Istanbul Technical University

About: Istanbul Technical University is a education organization based out in Istanbul, Turkey. It is known for research contribution in the topics: Fuzzy logic & Large Hadron Collider. The organization has 12889 authors who have published 25081 publications receiving 518242 citations. The organization is also known as: İstanbul Teknik Üniversitesi & Technical University of Istanbul.

Anthracene−Maleimide-Based Diels−Alder “Click Chemistry” as a Novel Route to Graft Copolymers

Abstract: Using the Diels−Alder (DA) “click chemistry” strategy between anthracene and maleimide functional groups, two series of well-defined polystyrene-g-poly(ethylene glycol) (PS-g-PEG) and polystyrene-g-poly(methyl methacrylate) (PS-g-PMMA) copolymers were successfully prepared The whole process was divided into two stages: (i) preparation of anthracene and maleimide functional polymers and (ii) the use of Diels−Alder reaction of these groups First, random copolymers of styrene (S) and chloromethylstyrene (CMS) with various CMS contents were prepared by the nitroxide-mediated radical polymerization (NMP) process Then, the choromethyl groups were converted to anthryl groups via the etherifaction with 9-anthracenemethanol The other component of the click reaction, namely protected maleimide functional polymers, were prepared independently by the modification of commercially available poly(ethylene glycol) (PEG) and poly(methyl methacrylate) (PMMA) obtained by atom transfer radical polymerization (ATRP) usin

Control of a Quadrotor Helicopter Using Dual Camera Visual Feedback

Abstract: In this paper we propose a vision-based stabilization and output tracking control method for a model helicopter. A novel two-camera method is introduced for estimating the full six-degrees-of-freedom pose of the helicopter. One of these cameras is located on-board the helicopter, and the other camera is located on the ground. Unlike previous work, these two cameras are set to see each other. The pose estimation algorithm is compared in simulation to other methods and is shown to be less sensitive to errors on feature detection. In order to build an autonomous helicopter, two methods of control are studied: one using a series of mode-based, feedback linearizing controllers and the other using a backstepping-like control law. Various simulations demonstrate the implementation of these controllers. Finally, we present flight experiments where the proposed pose estimation algorithm and non-linear control techniques have been implemented on a remote-controlled helicopter.

Disease detection on the leaves of the tomato plants by using deep learning

Abstract: The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or autonomously on the field or in the greenhouse. Likewise, diseases can also be detected from close-up photographs taken from plants by sensors built in fabricated greenhouses. The examined diseases in this study cause physical changes in the leaves of the tomato plant. These changes on the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architectures were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf images from the PlantVillage dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet.

Families of scroll grid attractors

Abstract: In this paper a new family of scroll grid attractors is presented. These families are classified into three called 1D-, 2D- and 3D-grid scroll attractors depending on the location of the equilibrium points in state space. The scrolls generated from 1D-, 2D- and 3D-grid scroll attractors are located around the equilibrium points on a line, on a plane or in 3D, respectively. Due to the generalization of the nonlinear characteristics, it is possible to increase the number of scrolls in all state variable directions. A number of strange attractors from the scroll grid attractor families are presented. They have been experimentally verified using current feedback opamps. Also Lur'e representations are given for the scroll grid attractor families.