Part I: Background and Fundamentals Introduction, Mohamed Gad-el-Hak, University of Notre Dame Scaling of Micromechanical Devices, William Trimmer, Standard MEMS, Inc., and Robert H. Stroud, Aerospace Corporation Mechanical Properties of MEMS Materials, William N. Sharpe, Jr., Johns Hopkins University Flow Physics, Mohamed Gad-el-Hak, University of Notre Dame Integrated Simulation for MEMS: Coupling Flow-Structure-Thermal-Electrical Domains, Robert M. Kirby and George Em Karniadakis, Brown University, and Oleg Mikulchenko and Kartikeya Mayaram, Oregon State University Liquid Flows in Microchannels, Kendra V. Sharp and Ronald J. Adrian, University of Illinois at Urbana-Champaign, Juan G. Santiago and Joshua I. Molho, Stanford University Burnett Simulations of Flows in Microdevices, Ramesh K. Agarwal and Keon-Young Yun, Wichita State University Molecular-Based Microfluidic Simulation Models, Ali Beskok, Texas A&M University Lubrication in MEMS, Kenneth S. Breuer, Brown University Physics of Thin Liquid Films, Alexander Oron, Technion, Israel Bubble/Drop Transport in Microchannels, Hsueh-Chia Chang, University of Notre Dame Fundamentals of Control Theory, Bill Goodwine, University of Notre Dame Model-Based Flow Control for Distributed Architectures, Thomas R. Bewley, University of California, San Diego Soft Computing in Control, Mihir Sen and Bill Goodwine, University of Notre Dame Part II: Design and Fabrication Materials for Microelectromechanical Systems Christian A. Zorman and Mehran Mehregany, Case Western Reserve University MEMS Fabrication, Marc J. Madou, Nanogen, Inc. LIGA and Other Replication Techniques, Marc J. Madou, Nanogen, Inc. X-Ray-Based Fabrication, Todd Christenson, Sandia National Laboratories Electrochemical Fabrication (EFAB), Adam L. Cohen, MEMGen Corporation Fabrication and Characterization of Single-Crystal Silicon Carbide MEMS, Robert S. Okojie, NASA Glenn Research Center Deep Reactive Ion Etching for Bulk Micromachining of Silicon Carbide, Glenn M. Beheim, NASA Glenn Research Center Microfabricated Chemical Sensors for Aerospace Applications, Gary W. Hunter, NASA Glenn Research Center, Chung-Chiun Liu, Case Western Reserve University, and Darby B. Makel, Makel Engineering, Inc. Packaging of Harsh-Environment MEMS Devices, Liang-Yu Chen and Jih-Fen Lei, NASA Glenn Research Center Part III: Applications of MEMS Inertial Sensors, Paul L. Bergstrom, Michigan Technological University, and Gary G. Li, OMM, Inc. Micromachined Pressure Sensors, Jae-Sung Park, Chester Wilson, and Yogesh B. Gianchandani, University of Wisconsin-Madison Sensors and Actuators for Turbulent Flows. Lennart Loefdahl, Chalmers University of Technology, and Mohamed Gad-el-Hak, University of Notre Dame Surface-Micromachined Mechanisms, Andrew D. Oliver and David W. Plummer, Sandia National Laboratories Microrobotics Thorbjoern Ebefors and Goeran Stemme, Royal Institute of Technology, Sweden Microscale Vacuum Pumps, E. Phillip Muntz, University of Southern California, and Stephen E. Vargo, SiWave, Inc. Microdroplet Generators. Fan-Gang Tseng, National Tsing Hua University, Taiwan Micro Heat Pipes and Micro Heat Spreaders, G. P. "Bud" Peterson, Rensselaer Polytechnic Institute Microchannel Heat Sinks, Yitshak Zohar, Hong Kong University of Science and Technology Flow Control, Mohamed Gad-el-Hak, University of Notre Dame) Part IV: The Future Reactive Control for Skin-Friction Reduction, Haecheon Choi, Seoul National University Towards MEMS Autonomous Control of Free-Shear Flows, Ahmed Naguib, Michigan State University Fabrication Technologies for Nanoelectromechanical Systems, Gary H. Bernstein, Holly V. Goodson, and Gregory L. Snider, University of Notre Dame Index

The MEMS Handbook

Sentiment analysis has drawn considerable interest among researchers owing to the realization of its fascinating commercial and business benefits. This paper deals with sentiment analysis in Arabic text from three perspectives. First, several alternatives of text representation were investigated. In particular, the effects of stemming, feature correlation and n-gram models for Arabic text on sentiment analysis were investigated. Second, the behaviour of three classifiers, namely, SVM, Naive Bayes, and K-nearest neighbour classifiers, with sentiment analysis was investigated. Third, the effects of the characteristics of the dataset on sentiment analysis were analysed. To this end, we applied the techniques proposed in this paper to two datasets; one was prepared in-house by the authors and the second one is freely available online. All the experimentation was done using Rapidminer. The results show that our selection of preprocessing strategies on the reviews increases the performance of the classifiers.

/pdf/a-study-of-the-effects-of-preprocessing-strategies-on-4d7ziqy1ho.pdf

A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

Genetic fuzzy self-tuning PID controllers for antilock braking systems

We present a class of Learning Classifier Systems that learn fuzzy rule-based models, instead of interval-based or Boolean models. We discuss some motivations to consider Learning Fuzzy Classifier Systems (LFCS) as a promising approach to learn mappings from real-valued input to real-valued output, basing on data interpretation implemented by fuzzy sets. We describe some of the approaches explicitly or implicitly referring to this research area, presented in literature since the beginning of the last decade. We also show how the general LFCS model can be considered as a framework for a wide range of systems, each implementing in a different way the modules composing the basic architecture. We also mention some of the applications of LFCS presented in literature, which show the potentialities of this type of systems. Finally, we introduce a general methodology to extend reinforcement distribution algorithms usually not designed to learn fuzzy models. This opens new application possibilities.

An Introduction to Learning Fuzzy Classifier Systems

Autonomous mobile robots are used in several application areas including manufacturing, mining, military, and transportation, search and protect missions, etc. For the navigation system it is necessary to locate the position of the mobile robot in surrounding environment. For avoiding obstacles efficiently and to reach the target under many different shapes of obstacle in environment, a fuzzy logic controller has been designed to improve the movement of mobile robot according to obstacles positions by defining or establishing input variables, output variables, fuzzy logic membership functions, fuzzy logic rule base ‘If-Then’ fuzzy inference system rules and defuzzification method. Then it has to plan a path towards desired goal. The navigation system of a mobile robot has to identify all potential obstacles in order to search for a collision free path. Obstacles avoidance and destination point can be achieved by changing the direction angle of the mobile robot. To make the mobile robot move in its environment, the basic path planning strategies have been used. While the mobile robot is navigating in its workspace environment, it avoids obstacles and look for the target. In this paper the simulation of path planning technique for an autonomous mobile robot is presented. The figure shows simulation of the mobile robot with four obstacles.

Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller

This paper explores a method that use WordNet concept to categorize text documents. The bag of words representation used for text representation is unsatisfactory as it ignores possible relations between terms. The proposed method extracts generic concepts from WordNet for all the terms in the text then combines them with the terms in different ways to form a new representative vector. The effects of this method are examined in several experiments using the multivariate chi-square to reduce the dimensionality, the cosine distance and two benchmark corpus the reuters-21578 newswire articles and the 20 newsgroups data for evaluation. The proposed method is especially effective in raising the macro-averaged F1 value, which increased to 0.714 for the Reuters from 0.649 and to 0.719 for the 20 newsgroups from 0.667.

Using WordNet for Text Categorization

This paper deals with automatic supervised classification of documents The approach suggested is based on a vector representation of the documents centred not on the words but on the n-grams of characters for varying n The effects of this method are examined in several experiments using the multivariate chi-square to reduce the dimensionality, the cosine and Kullback&Liebler distances, and two benchmark corpuses the reuters-21578 newswire articles and the 20 newsgroups data for evaluation The evaluation was done, by using the macroaveraged F1 function The results show the effectiveness of this approach compared to the Bag-OfWord and stem representations

Experimenting N-Grams in Text Categorization.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/spy2.7

An effective Bat algorithm for node localization in distributed wireless sensor network

By these days, most companies are more aware of than ever in providing quality of services over the web for reasons of economy, reliability, interoperability, flexibility, and universality. Enterprise application integration may rely on B2B scenarios where several candidate services with similar capabilities are provided by different service providers. The question is, how upon a request over a B2B integration scenario, the system chooses a service among several candidate services offering a capability satisfying its requests? This paper proposes a model that makes an automatic selection of best service provider that is based on mixed context and QoS ontology for a given set of parameters of QoS. We particularly show how this approach can be made to support an e-business framework and how it can add dynamics to B2B interactions by automating selection among heterogeneous services. We emphasize here on one dimensional quality of services. The approach is illustrated through a purchasing scenario to demonstrate consistency and effectiveness of the proposed method. Keyword: Selection of services, semantic Web service, quality of service, QoS Ontology, context Ontology, matching.

http://www.iajet.org/documents/vol.1/no.3/Web%20Services%20Selection%20Based%20on%20Context%20Ontology%20and%20Quality%20of%20Services.pdf

Web Services Selection Based on Context Ontology and Quality of Services

Fuzzy reasoning models are model-free estimators of control systems, which makes them very powerful tools in control applications. Their performances depend on several factors such as the completeness of the rule base, the subjective definitions of the fuzzy subset parameters or fuzzy partitioning, the fuzzy implication operator and the defuzzification method. In most cases these factors are decided by the subjective experience of the human expert. The authors attempt to improve the fuzzy systems performances by means of a genetic-based learning mechanism. They propose a new methodology based upon two genetic algorithm (GA) loops; the inner GA loop enables the generation of an optimal set of parameters of the fuzzy reasoning matrix based upon their selection by particular optimal fuzzy partitioning generated by an outer GA loop. Simulation of typical control problems shows the effectiveness and high performance of such a method. The effects of GA parameter settings on convergence and total performance are also discussed.

Abdellatif Rahmoun

Papers

Using WordNet for Text Categorization

Experimenting N-Grams in Text Categorization.

An effective Bat algorithm for node localization in distributed wireless sensor network

Web Services Selection Based on Context Ontology and Quality of Services

Genetic algorithm based methodology to generate automatically optimal fuzzy systems