Hand Movement Classification Using Burg Reflection Coefficients
نویسندگان
چکیده
منابع مشابه
Adaptive Classification of Hand Movement
The Hand Sign Classification (HSC) system classifies hand movement data into Australian Sign Language (AUSLAN) signs. It is built as a fuzzy expert system with an adaptive engine that trains the system to handle variations in the movement data, or to adapt to differences amongst signers. Adaptive fuzzy systems are often compared with neural networks in their adaptability, but unlike neural netw...
متن کاملHand Movement Classification Using An Adaptive Fuzzy Expert System
Hand sign recognition, in general, may be divided into two stages: the motion sensing, which extracts useful movement data from the signer's motion; and the classification process, which classifies the movement data as a sign. We have developed a prototype of the Hand Sign Classification (HSC) system that classifies a series of the full degrees-of-freedom kinematic data of a hand into sign lang...
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Media forensics tries to determine the originating device of a signal. We apply this paradigm to microphone forensics, determining the microphone model used to record a given audio sample. Our approach is to extract a Fourier coefficient histogram of near-silence segments of the recording as the feature vector and to use machine learning techniques for the classification. Our test goals are to ...
متن کاملGeneralized Reflection Coefficients
I consider general reflection coefficients for arbitrary one-dimensional whole line differential or difference operators of order 2. These reflection coefficients are semicontinuous functions of the operator: their absolute value can only go down when limits are taken. This implies a corresponding semicontinuity result for the absolutely continuous spectrum, which applies to a very large class ...
متن کاملMulti-class EEG classification of voluntary hand movement directions.
OBJECTIVE Studies have shown that low frequency components of brain recordings provide information on voluntary hand movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. APPROACH This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary h...
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ژورنال
عنوان ژورنال: Sensors
سال: 2019
ISSN: 1424-8220
DOI: 10.3390/s19030475