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Article
Fabrication and Characterization of InZnO TFTs Grown on Transparent Conductive Oxide Substrate by DC Sputtering Technique

Authors: M.N. Hussain --- J.M. Abdul-Jabbar
Journal: Iraqi Journal of Applied Physics المجلة العراقية للفيزياء التطبيقية ISSN: 18132065 23091673 Year: 2010 Volume: 6 Issue: 1 Pages: 41-46
Publisher: iraqi society for alternative and renewable energy sources and techniques الجمعية العراقية لمصادر وتقنيات الطاقة البديلة والمستجدة

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Abstract

In this work, depletion-mode transistors were made of InZnO thin films prepared and grown on transparent conductive substrates by DC sputtering technique. The SiO2-In2O3-ZnO system and N2 plasma incorporated InZnO film were grown to get a better controllability of the carrier concentration during the film growth. Hydrogen plasma and oxygen plasma effects on the TCO films and the TFTs were investigated. Devices were simulated in a device model to extract the parasitic parameters. The depletion-mode TFTs have been fabricated successfully on glass by using InZnO films as the channel layers.


Article
Neuro-Fuzzy Based ECG Signal Classification with A Gaussian Derivative Filter
تصنيف اشارة القلب باعتماد النظام العصبي المضبب ومرشح نوع مشتقة كاوس

Authors: H. N. Yahya وهبة نبيل يحيى --- S. N. M. Al-Faydi سما نزار محمد --- Dr. J. M. Abdul-Jabbar د. جاسم محمد عبد الجبار
Journal: AL Rafdain Engineering Journal مجلة هندسة الرافدين ISSN: 18130526 Year: 2015 Volume: 23 Issue: 2 Pages: 153-166
Publisher: Mosul University جامعة الموصل

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Abstract

In this paper, a neuro-fuzzy classification method is used for identifications of ECG signals. A feature extraction method with a QRS like filter (first order Gaussian derivative filter) is used. Five standard parameters (energy, mean value, standard deviation, maximum and minimum) are extracted from these diseasefeatures and then used as inputs for the neuro-fuzzy classification system. The ECG signals are importedfrom the standard MIT-BIH database. Five types of ECG signalsare used for classification; they are normal sinus rhythm (NSR), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC) and pacemaker (PM). The proposed system combines the neural network adaptive capabilities and fuzzy inference system with the suitable filter design to give a promising classification accuracy of 99%.

الخلاصةفي هذا البحث, تم استخدام طريقة التصنيف باعتماد (Neuro – fuzzy) لاغراض التعرف على اشارات ECG . واستعملت طريقة لاستخلاص السمات بالاعتماد على مرشح ذو استجابة بنوع مشتقة كاوس الاولى والتي تشبه تركيبة QRS في اشارة اﻟECG . تم بعد ذلك استخدام تلك السمات كادخالات لمنظومة التصنيف باعتماد (Neuro - fuzzy) .اٍن اشارات ECG المستخدمة في هذا البحث تم الحصول عليها من (The standard MIT-database) . واستخدم منها خمس انواع هي (NSR) ̦ (LBBB) ̦ (RBBB) ̦ (PVC) ̦ (PM) . ان المنظومة المستخدمة تجمع بين الامكانات المتكيفة للشبكات العصبية وبين الموائمة الضبابية مع التصميم المناسب للمرشح وهذا اعطى دقة تصنيف واعدة وبنسبة 99% .

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