Optimization of PSWAN in terms of cost and bandwidth

PSWAN is an internetworking project undertaken by Govt. of India at Pondicherry. It covers a vast area, under it there are various state headquarters and district headquarters. Approximately 3000 systems are using its internet services. Since the number of systems are more and the bandwidth required is less so optimization was needed. Optimization was required without hardware modifications, so we defined some of the parameters through which we can achieve the optimization of this network, these parameters are 1. Type of protocol 2. Type of Topology 3. Access policies 4. Load balancing 5. Traffic bottle neck 6. Bandwidth utilization. To make the network cost effective, some small networks were moved to broadband network so that bandwidth usage can be mitigated and consequently network will get optimized. Since this project (PSWAN) is using the CISCO devices only so it was easy to simulate the network, we used OPNET simulator as it is precise than other simulators. First the operational network was simulated and then the proposed one, proposed model showed evident positive results. The simulation tool used is Opnet. OPNET is extensive and powerful simulation software with wide variety of capabilities. It enables the possibility to simulate entire heterogeneous networks with various protocol

Early results after transatrial repair of RVOT obstruction including teratology of fallot

Background: Right ventricular (RV) dysfunction is a significant cause of morbidity and mortality after surgical correction of RVOT obstruction including tetralogy of Fallot (TOF). Transatrial repair avoids a ventriculotomy (in contrast to the transventricular approach) emphasizing maximal preservation of RV structure and function. We have adopted this technique as less traumatic for the right ventricle. This study evaluates the early surgical results of our approach.Methods: Between January 2005 to January 2014, 77 consecutive patients with RVOT obstruction were referred to our unit for surgical therapy. Of these, 14 were unsuitable for repair and underwent aortopulmonary shunting. In the remaining 63 patients (mean age of 2.67±0.38 years), complete transatrial/transpulmonary repair was performed. Previously placed shunts (four patients) were taken down. In all cases, subpulmonary resection and ventricular septal defect (VSD) closure were accomplished transatrially. In 51 patients, the main pulmonary artery was augmented with an autologous pericardial patch.Results: There were 7 (9%) deaths in this series. No patient required permanent pacemaker. Median ICU and hospital stay were 91 hours and 14 days, respectively. At median follow up of 54 (mean 51±12) months, all patients are asymptomatic, with no significant residual lesion.Conclusions: Transatrial/transpulmonary repair of TOF is associated with remarkably low morbidity and mortality in our early experience

From big data to smart data: a sample gradient descent approach for machine learning

Abstract This research paper presents an innovative approach to gradient descent known as ‘‘Sample Gradient Descent’’. This method is a modification of the conventional batch gradient descent algorithm, which is often associated with space and time complexity issues. The proposed approach involves the selection of a representative sample of data, which is subsequently subjected to batch gradient descent. The selection of this sample is a crucial task, as it must accurately represent the entire dataset. To achieve this, the study employs the use of Principle Component Analysis (PCA), which is applied to the training data, with a condition that only those rows and columns of data that explain 90% of the overall variance are retained. This approach results in a convex loss function, where a global minimum can be readily attained. Our results indicate that the proposed method offers faster convergence rates, with reduced computation times, when compared to the conventional batch gradient descent algorithm. These findings demonstrate the potential utility of the ‘‘Sample Gradient Descent’’ technique in various domains, ranging from machine learning to optimization problems. In our experiments, both approaches were run for 30 epochs, with each epoch taking approximately 3.41 s. Notably, our ‘‘Sample Gradient Descent’’ approach exhibited remarkable performance, converging in just 8 epochs, while the conventional batch gradient descent algorithm required 20 epochs to achieve convergence. This substantial difference in convergence rates, along with reduced computation times, highlights the superior efficiency of our proposed method. These findings underscore the potential utility of the ‘‘Sample Gradient Descent’’ technique across diverse domains, ranging from machine learning to optimization problems. The significant improvements in convergence rates and computation times make our algorithm particularly appealing to practitioners and researchers seeking enhanced efficiency in gradient descent optimization