Showing papers on "Multidimensional signal processing published in 2023"

Searching for Alternatives to the Savitzky–Golay Filter in the Spectral Processing Domain

Abstract: An elegant, well-established effective data filter concept, proposed originally by Abraham Savitzky and Marcel J.E. Golay, is undoubtedly a very effective tool, however not free from limitations and drawbacks. Despite the latter, over the years it has become a "monopolist” in many fields of spectra processing, claiming a "commercial" superiority over alternative approaches, which would potentially allow to obtain equivalent or in some cases even more reliable results. In order to show that basic operations performed on spectral datasets, like smoothing or differentiation, do not have to be equated to the application of the one particular single algorithm, several of such alternatives are briefly presented within this paper and discussed with regard to their practical realization. A special emphasis is put on the fast Fourier methodology (FFT), being widespread in the general domain of signal processing. Finally, a user-friendly Matlab routine, in which the outlined algorithms are implemented, is shared, so that one can select and apply the technique of spectral data processing more adequate for their individual requirements without the need to code it prior to use.

Signal Processing for Highly Resolved 2D NMR

Abstract: As the name implies, the “traditional” processing scheme for Fourier transform nuclear magnetic resonance (FT-NMR) signals is centred on the Fourier transform. However, other techniques can either replace or supplement the Fourier transform: extracting more information from fewer datapoints, improving sensitivity and/or resolution, reducing acquisition time (while maintaining spectral quality) and even reconstructing spectra whose experimental acquisition is too time-consuming to be feasible. Following an overview of “traditional” FT-NMR processing, including an analysis of apodization functions, this chapter will discuss alternatives to the Fourier transform applicable to 2D spectroscopy, including compressed sensing and covariance NMR. This chapter will evaluate processing techniques in light of the specific advantages of 2D NMR, such as the inherent ability to treat 2D datasets as matrices subject to well-studied matrix operations as well as the symmetry of certain 2D NMR experiments. On the other hand, this chapter will address certain challenges in processing rapidly acquired 2D NMR spectra, such as crowded signals and the inapplicability of certain multidimensional processing techniques to data with only a single indirect dimension. This chapter will also review software for NMR signal processing, such as NMRPipe and Mnova.

Data Processing Methods: Fourier and Beyond

Abstract: This chapter presents a compact overview of both practical and rigorously mathematical aspects of modern NMR signal processing. It discusses the properties of the Fourier transform (FT), which will be later useful to explain the effects of the experimental imperfections and signal processing procedures. The fast FT algorithm, used to calculate the discrete FT requires the same number of points in the input and output. However, one can increase the number of spectral points to any desired value by zero filling , that is, extending the free induction decay by adding artificial data points equal to zero at the end. The quadrature detection in one-dimensional spectra is realized through the acquisition of the two modulations, interpreted as real and imaginary parts of a complex NMR signal. The Projection Theorem is a powerful tool, useful in accelerating NMR experiments of dimensionality three and more.

Twenty-Five Years of Sensor Array and Multichannel Signal Processing: A review of progress to date and potential research directions

Abstract: In this article, a general introduction to the area of sensor array and multichannel signal processing is provided, including associated activities of the IEEE Signal Processing Society (SPS) Sensor Array and Multichannel (SAM) Technical Committee (TC). The main technological advances in five SAM subareas made in the past 25 years are then presented in detail, including beamforming, direction-of-arrival (DOA) estimation, sensor location optimization, target/source localization based on sensor arrays, and multiple-input multiple-output (MIMO) arrays. Six recent developments are also provided at the end to indicate possible promising directions for future SAM research, which are graph signal processing (GSP) for sensor networks; tensor-based array signal processing, quaternion-valued array signal processing, 1-bit and noncoherent sensor array signal processing, machine learning and artificial intelligence (AI) for sensor arrays; and array signal processing for next-generation communication systems.

Digital Signal Processing, Control and Hybrid Circuits

Abstract: In this chapter we make some remarks on digital and hybrid signal processing. As stated in the preface, digital signal processing is not the main topic of this book. Here, we make a “gentle introduction” to sampled data and digital signal processing. The main tool used in digital and discrete-time control and signal processing is the Z-transform. We recall here the properties and applications of this tool. However, no advanced theoretical considerations about the Z-transform are provided. We mention only the minimal amount of Z-transform properties, necessary to understand the sampled-data and digital signal processing. Practical applications in audio circuits are described, including analog and hybrid delay lines.

Six Transformations and Their Relationships in Digital Signal Processing

Abstract: An important content in the course of digital signal processing is the analysis of the time domain and frequency domain transformation of the system, which involves several important transformations, including the Fourier transform (FT) of continuous non-periodic signals, Fourier Series for Continuous Periodic Signals (FS), Discrete Time Fourier Transform (DTFT), Z Transform (ZT) of Discrete Signals, Fourier Series (DFS) of Discrete Periodic Signals, and Fast Fourier Transform (FFT),. The definitions and relationships of these transformations often confuse beginners. In response to this problem, this paper narrates the definitions of the six major transformations, and expounds the relationship of various transformations.

Signal parallel processing in high frequency surface wave radar based on CUDA

Abstract: The real-time problem of signal processing of high frequency surface wave radar (HFSWR) has become a hot point. The key of digital signal processing is the method based on two-second fast Fourier transformation (FFT). In this paper，the application of GPU to accelerate the signal processing based on CUDA is introduced. The principle of signal processing and GPU parallelization is explained and elaborated, and the comparison of performance is showed in this paper.

Retrospective and perspective for modification of digital methods for discrete Fourier transform

Abstract: The questions connected with the modified methods research of spectral analysis of a digital signal for reduction of hardware expenses for their hardware-software realization are considered. The purpose of the research is the comparative analysis of methods of discrete transformation of digital signal, allowing reducing the number of hardware-consuming operations of complex multiplication of its frequency-time samples. The methods of hardware-software modeling of digital signal processing algorithms were used in research. Fast Fourier transform and discrete Fourier transform methods with the minimal number of multiplications and also the method of theoretical numerical transformation allowing reducing the number of multiplications of digital algorithms of frequency signals selections were investigated. The results of the research showed and confirmed the promise of such a modification of the discrete Fourier transform method, which provides this transformation of a digital signal only by low-cost operations of successive addition of its frequency-time samples.

Algorithm Analysis and Application of Digital Signal Processing Technology

Abstract: Digital signal processing is a rapidly developing field of science and technology. In the 1960s and 1970s, it experienced the development stages of digital filtering theory, signal Fourier transform, convolution and fast correlation calculation. Subsequently, the rapid development of computer technology and microelectronics technology promoted the development of digital signal processing technology and pushed it to a climax. Algorithm analysis is the study of algorithms. An algorithm is a set of rules that explain how to solve computer programming problems. The most common algorithm is the search and sort algorithm, which tells you how to find what you need in a large group of items. It is also used to search files or databases, group data, or find patterns in a dataset. The method adopted in this paper is to use FPGA to complete the signal preprocessing and STFT algorithm implementation. The necessary parameters and data results obtained from FPGA processing are transferred to DSP through the interface in a handshake manner, and the DSP completes the subsequent modulation analysis and parameter estimation. This method is much more efficient and reliable than using DSP alone to complete all processes in the past, and greatly shortens the cycle of intra pulse analysis.

Properties of Low-Frequency Filters of One-Dimensional Signals with Limited Energy Spectrum

Abstract: In this paper, the properties of low-pass filters of signals with limited energy that are important for digital signal processing have been investigated. In particular, some conditions for the adequacy of signal smoothing have been established. The properties formulated in the work have been strictly justified for the case of using Fourier series of signals from the considered class. These properties have been generalized to the Fourier transformation of a signal and their implementation has been demonstrated for a number of specific examples. Other examples of functions that have the properties introduced in the work have been also given. Further research has revealed the universality of the functions for which the properties established in the work are fulfilled. In particular, the possibilities of their application in solving other problems of digital signal processing have been given.