A Module-based Wireless Node (MW-Node) is a Node with wireless and mobile capabilities added by means of modules. It is not a new node object derived from Node. Rather it is a new layout of mostly existing components. Rationale for this new design has been presented in [1]. The MW-Node provides a flexible support for wireless and mobile networking and in particular:
support for multiple interfaces/multiple channels, and
a common basis for the implementation of wireless routing protocols.
Whenever there is an application to write, breaking it up to small pieces is a great idea. The smallest piece of any application would probably be a data object, or a simple Bean in Java. Java Beans are simple Java classes that have a distinctive look and behavior. Their simplest purpose is to contain data and they accomplish this by having properties. Each property is actually a data-member containing/referencing the actual data and a couple of methods, a getter method and a setter method. Thus, the simplest data-bean describing a person could be expressed as such:
BPMLL is a package for training multi-label BP neural networks. The package includes the MATLAB code of the algorithm BP-MLL, which is designed to deal with multi-label learning. It is in particular useful when a real-world object is associated with multiple labels simultaneously
-- Simple Robot Control Program
--------------------------------------------------------------------------
-- Left is left IR sensor - 1=object to left
-- Right is rigth IR sensor - 1=object to right
-- Lmotor_dir 1=forward 0=reverse
-- Rmotor_dir 1=forward 0=reverse
-- Lmotor_speed 111=fast 000=slow
-- Rmotor_speed 111=fast 000=slow
A UML Documentation for an Elevator System：This paper is a PhD project report for the course Distributed Embedded Systems at Carnegie Mellon University. Throughout this course, a distributed real-time system – an elevator control system– is specified, designed, built, and simulated. Object Oriented Analysis and Design methods, in specific the Unified Modeling Language (UML) are used when designing the system.
We describe and demonstrate an algorithm that takes as input an
unorganized set of points fx1 xng IR3 on or near an unknown
manifold M, and produces as output a simplicial surface that
approximates M. Neither the topology, the presence of boundaries,
nor the geometry of M are assumed to be known in advance — all
are inferred automatically from the data. This problem naturally
arises in a variety of practical situations such as range scanning
an object from multiple view points, recovery of biological shapes
from two-dimensional slices, and interactive surface sketching.
Range imaging offers an inexpensive and accurate means for
digitizing the shape of three-dimensional objects. Because most
objects self occlude, no single range image suffices to describe the
entire object. We present a method for combining a collection of
range images into a single polygonal mesh that completely describes
an object to the extent that it is visible from the outside.
