<li id="yaiqy"></li> The purpose of this book is to present detailed fundamental information on a global positioning system (GPS) receiver. Although GPS receivers are popu- larly used in every-day life, their operation principles cannot be easily found in one book. Most other types of receivers process the input signals to obtain the necessary information easily, such as in amplitude modulation (AM) and frequency modulation (FM) radios. In a GPS receiver the signal is processed to obtain the required information, which in turn is used to calculate the user position. Therefore, at least two areas of discipline, receiver technology and navigation scheme, are employed in a GPS receiver. This book covers both areas.
標(biāo)簽： Fundamentals_of_Global_Positionin g_System_Receivers
上傳時(shí)間： 2020-06-09
上傳用戶：shancjb
I wrote this book so that students, hobbyists, and engineers alike can take advantage of the Arduino platform by creating several projects that will teach them about the engineering process. I also wanted to guide the reader through introductory projects so that they could get a firm grasp on the Arduino Language, and how to incorporate several pieces of hardware to make their own projects. This book offers so much information on the Arduino, not just the basic LED projects but it offers different peripherals such as Ultrasonic sensor, the Xbox? controller, Bluetooth, and much more. This book also teaches the non-engineer to follow a process that will help them in future project (not just Arduino projects).
標(biāo)簽： Engineering Practical Arduino
上傳時(shí)間： 2020-06-09
上傳用戶：shancjb
The present work, Advanced Process Engineering Control, is intended to be the continuation of the authors? Basic Process Engineering Control published by DeGruyter in 2014. It presents the main and conventional type control loops in process industries. Titles containing the concept of process engineering were deliberately chosen to suggest the inclusion, within the same approach, of processes other than the traditional ones. These come from outside the traditional fields of chemistry and petrochemistry: the sphere of pharmaceuticals, wastewater management, water puri- fication, water reserve management, construction material industry, food processing, household or automotive industries.
標(biāo)簽： Advanced_Process_Engineering_Cont rol
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
This book introduces students to the theory and practice of control systems engineer- ing. The text emphasizes the practical application of the subject to the analysis and design of feedback systems. The study of control systems engineering is essential for students pursuing degrees in electrical, mechanical, aerospace, biomedical, or chemical engineering. Control systems are found in a broad range of applications within these disciplines, from aircraft and spacecraft to robots and process control systems.
標(biāo)簽： Control Systems Engineering
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Control systems are becoming more important every day. At the beginning, the in- dustry used sequential controls for solving a lot of industrial applications in control systems, and then the linear systems gave us a huge increase in applying automatic linear control on industrial application. One of the most recent methods for control- ling industrial applications is intelligent control, which is based on human behavior or concerning natural process.
標(biāo)簽： Intelligent Control Systems LabVIEW with
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Modern information technologies and the advent of machines powered by artificial intelligence (AI) have already strongly influenced the world of work in the 21st century. Computers, algorithms and software simplify everyday tasks, and it is impossible to imagine how most of our life could be managed without them. However, is it also impossible to imagine how most process steps could be managed without human force? The information economy characterised by exponential growth replaces the mass production industry based on economy of scales
標(biāo)簽： AI-and-Robotics-IBA-GEI-April 2017
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.
標(biāo)簽： Auto-Machine-Learning-Methods-Sys tems-Challenges
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Design for manufacturability and statistical design encompass a number of activities and areas of study spanning the integrated circuit design and manufacturing worlds. In the early days of the planar integrated circuit, it was typical for a handful of practitioners working on a particular design to have a fairly complete understanding of the manufacturing process, the resulting semiconductor active and passive devices, as well as the resulting circuit - often composed of as few as tens of devices. With the success of semiconductor scaling, predicted and - to a certain extent even driven - by Moore’s law, and the vastly increased complexity of modern nano-meter scale processes and the billion-device circuits they allow, there came a necessary separation between the various disciplines.
標(biāo)簽： Manufacturability Statistical Design for and
上傳時(shí)間： 2020-06-10
上傳用戶：shancjb
Current field forecast verification measures are inadequate, primarily because they compress the comparison between two complex spatial field processes into one number. Discrete wavelet transforms (DWTs) applied to analysis and contemporaneous forecast fields prove to be an insightful approach to verification problems. DWTs allow both filtering and compact physically interpretable partitioning of fields. These techniques are used to reduce or eliminate noise in the verification process and develop multivariate measures of field forecasting performance that are shown to improve upon existing verification procedures.
標(biāo)簽： field forecast verification
上傳時(shí)間： 2020-07-22
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上傳時(shí)間： 2021-10-22
上傳用戶：
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