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The purpose of this paper is to propose applications of advanced signal-processing techniques for the diagnosis and detection of rotor fault in an induction machine. Two techniques are used: spectral analysis techniques and time frequency techniques for the diagnosis of an electrical machine. One is based on the power spectral density estimation techniques, such as periodogram and Welch periodogram. The second method is based on Hilbert transform (HT) to extract the envelope for the stator current. Then, this signal is processed via discrete wavelet transform (DWT) for determining the faulty components in the spectrum of the stator current envelope and identifying the eigenvalues of energies (HDWT).

First, this paper focused on theoretical development and a comparative study of these signal-processing techniques, which are based on the periodogram, Welch periodogram, HT and the DWT to extract the envelope for the stator current; it is used to compute the energy stored in each decomposition level obtained by the stator current envelope (HDWT). Moreover, the Welch periodogram is applied to obtain the envelope spectrum.

The simulation obtained and the experimental validation results of the proposed methods through MATLAB environment show the effectiveness of the proposed approaches with a good accuracy by power spectral density estimation techniques (periodogram and Welch periodogram). Moreover, the faults are manifested through the appearance of new frequencies components, as well as the envelope for the stator current (HT and DWT). This approach is effective for non-stationary and stationary signal to extract useful information for the detection of broken bar fault.

The current paper proposes a new diagnosis method for the detection and characterization of broken rotor bars defects early; it is founded primarily on theoretical development, and the comparison is based on the power spectral density technique (periodogram and Welch periodogram) and the computation of the energy stored in each decomposition level (precisely the HT and DWT). Moreover, the Welch periodogram is applied to obtain the envelope spectrum. The main advantages of the proposed techniques increase their reliability and availability.

Understanding cyclical activity is an important component of efficient portfolio management. Property appraisal models that do not explicitly take into account cyclical fluctuations may produce unrealistic valuation estimates resulting in property assets being incorrectly added to or removed from the general investment portfolio. In this paper we use conventional spectral analysis techniques to examine property and financial assets for evidence of cycles and co‐cycles. One finding is that the very pronounced cyclical patterns that appear in direct real estate markets and the economy as a whole are very much less obvious once they have filtered through to securitised property markets and financial assets markets.

The paper seeks to examine cycles and common cycles in the real estate markets of the UK, Japan, Singapore, Hong Kong and Malaysia using a combination of time domain and frequency domain methods.

The paper identifies the patterns of cyclical movement (if any) in the five public real estate markets, and searches for common cycle characteristics and patterns in international real estate markets. In addition to the time domain analyses, these empirical investigations are further empowered by a frequency domain method that includes spectral and co‐spectral analyses.

International real estate markets are characterized by cyclical behavior that exhibits phenomenal fluctuations. The markets are also pro‐cyclical; they do tend to move together. Furthermore, some differences in the patterns of the common cycles and their lead‐lag linkages are evident.

International investors would probably benefit from diversifying real estate stocks across the UK and Asian real estate markets, especially in the short and medium terms. However, the long‐term cyclical patterns across the national real estate stock markets are not sharply different, indicating that smaller diversification benefits are to be expected in the long term.

Common cycle analysis advances investors' understanding of the long‐term relationship and medium‐ and short‐term linkages across international real estate markets, thereby allowing investors and portfolio managers an opportunity to discern any contrasting cyclical patterns at all frequencies so as to assist in their portfolio decisions.

Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy.

This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image.

The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods.

In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

This paper aims to examine the weak form of efficiency for price series of four precious metals, i.e. gold, silver, platinum and palladium, using a generalized spectral method.

The method has the advantage of detecting both linear and non-linear serial dependence in the conditional mean, and it is robust to various forms of conditional heteroscedasticity. The authors use three different rolling windows for the purpose of robustness.

The authors report weak form of efficiency across metals series for almost all rolling windows. The optimum efficiency for Gold and Palladium is achieved through 250 days rolling window estimates whereas it is 500 days rolling window for silver. Platinum has similar efficiency levels across rolling windows. The degree of efficiency for metal prices is observed to be varying over time with silver market possessing highest levels of efficiency. The efficiency synchronization also varies across rolling windows and metals.

The results reveal that metal markets are efficient for most times implying the low predictability and the low likelihood of earning abnormal returns by speculating in these markets.

The study uses a relatively new statistical technique, the generalized spectral test, to capture linear and non-linear serial dependence. Therefore, the results possess adequate power against departure from market efficiency.

The purpose of this paper is to consider the simultaneous flow of Casson Williamson non Newtonian fluids in a vertical porous medium under the influence of variable thermos-physical parameters.

The model equations are a set of partial differential equations (PDEs). These PDEs were transformed into a non-dimensionless form using suitable non-dimensional quantities. The transformed equations were solved numerically using an iterative method called spectral relaxation techniques. The spectral relaxation technique is an iterative method that uses the Gauss-Seidel approach in discretizing and linearizing the set of equations.

It was found out in the study that a considerable number of variable viscosity parameter leads to decrease in the velocity and temperature profiles. Increase in the variable thermal conductivity parameter degenerates the velocity as well as temperature profiles. Hence, the variable thermo-physical parameters greatly influence the non-Newtonian fluids flow.

This study considered the simultaneous flow of Casson-Williamson non-Newtonian fluids by considering the fluid thermal properties to vary within the fluid layers. To the best of the author’s knowledge, such study has not been considered in literature.

A real‐time speech recognition system using spectral pattern matching techniques has been developed by Logica based on original work by the UK government's Joint Speech Research Unit. The user‐friendly system is considered likely to advance the date of man‐machine communication.

The purpose of this study is to explore numerical scrutinization of micropolar and Walters-B non-Newtonian fluids motion under the influence of thermal radiation and chemical reaction.

The two fluids micropolar and Walters-B liquid are considered to start flowing from the slot to the stretching sheet. A magnetic field of constant strength is imposed on their flow transversely. The problems on heat and mass transport are set up with thermal, chemical reaction, heat generation, etc. to form partial differential equations. These equations were simplified into a dimensionless form and solved using spectral homotopy analysis method (SHAM). SHAM uses the basic concept of both Chebyshev pseudospectral method and homotopy analysis method to obtain numerical computations of the problem.

The outcomes for encountered flow parameters for temperature, velocity and concentration are presented with the aid of figures. It is observed that both the velocity and angular velocity of micropolar and Walters-B and thermal boundary layers increase with increase in the thermal radiation parameter. The decrease in velocity and decrease in angular velocity occurred are a result of increase in chemical reaction. It is hoped that the present study will enhance the understanding of boundary layer flow of micropolar and Walters-B non-Newtonian fluid under the influences of thermal radiation, thermal conductivity and chemical reaction as applied in various engineering processes.

All results are presented graphically and all physical quantities are computed and tabulated.

The purpose of this paper is to investigate the dynamical behavior of heat and mass transfer of non-Newtonian nanofluid flow through parallel horizontal sheet with heat-dependent thermal conductivity and magnetic field. The effects of thermophoresis and Brownian motion on the Eyring‒Powell nanofluid heat and concentration are also considered. The flow fluid is propelled by squeezing force and constant pressure gradient. The hydromagnetic fluid is induced by periodic time variations.

The dimensionless momentum, energy and species balance equations are solved by the spectral local linearization method that is employed to numerically integrate the coupled non-linear differential equations.

The response of the fluid flow, temperature and concentration to variational increase in the values of the parameters is graphically presented and discussed accordingly.

The validity of the method used was checked by comparing it with previous related article.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.