There is a consensus in signal processing that the Gaussian kernel and its partial derivatives enable the development of robust algorithms for feature detection. Fourier analysis and convolution theory have a central role in such development. In this paper, we collect theoretical elements to follow this avenue but using the q-Gaussian kernel that is a nonextensive generalization of the Gaussian one. Firstly, we review the one-dimensional q-Gaussian and its Fourier transform. Then, we consider the two-dimensional q-Gaussian and we highlight the issues behind its analytical Fourier transform computation. In the computational experiments, we analyze the q-Gaussian kernel in the space and Fourier domains using the concepts of space window, cut-o frequency, and the Heisenberg inequality.
The article outlines how to use the convergence of collections to determine the position of a mobile device based on the WiFi radio signal strength with the use of fuzzy sets. The main aim is the development of the method for indoor position determination based on existing WiFi network infrastructure indoors. The approach is based on the WiFi radio infrastructure existing inside the buildings and requires operating mobile devices such as smartphones or tablets. An SQL database engine is also necessary as a widespread data interface. The SQL approach is not limited to the determination of the position but also to the creation of maps in which the system dening the position of the mobile device will operate. In addition, implementation issues are presented along with the distribution of the burden of performing calculations and the benets of such an approach for determining the location. The authors describe how to decompose the task of determining the position in a client-server architecture.
In the presented paper we discuss pure versions of pushdown automata that have no extra non-input symbols. More specifically, we study pure multi-pushdown automata, which have several pushdown lists. We restrict these automata by the total orders defined over their pushdowns or alphabets and determine the accepting power of the automata restricted in this way. Moreover, we explain the significance of the achieved results and relate them to some other results in the automata theory.
In this paper we present the analysis of the gas usage for different types of buildings. First, we introduce the classical theory of building heating. This allows the establishment of theoretical relations between gas consumption time series and the outside air temperature for different types of buildings, residential and industrial. These relations imply dierent auto-correlations of gas usage time series as well as different cross-correlations between gas consumption and temperature time series for different types of buildings. Therefore, the autocorrelation and the cross-correlation were used to classify the buildings into three classes: housing, housing with high thermal capacity, and industry. The Hurst exponent was calculated using the global DFA to investigate auto-correlation, while the Kendall's τ rank coeficient was calculated to investigate cross-correlation.
We present a novel quantum algorithm for the classification of images. The algorithm is constructed using principal component analysis and von Neuman quantum measurements. In order to apply the algorithm we present a new quantum representation of grayscale images.