In this research, we have designed, developed implemented a wireless sensor
networks based smart home for safe, sound and secured living environment for
any inhabitant especially elderly living alone. We have explored a methodology
for the development of efficient electronic real time data PROCESSing system to
recognize the behaviour of an elderly person. The ability to determine the
wellness of an elderly person living alone in their own home using a robust,
flexible and data driven artificially intelligent system has been investigated. A
framework integrating temporal and spatial contextual information for
determining the wellness of an elderly person has been modelled. A novel
behaviour detection PROCESS based on the observed sensor data in performing
essential daily activities has been designed and developed.
The continuous progress in modern power device technology is increasingly
supported by power-specific modeling methodologies and dedicated simulation
tools. These enable the detailed analysis of operational principles on the the device
and on the system level; in particular, they allow the designer to perform trade-
off studies by investigating the operation of competing design variants in a very
early stage of the development PROCESS. Furthermore, using predictive computer
simulation makes it possible to analyze the device and system behavior not only
under regularoperatingconditions, but also at the rim of the safe-operatingarea and
beyond of it, where destructive PROCESSes occur that limit the lifetime of a power
system.
It was the publisher’s idea that I write
RFID in the Supply Chain: A Guide
to Selection and Implementation
. Not only am I editor of
Enterprise Inte-
gration System
,
Second Edition Handbook
and author of
The
Complete Book
of Middleware
, I also had some innovative business PROCESS and project
management ideas on improving the effectiveness of integrating enterprise
systems with information on product traceability, the scope of which has
been widened by the RFID technology mandates.
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.
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).
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.
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.
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.
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
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.