The challenges in implementing SAE Level 4/5 autonomous vehicles are manifold, with intersection navigation being a pervasive one. We analyze a novel road topology invented by a co-author of this paper, Xiayong Hu. The topology eliminates the need for traditional traffic control and cross-traffic at intersections, potentially improving the safety of autonomous driving systems. The topology, herein called the Zonal Road Topology, consists of unidirectional loops of road with traffic flowing either clockwise or counter-clockwise. Adjacent loops are directionally aligned with one another, allowing vehicles to transfer from one loop to another through a simple lane change. To evaluate the Zonal Road Topology, a one km2 pilot-track near Changshu, China is currently being set aside for testing. In parallel, traffic simulations are being performed. To this end, we conduct a simulation-based comparison between the Zonal Road Topology and a traditional road topology for a generic Electric Vehicle (EV) using the Simulation for Urban MObility (SUMO) platform and MATLAB/Simulink. We analyze the topologies in terms of their travel efficiency, safety, energy usage, and capacity. Drive time, number of halts, progress rate, and other metrics are analyzed across varied traffic levels to investigate the advantages and disadvantages of the Zonal Road Topology. Our results indicate that vehicles on the Zonal Road Topology have a lower, more consistent drive time with greater traffic throughput, while using less energy on average. These results become more prominent at higher traffic densities.

Reconfigurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations in several domains because of their unique combination of flexibility, performance, and power efficiency. However, FPGAs have not been widely used for high-performance computing, primarily because of their programming complexity and difficulties in optimizing performance. We optimize Tensil AI's open-source inference accelerator for maximum performance using ResNet20 trained on CIFAR in this paper in order to gain insight into the use of FPGAs for high-performance computing. In this paper, we show how improving hardware design, using Xilinx Ultra RAM, and using advanced compiler strategies can lead to improved inference performance. We also demonstrate that running the CIFAR test data set shows very little accuracy drop when rounding down from the original 32-bit floating point. The heterogeneous computing model in our platform allows us to achieve a frame rate of 293.58 frames per second (FPS) and a %90 accuracy on a ResNet20 trained using CIFAR. The experimental results show that the proposed accelerator achieves a throughput of 21.12 Giga-Operations Per Second (GOP/s) with a 5.21 W on-chip power consumption at 100 MHz. The comparison results with off-the-shelf devices and recent state-of-the-art implementations illustrate that the proposed accelerator has obvious advantages in terms of energy efficiency.

Precise and reliable estimation of reference evapotranspiration (ET o ) is an essential for the irrigation and water resources management. ET o is difficult to predict due to its complex processes. This complexity can be solved using machine learning methods. This study investigates the performance of artificial neural network (ANN) and deep neural network (DNN) models for estimating daily ET o . Previously proposed ANN and DNN methods have been realized, and their performances have been compared. Six input data including maximum air temperature (T max ), minimum air temperature (T min ), solar radiation (R n ), maximum relative humidity (RH max ), minimum relative humidity (RH min ) and wind speed (U 2 ) are used from 4 meteorological stations (Adana, Aksaray, Isparta and Ni\u{g}de) during 1999-2018 in Turkey. The results have shown that our proposed DNN models achieves satisfactory accuracy for daily ET o estimation compared to previous ANN and DNN models. The best performance has been observed with the proposed model of DNN with SeLU activation function (P-DNN-SeLU) in Aksaray with coefficient of determination (R 2 ) of 0.9934, root mean square error (RMSE) of 0.2073 and mean absolute error (MAE) of 0.1590, respectively. Therefore, the P-DNN-SeLU model could be recommended for estimation of ET o in other climate zones of the world.

The first chapter is the Introductory chapter. The second chapter aims to provide an update of the recent advances in the field of rational design of PDE inhibitors. The third chapter includes designing a series of peptidic inhibitors that possessed a substrate transition-state analog and evaluating the structure-activity relationship of the designed inhibitors, based on docking and scoring, using the docking simulation software Molecular Operating Environment. The aim of the forth chapter is to develop structure-property relationships for the qualitative and quantitative prediction of the reverse-phase liquid chromatographic retention times of chlorogenic acids.

It is the efficient use of resources expected from an exam scheduling application. There are various criteria for efficient use of resources and for all tests to be carried out at minimum cost in the shortest possible time. It is aimed that educational institutions with such criteria successfully carry out central examination organizations. In the study, a two-stage genetic algorithm was developed. In the first stage, the assignment of courses to sessions was carried out. In the second stage, the students who participated in the test session were assigned to examination rooms. Purposes of the study are increasing the number of joint students participating in sessions, using the minimum number of buildings in the same session, and reducing the number of supervisors using the minimum number of classrooms possible. In this study, a general purpose exam scheduling solution for educational institutions was presented. The developed system can be used in different central examinations to create originality. Given the results of the sample application, it is seen that the proposed genetic algorithm gives successful results.1