Network load balancing using Ant Colony Optimisation
Ants first evolved around 120 million years ago, took form in over 11,400 different species, and are considered one of the most successful insects due to their highly organised colonies, sometimes consisting of millions of ants.
One particular notability of ants is their ability to create "ant streets". Long, bi-directional lanes of single file pathways in which they navigate landscapes in order to reach a destination in optimal time. These ever-changing networks are made possible by the use of pheromones which guide them using a shortest path mechanism. This technique allows an adaptive routing system which is updated should a more optimal path be found or an obstruction placed across an existing pathway.
Computer scientists began researching the behaviour of ants in the early 1990's to discover new routing algorithms. The result of these studies is Ant Colony Optimisation (ACO), and in the case of well implemented ACO techniques, optimal performance is comparative to existing top-performing routing algorithms.
This demonstration shows how ACO can be used to dynamically route traffic efficiently. An efficient routing algorithm will minimise the number of nodes that a call will need to connect to in order to be completed thus; minimising network load and increasing reliability.
Human Emotion Detection from Image
This Demonstration can detect human emotion from image. First, it takes an image, then by skin color segmentation, it detects human skin color, then it detect human face. Then it separates the eyes & lip from the face. Then it draws bezier curve for eyes & lips. Then it compares the bezier curve of eyes and lips to the bezier curves of eyes & lips that are stored in the data base. Then it finds the nearest bezier curse from the data base & gives that data base stored bezier curve emotion as this image emotion.
If the person’s emotion information is not available in the database, then the program calculates the average height for each emotion in the database for all people and then get a decision according to the average height.
The Voice Command Demo demonstrates a simple speech recognition by showing you the commands it recognizes.
The Voice Command interface is the high-level interface for speech recognition. It is designed to provide command and control speech recognition for applications. With this interface, a user gives the computer simple commands, such as "Open the file", and can answer simple yes/no questions. Command and Control does not allow speech dictation.
Artificial Neural Networks are a recent development tool that are modeled from biological neural networks. The powerful side of this new tool is its ability to solve problems that are very hard to be solved by traditional computing methods (e.g. by algorithms).
This sample application demonstrates and simulates Artificial Life. The app implements three main technologies which are used in the Gaming industry and Robotics for coding intelligent agents. These are:
- Neural Networks
- Genetic Algorithms
- Steering Behaviours
This demonstration will implement each of these, and demonstrate their usefulness in creating Intelligent Agents.
The algorithms demonstrated are the core of almost every game.
Natural Language Processing Tools
This application is a collection of natural language processing tools.
Currently it Demonstrates the following NLP tools:
- a sentence splitter
- a tokenizer
- a part-of-speech tagger
- a chunker (used to "find non-recursive syntactic annotations such as noun phrase chunks")
- a parser
- a name finder
- a coreference tool
- an interface to the WordNet lexical database
This application shows the generation of parse trees for English language sentences, as well as explores some of the other features of the Natural Language Processing.
Detect a written text's language
The language detection of a written text is probably one of the most basic tasks in natural language processing (NLP). For any language depending processing of an unknown text, the first thing to know is which language the text is written in.
The Wavelet sample application shows how to use the Wavelet transform filter to process images using wavelet transforms such as the Haar and CDF9/7.