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© 2022 IEEE.Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.

Most ultrasound (US) imaging techniques use spatially-constant speed-of-sound (SoS) values for beamforming. Having a discrepancy between the actual and used SoS value leads to aberration artifacts, e.g., reducing the image resolution, which may affect diagnostic usability. Accuracy and quality of different US imaging modalities, such as tomographic reconstruction of local SoS maps, also depend on a good initial beamforming SoS. In this work, we develop an analytical method for estimating mean SoS in an imaged medium. We show that the relative shifts between beamformed frames depend on the SoS offset and the geometric disparities in transmission paths. Using this relation, we estimate a correction factor and hence a corrected mean SoS in the medium. We evaluated our proposed method on a set of numerical simulations, demonstrating its utility both for global SoS prediction and for local SoS tomographic reconstruction. For our evaluation dataset, for an initial SoS under- and over-assumption of 5% the medium SoS, our method is able to predict the actual mean SoS within 0.3% accuracy. For the tomographic reconstruction of local SoS maps, the reconstruction accuracy is improved on average by 78.5% and 87%, respectively, compared to an initial SoS under- and over-assumption of 5%.

Unusual sensor data in smart homes may herald different problems based on sensor errors, security vulnerabilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.

The microwave imaging systems have the potential to present a cost effective and less hazardous alternative to conventional medical imaging techniques. In this paper, a Contrast Source Inversion method based microwave imaging scheme is proposed and tested for the detection of pulmonary edema and hemorrhage. To this end, a realistic human torso phantom is used, and the electromagnetic parameters of the human tissues is determined via Cole-Cole model. The scattered field is simulated via Method of Moments at the operating frequency of 350 MHz, and a 50 dB white Gaussian noise is added to model a realistic measurement setup. The numerical tests performed with the proposed technique suggest that the method can be used to locate the pulmonary edema and hemorrhage, and it is capable of distinguishing these two medical conditions successfully. © 2022 IEEE. 2-s2.0-85138721822

Electromagnetic interference issues are associated with high-speed switching of power converters. EMI modeling is an essential tool to study and control the EMI emission, enabling more efficient solutions. A comprehensive review and comparison of different modeling approaches for conducted emissions are provided in this paper, which can be used as a design guideline for engineers. For a motor drive application, common mode and differential mode conducted emissions are studied, and dominant noise production mechanisms are identified. Moreover, a review of various modeling techniques is presented for the main parasitic components of the system. Finally, time domain and frequency domain analysis approaches are explored along with the equivalent circuits which enable fast prediction of EMI emissions. This paper intends to help the reader develop an organized understanding of conducted emission modeling to assist them with a more efficient and electromagnetically-compatible design.

This paper presents a dynamic modeling method for a 3-phase mutually coupled switched reluctance machine (MCSRM) considering spatial harmonics, and saturation. The conventional modeling methods of MCSRMs are based on 3D look-up tables (LUTs), where the phase flux linkages are considered as state variables. These 3D LUTs describe the phase currents with respect to phase flux linkages, and rotor position. The 3D LUTs represent two $dq$ quadrants, and are obtained from finite element analysis (FEA) by multi-phase excitation where the excitation currents cover two quadrants in the $dq$ synchronous reference frame. The LUTs used in the proposed method represent the phase current, and electro-magnetic torque as vectors. The magnitude, and the angle of these vectors are represented by the sine, and cosine Fourier coefficients. Hence, rotor position is not an input to the LUTs, and the proposed method uses 2D LUTs. Additionally, the flux linkages in the four $dq$ quadrants possess symmetry for MCSRMs. Therefore, LUTs corresponding to only one $dq$ quadrant are required. The single-quadrant based LUTs reduces the number of FEA steps, and the size of the LUTs by 50% compared to the two-quadrant LUT based models. Finally, the proposed method is validated using FEA, and experiments for a 12/8 MCSRM.

Compared with internal-rotor switched reluctance motors (SRMs), external-rotor (ER) SRMs can show different vibration and acoustic noise behavior due to the differences in the designs, e.g. thinner rotor back iron to achieve smaller inertia, and longer stator pole height to improve the electromagnetic performance. This paper presents the considerations in the modeling and analysis of the acoustic noise sources of an ER SRM stator and rotor. The harmonic components of the stator tangential force density and rotor radial force density are analyzed and compared. The vibration modes, the vibration behavior, and acoustic noise of the stator and rotor are also compared. A 12/16 external-rotor SRM designed for a direct-drive E-bike application is used for the modeling, analysis, and experimental validation of vibration and acoustic noise. It is concluded that both the stator tangential vibration and the rotor radial vibration can be sources of the acoustic noise in an ER SRM.