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Varkonyi-Koczy A.R.,O buda University Nepszinhaz | Varkonyi-Koczy A.R.,Integrated Intelligent Systems Japanese Hungarian Laboratory
Journal of Advanced Computational Intelligence and Intelligent Informatics | Year: 2010

The never unseen information explosion in data transmission and communication called for new methods in signal coding and reconstruction. To minimize the channel capacity needed for the transmission urged researchers to find techniques which are flexible and can adapt to the available space and time. Anytime techniques are good candidates for such purposes. If the signal/data to be transmitted can be characterized as sequence of stationary intervals overcomplete signal representations can be applied. These techniques can be operated in an anytime manner as well, i.e., are excellent tools for handling the capacity problems. This paper introduces the concept of anytime recursive overcomplete signal representations using different recursive signal processing algorithms. The novelty of the approach is that an on-going set of signal transformations together with appropriate (e.g., L 1 norm) minimization procedures can provide optimal and flexible anytime on-going representations, ongoing signal segmentations into stationary intervals, and on-going feature extractions for immediate utilization in data transmission, communication, diagnostics, or other applications. The proposed technique may be advantageous if the transmission channel is overloaded and in case of processing non-stationary signals when complete signal representations can be used only with serious limitations because of their relative weakness in adaptive matching of signal structures. Source


Varkonyi-Koczy A.R.,Obuda University | Tusor B.,Integrated Intelligent Systems Japanese Hungarian Laboratory
SAMI 2010 - 8th International Symposium on Applied Machine Intelligence and Informatics, Proceedings | Year: 2010

Recently, the usage of smart environments has become popular, in order to make everyday living more comfortable and to improve the quality of live of humans. In this paper, a hand posture and gesture modeling and recognition system is introduced, which can be used as an interface to make possible communication with the Intelligent Space by simple hand gestures. The system transforms preprocessed data of the detected hand into a feature model by using fuzzy neural networks and based on this model determines the actual hand posture applying fuzzy inference. Finally, from the sequence of detected hand postures, the system can recognize the hand gesture of the user. ©2010 IEEE. Source


Varkonyi-Koczy A.R.,Obuda University | Tusor B.,Integrated Intelligent Systems Japanese Hungarian Laboratory
WISP 2011 - IEEE International Symposium on Intelligent Signal Processing, Proceedings | Year: 2011

Recently, Artificial Neural Networks (ANNs) have become popular because they can learn complex mappings from the input/output data and are relatively easy to implement in any application. Although, a disadvantageous aspect of their usage is that they need (usually a significant amount of) time to be trained, which scales with the structural parameters of the networks and with the quantity of the input data. However, the training can be done offline; it has a non-negligible cost and further, can cause a delay in the operation. Fuzzy Neural Networks (FNNs) are the combinations of ANNs and fuzzy logic in order to incorporate the advantages of both methods (the learning ability of ANNs and the thinking ability of fuzzy logic). FNNs have fuzzy values in their weight parameters and in the output of each neuron. Circular Fuzzy Neural Networks (CFNNs) are FNNs with their topology realigned to a circular topology and the connections between the input layer and hidden layer trimmed. This may result in a dramatic reduction in the training time, while the precision and accuracy of the network are not affected. To further increase the speed of the training of the ANNs, FNNs, or CFNNs used for classification, a new training procedure is introduced in this paper: instead of directly using the training data in the training phase, the data is first clustered and the neural networks are trained by using only the centers of the obtained clusters. © 2011 IEEE. Source


Tusor B.,Integrated Intelligent Systems Japanese Hungarian Laboratory | Varkonyi-Koczy A.R.,Obuda University
2010 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2010 - Proceedings | Year: 2010

Recently, the usage of smart environments has become popular, in order to make everyday living more comfortable and to improve the quality of live of humans. In this paper, a hand posture and gesture modeling and recognition system is introduced, which can be used as an interface to make possible communication with the Intelligent Space by simple hand gestures. The system transforms preprocessed data of the detected hand into a fuzzy hand posture feature model by using fuzzy neural networks and based on this model determines the actual hand posture applying fuzzy inference. Finally, from the sequence of detected hand postures, the system can recognize the hand gesture of the user. © 2010 IEEE. Source


Tusor B.,Integrated Intelligent Systems Japanese Hungarian Laboratory | Varkonyi-Koczy A.R.,Obuda University
12th IEEE International Symposium on Computational Intelligence and Informatics, CINTI 2011 - Proceedings | Year: 2011

Recently, with the spreading of machine intelligence, "smart environments" are becoming popular tools for humans to gather information, get assistance, form the environment, etc. Intelligent Space (or iSpace) based systems are good examples for such environments: they strive to create an intelligent, comfortable environment for higher quality, natural, and easy to follow lifestyle. The goal of this paper is to present a research that focuses on developing an Intelligent Space application that is able to comprehend and execute the detected and pre-processed commands given by human users. The presented solution is also able to learn commands that are given periodically under specific conditions and execute them if the conditions occur. © 2011 IEEE. Source

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