Continuous classifier training for myoelectrically controlled prostheses

Myoelectrically controlled prostheses use pattern recognition systems to classify input motions. Typically, these systems are initially trained offline using a set of training data. Changing conditions cause an increase in signal variation, leading to higher error rates. For better adaptability, a continuously trained classifier was developed. Data with valid class decisions are used to retrain the classifier with the class decisions used as classification targets. In this implementation the classifier validates decisions by using a retraining buffer to locate consecutive, identical majority vote decisions. Retraining is performed by incorporating new valid feature vectors, selected from the retraining buffer, into the training set, while discarding older vectors. Using the continuously trained classifier, an average improvement of 2.57% was seen over the non-continuously trained classifier

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy (vis-NIR DRS) has become an important area of research in the fields of remote and proximal soil sensing. The technique is considered to be particularly useful for acquiring data for soil digital mapping, precision agriculture and soil survey. In this study, 1581 soil samples were collected from 14 provinces in China, including Tibet, Xinjiang, Heilongjiang, and Hainan. The samples represent 16 soil groups of the Genetic Soil Classification of China. After air-drying and sieving, the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer. All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses. The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification. The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils. The results on the classification of the spectra are comparable to the results of other similar research. Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression (PLSR). This combination significantly improved the predictions of soil organic matter (R 2 = 0.899; RPD = 3.158) compared with using PLSR alone (R 2 = 0.697; RPD = 1.817)