Caching with Unknown Popularity Profiles in Small Cell Networks

A heterogenous network is considered where the base stations (BSs), small base stations (SBSs) and users are distributed according to independent Poisson point processes (PPPs). We let the SBS nodes to posses high storage capacity and are assumed to form a distributed caching network. Popular data files are stored in the local cache of SBS, so that users can download the desired files from one of the SBS in the vicinity subject to availability. The offloading-loss is captured via a cost function that depends on a random caching strategy proposed in this paper. The cost function depends on the popularity profile, which is, in general, unknown. In this work, the popularity profile is estimated at the BS using the available instantaneous demands from the users in a time interval $[0,\tau]$. This is then used to find an estimate of the cost function from which the optimal random caching strategy is devised. The main results of this work are the following: First it is shown that the waiting time $\tau$ to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite, provided the user density is greater than a predefined threshold. In this case, $\tau$ is shown to scale as $N^2$, where $N$ is the support of the popularity profile. Secondly, a transfer learning-based approach is proposed to obtain an estimate of the popularity profile used to compute the empirical cost function. A condition is derived under which the proposed transfer learning-based approach performs better than the random caching strategy.Comment: 6 pages, Proceedings of IEEE Global Communications Conference, 201

A Learning-Based Approach to Caching in Heterogenous Small Cell Networks

A heterogenous network with base stations (BSs), small base stations (SBSs) and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as $N^2$, where $N$ is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of $N$ and scales linearly with the dimension of the distribution parameter.Comment: 12 pages, 5 figures, published in IEEE Transactions on Communications, 2016. arXiv admin note: text overlap with arXiv:1504.0363

ENRICHMENT OF IN VIVO EFFICACY OF CATECHIN RICH EXTRACT WITH THE APPLICATION OF NANOTECHNOLOGY

Objective: The primary goal of this study was to convert a natural catechin-rich extract into nanoparticles by using a biodegradable and non-toxic polymer Eudragit L 100 to address the various biopharmaceutical problems of catechin.Methods: Nanoparticles were prepared by emulsion solvent evaporation technique using Eudragit L 100 in increasing concentration. Optimization of processing conditions like a selection of organic solvents, diluent and surfactant concentrations, drug and polymer ratio and method of drying to increase the biological efficiency were duly attempted. Parameters such as dynamic light scattering, zeta potential, SEM and energy-dispersive X-ray spectroscopy were assessed for the evaluation of nanoparticles.Results: The entrapment efficiency was found to be between 35-45 % with methanol compared to other organic solvents. The zeta potential values of all the formulations were in the range of±30 mV to±60 mV) which confirms moderate to good stability. A rapid or ‘burst' effect of the drug release in pH 6.8 buffer showing 92 % in the first 30 min which gradually decreased to 52 % by the end of 180 min but in the pH 7.4, the release was found to be moderate. SEM and DLS indicated particles were of spherical shape lying in a nanometer range of 100 to 200 nm with a proportional influence of polymer on the particles size.Conclusion: Nanoformulations were found to be more stable and confirmed the presence of major elements such as carbon and oxygen. The findings collectively indicate that it may be worthwhile to apply nanotechnology for the design of an advanced oral dosage form for an enhanced bioavailability and biological efficacy

Automated Modular Data Analysis and Visualization System with Predictive Analytics Using Machine Learning for Agriculture field

Economy of an India is majorly depending on growth of agricultural yields, and its allied agro industry products. Prediction of agricultural yield growth is a most difficult for the agriculture departments across iglobe. 1The agricultural yields growth is depending on several factors. In this paper historical data is analyzed and a predictive model was designed. 1Several Regression models such as linear model, multiple linear model and nonlinear models were tested for an effective prediction, or for forecasting the agricultural yield for a variety of crops. Along with this the crop trade for local farmers is a very complicated and tedious task and can get easily mislead by the system we are proposing helps them to analyze the crop availability and also according to market prices can be able to predict various characteristics of the trade. The proposed method is capable of producing the visual representation after data analysis and provides the prediction results in a visual format. And also the unstructured data analysis is implemented in the system. In the proposed method, the pre-processed input data will be sent to perform a descriptive analysis and a predictive analysis. In the descriptive analysis, the data is analyzed and the summary of the analysis is given as the output.In Predictive analysis, there are steps to be considered for the analysis. At the end summary of predicted results are given as output and summary of both descriptive analysis and predictive analysis is given as final report in visual format

Investigating the contribution of geometrical parameters and immersion time on fracture toughness of jute fabric composites using statistical techniques

The aim of this work is to evaluate the failure analysis of jute fabric reinforced composite using statistical technique for marine applications. A fracture mechanics approach is proposed to determine the failure load and fracture toughness of the composite. Jute fiber reinforced epoxy composites were fabricated by hand lay-up technique. The polymer composite is intended for marine applications as an alternative material, mainly in high moisture environments. Thus an investigation was conducted to evaluate the fracture mechanic parameter by immersing composite specimens in sea water. The statistical method of Taguchi was used for experimental design. Edge Notched Tension (ENT) and Single Edge Notched Bend (SENB) test were conducted according to the ASTM E1922 and ASTM D5045 respectively. Main effect graphs are obtained to study the effect of a/w (Crack length to width ratio), thickness and immersion time of composite on fracture mechanics parameters. The approach of calculating the percentage of contribution of control factors was established using by analysis of variance (ANOVA). The influence of fracture parameters of control factors can be analysed by using 3-D response surface graph (RSM). The Validation of experimental results for fracture parameters agreed well with the predicted values

Investigating the contribution of geometrical parameters and immersion time on fracture toughness of jute fabric composites using statistical techniques

The aim of this work is to evaluate the failure analysis of jute fabric reinforced composite using statistical technique for marine applications. A fracture mechanics approach is proposed to determine the failure load and fracture toughness of the composite. Jute fiber reinforced epoxy composites were fabricated by hand lay-up technique. The polymer composite is intended for marine applications as an alternative material, mainly in high moisture environments. Thus an investigation was conducted to evaluate the fracture mechanic parameter by immersing composite specimens in sea water. The statistical method of Taguchi was used for experimental design. Edge Notched Tension (ENT) and Single Edge Notched Bend (SENB) test were conducted according to the ASTM E1922 and ASTM D5045 respectively. Main effect graphs are obtained to study the effect of a/w (Crack length to width ratio), thickness and immersion time of composite on fracture mechanics parameters. The approach of calculating the percentage of contribution of control factors was established using by analysis of variance (ANOVA).  The influence of fracture parameters of control factors can be analysed by using 3-D response surface graph (RSM). The Validation of experimental results for fracture parameters agreed well with the predicted values

Effects of residual stresses on interlaminar radial strength of Glass-Epoxy L-bend composite laminates

The built-in heterogeneity of the composite laminates has been exploited to tailor the stiffness and strength requirements of modern structures to meet the specific functional demands. However, the non-homogeneity in these composites is the root cause for most of their failures. One of the undesirable consequences of the inherited heterogeneity is the development of cure-induced stresses during composite manufacturing. This work aims to investigate the influence of process-induced stresses on interlaminar radial strength in curved composite laminates. Glass-Epoxy (GE) laminates of two different thicknesses were prepared by hand lamination technique using V-shaped tooling and cured under room temperature. The state of residual stresses in GE laminates is varied by post-curing these laminates at different temperatures. Curved bending strength (CBS) and corresponding interlaminar radial stress for delamination of L-bend laminates were evaluated experimentally using four points bending test. The residual stress profile in each GE laminate is experimentally characterized by employing the Slitting method. The results indicate that the residual stresses have a negligible effect on the critical stress for initial delamination in GE laminates. But, the critical stress for delamination was found to be independent of the laminate thickness and increased with higher curing temperatures. The delaminated surfaces of L-bend laminates were studied using a scanning electronic microscope (SEM). The enhancement in the critical stress due to post-curing can be attributed to the improved fiber-matrix interfacial bonding with higher curing temperature