High-order statistics (HOS) are well suited for describing non-Gaussian random processes. These techniques are increasingly being employed in myoelectric research, on both time and frequency domain techniques. This work presents HOS-based techniques using only HOS time domain features to classify myoelectric signals. The auto-, cross- and full- (joint) third-order cumulants are evaluated as EMG-signal feature vectors to be compared between them. Four surface EMG signals were processed for classify motions from the upper limbs. Synergy among channels is characterized by the features in both auto and cross modes, and their incidences for classifying five or six movements are analyzed. In contrast to the third-order auto-cumulants, it had been verified that the third-order cross-cumulants have the same classification rate by working with five or six movements. A myoelectric control scheme and its experimental application were executed with normal and disabled subjects, reaching a classification rates of 90%, in average. Accuracy in online experiments was similar to the off-line classification rate.
A simple and single-step method for the production of Ln-doped YVO4 nanocrystals and their simultaneous encapsulation in a silica network based on the pyrolysis of liquid aerosols at 800 °C is reported. The procedure is illustrated for Yb,Er:YVO4-silica nanocomposites consisting of spherical particles, which present up-converted green luminescence after IR excitation whose efficiency increased on annealing up to 1000 °C due to the release of impurities (adsorbed water, and residual anions). XPS spectroscopy and TEM observations revealed that the surface of the composite particles was enriched in silica, which would facilitate their functionalisation required to use them in biological applications. The procedure can also be used to prepare other rare earth doped systems as illustrated for the case of Eu-doped YVO4/silica having down-converted red luminescence. 相似文献
A technique was developed to predict the freshness of packaged sliced chicken breast employing a nondestructive visible and short-wavelength near-infrared (SW-NIR) spectroscopy method. Spectra were recorded at 0, 7 and 14 days using a camera, spectral filter (400-1000 nm) and a halogen flood lighting system which were developed and calibrated for the purpose. Physicochemical, biochemical and microbiological properties such as moisture (xw), water activity (aw), pH, total volatile basic nitrogen (TVB-N), ATP breakdown compounds (K1 values) and mesophilic bacteria (cfu g− 1) were determined to predict freshness degradation. The spectra obtained were related to the storage time of the samples. The best wavelengths for modeling freshness were 413, 426, 449, 460, 473, 480, 499, 638, 942, 946, 967, 970 and 982 nm. A linear correlation was found between the visible and SW-NIR spectroscopy and parameters such as microbiological counts, K1 and T-VBN indexes. 相似文献