes to a new equation set for detecting watermarks based on fractional calculus principles. In addition, we provided the proper order for the derivative; this equation set replaces the previous one, given as a result that the detection rate increased up to 88% for the best case compared to the original equation set. The main modification was to replace the system’s equations to another set that we derived by using the Riemann–Liouville fractional operator. To verify that if this approach is effective, we performed tests on grayscale images of size $512 \times 512$ ; the test covered several attacks including noise addition, JPEG compression, and scaling. The results showed that the modified systems detected more watermarks than their counterparts. The proposed scheme presented better results for 15 out of 21 different tests and the other 6 ended in a draw. It is important to remark that the behavior holds for other embedding domains, for example, the discrete cosine transform coefficients and the singular values decomposition values, which suggests the regularity of the approach.

This paper presents a modified Laplace transform homotopy perturbation method with finite boundary conditions (MLT–HPM) designed to improve the accuracy of the approximate solutions obtained by LT–HPM and other methods. To this purpose, a suitable initial approximation will be introduced, in addition, the residual error in several points of the interest interval (RECP) will be canceled. In order to prove the efficiency of the proposed method a couple of nonlinear ordinary differential equations with mixed boundary conditions, indeed, difficult to approximate, are proposed. The square residual error (S.R.E) of the proposed solutions will result to be of hundredths and tenths, requiring only a first order approximation of MLT–HPM, unlike LT–HPM, which will require more iterations for the same cases study.

Fractional calculus is a promising and rich area of study as many applications to engineering are under development. As a result, many authors proposed new approaches for solving diverse problems with success. In particular, we focus on revisiting a problem in the digital watermarking field: the detection of Gaussian watermarks in the spatial domain. The novelty of this work is that we show that the use of fractional calculus principles reduces the false positive rate of a Gaussian watermarks detector whereas keeping the complexity of the system almost low. Most systems detect Gaussian watermarks using different strategies that increase the system complexity, this motivates our work which goal is to address the detection problem with a simpler approach.

There are many different applications for RFID tags, each one related to the way they are designed. In this document we present the basics to design antennas for UHF-RFID passive tags using some miniaturization techniques and inductive coupling. Also, we show the importance of the antenna parameters and the methodology for design our own antenna in a few steps using the methods explained in this work.

Methods of the electrocardiography (ECG) signal features extraction are required to detect heart abnormalities and different kinds of diseases. However, different artefacts and measurement noise often hinder providing accurate features extraction. One of the standard techniques developed for ECG signals employs linear prediction. Referring to the fact that prediction is not required for ECG signal processing, smoothing can be more efficient. In this paper, we employ the p-shift unbiased finite impulse response (UFIR) filter, which becomes smooth by p<0. We develop this filter to have an adaptive averaging horizon: optimal for slow ECG behaviours and minimal for fast excursions. It is shown that the adaptive UFIR algorithm developed in such a way provides better denoising and suboptimal features extraction in terms of the output signal-noise ratio (SNR). The algorithm is developed to detect durations and amplitudes of the P-wave, QRS-complex, and T-wave in the standard ECG signal map. Better performance of the algorithm designed is demonstrated in a comparison with the standard linear predictor, UFIR filter, and UFIR predictive filter based on real ECG data associated with normal heartbeats.

Electrical power processing is an essential sub-system of Proton exchange membrane fuel cell PEMFC systems, it is usually composed by a dc-dc converter for customizing the electrical energy before feeding the load, the Single-Ended Primary-Inductor Converter or simply SEPIC, is one of the converters with continuous input current, and it has been applied to a variety of industrial applications. This work explores the capability of the traditional dc-dc SEPIC to achieve high voltage gain by combining the converter with a diode-capacitor voltage multiplier and their applications to PEMFC systems. The continuous input current and high voltage gain is adequate for the power conditioning in fuel cell systems. For the discussed application, the voltage gain is regulated by PWM and may be extended by increasing the diode-capacitor multiplier structure. Main characteristics of the converter are: continuous input current, high-voltage gain, avoiding the use of extreme duty cycles and transformers, low voltage stresses in the power switches, besides the converter topology only employs a switch controlled and two inductors which allows a relatively simple implementation. Simulation and experimental results are provided to verify the theoretical analysis and proof of the operating principle for the dc-dc power converter proposed.

Jitter is inherent to the breakpoints of measured genome somatic copy number alterations (SCNAs). Therefore, an analysis of jitter is required to reduce errors in the SCNA estimation. The SCNA measurements are accompanied with intensive noise that may cause errors and ambiguities in the breakpoint detection with low signal-to-noise ratios (SNRs). We show that the asymmetric exponential power distribution (AEPD) provides much better approximation to the jitter distribution than the earlier proposed discrete skew Laplace distribution. Furthermore, we confirm that (AEP) distribution its suitable for computing the confidence upper and lower boundary limits used to guarantee an existence of genomic changes with a required probability. We test some simulated SCNAs measurements by the upper and lower confidence bound masks with several probabilities.

Efficient implementation of low cost transducers for industrial applications requires smart sensor with embedded accurate and blind filtering algorithms. In this paper an iterative, blind, and unbiased finite impulse response (UFIR) filter having prediction capabilities is proposed as an alternative to the Kalman filter (KF) for smart sensors design. The robustness of the UFIR filter is proved analytically. The predictive properties of UFIR filter allow getting a high accuracy and precision when measurements are provided with missing data, which is demonstrated based on a short-time and long-time temperature probing.

We present the fractional wave equation in a conducting material. We used a Maxwell’s equations with the assumptions that the charge density and current density J were zero, and that the permeability and permittivity were constants. The fractional wave equation will be examined separately; with fractional spatial derivative and fractional temporal derivative, finally, consider a Dirichlet conditions, the Fourier method was used to find the full solution of the fractional equation in analytic way. Two auxiliary parameters and are introduced; these parameters characterize consistently the existence of the fractional space-time derivatives into the fractional wave equation. A physical relation between these parameters is reported. The fractional derivative of Caputo type is considered and the corresponding solutions are given in terms of the Mittag-Leffler function show fractal space-time geometry different from the classical integer-order model.

This article proposes the application of Laplace transform---homotopy perturbation method with variable coefficients, in order to find analytical approximate solutions for nonlinear differential equations with variable coefficients. As case study, we present the oxygen diffusion problem in a spherical cell including nonlinear Michaelis---Menten uptake kinetics. It is noteworthy that this important problem introduces the Robin boundary conditions as an additional difficulty. In fact, after comparing figures between approximate and exact solutions, we will see that the proposed solutions are highly accurate. What is more, we will see that the square residual error of our solutions is 1.808511632 × 10ź7 and 2.560574954 × 10ź10 which confirms the accuracy of the proposed method, taking into account that we will just keep the first-order approximation.

This work presents an alternative solution for the mathematical analysis of the fractional waves in dielectric media. For the fractional wave equation, the Caputo fractional derivative was considered, the order of the spatial and temporal fractional derivatives are , respectively. In this analysis, we introduce the appropriate fractional dimensional parameters which characterize consistently the existence of the fractional space and time derivatives into the fractional wave equation. We will consider source free Maxwell equations in isotropic and homogeneous dielectric medium. The general solutions obtained in our research have been expressed in terms of the multivariate Mittag–Leffler functions, these functions depend only on the parameters and preserving the appropriated physical units according to the system studied.

It is recognized that one of the main causes of human death is associated with heart disease. An electrocardiogram (ECG) is used to identify abnormalities related to the electrical activities of the heart. In this paper, we discuss applications of the unbiased finite impulse response (UFIR) estimation approach to denoise the ECG measurements and extract useful features from. One of the standard methods providing denoising and extracting features of the ECG signals is based on the one-step prediction. We show that better accuracy can be obtained by using the UFIR filter and the UFIR smoothing filter with adaptive averaging horizon length.

Applications of tiie unbiased finite impulse response (UFIR) estimation approach are discussed to improve measurements of tiie electrocardiography (ECG) signals. One of the standard methods providing denoising and extracting features of the ECG signals is based on the one-step prediction. We show that better accuracy is obtained by using the UFIR filter and the UFIR smoothing filter with adapted averaging horizon length. To investigate the trade-off between the predictive and UFIR filtering algorithms, we exploit the ECG measurements data for normal heart beating. Better performance of the designed UFIR denoising algorithms is demonstrated.