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This textbook covers both fundamental and advanced Java database programming techniques for beginning and experienced students as well as programmers (courses related to database programming in Java with Apache NetBeans IDE 12 environment). A sample SQL Server 2019 Express database, CSE_DEPT, is created and implemented in all example projects throughout this textbook. Over 40 real sample database programming projects are covered in this textbook with detailed illustrations and explanations to help students understand the key techniques and programming technologies. Chapters include homework and selected solutions to strengthen and improve students' learning and understanding for topics they study in the classroom. Both Java desktop and Web applications with SQL Server database programming techniques are discussed and analyzed. Some updated Java techniques, such as Java Server Pages (JSP), Java Server Faces (JSF), Java Web Service (JWS), JavaServer Pages Standard Tag Library (JSTL), JavaBeans and Java API for XML Web Services (JAX-WS) are also discussed and implemented in the real projects developed in this textbook. This textbook targets mainly advanced-level students in computer science, but it also targets entry-level students in computer science and information system. Programmers, software engineers and researchers will also find this textbook useful as a reference for their projects.

Programming --- Computer architecture. Operating systems --- Information systems --- Computer. Automation --- computers --- programmeren (informatica) --- database management --- software engineering --- Databases. --- Java (Computer program language) --- SQL server. --- JavaScript (Llenguatge de programació) --- Bases de dades

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The ability of fuzzy systems to provide shades of gray between "on or off" and "yes or no" is ideally suited to many of today’s complex industrial control systems. The static fuzzy systems usually discussed in this context fail to take account of inputs outside a pre-set range and their off-line nature makes tuning complicated. Advanced Fuzzy Logic Technologies in Industrial Applications addresses the problem by introducing a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs. The tuning process is a major focus in this volume because it is the most difficult stage in fuzzy control application. Using new methods such as µ-law technique, histogram equalization and the Bezier-based method, all detailed here, the tuning process can be significantly simplified and control performance improved. The other great strength of this book lies in the range and contemporaneity of its applications and examples which include: laser tracking and control; robot calibration; image processing and pattern recognition; medical engineering; audio systems; autonomous underwater vehicles and data mining. Advanced Fuzzy Logic Technologies in Industrial Applications is written to be easily understood by readers not having specialized knowledge of fuzzy logic and intelligent control. Design and application engineers and project managers working in control, as well as researchers and graduate students in the discipline will find much to interest them in this work. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Automatic control -- Mathematics. --- Fuzzy logic. --- Fuzzy systems. --- Intelligent control systems. --- Automatic control --- Fuzzy logic --- Intelligent control systems --- Fuzzy systems --- Mechanical Engineering --- Engineering & Applied Sciences --- Mechanical Engineering - General --- Mathematics --- Mathematics. --- Information Technology --- Artificial Intelligence --- Systems, Fuzzy --- Intelligent control --- Intelligent controllers --- Nonlinear logic --- Control engineering --- Control equipment --- Engineering. --- Artificial intelligence. --- Pattern recognition. --- Automotive engineering. --- Control engineering. --- Robotics. --- Mechatronics. --- Biomedical engineering. --- Control. --- Artificial Intelligence (incl. Robotics). --- Control, Robotics, Mechatronics. --- Pattern Recognition. --- Automotive Engineering. --- Biomedical Engineering. --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- Mechanical engineering --- Microelectronics --- Microelectromechanical systems --- Automation --- Machine theory --- Control theory --- Engineering instruments --- Programmable controllers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- System analysis --- Fuzzy mathematics --- Logic, Symbolic and mathematical --- Optical pattern recognition. --- Control and Systems Theory. --- Artificial Intelligence. --- Biomedical Engineering and Bioengineering. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination