Ali Gulbag

Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques

Abstract The purpose of this article is to demonstrate the use of feedforward neural networks (FFNNs), adaptive neural fuzzy inference systems (ANFIS), and probabilistic neural networks (PNNs) to discriminate between earthquakes and quarry blasts in Istanbul and vicinity (the Marmara region). The tectonically active Marmara region is affected by the Thrace-Eskisehir fault zone and especially the North Anatolian fault zone (NAFZ). Local MARNET stations, which were established in 1976 and are operated by the Kandilli Observatory and Earthquake Research Institute (KOERI), record not only earthquakes that occur in the region, but also quarry blasts. There are a few quarry-blasting areas in the Gaziosmanpasa, Catalca, Omerli, and Hereke regions. Analytical methods were applied to a set of 175 seismic events (2001–2004) recorded by the stations of the local seismic network (ISK, HRT, and CTT stations) operated by the KOERI National Earthquake Monitoring Center (NEMC). Out of a total of 175 records, 148 are related to quarry blasts and 27 to earthquakes. The data sets were divided into training and testing sets for each region. In all the models developed, the input vectors consist of the peak amplitude ratio (S/P ratio) and the complexity value, and the output is a determination of either earthquake or quarry blast. The success of the developed models on regional test data varies between 97.67% and 100%.

BZK.SAU: Implementing a hardware and software-based Computer Architecture simulator for educational purpose

The most ideal learning about a topic is to put the theoretical knowledge into practice. Computer Architecture and Organization course plays a significant role in the electronics engineering, computer engineering and similar disciplines. To convert practice the concepts handled in this course is not easy task. In this paper, we present a computer architecture simulator design named BZK.SAU. It has fifty-nine instructions that are commonly used in commercial microprocessors. Also, it has eleven registers and implements interrupt, stack and input-output operations. This simulator with memory of 64 KB uses sixteen bits data bus to communicate between registers, memory, and peripheral devices. This simulator is a useful tool for Computer Architecture and Organization course since all units of this simulator can be examined in detail. Many of the simulators in the literature have been designed using a software programming language. These simulators can help to enhance learning the concepts in Computer Architecture and Organization course. However, examining the internal structure of the units in these simulators is not possible. The simulator designed in this paper allows being able to analyze the internal structures since all units of this simulator was designed at logic gate level. Additionally, the Assembler and Compiler Programs was written for this Simulator in the Microsoft Visual Studio. NET platform. The students can watch the execution of the assembly code written using these programs. Also, they can watch step by step changes on its registers and other units by adjusting the clock speed in the simulator.

BZK.SAU.FPGA10.1: A modular approach to FPGA-based micro computer architecture design for educational purpose

This article describes a modular approach to FPGA‐based micro computer architecture design for supporting undergraduate courses in computer science and related discipline. We have adopted the modular approach to the second FPGA version of the BZK.SAU[6] named BZK.SAU.FPGA10.1. The BZK.SAU.FPGA10.1 platform has modular units that enable development of their own micro computer designs, particularly arithmetic and logic unit (ALU), memory and system bus, by integrating of simple building blocks. So the proposed modular approach will create high level interest in the faculty teaching computer organization and architecture course and produce significant contribution in the education of engineers. Also, such an approach could greatly increase the understanding of the architectural concept of the Microcomputer Architecture. All the modules in the BZK.SAU.FPGA10.1 design are entirely realized using schematic structure on Alteras Cyclone II Development board. So, students can implement on a hardware platform to test their own designs by downloading since it is an affordable cost for the institution.

A neural network implemented microcontroller system for quantitative classification of hazardous organic gases in the ambient air

In this study, a microcontroller-based gas mixture classification system is proposed to use real-time analyses of the trichloroethylene and acetone binary mixture. A Feed Forward Neural Network (FFNN) structure is performed for quantitative identification of individual gas concentrations (trichloroethylene and acetone) in their gas mixtures. The phthalocyaninecoated Quartz Crystal Microbalance (QCM) type sensors were used as gas sensors. A calibrated Mass Flow Controller (MFC) was used to control the flow rates of carrier gas and trichloroethylene and acetone gas mixtures streams. The components in the binary mixture were quantified by applying the sensor responses from the QCMs sensor array as inputs to the FFNN. The microcontroller-based gas mixture classification system performs Neural Network (NN)-based estimation, the data acquisition and user interface tasks. This system can estimate the gas concentrations of trichloroethylene and acetone with the average errors of 0.08% and 0.97%, respectively.

BZK.SAU.FPGA10.0: Microprocessor architecture design on reconfigurable hardware as an educational tool

The Computer Architecture and Organization course in Computer and Electrical Engineering departments faces with a big problem: the migration from theory to practice. In order to solve this problem, a Computer Architecture simulator named BZK.SAU[1] is designed using an emulator program for educational purpose. This approach has important limitations. While students can complete and simulate their designs using software, they do not have the chance to implement and to run their designs in actual hardware. This work presents our solution to this problem: the FPGA implementation of BZK.SAU so that it would look and behave like the computer architecture simulator published in [1]. So we have implemented BZK.SAU Computer Architecture Simulator by using Altera® FPGA board that is a reconfigurable hardware prototyping development platform.

A Study on Neural Networks Using Taylor Series Expansion of Sigmoid Activation Function

The use of microcontroller in neural network realizations is cheaper than those specific neural chips. However, realization of complicated mathematical operations such as sigmoid activation function is difficult via general microcontrollers. On the other hand, it is possible to make approximation to the sigmoid activation function. In this study, Taylor series expansions up to nine terms are used to realize sigmoid activation function. The neural network (NN) structures with Taylor series expansions of sigmoid activation function are used for the concentration estimation of Toluene gas from the trend of the transient sensor responses. The Quartz Crystal Microbalance (QCM) type sensors were used as gas sensors. The appropriateness of the NNs for the gas concentration determination inside the sensor response time is observed with five different terms of Taylor series expansion.

An intelligent gas concentration estimation system using neural network implemented microcontroller

The use of microcontroller in neural network realizations is cheaper than those specific neural chips In this study, an intelligent gas concentration estimation system is described A neural network (NN) structure with tapped time delays was used for the concentration estimation of CCI4 gas from the trend of transient sensor responses After training of the NN, the updated weights and biases were applied to the embedded neural network implemented on the 8051 microcontroller The microcontroller based gas concentration estimation system performs NN based concentration estimation, the data acquisition and user interface tasks This system can estimate the gas concentrations of CCI4 with an average error of 1.5 % before the sensor response time The results show that the appropriateness of the system is observed.

Leaf recognition using K-NN classification algorithm

Plants play a crucial role in terms of the lives of human and other creatures since the existence of the universe. Despite the studies of plant scientists, there are many undiscovered and unidentified species in our environment. This paper is aimed to add the leaves, whose images have been clearly attained, to the system and to provide a proper analysis of those leaves. The images could be either the ones taken before or the ones obtained by means of a camera that is connected transiently. Leaf images went through pretreatment phases first, and then their features were extracted. Finally, classification processing was accomplished by using K-NN algorithm. The System is working successfully.

A Modular Approach to Arithmetic and Logic Unit Design on a Reconfigurable Hardware Platform for Educational Purpose

The Arithmetic and Logic Unit (ALU) design is one of the important topics in Computer Architecture and Organization course in Computer and Electrical Engineering departments. There are ALU designs that have non-modular nature to be used as an educational tool. As the programmable logic technology has developed rapidly, it is feasible that ALU design based on Field Programmable Gate Array (FPGA) is implemented in this course. In this paper, we have adopted the modular approach to ALU design based on FPGA. All the modules in the ALU design are realized using schematic structure on Altera’s Cyclone II Development board. Under this model, the ALU content is divided into four distinct modules. These are arithmetic unit except for multiplication and division operations, logic unit, multiplication unit and division unit. User can easily design any size of ALU unit since this approach has the modular nature. Then, this approach was applied to microcomputer architecture design named BZK.SAU.FPGA10.0 instead of the current ALU unit.

On the improvement of the teaching quality and learning effectiveness in the computer organization course through FPGA and modular centered microcomputer design

This manuscript focuses on the results of a project study on the field programmable gate array (FPGA) and modular based microcomputer architecture design for improving the teaching and learning effectiveness in computer organization course and similar courses. The design mentioned in this manuscript is a unique system allowing to students to directly write their program and/or include their own designs. The main objective of this study is to improve the teaching effectiveness in the computer organization course at graduate level via hands‐on learning. The goal of the project is to encourage students to create their unique microcomputer designs since this design has modular structure and the fundamental elements which a simple computer has. Also it is to make appropriate modifications using students’ concerns and the results of project study for computer architecture and organization course for next semesters. To evaluate the improvement of the teaching and learning effectiveness, the students enrolled in the computer organization course in computer engineering department are divided into two groups. A student survey is conducted to provide students with an opportunity to have their views about this methodology at computer organization course. The results of the survey acquired with the probability density distribution function reveal the effectiveness of this methodology with FPGA development environment.