This the specification of the Enterprise JavaBeansTM architecture.The Enterprise JavaBeans
architecture is a component architecture for the development and deployment of componentbased
distributed business applications. Applications written using the Enterprise JavaBeans
architecture are scalable, transactional, and multi-user secure. These applications may be written
once, and then deployed on any server platform that supports the Enterprise JavaBeans
specification.
This a full 3-tier dababase application which includes a activex dll project(business objects) and a standard exe(UI). Besides all the database techniques it demonstrates, it also shows how to make MSHFlexgrid a editable grid(with combobox, checkbox, datetimepicker) and how to merge a toolbar for multi forms.
A collection of math routines including 8-bit, 16-bit, 32-bit signed and unsigned addition, subtraction, multiplication, and division. Very nice code library with heavy in-line documentation! Been looking for multi-byte divide? Here it is.
This zip describes an ISO7816 configuration to read the ATR. Includes main.html file for help. For use under Green Hills 3.6.1 Multi?2000 Software Tool.
This zip describes SPI communication with a Serial DataFlash AT45DB and/or with a DataFlashCard AT45DCB. It shows how to configure the SPI peripheral on the AT91RM9200EK. Includes main.html file for help. For use under Green Hills 3.6.1 Multi?2000 Software Tool
This zip describes an AT91 USART with PDC Transmission and Reception chain.Includes main.html file for help. For use under Green Hills 3.6.1 Multi?2000 Software Tool.
annie is an ANN, ie, Artificial Neural Network library developed in C++. It can be used to implement various kinds of neural networks like Multi-Layer Perceptron, Radial basis function networks, Hopfield networks etc.
Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks while the third can be generalized to multi-layer perceptrons.
Many applications use connection/object pool. A program may require a IMAP connection pool and LDAP connection pool. One could easily implement a IMAP connection pool, then take the existing code and implement a LDAP connection pool. The program grows, and now there is a need for a pool of threads. So just take the IMAP connection pool and convert that to a pool of threads (Copy, paste, find, replace????). Need to make some changes to the pool implementation? Not a very easy task, since the code has been duplicated in many places. Re-inventing source code is not an intelligent approach in an object oriented environment which encourages re-usability. It seems to make more sense to implement a pool that can contain any arbitrary type rather than duplicating code. How does one do that? The answer is to use type parameterization, more commonly referred to as templates.