Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals
Journal of Medical Systems
Max-plus algebra-based wavelet transforms and their FPGA implementation for image coding
Information Sciences: an International Journal
A modified support vector machine and its application to image segmentation
Image and Vision Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Information Sciences: an International Journal
Information Sciences: an International Journal
Multi-sensor data fusion using support vector machine for motor fault detection
Information Sciences: an International Journal
Discrete fractional wavelet transform and its application to multiple encryption
Information Sciences: an International Journal
Information Sciences: an International Journal
A support vector machine-based context-ranking model for question answering
Information Sciences: an International Journal
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This paper investigates two new multisensor data fusion algorithms for object detection in monitoring of industrial processes. The goals were to reduce the rate of false detection and obtain reliable decisions on the presence of target objects. The monitoring system uses acceleration sensors and is used as a sensor-cluster. In principle the approach can include arbitrary data acquisition techniques. Two approaches were proposed. The first uses a short-time Fourier transform (STFT) as a prefilter to extract relevant features from the acceleration signals. The features extracted from different sensor channels are first classified using support vector machine (SVM)-based filters. A novel decision fusion process to combine individual decisions was developed. The second approach uses a continuous wavelet transform (CWT) as a prefilter to extract relevant features from the acceleration signals. The features extracted from different sensor signals are subjected to further prefiltering processes before SVM-based classification. The individual decision functions are then combined in a decision fusion module. The classification system was trained and validated using real industrial data. The two approaches were tested using the same data and their performance and modeling complexity are compared. The developed approaches show strong improvements in detection and false alarm rates.