Abstract The recognition of stomatological disorders and the classification of dental caries are important areas of biomedicine that can hugely benefit from machine learning tools for the construction of relevant mathematical models. This paper explores the possibility of using reflectivity data to distinguish between healthy tissues and caries by deep learning and multilayer convolutional neural networks. The experimental data set includes more than 700 observations recorded in the stomatology laboratory. For rigor, the results obtained from the deep learning systems are compared with those evaluated for selected sets of features estimated for each observation and classified by a decision tree, support vector machine (SVM), k-nearest neighbor, Bayesian methods, and two-layer neural networks. The classification accuracy obtained for the deep learning systems was 98.1% and 94.4% for data in the signal and spectral domains, respectively, in comparison with an accuracy of 97.2% and 87.2% evaluated by the SVM method. The proposed method conclusively demonstrates how the artificial intelligence and deep learning methodology can contribute to improved diagnosis of dental problem in stomatology.

Bursts of sleep spindles and paroxysmal fast brain activity waveforms have frequency overlap whilst generally, paroxysmal waveforms have shorter duration than spindles. Both resemble bursts of normal alpha activity during short rests while awake with closed eyes. In this paper, it is shown that for a proposed cooperative adaptive line enhancer, which can both detect and separate such periodic bursts, the combination weights are consistently different from each other. The outcome suggests that for accurate modelling of the brain neuro-generators, the brain connectivity has to be precisely estimated and plugged into the adaptation process.

In this article, detection of sleep apnea or hypopnea events is addressed using a single channel electrocardiography (ECG) signal by analysis of respiratory extracted modulation. First, R peaks are detected from ECG signal. Then, a time-series with the amplitude (height) and timing of R peaks representing respiratory-induced amplitude modulation is constructed. This signal is resampled evenly at 4Hz. Synchrosqueezed wavelet transform (SSWT) together with an iterative time-frequency ridge estimation is applied to provide a robust estimation of instantaneous respiratory frequency and detect the regions with/without sleep apnea/hypopnea events. Signal reconstruction using inverse synchrosqueezed wavelet transform (ISSWT) has been performed. The appeared peaks can identify and measure the duration of apnea/hypopnea events.

This paper presents improvements in image gap restoration through the incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed: 1) the local edges or textures, inferred from the gradients of the neighboring pixels and 2) the global edges between image objects or segments, inferred using a Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer, an edge skeleton image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of upsampling), up-sampled, and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves peak-signal-to-noise-ratio by 1-5 dB compared with a range of best published works.

Movement disorders, problems with motion and gait stability related to aging form a very intensively studied research area. The paper presents a contribution to these topics through the use of data acquired by motion sensors and namely image and depth sensors of the MS Kinect. While video sequences obtained by complex camera systems can be used for the precise gait features evaluation, it is possible to use much cheaper devices for diagnostic purposes accurate enough in many cases. The experimental part of the study presents video sequences and depth sensors data acquisition for 18 individuals with the Parkinson's disease and 18 healthy age-matched controls using the proposed graphical user interface in the clinical environment. Results presented include the estimation of gait features to distinguish gait disorders and to classify individuals in the early stage of possible serious diseases. The accuracy achieved was higher then 90 % for given sets of individuals.

The organizing committee of ICASSP 2011 is delighted to welcome you to the 36th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), which is being held at the Prague Congress Centre, May 22–27, 2011. This is the flagship conference for the IEEE Signal Processing Society. In 1997, ICASSP was held in Munich, Germany, and now, in 2011, ICASSP is back in Central Europe. During the intervening years, the event has been held across the continents and next year will return to Asia, when Kyoto in Japan will be the venue. The ICASSP conference is the world's largest and most comprehensive technical event, focused on signal processing and its applications. The conference includes overview lectures, detailed scientific sessions and exhibitions, together with social and cultural events.

Environmental sensing is closely related to digital processing of observed signals and images. The paper is devoted to the analysis of mathematical methods allowing for detection of concentration of aerosol particles observed at ground measuring stations and by satellites. The first part of the contribution presents basic methods of two-dimensional interpolation allowing for the estimation of the observed variables over the whole region of interest. The whole paper is focused on satellite images and their processing. Owing to simultaneous observations at different frequencies, it is possible to detect image differences and to compare them to surface measurements. The paper is devoted to the design and verification of algorithms of image denoising including wavelet use and their correlation. General methods studied in the paper allow further image segmentation, feature extraction, classification, detection of the most important sources of pollution, prediction and control of pollution sources.