Waldir S. S. Junior

Electromyographic signal compression based on preprocessing techniques

Recently, electromyographic records have been rearranged into two-dimensional arrays and encoded with image compressors, in the same way as image data. However, as a consequence of this reshaping, the correlation among signal segments is generally lost, which reduces the compression efficiency. In the present work, new preprocessing techniques for encoding electromyographic signals as two-dimensional matrices are presented, namely percentage difference sorting and relative complexity sorting, which have the potential to favor the exploitation of the intersegment dependencies. The experiments were carried out with real isometric records acquired in laboratory, that were first preprocessed and then compressed with a JPEG2000 encoder, showing that the proposed framework is effective and outperforms even state-of-the-art schemes present in the literature, in terms of PRD × Compression Ratio.

Weak Classifier for Density Estimation in Eye Localization and Tracking

In this paper, we propose a fast weak classifier that can detect and track eyes in video sequences. The approach relies on a least-squares detector based on the inner product detector (IPD) that can stimate a probability density distribution for a feature’s location–which fits naturally with a Bayesian estimation cycle, such as a Kalman or particle filter. As a least-squares sliding window detector, it possesses tolerance to small variations in the desired pattern while maintaining good generalization capabilities and computational efficiency. We propose two approaches to integrating the IPD with a particle filter tracker. We use the BioID, FERET, LFPW, and COFW public datasets as well as five manually annotated high-definition video sequences to quantitatively evaluate the algorithms’ performance. The video data set contains four subjects, different types of backgrounds, blurring due to fast motion, and occlusions. All code and data are available.

SEMG signal compression based on two-dimensional techniques

BackgroundRecently, two-dimensional techniques have been successfully employed for compressing surface electromyographic (SEMG) records as images, through the use of image and video encoders. Such schemes usually provide specific compressors, which are tuned for SEMG data, or employ preprocessing techniques, before the two-dimensional encoding procedure, in order to provide a suitable data organization, whose correlations can be better exploited by off-the-shelf encoders. Besides preprocessing input matrices, one may also depart from those approaches and employ an adaptive framework, which is able to directly tackle SEMG signals reassembled as images.MethodsThis paper proposes a new two-dimensional approach for SEMG signal compression, which is based on a recurrent pattern matching algorithm called multidimensional multiscale parser (MMP). The mentioned encoder was modified, in order to efficiently work with SEMG signals and exploit their inherent redundancies. Moreover, a new preprocessing technique, named as segmentation by similarity (SbS), which has the potential to enhance the exploitation of intra- and intersegment correlations, is introduced, the percentage difference sorting (PDS) algorithm is employed, with different image compressors, and results with the high efficiency video coding (HEVC), H.264/AVC, and JPEG2000 encoders are presented.ResultsExperiments were carried out with real isometric and dynamic records, acquired in laboratory. Dynamic signals compressed with H.264/AVC and HEVC, when combined with preprocessing techniques, resulted in good percent root-mean-square difference

Parameter selection for SVM in automatic modulation classification of analog and digital signals

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Electromyographic Signal Compression Through Image Encoders and Preprocessing Techniques

× compression factor figures, for low and high compression factors, respectively. Besides, regarding isometric signals, the modified two-dimensional MMP algorithm outperformed state-of-the-art schemes, for low compression factors, the combination between SbS and HEVC proved to be competitive, for high compression factors, and JPEG2000, combined with PDS, provided good performance allied to low computational complexity, all in terms of percent root-mean-square difference

A Distributed Extension of the Hybrid PRS System using Video Processing to Command a Robot via Bluetooth

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