No abstract

No abstract

No abstract

In Electrocardiogram (ECG) application, we face a main problem of power line noise added to the ECG signal. Different Researchers have been interested in this problem due to its importance. In this paper we introduce a study of different algorithms and their effects on the performance of the ECG noise canceller. We have used many kinds of algorithms such as: LMS (Least Mean Square), NLMS (Normalized Least Mean Square), Signed Regressor LMS (SRLMS), Sign LMS (SLMS), Sign-Sign LMS (SSLMS) and a new proposed modified LMS called Variable Step size Least Mean Square (VSLMS) using MATLAB software package as well as Unbiased Linear output Neural Network (ULNN) and Unbiased Non Linear output Neural Network (UNLNN). It is promising to clarify the difference among these algorithms with the aim of obtaining better performance.

Background Acute myocardial infraction (AMI) is a leading cause of morbidity. As anti-diabetic drugs affect the cardiovascular risk of diabetic patients independent of their glucose lowering effect, this study was aimed to explore the cardioprotective effects of metformin, sitagliptin and dapagliflozin on electrocardiogram (ECG) changes, IL-1β, troponin I, caspase 3 in isoprenaline (ISO) induced MI in non-diabetic rats. The present study was conducted on 40 adult male Wistar albino rats. The rats were randomly assigned into 5 groups, 8 each: I-Normal Control (NC) group, II-ISO-induced MI control (ISO-MI) injected with ISO subcutaneously at a dose of 100 mg/kg to induce experimental AMI. III-A- Metformin treated ISO-induced MI group (300 mg/kg/day), III-B-Sitagliptin treated ISO-induced MI group (10 mg/kg/day) and III-C- Dapagliflozin treated ISO-induced MI group (5 mg/kg/day). Results Treated groups showed significant improvement at p < 0.05 of ECG parameters with a decrease HR, ST amplitude and QT interval as compared to ISO-MI group. There was significant reduction at p < 0.05 of serum levels of IL-1β, troponin I and caspase 3 in the treated groups. Conclusions All medications proved to be effective in alleviating the harmful effects caused by ISO-induced MI evidenced by ECG readings and biochemical parameters. However, Dapagliflozin demonstrated a superior effect to Metformin and Sitagliptin.

Power level control is a <span>critical issue in nuclear power stations due to its nonlinear dynamics. One of the most commonly used controllers is fractional order proportional–integral–derivative (FOPID). The FOPID is an enhanced and modern controlling system that has two additional added parameters. In this paper, comparison between particle swarm, gray wolf and ant lion optimization techniques is performed to determine the FOPID controller parameters. The nuclear reactor is a pressurized water reactor which is a fifth order nonlinear reactor model and is simulated using MATLAB software based on the point kinetic model. The integral square error (ISE) performance index is used to evaluate the performance of the three optimization techniques. The simulation results show that ant lion optimization for tuning the FOPID controller parameters gives the best performance and integral square error index better than the two other optimization techniques.</span>

No abstract

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.