- Investigation of Chaotic-Type Features In Hyperspectral Satellite Data
Osman Kocal and Mufit Cetin , Yalova University, Turkey.
Hyperspectral images provide detailed spectral information with more than several hundred channels. On the other hand, the high dimensionality in hyperspectral images also causes to classification problems due to the huge ratio between the number of training samples and the features. In this paper, Lyapunov Exponents (LEs) are used to determine chaotic-type structure of EO1 Hyperion hyperspectral image, a mixed forest site in Turkey. Experimental results demonstrate that EO-1 Hyperion image has a chaotic structure by checking distribution of Lyapunov Exponents (LEs) and they can be used as discriminative features to improve classification accuracy for hyperspectral images.
- A noval approach for detecting emotion in text
Satyendra Singh Jadon and Sudhanshu prakash tiwari,India
In this paper, we present a experiment, which concerned with detection of emotion class at sentence level. Approach is based upon combination of both machine leaning and key word based approach. There is a large annotated data set which manually classified a sentence beyond six basic emotions: love, joy, anger, sadness, fear, surprise. Using annotated data set define an emotion vector of key word in input sentence. Using an algorithm calculate the emotion vector of sentence by emotion vector of word. Then on the basis of emotion vector categorized the sentence into appropriate emotion class.
- Bandwidth-Performance Tradeoffs for a Transmission with Concurrent Signals
Aminata A. Garba ,Carnegie Mellon University,USA
We consider a bandwidth-efficient transmission scheme, where two signals are sent concurrently. The BER and the achievable minimum distances for the signals’ constellations at the receivers are derived as functions of the signals’ energies and their input probability distributions. Finally, trade-offs between bandwidth, signals’ energies and achievable performances are discussed.