Equilibrium isotherms of red beetroots

The equilibrium moisture content of red beetroot has been studied and the corresponding sorption- desorption curves have been obtained at temperature 20°C. The strain measurement method has been used to establish the sorption curves. Analytical dependence describing the sorption and desorption curves have also been derived. Values of equilibrium moisture contents for temperatures higher than 20°C have been obtained by the Pass and Slepchenko’s method. The results are presented in graphical and table form

HRTF PHASE SYNTHESIS VIA SPARSE REPRESENTATION OF ANTHROPOMETRIC FEATURES

We propose a method for the synthesis of the phases of Head-Related Transfer Functions (HRTFs) using a sparse representation of anthropometric features. Our approach treats the HRTF synthesis problem as finding a sparse representation of the subjects anthropometric features w.r.t. the anthropometric features in the training set. The fundamental assumption is that the group delay of a given HRTF set can be described by the same sparse combination as the anthropometric data. Thus, we learn a sparse vector that represents the subjects anthropometric features as a linear superposition of the anthropometric features of a small subset of subjects from the training data. Then, we apply the same sparse vector directly on the HRTF group delay data. For evaluation purpose we use a new dataset, containing both anthropometric features and HRTFs. We compare the proposed sparse representation based approach with ridge regression and with the data of a manikin (which was designed based on average anthropometric data), and we simulate the best and the worst possible classifiers to select one of the HRTFs from the dataset. For objective evaluation we use the mean square error of the group delay scaling factor. Experiments show that our sparse representation outperforms all other evaluated techniques, and that the synthesized HRTFs are almost as good as the best possible HRTF classifier

ULTRASOUND BASED GESTURE RECOGNITION

ABSTRACT In this study, we explore the possibility of recognizing hand gestures using ultrasonic depth imaging. The ultrasonic device consists of a single piezoelectric transducer and an 8 -element microphone array. Using carefully designed transmit pulse, and a combination of beamforming, matched filtering, and cross-correlation methods, we construct ultrasound images with depth and intensity pixels. Thereafter, we use a combined Convolutional (CNN) and Long Short-Term Memory (LSTM) network to recognize gestures from the ultrasound images. We report gesture recognition accuracies in the range 64.5-96.9%, based on the number of gestures to be recognized, and show that ultrasound sensors have the potential to become low power, low computation, and low cost alternatives to existing optical sensors