Perform Signal Processing Research Articles

Determination of steady-state for continuous powder mixing: Analysis of mixture properties and signal processing

Continuous powder mixing offers many advantages over batch processes, but its industrial implementation requires control of transient phases due to startup or variations in operating conditions. Detecting the steady-state by observing the mixture properties, obtained through in-line analysis of the mixture composition, is challenging due to the significant fluctuations in these properties. However, there is no quantitative method for identifying the steady-state for continuous powder mixing. We therefore propose in this work a methodology for steady-state detection by comparing several methods of mixture property analysis and signal processing. We show that conventional methods of mixture property analysis cannot reliably and accurately detect steady-state. We conclude that the best methodology is to perform signal processing on the content of the key component in the mixture. To do this, the signal of the content of the key component is first smoothed by the Savitzky-Golay filter. Then, the standard deviation of the filtered signal is compared with a tuning parameter to detect the beginning of steady-state. This method could be used to determine the mixing conditions leading to the smallest startup time, hence avoiding costly waste.

On-board radar complex for high-precision direction finding of radio sources for autonomous guidance of winged UAVs

The subject of this article is the design of a cheap, simple, and highly accurate on-board radar system for determining the angular position of radio-signal emission stations, which can be used to guide UAVs to a specified area of space. The aim of this study is to develop high-precision functioning algorithms, practical recommendations for implementation, and experimental measurements of the main parameters of the onboard radio direction finders of radio radiation sources placed on winged UAVs. Objectives of the research: 1) analysis of the statistical theory of optimization of signal processing algorithms in radar systems; 2) synthesis of direction-finding algorithms for radio measurement sources capable of performing measurements with high accuracy and in a wide range of unambiguous measurement angles; 3) synthesis of the radio direction finder structural scheme and its simulation modelling; 4) design of radio direction finder receiving antennas capable of operating separately and in a complex; 5) justification of the choice of components for the input paths of receivers, parameters of ADC and microcomputers that make up the main components of the radar complex; 6) manufacturing of a working model of the radar complex; 7) experimental measurements in the laboratory. The methods for solving the tasks are based on the statistical theory of optimization of radio engineering systems for remote sensing and radar, existing software tools for simulation modelling, and the theory of radio measurements. The main idea behind creating a cheap, simple, and highly accurate on-board radar system for radio emitter direction finding is to combine the results of angular position measurements from several amplitude and phase meters. This approach allows us to create an easy-to-implement radio system with the advantages of different measuring instruments. The following results were obtained: 1) theoretical studies and simulation modelling confirmed that by combining measurements from a two-antenna amplitude direction finder with narrow diagrams, a two-antenna direction finder with wide diagrams, and a two-antenna phase finder, it is possible to achieve both high accuracy and a wide range of unambiguous measurements; 2) a radar system based on a cheap and commonly available element base was developed; 3) antennas were developed that can be easily installed under the wings of UAVs; 4) experimental studies confirmed the performance of complex signal processing in an on-board six-antenna radio direction finder. The material of this work forms the basis for further experimental development of radio-direction finders for various purposes, opens up opportunities for overcoming the contradiction between accuracy and range of unambiguous measurements, and highlights an additional direction for increasing the autonomy of winged UAVs.

Transformer and cross-attention-based multi-sensor in-situ monitoring of molten pool stability and part quality in laser powder bed fusion

The fluctuations in process parameters, as a random phenomenon in the laser powder bed fusion (LPBF) process, is occasional. These subtle variations can lead to defects such as porosity if not monitored and corrected promptly. Therefore, monitoring the stability of the molten pool and small changes in process parameters is significant. For process monitoring, the commonly used Transformer architectures have demonstrated remarkable performance in single-sensor signal processing. However, as the core component of the Transformer, the self-attention mechanism has limitations in data fusion of multi-sensor signals. To this end, a novel Transformer-based deep learning method associated with the cross-attention mechanism (Trans-Cross) is proposed. Trans-Cross leverages the collected photodiode and acoustic signals to monitor the fluctuation of process parameters as well as the quality of parts. Specifically, the cross-attention mechanism is introduced to enhance representative feature extraction from photodiode and acoustic signals, exploiting their complementary information. To better characterize the raw signals, an improved adaptive variational mode decomposition (VMD) algorithm is proposed for data preprocessing. The proposed Trans-Cross achieves impressive prediction accuracies of 97.41 % for identifying the fluctuations in the process parameters and 99.73 % for part quality classification. Trans-Cross exhibits superior performance and strong robustness when dealing with constraints such as limited input signal length and training data ratio. These results validate the feasibility of the proposed method in accurately identifying the fluctuations in process parameters as well as the part quality. This work provided data guidance to enhance the stability of the LPBF process.

Simulation of neural functions based on organic semiconductor/MXene synaptic transistors

Artificial synaptic devices, which are the basic units of neuromorphic computing systems, can perform signal processing with low power consumption. Organic synaptic transistors have attracted significant attention owing to their lightweight and good compatibility with flexible substrates. As per the current state of research both domestically and internationally, the majority of the existing organic synaptic transistors are based on floating gate structures, electret configurations, ferroelectric types. Furthermore, additional capture layers are required for the preparation of these devices. Two-dimensional MXenes have great potential in the preparation of synaptic transistors owing to their efficient multiple energy storage capabilities, excellent metallic conductivity, abundant surface functional groups, hydrophilicity, and layered structure. Therefore, high-performance synaptic transistors based on the two-dimensional material MXene were developed in this study. These transistors not only exhibited excellent memory performance with a memory window above 20 V, but also successfully simulated typical synaptic behaviors, including excitatory postsynaptic current/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation/paired-pulse depression (PPF/PPD), and long-term plasticity (LTP). Synaptic transistors based on MXenes represent a promising approach for the preparation of high-performance organic synaptic transistors.

The Optimization Research on RF Chip Signal Processing Based on Deep Learning Algorithms

With the rapid development of wireless communication technology, radio frequency (RF) chips, as the core components of communication systems, directly influence the overall system performance through their signal processing capabilities. Traditional signal processing methods often struggle to achieve optimal results in the face of complex and variable communication environments. In recent years, deep learning technology, due to its powerful feature learning and pattern recognition capabilities, has shown tremendous potential in various fields. This paper addresses the signal processing issues of RF chips and proposes an optimization method based on deep learning algorithms. Firstly, the status and challenges of RF chip signal processing are analyzed, followed by the design of a deep learning model suitable for RF signal processing. Through data pre-processing and feature extraction, the generalization ability of the model is enhanced. Experimental results demonstrate that the proposed method outperforms traditional methods in signal noise suppression, interference cancellation, and signal distortion compensation, significantly improving the signal processing performance of RF chips. This research provides new ideas and methods for the application of deep learning in the field of RF chip signal processing and holds significant theoretical and practical importance for the future development of wireless communication systems.

Assessing Student Performance in Digital Signal Processing (DSP) Subjects: A Comparative Study of Traditional and Online Learning Environments

Assessing Student Performance in Digital Signal Processing (DSP) Subjects: A Comparative Study of Traditional and Online Learning Environments

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes.

Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed myoelectric hands still adopt two-electrode sEMG systems, detailed studies on signal processing and classification performance are still lacking. In this study, nine able-bodied participants were recruited to perform six typical hand actions, from which sEMG signals from two electrodes were acquired using a Delsys Trigno Research+ acquisition system. Signal processing and machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines (SVM), were used to study classification accuracies. Overall classification accuracy of 93 ± 2%, action-specific accuracy of 97 ± 2%, and F1-score of 87 ± 7% were achieved, which are comparable with those reported from multi-electrode systems. The highest accuracies were achieved using SVM algorithm compared to LDA and KNN algorithms. A logarithmic relationship between classification accuracy and number of features was revealed, which plateaued at five features. These comprehensive findings may potentially contribute to signal processing and machine learning strategies for commonly prescribed myoelectric hand systems with two sEMG electrodes to further improve functionality.

Lateral heterostructures of WS2 and MoS2 monolayers for photo-synaptic transistor

Von Neumann architecture-based computing, while widely successful in personal computers and embedded systems, faces inherent challenges including the von Neumann bottleneck, particularly amidst the ongoing surge of data-intensive tasks. Neuromorphic computing, designed to integrate arithmetic, logic, and memory operations, has emerged as a promising solution for improving energy efficiency and performance. This approach requires the construction of an artificial synaptic device that can simultaneously perform signal processing, learning, and memory operations. We present a photo-synaptic device with 32 analog multi-states by exploiting field-effect transistors based on the lateral heterostructures of two-dimensional (2D) WS2 and MoS2 monolayers, formed through a two-step metal–organic chemical vapor deposition process. These lateral heterostructures offer high photoresponsivity and enhanced efficiency of charge trapping at the interface between the heterostructures and SiO2 due to the presence of the WS2 monolayer with large trap densities. As a result, it enables the photo-synaptic transistor to implement synaptic behaviors of long-term plasticity and high recognition accuracy. To confirm the feasibility of the photo-synapse, we investigated its synaptic characteristics under optical and electrical stimuli, including the retention of excitatory post-synaptic currents, potentiation, habituation, nonlinearity factor, and paired-pulse facilitation. Our findings suggest the potential of versatile 2D material-synapse with a high density of device integration.

Signal processing collaborated with deep learning: An interpretable FIRNet for industrial intelligent diagnosis

Due to the neglect of prior characteristics and the lack of explicit constraints on fault knowledge, conventional intelligent diagnosis methods suffer from great hardships in exacting fault-sensitive information and making explainable decisions, resulting in poor interpretability and inferior robustness. Motivated by the excellent multiscale analysis performance of signal processing and the powerful feature mining ability of deep learning, this study proposes an interpretable FIRNet for equipment intelligent diagnosis under strong noise environments. It consists of a well-crafted FIRLayer and a deep learning backbone. Inspired by the modulation principle that fault-sensitive components are normally modulated into multiscale mode characteristics, multiple ex-ante interpretable filters with two learnable parameters, including center frequency and bandwidth, are analytically designed to process the sequence signal sets, represented as signal-processing-based FIRLayer where the extracted multiscale feature maps are taken as an interpretable status information expression. Subsequently, multiscale convolutional kernels are established to extract the high-level feature maps and further make the final diagnostic decisions, represented as the deep learning backbone. The simulated and experimental results show that the proposed FIRNets have higher identification precision compared to the other nine deep learning models. Specially, three aspects, including model interpretability, noise robustness capacity and edge intelligent diagnosis, are further analyzed to illustrate the interpretable advantages of FIRNets. Hereinto, feature visualizations of the FIRLayer and mode decision contributions of the deep learning backbone are in-depth analyzed to verify the interpretable feature representation and decision-making principle of FIRNets. The results indicate that FIRNets have superior ex-post interpretability compared to other methods. Focusing on industrial practices, an efficient edge diagnosis system based on a pruned FIRNet is established, and an online diagnosis accuracy of more than 99% has been achieved. It can be foreseen that the proposed FIRNets show great potential and competitiveness to promote the edge computing application of equipment intelligent diagnosis.

Great-length wavelets on quantum computing platform: Applications and fast approximations

Wavelets with great filter length, also known as high-order wavelets, exhibit favorable properties and superior performance in classical signal processing. We study the performance of great-length wavelets in the quantum computation of a dynamical model. Numerical results indicate that certain classical properties of wavelets can significantly improve their quantum localization capability. In particular, smooth wavelets have a quantum localization capability up to 10 times stronger than non-smooth ones, as measured by the Inverse Participation Ratio (IPR).Also, we propose an efficient quantum algorithm for approximating wavelets of any length m in time Opoly(logN,log1ϵ,m), achieving a poly-logarithmic dependence on the inverse of precision 1ϵ and the system dimension N. We show analytically and numerically that the quantum wavelet-based denoising algorithm succeeds with a constant probability, independent of the dimension N.

Evaluation on improved sparse signal reconstruction algorithm for trusted AI and DCS technology

The improved Sparse Signal Reconstruction (SR) algorithm for Trusted Artificial Intelligence (AI) and Distributed Compressed Sensing (DCS) technology was thoroughly investigated. The study verified its effectiveness and advantages in trusted AI and DCS systems, which have significant implications for enhancing the credibility, security, and performance of signal processing and AI algorithms. The reconstruction performance was evaluated using Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), and Least Absolute Shrinkage and Selection Operator (LASSO). The analysis primarily focused on runtime, refactoring errors, and the number of successful reconstruction attempts. When K = 4, K = 6, K = 8, and K = 10, OMP outperformed BP and LASSO in terms of successful reconstructions, demonstrating better performance and higher reconstruction precision.

Improving estimation performance of compressive sensing‐based multiple‐input multiple‐output radar using electronic beamsteering

AbstractTwo‐dimensional direction‐of‐arrival (DoA) estimation in azimuth and elevation via radar systems equipped with uniform rectangular arrays (URAs) will play an important role in various application areas—most distinctively in future urban air mobility settings with unmanned aerial vehicles. A key factor is the fast and reliable provision of target detections in terms of range and DoA for safe autonomous operation of the vehicle using on‐board antenna arrays with compact installation size. The authors present a technique for improving the performance of DoA estimation using compressive sensing in conjunction with multiple‐input multiple‐output arrays with electronically steered beams in the transmit direction. The simulation study investigates the impact of different design considerations on radar signal processing performance. An optimisation of a radar system using electronic beamsteering in the transmit domain is presented numerically. Based on the architecture of the URAs used, performance and detection accuracy can be improved.

Fully Reconfigurable Photonic Filter for Flexible Payloads

Reconfigurable photonic filters represent cutting-edge technology that enhances the capabilities of space payloads. These advanced devices harness the unique properties of light to deliver superior performance in signal processing, filtering, and frequency selection. They offer broad filtering capabilities, allowing for the selection of specific frequency ranges while significantly reducing Size, Weight, and Power (SWaP). In scenarios where satellite communication channels are crowded with various signals sharing the same bandwidth, reconfigurable photonic filters enable efficient spectrum management and interference mitigation, ensuring reliable signal transmission. Furthermore, reconfigurable photonic filters demonstrate their ability to adapt to the dynamic space environment, withstanding extreme temperatures, radiation exposure, and mechanical stress while maintaining stable and reliable performance. Leveraging the inherent speed of light, these filters enable high-speed signal processing operations, paving the way to various space payload applications, such as agile frequency channelization. This capability allows for the simultaneous processing and analysis of different frequency bands. In this theoretical study, we introduce a fully reconfigurable filter comprising two decoupled ring resonators, each with the same radius. Each resonator can be independently thermally tuned to achieve reconfigurability in both central frequency and bandwidth. The precise reconfiguration of both central frequency and bandwidth is achieved by using the thermo-optic effect along the whole ring resonator path. A stopband rejection of 45 dB, with a reconfigurable bandwidth and central frequency of 20 MHz and 180 MHz, respectively, has been numerically achieved, with a maximum electrical power of 11.50 mW and a reconfiguration time of 9.20 µs, by using the scattering matrix approach, where the elements have been calculated through Finite Element Method-based and Beam Propagation Method-based simulations. This performance makes the proposed device suitable as key building block of RF optical filters, useful in the next-generation telecommunication payload domain.

Achieving Ultrawide Tunability in Monolithically Fabricated Si Nanoresonator Devices.

Nanoresonators are powerful and versatile tools promising to revolutionize a wide range of technological areas by delivering unparalleled performance in physical, chemical, biological sensing, signal and information processing, quantum computation, etc., via their high-frequency resonant vibration and rich dynamic behavior. Having the ability to tune the resonance frequency and dynamic behavior at the application stage promises further improvement in their effectiveness and enables novel applications. However, achieving significant room-temperature tunability in conventional (monolithically fabricated) nanoresonators is considered challenging. Here we demonstrate ultrawide electrostatic tuning (∼70%) of (initial) resonance-frequency (∼7% V-1) at room temperature in a monolithically fabricated ultrathin Si nanoresonator (width ∼ 40 nm, length ∼ 200 μm) device. Extreme electrostatic tuning of nonlinear behavior is also demonstrated by canceling the cubic-nonlinear coefficient and subsequently flipping its sign. Thus, these results are expected to provide remarkable operational flexibility and new capabilities to microfabricated resonators, which will benefit many technological areas.

A microfluidic transistor for automatic control of liquids

Microfluidics have enabled notable advances in molecular biology1,2, synthetic chemistry3,4, diagnostics5,6 and tissue engineering7. However, there has long been a critical need in the field to manipulate fluids and suspended matter with the precision, modularity and scalability of electronic circuits8–10. Just as the electronic transistor enabled unprecedented advances in the automatic control of electricity on an electronic chip, a microfluidic analogue to the transistor could enable improvements in the automatic control of reagents, droplets and single cells on a microfluidic chip. Previous works on creating a microfluidic analogue to the electronic transistor11–13 did not replicate the transistor’s saturation behaviour, and could not achieve proportional amplification14, which is fundamental to modern circuit design15. Here we exploit the fluidic phenomenon of flow limitation16 to develop a microfluidic element capable of proportional amplification with flow–pressure characteristics completely analogous to the current–voltage characteristics of the electronic transistor. We then use this microfluidic transistor to directly translate fundamental electronic circuits into the fluidic domain, including the amplifier, regulator, level shifter, logic gate and latch. We also combine these building blocks to create more complex fluidic controllers, such as timers and clocks. Finally, we demonstrate a particle dispenser circuit that senses single suspended particles, performs signal processing and accordingly controls the movement of each particle in a deterministic fashion without electronics. By leveraging the vast repertoire of electronic circuit design, microfluidic-transistor-based circuits enable fluidic automatic controllers to manipulate liquids and single suspended particles for lab-on-a-chip platforms.

High-frequency source localization based on steered frequency-wavenumber analysis using sparse array

In this study, we address the problem of estimating the position of a nearby target emitting a high-frequency signal using sparse array. One of the representative localization techniques, Matched Field Processing (MFP), is known to demonstrate reliable performance below 1 kHz. However, at frequencies higher than the design frequency of the array sensor, the localization becomes challenging due to spatial aliasing. Nevertheless, Frequency-Difference Matched Field Processing (FDMFP) is a technique that successfully estimates the position of the target even in the presence of spatial aliasing. It performs signal processing in the low-frequency band using low-frequency components obtained from two high-frequency components. To ensure reliability, we compared and validated the results of this study with the results of FDMFP. In this research, we completed the algorithm by controlling the phenomenon of striation shifting caused by spatial aliasing in the conventional frequency-wavenumber(f-k) analysis through beam steering. We steered the received signal to the location of signal reception and identified the target's position using the f-k spectrum of the steered signal. The data used in the study is the sound of snapping shrimp from SAVEX15, with a frequency range of 5–24 kHz wideband impulse signals.

Stochastic resonance in a single autapse–coupled neuron

The signal detection ability of nervous system is highly associated with nonlinear and collective behaviors in neuronal medium. Neuronal noise, which occurs as natural endogenous fluctuations in brain activity, is the most salient factor influencing this ability. Experimental and theoretical research suggests that noise is beneficial, not detrimental, for regular functioning of nervous system. In this regard, there is a general agreement that noise at an adequate intensity can engage rhythmic activity in brain and noise-induced oscillations enhances performance of the weak signal processing, especially when frequency of the signal is around that of the noise-induced rhythmic oscillation. This behavior in biological neural systems is explained by the notion of “stochastic resonance”. Another factor that plays a key role in regulating neuronal behaviors, including motor and cognitive tasks by maintaining signaling between cells, is characteristics of synapses different in structure and functioning. Here, we study stochastic resonance in Hodgkin–Huxley neuron that has a peculiar synaptic connection called autapse, known as a biophysical feedback mechanism, under presynaptic noise originating from superposition of inhibitory and excitatory Poisson bombardment. Our results show that, under certain conditions, autapse dynamics are able to improve the weak signal detection performance of Hodgkin–Huxley neuron via stochastic resonance. This study provides novel insights into functional role of autapse in neural information processing by revealing a biophysical aspect of stochastic resonance with numerical computations.

Exploring the Potential of Mixed Fourier Series in Signal Processing Applications Using One-Dimensional Smooth Closed-Form Functions with Compact Support: A Comprehensive Tutorial

This paper studies and analyzes the approximation of one-dimensional smooth closed-form functions with compact support using a mixed Fourier series (i.e., a combination of partial Fourier series and other forms of partial series). To explore the potential of this approach, we discuss and revise its application in signal processing, especially because it allows us to control the decreasing rate of Fourier coefficients and avoids the Gibbs phenomenon. Therefore, this method improves the signal processing performance in a wide range of scenarios, such as function approximation, interpolation, increased convergence with quasi-spectral accuracy using the time domain or the frequency domain, numerical integration, and solutions of inverse problems such as ordinary differential equations. Moreover, the paper provides comprehensive examples of one-dimensional problems to showcase the advantages of this approach.

Design of a Digital Array Signal Processing System with Full Array Element

For the application of a digital array signal processing system with a high sampling rate and large bandwidth, and considering the constraints of limited resources (performance, volume, power consumption, weight, etc.), this paper proposes a design and implementation method of broadband digital array signal processing system based on radio frequency system on a chip. The system focuses on multi-channel synchronization under a high sampling rate and multi-channel amplitude and phase calibration under large bandwidth to form an all-digital, low-power, multi-channel, high-speed, large bandwidth hardware platform suitable for broadband digital array signal processing. Through the implementation of the Multiple-Input Multiple-Output phased-array digital beamforming algorithm, the test conducted verifies that the system has superior signal processing performance of the broadband digital array.

Personal Health Monitoring with Bluetooth Sensors and Android Smartphones

The objective of this research project is to utilize a multi-modal biosensor platform incorporating multiple MEMS sensors for continuous vital signs monitoring, and integrate it with a smartphone unit for cellular network connectivity and communication.Using Bluetooth interface, sensor data is transferred to smartphone devices equipped with Android operating system. This enables real time data analysis with smartphone devices. Smartphones are used for displaying incoming sensor data and performing signal processing. With the use of internet enabled smartphones, patients and doctors can be more in touch, and doctors can monitor/keep track of their patients’ health status. The miniature size of the sensors allows them to be practical and convenient to be carried around in everyday activities. Since most patients already carry personal smartphones, health monitoring technology can be deemed affordable, requiring only Bluetooth enabled sensors and an application software.Although several patient health monitoring applications exist, these solutions typically have a separate device to process the data and send it wirelessly. The goal of this research is to replace this extra hardware device with the patient’s personal phone to do the processing and the sharing of the data. The proposed solution makes it easier for the patients to carry the device around, since it is comprised of only the sensors and an application software running on the host smartphone platform that we all carry in our daily lives.