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Preprocessing Trajectory Learning Techniques For Robots: A comparative study

Many applications in our everyday living are totally depending on using the robots. So that, the need for having smart and more productive robots is increasing. Developing such robots necessitates the programming of the robot. Hence, the machine learning approaches are widely employed to accomplish this objective successfully. Programming the robot can be applied by demonstration such that the

Deep stacked ensemble learning model for COVID-19 classification

COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography

Software and Communications

Engineered magnetic oxides nanoparticles as efficient sorbents for wastewater remediation: a review

The rapid urbanization and industrialization is causing worldwide water pollution, calling for advanced cleaning methods. For instance, pollutant adsorption on magnetic oxides is efficient and very practical due to the easy separation from solutions by an magnetic field. Here we review the synthesis and performance of magnetic oxides such as iron oxides, spinel ferrites, and perovskite oxides for

Energy and Water

A 3D Multiple-Slip Crystal-Plasticity Model for Precipitate Hardening in Additively Manufactured High Strength Steels

Additive Manufacturing (AM) revolutionized the manufacturing of complex geometry products, especially in medical and aerospace fields. High-strength precipitate hardened (PH) stainless steels provide unique properties in term of strength and corrosion resistance for critical applications in both fields. In the current study, a 3D multiple-slip crystal-plasticity dislocation densities-based model

Energy and Water

Graph transformer for communities detection in social networks

Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this

Artificial Intelligence
Software and Communications

Ultrasonic characterization of expanded polystyrene used for shallow tunnels under seismic excitation

Expanded Polystyrene (EPS) is used as an inclusion to mitigate stresses acting on tunnels. In this study, the efficiency of utilizing EPS in reducing dynamic loads acting on shallow tunnels was studied. To gauge this, dynamic modulus of elasticity (Ed) and shear modulus (G) of EPS with densities equal to 25, 30, and 35 kg/m3 were characterized based on series of ultrasonic tests, where Ed ranged

Energy and Water

Estimating phase error using a Hilbert transform-based time-domain technique

Measuring phase noise in oscillators is crucial in communication systems, vibration analysis, and frequency synthesizers. Traditionally, this measurement is done in frequency domain by estimating the ratio of the power density at an offset frequency from the carrier to the power of the carrier signal. This approach is hardware intensive and dependent on the the offset frequency, for which there

Circuit Theory and Applications

Enhancing Parkinson's disease diagnosis accuracy through speech signal algorithm modeling

Parkinson's disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning

Software and Communications

Plant stem tissue modeling and parameter identification using metaheuristic optimization algorithms

Bio-impedance non-invasive measurement techniques usage is rapidly increasing in the agriculture industry. These measured impedance variations reflect tacit biochemical and biophysical changes of living and non-living tissues. Bio-impedance circuit modeling is an effective solution used in biology and medicine to fit the measured impedance. This paper proposes two new fractional-order bio

Circuit Theory and Applications

Second-order cascode-based filters

In this paper, we report on the design of a class of analog filters based on the cascode circuit structure surrounded by four impedances. The proposed topology is systematically investigated using two-port network techniques and symbolic math CAD tools. A total of 106 second-order filter circuits can be obtained from this class including 9 low-pass filters, 6 high-pass filters, 73 bandpass filters

Circuit Theory and Applications