A major goal of this book is to show to make devices that are inherently reliable by design. While a lot of attention has been given to “quality improvement,” the majority of the emphasis has been placed on the processes that occur after the design of a product is complete. Design deficiencies are a significant problem, and can be exceedingly difficult to identify in the field. These types of quality problems can be addressed in the design phase with relatively little effort, and with far less expense than will be incurred later in the process. Unfortunately, there are many hardware designers and organizations that, for various reasons, do not understand the significance and expense of an unreliable design. The design methodology presented in this text is INtended to address this problem.
Abstract—Wireless networks in combination with image
sensors open up a multitude of previously unthinkable sensing
applications. Capable tools and testbeds for these wireless image
sensor networks can greatly accelerate development of complex,
yet efficient algorithms that meet application requirements. In this
paper, we introduce WiSNAP, a Matlab-based application
development platform INtended for wireless image sensor
networks. It allows researchers and developers of such networks
to investigate, design, and evaluate algorithms and applications
using real target hardware. WiSNAP offers standardized and
easy-to-use Application Program Interfaces (APIs) to control
image sensors and wireless motes, which do not require detailed
knowledge of the target hardware. Nonetheless, its open system
architecture enables support of virtually any kind of sensor or
wireless mote. Application examples are presented to illustrate the
usage of WiSNAP as a powerful development tool.
This application report presents basic code for initializing and operating the TMS320LF240x DSP devices. Two functionally equivalent example progra ms are presented: one written in assembly language and the other in C language. Detailed discussions of each program are provided that explain numerous compiler and assembler directives, code requirements, and hardware-related requirements. The programs are ready to run on either the TMS320LF2407 Evaluation Module (EVM) or the eZdsp LF2407 development kit. However, they are also INtended for use as a code template for any TMS320LF240x (LF240x) or TMS320LF240xA (LF240xA) DSP target system.
Testability is the concern most often voiced by Texas Instruments (TIä )
application specific integrated circuit (ASIC) users. This document is INtended
to consolidate TI policies into a coherent approach to designing for testability.
It is not INtended as a specification, but as a guide you can use for developing
test strategies when designs are being initiated
C 開發的有限元軟件，界面還可以，不錯，可以試試。 FElt is a free system for introductory level finite element analysis. It is
primarily INtended as a teaching tool for introductory type courses in finite
elements - probably in the mechanical/structural/civil fields. In a command
line environment, FElt uses an intuitive, straightforward input syntax to
describe problems. It also includes a graphical user interface for
workstations that allows the user to set-up, solve and post-process the
problem in a single CAD-like environment.
We propose a technique that allows a person to design a new photograph
with substantially less effort. This paper presents a method that generates a composite image when a user types
in nouns, such as “boat” and “sand.” The artist can optionally design an INtended image by specifying other
constraints. Our algorithm formulates the constraints as queries to search an automatically annotated image
database. The desired photograph, not a collage, is then synthesized using graph-cut optimization, optionally
allowing for further user interaction to edit or choose among alternative generated photos. An implementation of
our approach, shown in the associated video, demonstrates our contributions of (1) a method for creating specific
images with minimal human effort, and (2) a combined algorithm for automatically building an image library with
semantic annotations from any photo collection.
Boost provides free peer-reviewed portable C++ source libraries.
We emphasize libraries that work well with the C++ Standard Library. Boost libraries are INtended to be widely useful, and usable across a broad spectrum of applications. The Boost license encourages both commercial and non-commercial use.
We aim to establish "existing practice" and provide reference implementations so that Boost libraries are suitable for eventual standardization. Ten Boost libraries are already included in the C++ Standards Committee s Library Technical Report (TR1) as a step toward becoming part of a future C++ Standard. More Boost libraries are proposed for the upcoming TR2.
Boost works on almost any modern operating system, including UNIX and Windows variants. Follow the Getting Started Guide to download and install Boost. Popular Linux and Unix distributions such as Fedora, Debian, and NetBSD include pre-built Boost packages. Boost may also already be available on your organization s internal web server.
The System Management BIOS Reference Specification addresses how motherboard and system vendors present
management information about their products in a standard format by extending the BIOS interface on Intel
architecture systems. The information is INtended to allow generic instrumentation to deliver this data to
management applications that use CIM (the WBEM data model) or direct access and eliminates the need for error
prone operations like probing system hardware for presence detection.
The Linux GPIB Package is a support package for GPIB (IEEE 488) hardware. The package contains kernel driver modules, and a C user-space library with Guile, Perl, PHP, Python and TCL bindings. The API of the C library is INtended to be compatible with National Instrument s GPIB library. The Linux GPIB Package is licensed under the GNU General Public License .
Requirements:
Linux kernel version 2.4.x (use Linux-GPIB version 3.1.x). Earlier kernel versions are not supported.
* Lightweight backpropagation neural network.
* This a lightweight library implementating a neural network for use
* in C and C++ programs. It is INtended for use in applications that
* just happen to need a simply neural network and do not want to use
* needlessly complex neural network libraries. It features multilayer
* feedforward perceptron neural networks, sigmoidal activation function
* with bias, backpropagation training with settable learning rate and
* momentum, and backpropagation training in batches.
