Signal Processing Technology Research Articles

Unraveling the Complexity with Applications of Nonlinear Analysis in Signal Processing and Communication Engineering

Nonlinear analysis is an essential area of signal and communication engineering. Many different kinds of nonlinear analysis can be used in this study. Linear signal processing techniques had previously struggled to accurately capture complex data pattern and communication signals. However, the development of non-linear analysis is a game-changing advancement since it provides a solid framework for understanding and manipulating these complex and non-trivial dynamics. In order to understand the behaviour of a wide variety of signals, from financial market trends to biological and environmental data, non-linear techniques are essential in signal processing. Improved understanding of system behaviour and predictive models may result from the use of such techniques to extract complex data patterns that are not often used. Non-linear approaches also contribute to the development of image and video processing by easing the development of advanced compression, tracking, and object recognition algorithms. Non-linear analysis is crucial in communication engineering as it aids in comprehending and optimising the flow of information across various channels. A greater understanding of signal distortion is made possible by the nonlinear perspective, which makes it possible to build reliable communication systems that can withstand interference and noise. By facilitating the creation of sophisticated modulation systems, adaptive equalisation methods, and error control coding, it raises the communication networks' dependability and effectiveness. Designing complex systems that can handle the complexities of today's data and communication demands is made possible by the paradigm-shifting synergy of nonlinear analysis, signal processing, and communication engineering. This abstract highlights the role that nonlinear analysis plays in transforming various fields and highlights how it can be used to solve changing problems related to information comprehension, processing, and transmission in a technologically sophisticated world.

Design and Implementation of K-Band Electromagnetic Wave Rain Gauge System

In order to prevent and manage damage caused by localized torrential downpours, the quantitative observation of rainfall is crucial. Considering the spatial complexity and vertical variability of rainfall, it is important to obtain low-altitude, high-resolution radar observations to reduce uncertainty in radar rainfall estimates. In this paper, we present an electromagnetic wave rainfall gauge system (EWRG) that detects rainfall within the observation area and estimates the areal rainfall using electromagnetic waves. The EWRG system was developed based on a subminiature size antenna, a K-band dual-polarization transceiver, and advanced high-resolution, high-speed signal processing technology. The system design and signal processing techniques are described in detail. The EWRG has the advantage of overcoming the limitations of conventional cylindrical ground rain gauges, such as the contamination and spatial inaccuracy of rain gauges, which cause uncertainty in quantitative precipitation measurement.

STRUCTURES OF ADAPTIVE SIGNAL PROCESSING SYSTEMS FOR RADAR SENSORS OF EXTERNAL INFORMATION FOR CORRELATION-EXTREME AIRCRAFT NAVIGATION SYSTEMS

During the guidance of the aircraft on the final part of the flight, it is affected, along with other external factors, by interferences of various (artificial or natural) origins. These interferences have various effects on the receiving elements of the antenna array of the radar sensor of external information. Due to the variability and rapidity of the complex interference situation, adaptive interference protection systems are the most effective in combating these interferences. It is known that the use of adaptive processing systems allows for overcoming the practically inevitable a priori uncertainty of statistical characteristics of signals and interference of various origins. At present, due to the development of digital technology, new methods and devices for adaptive signal processing against the back- ground of interference have appeared. Thus, the arsenal of methods of adaptation to Gaussian disturbances has been supplemented by methods involving the inversion (direct or recurrent) of the most plausible estimates of correlation matrices of disturbances or their regularized varieties. Wide possibilities of adaptation are opened up in modern radar stations with multi-element phased antenna ar- rays, which provide for digital information processing. Due to the very high speed of the aircraft during the operation of its correlation- extreme guidance system, as well as due to the dynamic and non-stationary interference environment, an important requirement for adaptive anti-jamming systems is their speed. The effectiveness of adaptive processing of signals against the background of interference can be significantly increased by using reliable a priori information. The paper considers a method of increasing the speed of adaptive protection systems against radar interference of various origins by taking into account a priori information about the central symmetry of the receiving channels of radar sensors of external information of correlation-extreme aircraft navigation systems. It is shown that taking into account such a priori information as the central symmetry of the receiving channels of radar sensors of external informa- tion leads to a corresponding change in the structure of devices for adaptive signal processing in the conditions of interference in these sensors and as a consequence, to an increase in their speed.

Novel Optical Kerr Switching Photonic Device Based on Nonlinear Carbon Material.

In the context of current communication systems, there is an urgent demand for more efficient and higher-speed optical signal processing technologies. Researchers are actively exploring new materials and devices to harness nonlinear optical phenomena, seeking advancements in this field. Nonlinear carbon materials, especially promising 2D materials, have garnered attention for their potential interaction with light and have become integral to the development of all-optical signal processing devices. This study focuses on utilizing a photonic device based on a nonlinear Au/CB composite material for optical Kerr switching. The application of Au/CB as a nonlinear material in the Kerr switch represents a noteworthy advancement, demonstrating its capability to modulate optical signals. By appropriately applying a pump light, the study achieves optical Kerr switching with an extinction ratio of approximately 15 dB in the fully off state of the signal light carrying a 10 GHz analog signal, marking a pioneering achievement in the field to the best of our knowledge. The experimental results, encompassing extinction ratios, signal control, and stability, not only validate the feasibility of this technology but also underscore its potential applicability within optical communication systems. The successful modulation and control of a 10 GHz analog signal showcase the practicality and effectiveness of the Au/CB-based optical Kerr switch. This progress contributes to the continuous evolution of optical Kerr switching, a crucial component in modern optical communication systems. Therefore, we believe that the Au/CB-based optical Kerr switch is an exceptionally promising and stable all-optical signal processing device. As the contemporary communication landscape evolves, the integration of this technology holds the potential to enhance the efficiency and speed of optical signal processing.

Speech for Intelligent cognition change tracking and DEtection of AD (SIDE‐AD) Research Program: SIDE‐AD

AbstractBackgroundThere is emerging evidence that speech could be a potential indicator and manifestation of early Alzheimer’s disease (AD) pathology. Symptomatic AD may change an individual’s language, especially certain elements in speech such as elaboration and attribution. Therefore, the University of Edinburgh and Sony Group Corporation are collaborating within the Sony Research Award Program to create the SIDE‐AD study which aims to develop digital speech‐based biomarkers technology for AD. The study is designed to assess disease status in the pre‐dementia stages in a real‐world at‐risk population in Brain Health Services.MethodSIDE‐AD is an observational longitudinal study developing speech biomarkers for use in neurodegenerative disease. Participants are recruited from Brain Health Services in Scotland with data collection starting in spring 2023. In the context of the SIDE‐AD study, we have developed a secured online data collection infrastructure for collecting voice data by prompting individuals to record a voice sample talking about their brain health. Individuals are asked to rate their mood, anxiety and apathy. The baseline speech biomarkers will be compared to the follow‐up visit’s speech recordings as well as to other variables from routinely collected health care data. The SIDE‐AD study will employ signal processing and machine learning technologies to automate the assessment of the respondents’ speech.ResultThe objective of the SIDE‐AD study is to recruit participants (n = 150) across the AD spectrum. The developed infrastructure will be used to collect data of participants with an objective of analysing baseline speech markers in relation to self‐reported mood, anxiety and apathy as well as clinical outcomes from patients’ health care records.ConclusionThe SIDE‐AD study is carrying out research and development of a speech‐based application recording data on individuals’ self‐perceived brain health. A secured online data collection infrastructure is developed to collect speech data with an objective to analyse speech data in combination with routinely available clinical data in Brain Health Clinics, enabling real world validation of speech‐based digital biomarkers for the early detection of AD. We plan to use automatic speech recognition and acoustic patterns for the automatic assessment of brain health status relevant to emergent AD‐specific speech biomarkers.

Error correction algorithm of array time-varying amplitude and phase based on autoencoder

As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology. A deep learning-based algorithm for the correction of time-varying amplitude and phase errors in arrays is proposed in terms of the idea of autoencoder. The algorithm makes full use of the data feature extraction and reconstruction capability of the autoencoder network, designs a deep learning network for the correction of time-varying amplitude phase error of the channel, gives a double-driven learning mechanism without time-varying amplitude phase error data (unperturbed data) and time-varying amplitude phase error data (perturbed data), completes the extraction of the array stream shape hidden features based on the principle of minimising the mean square error of the desired output and the ideal model. The simulated experiments show that the algorithm can effectively correct the time-varying amplitude and phase errors of each channel, and the mean square error of the corrected amplitude and phase errors are within 0.5% and 1.5% respectively when there are ±80% random time-varying amplitude errors and ±5° random time-varying phase errors. The effectiveness of the proposed algorithm is verified.

An effective hybrid deep neural network for underwater acoustic target recognition

Underwater acoustic target recognition is an important support technology for underwater information detection and signal processing. Due to the high complexity of the marine environment, it is not easy to obtain clear and reliable features from ship-radiated noise, which makes underwater acoustic target recognition difficult. We propose a new hybrid network of transformer networks and convolutional neural networks to alleviate this problem. It consists of three convolution modules and a transformer module. The convolution modules are used to learn the local features of the time-frequency map, and the features are enhanced using the residual structure and channel attention. The transformer module is used to extract the global features of the time-frequency map. The proposed network can obtain global and local features of the ship-radiated noise from the time-frequency map of the raw data, which can improve the accuracy of recognition. Experiments were conducted on DeepShip, a public benchmark dataset of ship-radiated noise, and compared with the current state-of-the-art methods. The results show that the proposed hybrid network achieves the highest recognition accuracy on the DeepShip dataset.

Graphene-integrated hybrid plasmonic waveguide for Kerr nonlinear application

Graphene, with its broadband optical properties and high optical nonlinearity, shows promise as an ultrathin material compatible with CMOS technology for all-optical signal processing in integrated photonics. This paper presents simulation, analysis, and design of a novel graphene-integrated nonlinear hybrid plasmonic waveguide. Finite element simulations study the Kerr nonlinear effect of extra graphene layers. The enhanced nonlinear parameter of [Formula: see text][Formula: see text]W[Formula: see text][Formula: see text]Km[Formula: see text] and nonlinear figure-of-merit (FOM) of 2.10 achieved at 1,550[Formula: see text]nm exceed those of silicon-on-insulator (SOI) hybrid plasmonic waveguides. The proposed graphene-integrated hybrid plasmonic waveguide (GHPW) demonstrates high Kerr nonlinearity and FOM, indicating potential for nonlinear all-optical signal processing.

Capturing the pulse: a state-of-the-art review on camera-based jugular vein assessment.

Heart failure is associated with a rehospitalisation rate of up to 50% within six months. Elevated central venous pressure may serve as an early warning sign. While invasive procedures are used to measure central venous pressure for guiding treatment in hospital, this becomes impractical upon discharge. A non-invasive estimation technique exists, where the clinician visually inspects the pulsation of the jugular veins in the neck, but it is less reliable due to human limitations. Video and signal processing technologies may offer a high-fidelity alternative. This state-of-the-art review analyses existing literature on camera-based methods for jugular vein assessment. We summarize key design considerations and suggest avenues for future research. Our review highlights the neck as a rich imaging target beyond the jugular veins, capturing comprehensive cardiac signals, and outlines factors affecting signal quality and measurement accuracy. Addressing an often quoted limitation in the field, we also propose minimum reporting standards for future studies.

Trade-offs Between Delay and Energy in 4-Bit Absolute Value Detector and its application analysis

With the rapid advancements in digital signal processing and communication technology, the utilization of digital detectors has become prevalent across numerous domains. Among these detectors, absolute value detectors hold particular significance. This article primarily delves into the delicate balance between latency and energy consumption within the realm of 4-bit absolute value detectors. To commence, we provide an exhaustive exposition of the fundamental design and operational principles underpinning absolute value detectors. Subsequently, we embark on a comprehensive comparison of various optimization approaches, with a special focus on their efficacy concerning energy consumption and latency. The research endeavors have unveiled that the detector's optimal aspect ratio, as well as the choice of novel materials like graphene and silicon germanium, can wield a substantial influence over its performance. As its peer into the horizon, it is evident that the continuous evolution of new materials and cutting-edge technologies promises further enhancements in the performance of 4-bit absolute value detectors.

From Theory to Practice: A Multidimensional Optimization Method for Improving the Charging Efficiency of Electric Vehicles

This paper focuses on the optimization of dynamic wireless charging technology for electric vehicles to address challenges such as long charging time and few charging locations. The research covers transmit and receive unit optimization, system and power management improvements, and the impact of environmental conditions on charging efficiency. In terms of the optimization of transmitting and receiving devices, the paper discusses the optimization of power, frequency and directivity to improve the energy transmission distance and efficiency. In terms of system and power management optimization, energy loss is reduced through methods such as magnetic disc structure improvement, and the importance of proper power management for battery life and charging safety is emphasized. In addition, the influence of environmental conditions (such as temperature, humidity, and distance) on charging efficiency is also elaborated. High temperature, high humidity and long distance may affect the system performance. The paper puts forward some countermeasures for these effects, such as heat dissipation design, humidity cancellation technology and signal processing technology. However, further field validation and technical, cost and standard issues need to be resolved to achieve commercial application of the technology.

Design of a multiparameter data acquisition and control system for in situ seabed observation base stations

AbstractWith the exploration, development, and research of deep‐sea resources, there is an urgent need for long‐term and continuous observation data of the deep‐sea seabed boundary layer. The traditional method of deep‐sea seabed survey and sampling based on scientific research vessels has the discontinuity of observation data in space and time scales. There are some problems in the seabed in situ observation method based on the seabed observation network for low mobility and high operation and maintenance costs, restricting the in‐depth understanding of the dynamic change process of the deep‐sea floor. To solve the above problems, an open and modular data acquisition control system was designed based on an embedded system and signal processing technology. In terms of the physical, chemical, geological, and ecosystem characteristics of the seafloor or near the seafloor boundary layer, various functional sensors and instrumentation were matched to form an independent underwater integrated measurement or experimental device, eventually realizing in situ multiparameter and long‐time series observations of the seafloor. The system data acquisition and control test were completed through laboratory experiments, which verified the feasibility of the system design. The research showed important theoretical and technical reference significance for the exploration and development of resources in the submarine boundary layer and the promotion of deep‐sea scientific research.

Combining circular laser sensing array with MUSIC algorithm for fast damage localization

Large plate shell structures are key supporting components for aerospace and transportation equipment. Rapid localization of damages in large-scale thin-walled structures is an urgently needed technology for on-site detection and maintenance. This paper proposed a rapid damage localization method based on a laser ultrasonic circular arrays, combining the advantages of the circular arrays and the MUSIC algorithm. It does not need reference signals in advance, the direct waves and scattered waves can be quickly distinguished and extracted according to the time of arrival (TOA). Then, the two-dimensional multiple signal classification method(2D-MUSIC) under near-field conditions is used to quickly locate damage, which is a feature subspace algorithm in array signal processing technology. Compared with other commonly used imaging algorithms, this method has great advantages in detection efficiency and angular resolution. According to the detection method proposed in this paper, the position of single damage and multiple incoherent damages within 0.12 m2 can be quickly found within 1.3 s, and the angle error and distance error are not more than 5° and 5 cm. However, for the damage of coherent sources, there may be artifacts interfering with the imaging results.

Design and Implementation of Hardware-in-the-Loop Simulation Environment Using System Identification Method for Independent Rear Wheel Steering System

In the automotive field, with the advancement of electronic and signal processing technologies, active control-based chassis systems have been developed to enhance vehicle stability. In this study, a Hardware-in-the-Loop (HiL) simulation environment was developed to effectively improve time and cost during the development process of an independent rear-wheel steering system. The HiL Simulation Environment was developed—a specific test bench capable of simulating driving loads on the prototype. Based on the system identification method, a reaction force modeling technique for the target driving loads was proposed. The full vehicle dynamics simulation model was developed with a lateral maximum error of 4.5% and a correlation coefficient of 0.98, as well as a longitudinal maximum error of 0.1% and a correlation coefficient of 0.99. The reaction force generation system had a maximum error of 2.9%. Using the developed HiL simulation environment, performance verification and analysis of the independent rear-wheel steering system were conducted, showing reductions of 5.1% in lateral acceleration and 5.2% in yaw rate.

Analysis and application of key technologies in digital signal processing

With the uncreasing reform of times and the continuous advancement of my science and technology, in recent years, my overall national strength is strengthening gradually. I have entered an information age, one digital age, the use of digital signal processing is becoming more and more frequent and important. Therefore, this paper mainly analyzes the application of DSP, the digital signal processing technology show to make the exposition. Digitalization is a major trend in today's society and the main theme of the development of the times, while digital signal processor is a component born with the times. The so-called digital signal processing is to use a universal digital signal chip to extract useful information from the signal by digital calculation. In recent years, in the wake of the constantly improving of science and technology and the continuous reform and innovation of technology in China, DSP is more and more close to people's production and life, and plays an important role in practical applications. Therefore, it is necessary to discuss and analyze its development.

Design and implementation of 4-bit absolute value detector based on verilog HDL

Absolute value detectors are widely utilized in many disciplines, such as image processing, speech recognition, and control systems, due to the advancement of digital signal processing and computer vision technologies. This paper presents a 4-bit absolute value detector based on the Verilog HDL programming language. Absolute value detectors are extensively used in signal processing, computer vision, and other fields to determine if the absolute value of the input signal meets specific conditions. This paper describes in detail the design process, including module analysis, the drawing of state transfer diagrams, the writing of Verilog HDL code, and simulation verification with a focus on practical applications. The results demonstrate that the absolute value detector is accurate and reliable in determining whether the absolute value of the input signal is greater than or equal to the threshold value.

Signal detection and material identification method for loose particles inside sealed relays based on fusion classification model

In this study, the authors combined the research on loose particle signal and component signal identification with the research on loose particle material identification for the first time, providing comprehensive and reliable loose particle detection results. Based on this, a signal detection and material identification method for loose particles inside sealed relays based on fusion classification model is proposed. Due to the limitations of technical means and confidentiality management, the authors made a real sealed relay sample, and took it as the research object. Through the steps of data acquisition, signal processing, feature engineering, and model training, the dedicated component identification feature library and material identification feature library was constructed, respectively, the component identification model and material identification model based on parameter-optimized SVM with linear kernel and XGBoost was trained, respectively. For the seal relay to be tested, through the steps of data acquisition, signal processing and feature engineering, the data set to be tested was created. The component identification model was used to identify component signals with loose particle signals, and the material identification model was used to identify the materials of loose particles. The majority voting process was used to convert the classification results into identification results, resulting in loose particle detection and material identification results. In addition, the general procedure steps of the proposed method for physical testing were given, and the identification accuracy for device-level loose particle detection was newly proposed. The loose particle testing event containing thirty-seven identification tasks shows that, the achieved identification accuracy was 97.30%, and 92.16% of the average classification accuracy was achieved by the component identification model, 80.41% of the average classification accuracy was achieved by the material identification model. This effectively demonstrates the feasibility and practicality of the proposed method in this paper. It is an important supplement to the loose particle detection research, and provides references for signal detection in similar fields.

Key Technologies for a Beyond-100G Next-Generation Passive Optical Network

The explosive development of emerging telecommunication services has stimulated a huge growth in bandwidth demand as people seek universal access to telecommunication networks. In addition, the kinds of services of an existing optical access network are becoming more flexible. In order to provide higher capacity and meet higher transmission performance requirements, it is necessary to further explore the application of the beyond-100G passive optical network (PON). This paper offers a comprehensive review and outline of the prospects of technologies for bringing a beyond-100G PON to practical applications in the future. We review the current existing technologies, mainly in terms of the physical layer and higher media access control layer. These key technologies for the beyond-100G PON, which plays an increasingly significant role, include the advanced multiplexing technology, physical layer digital signal processing technology, infrastructure-sharing technology, security protection technology, and intelligent control management key technologies. Finally, open issues and new challenges for the next-generation PON are focused upon.

An absolute ranging method based on FMCW principle using dual laser interferometry

The FMCW (Frequency Modulated Continuous Wave) laser interferometer can achieve high-precision displacement measurement by calculating the phase of the interference signal. Due to the need for continuous superposition of signal phases, the optical path and power supply cannot be interrupted during the measurement process. To solve this problem, this article proposes a new absolute ranging method—the decimal comparison method. Two distributed feedback lasers with different central wavelengths are used to measure the same target. The digital signal processing technology is used to solve the sampled interference signals, and the initial phase value corresponding to the decimal of interference is taken. Combined with the central wavelength of the two laser sources, the length variation of the F–P cavity corresponding to the number of interference periods is calculated. Finally, the optical path difference of the target is calculated by the established mathematical model. The experimental results show that this method can realize the absolute measurement of laser interferometry and can expand the application range of FMCW interferometry.

Probabilistic short-term wind speed forecasting using a novel ensemble QRNN

Neural Networks are widely used as the intelligent tool for short-term wind speed forecasting. Owing to the inherent randomness and intermittency of the wind, it is hard to ensure sufficient forecasting precision when using single conventional Network models to conduct deterministic forecasting. Thanks to the recent achievements of ensemble methods, a novel ensemble Quantile Regression Neural Network (QRNN) based on Wavelet decomposition (WD) and least absolute shrinkage and selection operator (LASSO) for probabilistic short-term wind speed forecasting is developed here. The signal processing technology, feature extraction method and predicting model are ingeniously combined in the proposed ensemble QRNN. More specifically, the WD is utilized to extract the signal features into a collection of easy-to-analyze subseries. LASSO regression is combined with the QRNN to conduct the robust prediction. Finally, the kernel density estimation method is adopted to further enhance the forecasting precision. The forecasting performance between the proposed ensemble QRNN and several benchmark models are evaluated using a piece of field measured 10-min mean wind speed data. Results show that the proposed ensemble model can adequately enhance the performance of short-term wind speed forecasting, which can also quantify and reduce the uncertainty of the prediction results.