Multidimensional Signal Processing Research Articles

Multidimensional complex signal processing and feature extraction for fatigue testing data

AbstractThis study focuses on implementing a multiaxial racetrack amplitude filter designed for processing fatigue testing data. The filter utilizes the concept of a multiaxial racetrack to map a multidimensional signal into a multidimensional space and employs amplitude filtering to eliminate noise while preserving structural outliers and sequential characteristics inherent in the signal. This research evaluates the effectiveness of this method in processing multidimensional data and compares it to conventional data preprocessing methods in the context of fatigue test data. Furthermore, the effectiveness of two optimization strategies proposed to address the limitations related to the filter's “filtering radius” and “filtering direction” has been validated. The findings reveal that the proposed filter is a versatile multidimensional signal processing technique suitable for diverse domains requiring signal order and shape preservation, particularly in fatigue analysis.

Research on an Indoor Light Environment Comfort Evaluation Index Based on Electroencephalogram and Pupil Signals

With the development of modern technology, many people work for a long time around various artificial light sources and electronic equipment, causing them to feel discomfort in their eyes and even eye diseases. The industry currently lacks an objective quantitative environmental–visual comfort index that combines subjective and objective indicators. For this experiment, objective eye movement and electroencephalogram (EEG) signals were collected in combination with a subjective questionnaire survey and a preference inquiry for comprehensive data mining. Finally, the results on a Likert scale show that high screen brightness can reduce the visual fatigue of subjects under high illuminance and high correlated color temperature (CCT). Pupil data show that, under medium and high ambient illuminance, visual perception sensitivity is more likely to be stimulated, and visual fatigue is more likely to deepen. EEG data show that visual fatigue is related to illuminance and screen brightness. On this basis, this study proposes a new evaluation index, the visual comfort level (0.6404 average at a low screen brightness, 0.4218 average at a medium screen brightness, and 0.5139 average at a high screen brightness), where a higher score for the visual comfort level represents a better visual experience. The visual comfort level provides a useful reference for enhancing the processing of multi-dimensional and biomedical signals and protecting the eyes.

ResNet diagnosis of rotor faults in oil transfer pumps

To address rotor imbalance and misalignment in oil transfer pumps, an innovative diagnostic framework using Residual Network (ResNet) is proposed. The model incorporates advanced signal processing algorithms and strategic sensor placement to enhance diagnostic efficacy. A fault simulation test rig captured vibration signals from eight key measurement points on the pump. One-dimensional and multi-dimensional signal processing techniques generated comprehensive datasets for training and validating the model. Sensor placement optimization, focusing on the bearing seat's axial direction, inlet flange's vertical direction, and outlet flange's axial direction, increased rotor fault sensitivity. Time-frequency data processed via Short-Time Fourier Transform (STFT) achieved the highest diagnostic accuracy, surpassing 98 %. This study highlights the importance of optimal signal processing and precise sensor placement in improving the accuracy of diagnosing rotor faults in oil transfer pumps, thus enhancing the operational reliability and efficiency of energy transportation systems.

Two-dimensional flow vector measurement based on all-fiber laser feedback frequency-shifted multiplexing technology

The decomposition and identification of signals are crucial for flow vector acquisition in a multi-dimensional measurement. Here, we proposed a two-dimensional (2D) flow vector measurement system based on all-fiber laser feedback frequency-shifted multiplexing technology. The reliable performance of the system is characterized by experimental verification and numerical simulation. An orthogonal dual-beam structure is employed to eliminate the impact of an unknown incident angle in the practical application. Meanwhile, the vector velocity signals in 2D can be decomposed into one-dimensional (1D) scalar signals by adopting the frequency-shifted multiplexing, which makes it easy to obtain the vector information and velocity distribution of fluid motion through the self-mixing interference frequency spectrum. Moreover, the measured flow rates present a high linearity with syringe pump speeds ranging from 200 to 2000 μL/min, and the velocity information of the different incidence angles is easily obtained with high precision. This work may pave the way for the acquisition and processing of multi-dimensional flow vector signals, with potential applications in biomedical monitoring and microflow velocity sensing.

Multidimensional Fiber‐to‐Chip Optical Processing Using Photonic Integrated Circuits

AbstractMultidimensional multiplexing technologies have been proven to be a promising scheme to meet the demands of high‐capacity optical interconnects and optical processing networks. However, challenges remain to realize multidimensional hybrid multiplexing data transmission and signal processing between few‐mode fiber transmission links and on‐chip optical processing networks, since the optical coupling between fiber and chip is almost exclusively in the single‐mode regime. Here, a multidimensional fiber‐to‐chip optical processing system is proposed and demonstrated for hybrid wavelength‐, mode‐, and polarization‐division multiplexing signals, where multidimensional coupling between few‐mode fiber transmission link and on‐chip multi‐mode processing network is realized by using a 3D photonic integrated (de)multiplexers on a glass chip as well as 2D photonic integrated (de)multiplexers on a silicon chip, and the on‐chip multidimensional optical processing network composed by parallel cascaded micro‐ring resonator array performs a function of reconfigurable optical add/drop multiplexer. By operating wavelength‐division multiplexing in conjunction with mode‐division multiplexing and polarization‐division multiplexing, the communication bandwidth of fiber‐to‐chip optical processing systems can be further scaled.

An Efficient VLSI Architecture for Bi-Cubic Interpolation using Carry Skip Adder

In any signal processing system, signal distortion can occur. A compensation scheme would adjust the captured signal to correct for any deviations and ensure accurate signal transmission and preservation. A compensation scheme is used to correct such deviations for mostly linear is used to enhance the quality of reconstructed signal suitable for high-quality super-resolution techniques. This technique is applicable for multidimensional signal processing such as image processing. Except for the resolution of the acquired images, the extracting of colour information is greatly affected by the number of pixels. This scheme enhances the correlation of interpolated pixels and their immediate neighbouring pixels. This research aims to develop a super-resolution demosaicking (SRD) technique to reconstruct high-resolution, full-colour images from samples of the same previously seen (in-painted) image without a sophisticated, time-consuming training process. Hardware-oriented colour demosaicking methods, which give importance to green colour pixels, have thus enhanced the quality of the restored image. A Boundary Mirror and Detector mechanism also considered here for improving the pixel quality up to the boundaries. Three interpolators were economized by using a method called hardware sharing. The present Bi-Cubic interpolation technique has been designed as part of our overall effort to enhance and advance the SRD approach toward better performance and broader acceptance. This had been implemented by VLSI architecture and VHDL, whereas MATLAB had executed the image conversion and de-conversion methods. Comparison in the evaluation of results was done based on PSNR and SSIM values, followed by the analysis of the output results after the synthesis process using a Xilinx Vivado ZYNQ-7-ZC702 FPGA.

Back Cover Image

AbstractThe cover image focuses on neuronal circuit motif with specialized excitatory–inhibitory connectivity pattern. The neuronal circuit is an advanced functional unit of the brain beyond neurons and synapses. Neurons do not function in isolation and are linked to ensembles or circuit motifs that process specific types of information, enables multidimensional signal processing in the information flow of the brain. The authors demonstrate a core processor that can be employed to construct commonly used neuronal circuits and further perform bio‐realistic neuromorphic computing. Exploring the working principle, physical configuration, scalable design, and extensive signal‐processing capabilities of core processing neuron is crucial for advancing hardware development for brain‐inspired integrated neuromorphic systems. image

N$$ N $$‐dimensional wave packet transform and associated uncertainty principles in the free metaplectic transform domain

The free metaplectic transformation (FMT) is an ‐dimensional linear canonical transform. This transform is much useful, especially in multidimensional signal processing and applications. In this paper, our aim is to achieve an efficient time‐frequency representation of higher‐dimensional nonstationary signals by introducing the novel free metaplectic wave packet transform (FM‐WPT) in , based on the elegant convolution structure associated with the free metaplectic transforms. The FM‐WPT preserves the properties of classical wave packet transform (WPT) in and has better mathematical properties. Further, the validity of the proposed transform is demonstrated via a lucid example. The preliminary analysis encompasses the derivation of fundamental properties of the novel FM‐WPT, including boundedness, reconstruction formula, Moyal's formula, and the reproducing kernel. To extend the scope of the study, we formulate several uncertainty inequalities, including Lieb's inequality, Pitt's inequality, logarithmic inequality, Heisenberg's uncertainty inequality, and Nazarov's uncertainty inequality for the proposed transform.

Multidimensional Signal Processing for High Areal Density Heated-Dot Magnetic Recording

In this article, a new multidimensional signal processing scheme for a heated-dot magnetic recording (HDMR) system using double-layered bit-patterned media (BPM) is proposed. This proposed signal processing scheme is capable of simultaneously detecting data sequences recorded on two tracks in each layer sequentially. The effective transmission rate of the HDMR system with two-track simultaneous log-likelihood ratio (LLR) detection and double-layered recording can be increased about four times that of the HDMR system with single-track LLR detection and single-layered recording if the data sequence recorded on each track has the same rate. It is shown that the error rate performance of the HDMR system with two-track simultaneous LLR detection and double-layered recording shows a significant improvement over the HDMR system with single-track LLR detection and single-layered recording by computer simulation.

Trends in EEG signal feature extraction applications.

This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature extraction techniques in the time domain, frequency domain, decomposition domain, time-frequency domain, and spatial domain. We also provide pseudocode for the methods discussed so that they can be replicated by practitioners and researchers in their specific areas of biomedical work. Furthermore, we discuss artificial intelligence applications such as assistive technology, neurological disease classification, brain-computer interface systems, as well as their machine learning integration counterparts, to complete the overall pipeline design for EEG signal analysis. Finally, we discuss future work that can be innovated in the feature extraction domain for EEG signal analysis.

Motion Analysis Using Global Navigation Satellite System and Physiological Data

Motion analysis by wearable sensors forms a very important research topic with a general mathematical background and applications in different areas including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body and the simultaneous acquisition of signals from further sensors. The application is related to the monitoring of physical activity and the analysis of the heart rate dynamics during the run at route segments of different slopes with changing speed, forming an alternative to the heart monitoring on the treadmill ergometer. The proposed computational method includes digital methods of data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The detection of the patterns of motion includes segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This method of classification provides a comparison of neural networks, support vector machine, Bayesian, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface suggest the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems as well.

Learning Asymmetric and Local Features in Multi-Dimensional Data Through Wavelets With Recursive Partitioning

Effective learning of asymmetric and local features in images and other data observed on multi-dimensional grids is a challenging objective critical for a wide range of image processing applications involving biomedical and natural images. It requires methods that are sensitive to local details while fast enough to handle massive numbers of images of ever increasing sizes. We introduce a probabilistic model-based framework that achieves these objectives by incorporating adaptivity into discrete wavelet transforms (DWT) through Bayesian hierarchical modeling, thereby allowing wavelet bases to adapt to the geometric structure of the data while maintaining the high computational scalability of wavelet methods-linear in the sample size (e.g., the resolution of an image). We derive a recursive representation of the Bayesian posterior model which leads to an exact message passing algorithm to complete learning and inference. While our framework is applicable to a range of problems including multi-dimensional signal processing, compression, and structural learning, we illustrate its work and evaluate its performance in the context of image reconstruction using real images from the ImageNet database, two widely used benchmark datasets, and a dataset from retinal optical coherence tomography and compare its performance to state-of-the-art methods based on basis transforms and deep learning.

Multi-dimensional graph fractional Fourier transform and its application to data compression

Many multi-dimensional signals appear in the real world, such as digital images and data that has spatial and temporal dimensions. Designing a transform method to process these multi-dimensional signals in graph fractional domain is a key challenge in graph signal processing. This paper investigates the novel transform for multi-dimensional graph signals defined on Cartesian product graph and studies several related properties. Our work includes: (i) proposing the two-dimensional graph fractional Fourier transforms using two basic graph signal processing methods i.e. based on Laplacian matrix and adjacency matrix; (ii) extending the two-dimensional transforms to multi-dimensional graph fractional Fourier transforms (MGFRFT). MGFRFT provides an additional fractional analysis tool for multi-dimensional graph signal processing; (iii) exploring the advantages of MGFRFT in terms of spectrum, directional characteristics and computational time; (iv) applying the proposed transform to data compression to highlight its utility and effectiveness.

DANC-Net: Dual-Attention and Negative Constraint Network for Point Cloud Classification

Convolutional neural networks, as a branch of deep neural networks, have been widely used in multidimensional signal processing, especially in point cloud signal processing. Nevertheless, in point cloud signal processing, most point cloud classification networks currently do not consider local feature correlation. In addition, they only adopt ground-truth as positive information to guide the training of networks while ignoring negative information. Therefore, this paper proposes a network model to classify point cloud signals based on feature correlation and negative constraint, DANC-Net (dual-attention and negative constraint on point cloud classification). In the DANC-Net, the dual-attention mechanism is utilized to strengthen the interaction between local features of point cloud signal from both channel and space, thereby improving the expression ability of extracted features. Moreover, during the training of the DANC-Net, the negative constraint loss function ensures that the features in the same categories are close and those in the different categories are far away from each other in the representation space, so as to improve the feature extraction capability of the network. Experiments demonstrate that the DANC-Net achieves better classification performance than the existing point cloud classification algorithms on synthetic datasets ModelNet10 and ModelNet40 and real-scene dataset ScanObjectNN. The code is released at https://github.com/sunhang1986/DANC-Net.

A Review of Environmentally Friendly Approaches in Fire Extinguishing: From Chemical Sciences to Innovations in Electrical Engineering.

After the invention of fire, the fight against fire probably started, and it has been going on for thousands of years. During this time, the aim has always been to extinguish the fire as soon as possible and to produce fire-resistant materials. Symmetry plays an important role in reducing problems, as it is a common feature of modern life. Multidimensional signal processing has many applications, an example of which is the use of appropriately timed acoustic waves to extinguish flames. This article provides a brief review of issues related to the use of acoustic waves for flame suppression based on studies in the literature. In addition, measurement results available in the literature obtained using a high-power acoustic extinguisher are discussed as a review of the scientific literature. Furthermore, we provide the latest information on the situation of flame retardants, including the latest innovations in basic sciences. In this sense, from intrinsically fire-resistant materials to flame-retardant additives and nanocomposites, new processes and applications are briefly mentioned.

Mobile Performance Intelligent Evaluation of IoT Networks Based on DNN

The rapid development of the sensor equipment has promoted the rapid growth of the Internet of Things (IoT). The IoT has been widely employed in the multidimensional signal processing and gradually formed the IoT networks. Mobile communication promotes the wide application of the IoT networks. In this study, the transmit antenna selection (TAS) scheme is employed to investigate the average symbol error probability (ASEP) performance of mobile IoT networks over the 2-Rayleigh channels. We first employ moment-generating function (MGF) approach to derive the exact ASEP expressions. We also investigate the outage probability (OP) performance and derive OP expressions. Employing the deep neural network (DNN), an OP intelligent prediction algorithm is proposed. Then, the numerical simulations are conducted to confirm the ASEP and OP performance analysis. The effect of different channel parameters is also analyzed. Compared with Nakagami and Rayleigh channel models, the 2-Rayleigh model has 83.6% and 59.1% increase in ASEP values, respectively. Compared with ELM and RBF models, the DNN model has 31.7% and 22.5% increase in OP prediction accuracy, respectively.

Development of a Cluster with Cloud Computing Based on Neural Networks With Deep Learning for Modeling Multidimensional Fields

Introduction. The modeling of multidimensional fields on multiprocessors, with a neural network architecture, which is rebuilt in the process of solving the problem by means of deep learning, is considered. This architecture of the calculator allows the device to be used to solve the problems of passive location, monitoring station, active LPI location station, base telecommunications station at the same time. Particular attention is paid to the use of bionic principles in the processing of multidimensional signals. A cluster computer with cloud computing is proposed for creating a modeling complex for processing multidimensional signals and debugging the target system. The cluster is made in the form of a multiprocessor based on neural network technology with deep learning. Biomimetic principles are used in the architecture of the modeling complex. The purpose of the work. Creation of a modeling complex as a cluster with cloud computing using neural networks with deep learning. The cluster is a neuromultiprocessor that is rebuilt in the process. Results. In the process, we managed to create a multiprocessor, which in the process of computing is rebuilt, to simulate a terahertz 3D Imager scanner using cloud computing. Conclusions. In the process of performing the work a complex for modeling multidimensional signals was created. As the basis of the computer used a cluster that is rebuilt in the process. The computing base consists of neural networks with cloud computing. Keywords: cognitive space, deep learning, convolutional neural network, neural network architectures, cluster.

МАТЕМАТИЧЕСКИЙ ФОРМАЛИЗМ ДЛЯ РАСЧЕТОВ СЕЧЕНИЯ УПРУГОГО р7Ве- РАССЕЯНИЯ В РАМКАХ ТЕОРИИ ГЛАУБЕРА

In this paper, we solve the problem of constructing a commutative algebra of quaternions and octonions. A proof of the theorem is given that the commutativity of quaternions can be ensured by specifying a set of sign coefficients of the directions of reference of the angles between the radius vectors in the coordinate planes of the vector part of the coordinate system of the quaternion space. The method proposed in the development of quaternions possessing the commutative properties of multiplication is used further to construct a commutative octonion algebra. The results obtained on improving the algebra of quaternions and octonions can be used in the development of new hypercomplex numbers with division over the field of real numbers, and can also find application for solving a number of scientific and technical problems in the areas of field theory, physical electronics, robotics, and digital processing of multidimensional signals.

Low-rank tensor completion based on tensor train rank with partially overlapped sub-blocks

Recently, low-rank tensor based methods using tensor train (TT) rank have achieved promising performance on multidimensional signal processing. Especially taking advantage of a tensor augmentation technique called ket augmentation (KA), methods based on TT rank can more efficiently capture correlation of the generated higher-order tensor, but serious block artifacts are caused. In this paper, a tensor completion method using parallel matrix factorization based on TT rank with partially overlapped sub-blocks is proposed. Combined with the partially overlapped sub-blocks scheme and KA technique, an improved tensor augmentation technique is proposed to further increase the order of generated tensor, enhance the low-rankness, and alleviate block artifacts. To reduce the computational time, parallel matrix factorization is utilized to minimize the TT rank. Besides, a fixed weighting function is also developed to reduce the blockiness effect according to the shortest distance between the pixel and the corresponding sub-block boundaries. Numerical experiments demonstrate the superiority of the proposed method over the existing state-of-the-art methods in terms of quality and quantity.

On two-channel circuits of subband filtration of multidimensional signals

We propose the method to construct high-frequency and low-frequency analysing and restoring filters for digital processing of multidimensional signals.