The aim of this study is to compare two supervised artiﬁcial neural network models for diagnosing a child with learning disability. Once diagnosed, then a fuzzy expert system is applied to correctly classify the type of learning disability in a child. The endeavor is to support the special education community in their quest to be with the mainstream. The initial part of the paper gives a comprehensive study of the different mechanisms of diagnosing learning disability. Models are designed by implementing two soft computing techniques called Single-Layer Perceptron and Learning Vector Quantization. These models classify a child as learning disabled or nonlearning disabled. Once diagnosed with learning disability, fuzzybased approach is used further to classify them into types of learning disability that is Dyslexia, Dysgraphia, and Dyscalculia. The models are trained using the parameters of curriculum-based test. The paper proposes a methodology of not only detecting learning disability but also the type of learning disability
مقاله شبکه های حسگر بیسیم القای مغناطیسی
مقاله شبکه های حسگر بیسیم القای مغناطیسیمعرفی :MISE-PIPE شبکه های حسگر بیسیم مبتنی بر القای مغناطیسی برای نظارت بر خطوط لوله زیر زمینی در این محصول یک مقاله پایه که در ذیل نام آن امده است به همراه فایل پاور پوینت جهت ارائه در سرکلاس درس موجود است مقاله مربوط به سال 2010 بوده که در آن درخصوص حسگر های سرعت سنج تعبیه شده در خطوط لوله و نحوه نظارت بر این خطوط انتقال آب یا نفت راهکارهایی ارائه شده است MISE-PIPE- Magnetic induction-based wireless sensor networks for underground pipeline monitoring
abstract Underground pipelines constitute one of the most important ways to transport large amounts of ﬂuid (e.g. oil and water) through long distances. However, existing leakage detection techniques do not work well in monitoring the underground pipelines due to the harsh underground environmental conditions. In this paper, a new solution, the magnetic induction (MI)-based wireless sensor network for underground pipeline monitoring (MISE-PIPE), is introduced to provide low-cost and real-time leakage detection and localization for underground pipelines. MISE-PIPE detects and localizes leakage by jointly utilizing the measurements of different types of sensors that are located both inside and around the underground pipelines. By adopting an MI waveguide technique, the measurements of different types of the sensors throughout the pipeline network can be reported to the administration center in real-time. The system architecture and operational framework of MISE-PIPE is ﬁrst developed. Based on the operational framework, research challenges and open research issues are then discussed.