A Design of a low-pass FIR filter using Hamming Window Functions in Matlab

The reduction and filtering of the input components of an original signal in one or more frequency bands using a finite impulse response, better known as FIR, is designed using a function of the Hamming window. Although there are various window functions such as the Blackman window function, the Hanning window function and the rectangular window functions that can be used as digital filters, the Hamming window function was used in this study for the reason of its minimum damping/decibel of the stopband with a reduced transition bandwidth. Among the other three widow functions that can be used, the Blackman window function is closest to the Hamming window function in terms of minimum bandstop attenuation/decibel, since both have a dB value greater than -50. However, in terms of transition bandwidth (Δω), the Hamming window has a narrower bandwidth than the Blackman window, making it more appropriate to use in this FIR filter design. This type of filter is important for analyzing the different types of signals that are essential in a world where digital filters play a major role in DSP applications. This research paper offers a Matlab-based low-pass FIR digital filter that uses Hamming window functions. Keywords: FIR filters, Hamming window, Blackman window Hanning window, Matlab. DOI: 10.7176/CEIS/11-2-04 Publication date: February 29th 202

Effect of Performance Appraisal System on Employee Productivity;(Selected Public Senior High Schools, Ho Municipality, Ghana)

Performance appraisals improve the work performance of employees by helping them realize their full potentials in carrying out their firm's mission and also, to provide information to employees and managers for use in making work-related decisions. They, also, provide feedback to employees and thereby serve as a vehicle for personal and career development. Performance appraisals, however, are beset with difficulties as a result of their complex nature. The general objective of the study, therefore, was to evaluate the performance appraisal system and its effect on employee productivity (performance) at the Ghana Education Service (GES). The research design used in the study was the descriptive study because it was appropriate for the achievement of the research objectives. The population for the study was 153 and a sample size of 108 respondents was selected using the probability and non-probability sampling method. Interview and questionnaires were used as instruments for the study and out of 106 questionnaires distributed, all of them were retrieved from respondents and interview conducted for two respondents. The data was analyzed with tables, bar charts. Among the main findings of the study was that the GES only carried out performance appraisal when teachers were due for promotion. The finding indicated a negative relationship between performance appraisal and productivity of teachers. It means that performance appraisal has no link with the WASSCE results. The study recommends that the Ghana Education Service should adopt performance apprnaisal that is tailored to the job description and the job analysis, that is, there should be a clear cut policy on the conduct of performance appraisal in the GES in order to improve on its conduct. Keywords: High School, Productivity, Appraisal, Performance, Employee DOI: 10.7176/JESD/12-2-01 Publication date: January 31st 202

An Alternative to the MVU Estimator to Estimate the Level of DC in AWGN

In statistics, Maximum Likelihood Estimation (MLE) is a method of estimating the parameters of a particular statistical model, finding parameter values that maximize probability, observations, and the parameters are specified. The MLE can be seen as a special case of maximum post-positive estimation (MAP), which includes a uniform preventive distribution of parameters, or as a variant of the MAP that ignores the above and is therefore unregulated. Now let's look at an alternative to the MVU estimator, which is desirable in situations where the minimum variance unbiased (MVU) estimator does not exist or cannot be found, even if it exists. This estimator, which relies on the principle of maximum likelihood, is primarily the common method for obtaining a practical estimator. It has the clear advantage of being a crank turning procedure, which allows you to implement it for complicated estimation problems. A clear advantage of MLE is that it can be found numerically for a given data-set. The safest way to find the MLE is to search the grid, as long as the space between the searches are small enough, we are sure to find the MLE. Keywords: Maximum Likelihood Estimation, minimum variance unbiased, Estimator, Probability Distribution Function. DOI: 10.7176/ISDE/11-3-05 Publication date: June 30th 202

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance

Spatial and Temporal Normalization for Multi-Variate Time Series Prediction Using Machine Learning Algorithms

Multi-variable time series (MTS) information is a typical type of data inference in the real world. Every instance of MTS is produced via a hybrid dynamical scheme, the dynamics of which are often unknown. The hybrid species of this dynamical service are the outcome of high-frequency and low-frequency external impacts, as well as global and local spatial impacts. These influences impact MTS’s future growth; hence, they must be incorporated into time series forecasts. Two types of normalization modules, temporal and spatial normalization, are recommended to accomplish this. Each boosts the original data’s local and high-frequency processes distinctly. In addition, all components are easily incorporated into well-known deep learning techniques, such as Wavenet and Transformer. However, existing methodologies have inherent limitations when it comes to isolating the variables produced by each sort of influence from the real data. Consequently, the study encompasses conventional neural networks, such as the multi-layer perceptron (MLP), complex deep learning methods such as LSTM, two recurrent neural networks, support vector machines (SVM), and their application for regression, XGBoost, and others. Extensive experimental work on three datasets shows that the effectiveness of canonical frameworks could be greatly improved by adding more normalization components to how the MTS is used. This would make it as effective as the best MTS designs are currently available. Recurrent models, such as LSTM and RNN, attempt to recognize the temporal variability in the data; however, as a result, their effectiveness might soon decline. Last but not least, it is claimed that training a temporal framework that utilizes recurrence-based methods such as RNN and LSTM approaches is challenging and expensive, while the MLP network structure outperformed other models in terms of time series predictive performance