Application of signal processing techniques for measurement of muscle fiber conduction velocity

The objectives of this study were to evaluate if muscle fiber conduction velocity (MFCV) could be used as a reliable indicator of fatigue and to characterize the recovery of MFCV after a fatiguing contraction. The decline of MFCV with fatigue was modelled using linear regression and compared with the decline in median frequency (MF). It was found that the percent decline in MF with fatigue was greater than that of MFCV with fatigue and that the decline of MFCV was consistent in all subjects tested. It was thus determined that MFCV could be used as a reliable indicator of fatigue. Possible explanations for the recovery of MFCV after fatigue were given. The recovery curves for all subjects were curve fit using the exponential peeling technique. A comparison of the time constants showed that 8 out of 9 subjects had values between 2-4 minutes, indicating that the recovery process had a similar response in these 8 subjects. Decomposition of the EMG is a useful tool which helps us better understand the functioning of the neuromuscular system. An algorithm was developed to decompose the EMG into its constituent motor units based on the work done by Deluca et al. Preliminary results were obtained. However, further research is needed in this area

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks