Showing papers on "Multidimensional signal processing published in 2020"

Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications

Abstract: Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications

Data Analytics on Graphs Part II: Signals on Graphs

Abstract: The area of Data Analytics on graphs deals with information processing of data acquired on irregular but structured graph domains. The focus of Part I of this monograph has been on both the fundamental and higher-order graph properties, graph topologies, and spectral representations of graphs. Part I also establishes rigorous frameworks for vertex clustering and graph segmentation, and illustrates the power of graphs in various data association tasks. Part II embarks on these concepts to address the algorithmic and practical issues related to data/signal processing on graphs, with the focus on the analysis and estimation of both deterministic and random data on graphs. The fundamental ideas related to graph signals are introduced through a simple and intuitive, yet general enough case study of multisensor temperature field estimation. The concept of systems on graph is defined using graph signal shift operators, which generalize the corresponding principles from traditional learning systems. At the core of the spectral domain representation of graph signals and systems is the Graph Fourier Transform (GFT), defined based on the eigendecomposition of both the adjacency matrix and the graph Laplacian. Spectral domain representations are then used as the basis to introduce graph signal filtering concepts and address their design, including Chebyshev series polynomial approximation. Ideas related to the sampling of graph signals, and in particular the challenging topic of data dimensionality reduction through graph subsampling, are presented and further linked with compressive sensing. The principles of time-varying signals on graphs and basic definitions related to random graph signals are next reviewed. Localized graph signal analysis in the joint vertex-spectral domain is referred to as the vertex-frequency analysis, since it can be considered as an extension of classical time-frequency analysis to the graph serving as signal domain. Important aspects of the local graph Fourier transform (LGFT) are covered, together with its various forms including the graph spectral and vertex domain windows and the inversion conditions and relations. A link between the LGFT with a varying spectral window and the spectral graph wavelet transform (SGWT) is also established. Realizations of the LGFT and SGWT using polynomial (Chebyshev) approximations of the spectral functions are further considered and supported by examples. Finally, energy versions of the vertex-frequency representations are introduced, along with their relations with classical timefrequency analysis, including a vertex-frequency distribution that can satisfy the marginal properties. The material is supported by illustrative examples.

Vibration Analysis, Instruments, and Signal Processing

Abstract: Features Integrates theory, measurements, experiment, and signal processing into vibration problems Includes signal processing topics such as aliasing effect and computation of power spectral density Discusses finite element modeling including modeli

Spectral Characteristics of Spatiotemporal Signals and Interference on a Linear Antenna Array

Abstract: The paper considers an approach to the description of a mathematical model of signals arriving at spatially distributed antenna elements of a digital antenna array in radar systems. The spectral and correlation characteristics of the formed spatiotemporal processes are studied and one of the possible signal processing methods based on these properties is proposed.

Multidimensional Digital Signal Processing for Printed Circuit Boards Testing

Abstract: A new method for multidimensional digital signal processing for Printed Circuit Boards (PCB) testing is described. The considered method comes to solve such problems as competition and synchronization, which are present in the multidimensional signals processing. The solution is achieved by performing in parallel all operations, replacing the analog-to-digital conversion (ADC) with differentiation operations and analyzing the signal variation rate based on the Fuzzy logic elements. As a result of these operations, using digital integration models, binary code streams are obtained that allow the reconstruction of the signal shape. Mathematical models applied for the transformation and processing of multidimensional signals are presented. The designed system for the multidimensional signal processing consists of a computing unit, the test signals generator, the data storage unit, the Printed Circuit Board with nodes for test signals application and retrieval, and finally the processing elements for differentiation and analysis based on Fuzzy logic.