A creative intelligent object classification system using Google's™ images import search function

Published ArticleLimits of artificial intelligent, expert systems are defined by the specific hardware limitation of the specific system. Limits can be overcome, or addressed, by giving an intelligent system web access; therefore giving it access to Google's™ vast hardware, search functions and databases. Reverse image searches can be done directly in Google's™ image search bar since October 2011. This reverse image search function is used by the proposed system to do object recognition. Computational creativity, or the ability of a program or computer to show human-level creativity and interaction, is achieved by means of a voice communication of the object identification result to the user. The proposed system interprets the result by doing a definition web search and communicating this to the user. The results show that with the novel interpretation software, it should be possible to use Google™ as an artificial intelligent, computational creative system. This proposed system thus has the ability to do object classification by accessing Google's™ vast hardware, search functions and databases, thereafter would the proposed system search a suitable definition for the classification. All of this information is communicated to the user using voice. These techniques could be used on an automatic guided vehicle, robots or expert system

External Thermal Camera Exposure Control Workaround for Low-Cost Security Thermal Cameras

Published ArticleThermal cameras have an advantage in feature extraction, namely the ability to show only objects with/according to temperature; for example, a person with a higher temperature than the immediate surroundings could be extracted from the background with simple machine vision techniques. The problem with industrial thermal cameras for security use is that they are very expensive. A solution would be to obtain less expensive security thermal cameras. However, a problem inherent to these cameras is their automatic self-calibration system. This is problematic in fixed constant comparator programs. This paper proposes a calibration system to regulate the optimal exposure parameter for the analysis of a single, possibly under- or over-exposed image. This calibration system provides an optimal exposure reference for the camera, based on image clarity and reduced image noise. The phenomenon of image noise is caused by under- or over-exposed images. To estimate the exposure quality in the presence of saturated and unsaturated pixels, a temperature-controlled surface is introduced into the camera's field of view. The camera's calibrating point is aimed at this surface; therefore, this is a reference temperature for the camera. Experimental results are presented comparing different reference temperatures to target visibility. The experiment was conducted in a controlled environment

Google search by image : a system evaluation of adjusted images for the detection of visual plagiarism

Published ArticleThis paper investigates the precision of Google™'s Search by Image (SBI) system which lecturers can use to establish a workflow that will combat visual plagiarism in photography programmes. Currently no efficacious visual plagiarism detection method exists for implementation by photography lecturers. Content-based image retrieval systems like Google™ SBI have not yet been tested systemically for the detection of visual plagiarism. Using the Precision method to calculate the accuracy of the system, 300 images were randomly sampled through Google™ Images and altered with different adjustments. The images were uploaded to Google™ SBI and the results indicated a system of high quality

Using neural networks modelling as motivation for alternative assessment practices in higher engineering education

Published ArticleThe human brain has about 100 billion neurons. These neural networks can be simulated in the science of artificial intelligence. Thus are these mathematical models in artificial intelligence based on their biological neural network counterpart. One can use these mathematical models to model learning. Neural networks are based on collections of nodes or neurons that are connected in a tree pattern to allow communication between them. A single node is a simple processor but a multilayered network with supervised training is capable of complex tasks. Learning can be divided into surface or deep learning. Surface learning is a low energy, low cognitive approach. Deep learning are recognized by, leaner's accepting personal responsibility, enjoying the experience of learning and the ability to identify where to apply learning in industry or future work. It is thus beneficial if the neural networks are stimulated to a deep, constructive learning approach. Assessment can be a good method to shape learning. This article argues that by shifting to an alternative assessment approach one can shift a learner's neural networks from surface learning to deep constructive learning

A technique for tracking an indoor unmanned aerial or automated guided vehicle using a stationary camera and hue colour characteristics

Published ArticleToday's industries are based on an automated workplace. These automated workplaces are efficient, reconfigurable and intelligent automated environments. They are filled with technology, robotics, Automated Guided Vehicle (AGV) and, or Unmanned Aerial Vehicles (UAV) etc. For full automation will one need to effectively track an object, unmanned aerial vehicle (UAV) or automated guided vehicle (AGV). Effective tracking of vehicles can be used for control. This could result in less hardware on the craft that leads to a longer battery life, a bigger pay load or more processing power. This system track by using a stationary colour camera placed at an optimal placing in the automated workplace. The vehicle or objects are painted in two colours (colour A and colour B) that are not present in the automated workplace. The images from the camera are hue colour filtered to extract only the object or vehicle. The area, placement in frame and relationship between colour A and B are used for position and determine the orientation of AGV, UAV or object

Fibonacci numbers and the golden rule applied in neural networks

Published ArticleIn the 13th century an Italian mathematician Fibonacci, also known as Leonardo da Pisa, identified a sequence of numbers that seemed to be repeating and be residing in nature (http://en.wikipedia.org/wiki/Fibonacci) (Kalman, D. et al. 2003: 167). Later a golden ratio was encountered in nature, art and music. This ratio can be seen in the distances in simple geometric figures. It is linked to the Fibonacci numbers by dividing a bigger Fibonacci value by the one just smaller of it. This ratio seems to be settling down to a particular value of 1.618 (http://en.wikipedia.org/wiki/Fibonacci) (He, C. et al. 2002:533) (Cooper, C et al 2002:115) (Kalman, D. et al. 2003: 167) (Sendegeya, A. et al. 2007). Artificial Intelligence or neural networks is the science and engineering of using computers to understand human intelligence (Callan R. 2003:2) but humans and most things in nature abide to Fibonacci numbers and the golden ratio. Since Neural Networks uses the same algorithms as the human brain does, the aim is to experimentally proof that using Fibonacci numbers as weights, and the golden rule as a learning rate, that this might improve learning curve performance. If the performance is improved it should prove that the algorithm for neural network's do represent its nature counterpart. Two identical Neural Networks was coded in LabVIEW with the only difference being that one had random weights and the other (the adapted one) Fibonacci weights. The results were that the Fibonacci neural network had a steeper learning curve. This improved performance with the neural algorithm, under these conditions, suggests that this formula is a true representation of its natural counterpart or visa versa that if the formula is the simulation of its natural counterpart, then the weights in nature is Fibonacci values

Facilitation of a diverse higher education student community from conventional to alternative assessment practices

Published ArticleThe higher education classroom of today is filled with students of vast diversity which applies to culture, sex and nationality. This can be seen in the fact that some students only study for the sake of a higher education or the availability of a bursary. These factors influence the persuasion and commitment towards more surface learning. It is stated that the principle of assessment is not only a tool to indicate achievement or outcomes met, but a good assessment method can also shape learning. By shifting to an alternative assessment method one can shift a learner's persuasion and commitment from surface learning to deep, constructive learning