On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique.

A decentralized hybrid computing consumer authentication framework for a reliable drone delivery as a service

The thriving adoption of drones for delivering parcels, packages, medicines, etc., is surging with time. The application of drones for delivery services results in faster delivery, fuel-saving, and less energy consumption. Giant companies like Google, Amazon, Facebook, etc., are actively working on developing, testing, and improving drone-based delivery systems. So far, a lot of work has been done for improving the design, speed, operating range, security of the delivery drones, etc. However, very limited work has been done to ensure a complete and reliable last-mile delivery from the merchant’s store to the hands of the actual customer. To ensure a complete and reliable last-mile delivery, a drone must authenticate the consumer before dropping the package. Therefore, in this work, we propose a consumer authentication (Consumer-Auth) hybrid computing framework for drone delivery as a service to make sure that the parcel is perfectly delivered to the intended customer. The proposed Consumer-Auth framework enables a drone to reach the exact destination by using the GPS coordinates of the customer autonomously. After reaching the exact location, the drone waits for the customer to come to the specific pinned location then it starts a two-factor consumer authentication process, i.e., one-time password (OTP) verification and face Recognition. The experimental results manifest the effectiveness of the proposed Consumer-Auth framework to ensure a complete and reliable drone-based last-mile delivery.

Computational Load Balancing Method for Hybrid Computing Systems

The paper considers the issues of the computations distributing within one node of a hybrid computing system for applied programs with computation-intense operations. A method is proposed for static distribution of computations, as well as a method for automatic balancing of the computational load during program execution, which is based on periodic analyzing the CPU load by the executed program and making decision to redistribute computational load if necessary. The proposed methods are implemented in an applied program that solves a gas dynamic problem using the computing resources of the multicore central processor and graphics accelerators. The results of program execution with various data distributions were obtained and analyzed, both with and without the mechanism for automatic balancing of the computational load.

The cubature formulas computing modeling

Presented paper is related with the problem of hybrid computing structures design and their methodology. The main purpose is the theoretical basis for the automated design supercomputing devices development and the different restrictions accounting. The theory of space granular models and granular computing are used for the new theoretical basis development. As a result, the mathematical models, provided in this paper, may be implemented to the FPGA units by the special COLAMO programming language. The new approach provides the universal computing devices for the multidimensional cubature formulas for the very wide domain of engineering problems.