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.
Time Series Prediction
This demonstration tries to solve yet another task with Genetic Programming and Gene Expression Programming. For the given time series it tries to build an algebraic expression, which calculates next time series value from the given known past values. Once a good expression is found during training phase, the expression may be tried to predict future data points from the last known values.
Speech Recognition & Text to Speech
If you are interested in computer text to speech (TTS) and speech recognition (SR), this demonstration is for you, it will demonstrates the speech technologies for more than 26 different languages :
Multiple face detection and recognition in real time
The facial recognition has been a problem very worked around the world for many persons; this problem has emerged in multiple fields and sciences, especially in computer science, others fields that are very interested In this technology are: Mechatronic, Robotic, criminalistics, etc. In this demonstration the main goal is showing a face detector and recognizer in real time for multiple persons using Principal Component Analysis (PCA) with eigenface for implement it in multiple fields.
An example of EigenFaces:
Traffic Sign Detection
Traffic sign detection is a crucial component in an autonomous vehicle navigation system. For an automobile to navigate itself safely in an urban environment, it must be able to understand traffic signs
- It should be able to read the speed limit, such that it will not received tickets for speeding and paid a premium on its insurance
- It should be able to read traffic lights and stop on red
- It should be able to read stop sign and yield to other vehicles which are also crossing the same intersection.
This demonstration aims to solve a small part of a autonomous vehicle navigation system, which detect stop sign from images captured by camera.
Demonstrates performing image classification using the Bag of Visual Words (BoW) model with SURF features and the Binary Split algorithm.
The BoW model is used to transform the many SURF feature points in a image in a single, fixed-length feature vector. The feature vector is then used to train a Support Vector Machine (SVM) using a variety of kernels.