Waveform Selection Research Articles

Recognition of Radix Bupleuri origin using laser-induced breakdown spectroscopy (LIBS) combined with deep learning and machine learning algorithms

In this study, laser-induced breakdown spectroscopy (LIBS) was utilized to classify Radix Bupleuri from six origins to acquire good results. Several feature variable selection methods have been used to preprocess the experimental data on the LIBS. Machine learning and deep learning algorithms were used to build classification models, respectively. The accuracy of all four classification models on the prediction set is higher than 99%. Among them, the classification model constructed by the residual neural network (ResNet) algorithm demonstrated the optimal prediction accuracy both in the full waveform and feature variable selection. For the independent prediction set, the ResNet classification model still demonstrated optimal prediction with 88.33% accuracy. On the importance ranking of feature variables, the elements as Cl, Mg, Fe, Ba, Li, O, and He were considered to play an important role in the identification of the origin of Radix Bupleuri. This study demonstrated that LIBS combined with deep learning and machine learning algorithms can provide a more efficient and reliable method for the origin identification of Radix Bupleuri. In addition, it can be found that the application of deep learning algorithms can realize the prediction output more efficiently and accurately for the analysis of high-dimensional data of LIBS.

Cognitive Radar Waveform Selection for Low-Altitude Maneuvering-Target Tracking: A Robust Information-Aided Fusion Method

In this paper, we introduce an innovative interacting multiple-criterion selection (IMCS) idea to design the optimal radar waveform, aimingto reduce tracking error and enhance tracking performance. This method integrates the multiple-hypothesis tracking (MHT) and Rao–Blackwellized particle filter (RBPF) algorithms to tackle maneuvering First-Person-View (FPV) drones in a three-dimensional low-altitude cluttered environment. A complex hybrid model, combining linear and nonlinear states, is constructed to describe the high maneuverability of the target. Based on the interacting multiple model (IMM) framework, our proposed IMCS method employs several waveform selection criteria as models and determines the optimal criterion with the highest probability to select waveform parameters. The simulation results indicate that the MHT–RBPF algorithm, using the IMCS method for adaptive parameter selection, exhibits high accuracy and robustness in tracking a low-altitude maneuvering target, resulting in lower root mean square error (RMSE) compared with fixed- or single-waveform selection mechanisms.

Investigating worldwide strong motion databases to derive a collection of free-field records to select design-compatible waveforms for Switzerland

The process of choosing ground motions typically relies on assembling a collection of ground motions that match a desired spectrum. This selection process is guided by specific seismological criteria, including factors like earthquake magnitude, distance from the epicenter, site soil type, and the range of spectral periods that need to fit with the target spectrum. The selection algorithm and the available dataset of waveforms obviously play significant roles in this process. In many engineering and site response applications, it is essential that the input ground motion is representative for the shaking at the free surface of the Earth, and at times also a specific soil type may be required. However, real waveform databases often lack sufficient and/or consistent metadata related to the installation type and soil characterization of recording stations, as well as to the earthquake seismological parameters. This deficiency can lead to the selection of inappropriate waveforms, such as those recorded by stations situated within manmade structures (buildings, bridges, dams) or on a soil type different than the intended one. To address this issue, our approach for creating an appropriate waveform database applicable to Switzerland starts with the computation of seismic hazard disaggregation for return periods of 475 and 975 years. This computation helps identifying the magnitude-distance scenarios most relevant for the five seismic hazard zones defined in the Swiss building code. Once these magnitude-distance ranges are identified, we adhere to established standards regarding the quality control of three-component waveforms and their associated metadata. We assemble a database of waveforms by collating and homogenizing data from available global databases. In the interest of comprehensiveness, we also incorporate data obtained from 3D physics-based numerical simulations of strong-motion near the seismic source. Finally, we employ an algorithm that integrates the Eurocode 8 waveform selection criteria. This algorithm allows us to select and scale waveforms suitable for microzonation and structural analysis studies within each of Switzerland’s five seismic hazard zones. Selecting waveforms compatible with the target design spectra proves to be challenging due to the stringent criteria imposed by Eurocode 8. This challenge arises from the scarcity of recorded waveforms with verified metadata and precise site characterization in the desired magnitude-distance ranges.

An Hybrid Framework OTFS-OFDM Based on Mobile Speed Estimation

The Future wireless communication systems face the challenging task of simultaneously providing highquality service (QoS) and broadband data transmission, while also minimizing power consumption, latency, and system complexity. Although Orthogonal Frequency Division Multiplexing (OFDM) has been widely adopted in 4G and 5G systems, it struggles to cope with a significant delay and Doppler spread in high mobility scenarios. To address these challenges, a novel waveform named Orthogonal Time Frequency Space (OTFS). Designers aim to outperform OFDM by closely aligning signals with the channel behaviour. In this paper, we propose a switching strategy that empowers operators to select the most appropriate waveform based on an estimated speed of the mobile user. This strategy enables the base station to dynamically choose the waveform that best suits the mobile user’s speed. Additionally, we suggest retaining an Integrated Sensing and Communication (ISAC) radar approach for accurate Doppler estimation. This provides precise information to facilitate the waveform selection procedure. By leveraging the switching strategy and harnessing the Doppler estimation capabilities of an ISAC radar.Our proposed approach aims to enhance the performance of wireless communication systems in high mobility cases. Considering the complexity of waveform processing, we introduce an optimized hybrid system that combines OTFS and OFDM, resulting in reduced complexity while still retaining performance benefits.This hybrid system presents a promising solution for improving the performance of wireless communication systems in higher mobility.The simulation results validate the effectiveness of our approach, demonstrating its potential advantages for future wireless communication systems. The effectiveness of the proposed approach is validated by simulation results as it will be illustrated.

Radar Waveform Selection for Maneuvering Target Tracking in Clutter with PDA-RBPF and Max-Q-Based Criterion

Radar Waveform Selection for Maneuvering Target Tracking in Clutter with PDA-RBPF and Max-Q-Based Criterion

A Novel Tracking Algorithm Based on Waveform Selection for Maneuvering Targets in Clutter

The cognition radar utilizes the dynamic waveform configuration to adapt to the environment to improve tracking performance. A novel tracking algorithm based on waveform selection is proposed to address the maneuvering aircraft tracking problem in clutter. Based on the modified current statistical (MCS) model, the modified probabilistic data association filter (MPDAF) is integrated with the square-root cubature Kalman filter (SCKF) to deal with the nonlinear measurement and the clutter. Additionally, the waveform library is established by applying the fractional Fourier transform (FRFT) to a base waveform, and a direct method is proposed to select the optimal waveform to minimize the posterior state error covariance matrix. The simulation results showed that, compared with the filters with the fixed waveform as well as the state-of-the-art algorithms, the proposed algorithm achieved higher estimation precision and lower track loss percentage while maintaining a low computational burden.

Investigating Different Paradigms of Electrical Stimulation for Retinal Prosthesis

Background and Aims: Retinal prostheses have been suggested to restore vision for those with retinal diseases. This study aimed to identify the most effective stimulation approaches for retinal prostheses. Methods: This study examined various return electrode configurations on a realistic retinal ganglion cell model. Two return electrode configurations, monopolar and hexapolar, were implanted on epiretinal side. There were four waveforms used to send electrical current through the active electrode. The study also compared two stimulation methods: single stimulation and concurrent (parallel) stimulation. Results: Directly placing the retinal ganglion cell beneath the active electrode resulted in no significant difference in the required current for stimulating retinal ganglion cells between monopolar and hexapolar return electrodes, regardless of the stimulation waveform. For retinal ganglion cells located away from the active electrode, differences in the required current were noted between the two return electrode configurations. The study discovered that parallel stimulation with two or three active elec¬trodes lowered current requirements by up to 70% compared to single stimulation. Monophasic anod¬ic stimulation yielded the most effective pulse waveform, regardless of the configuration (monopolar or hexapolar) or stimulation mode (single or parallel). Moreover, monophasic cathodic stimulation usually targets retinal ganglion cells around the active electrode. Conclusions: These results illuminate the best stimulation strategies for enhancing the efficacy of retinal prosthe¬sis. The study highlights the importance of electrode arrangement, waveform selection, and single or concurrent stimulation for improving retinal ganglion cell activation.

Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System

Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System

A Radar System with Adaptive Waveform Selection against Dynamic Spoofing Attacks

A Radar System with Adaptive Waveform Selection against Dynamic Spoofing Attacks

Waveform Selection Based on Discrete Prolate Spheroidal Sequences for Near-Optimal SNRs for Photoacoustic Applications

Waveform engineering is an important topic in imaging and detection systems. Waveform design for the optimal Signal-to-Noise Ratio (SNR) under energy and duration constraints can be modelled as an eigenproblem of a Fredholm integral equation of the second kind. SNR gains can be achieved using this approach. However, calculating the waveform for optimal SNR requires precise knowledge of the functional form of the absorber, as well as solving a Fredholm integral eigenproblem which can be difficult. In this paper, we address both those difficulties by proposing a Fourier series expansion method to convert the integral eigenproblem to a small matrix eigenproblem which is both easy to compute and gives a heuristic view of the effects of different absorber kernels on the eigenproblem. Another important result of this paper is to provide an alternate waveform, the Discrete Prolate Spheroidal Sequences (DPSS), as the input waveform to obtain near optimal SNR that does not require the exact form of the absorber to be known apriori.

Collaborative LEO Satellites for Secure and Green Internet of Remote Things

The Internet of Remote Things (IoRT) supported by Low Earth Orbit (LEO) satellites is becoming indispensable for remote sensing, and it will play an important role in the forthcoming sixth generation (6G) communication network. In exploring its applications in remote mining and smart grid, etc., it is found that the implementation of IoRT faces challenges, including limited energy supplies, high mobility, and security vulnerabilities. To address these challenges, we propose employing collaborative LEO satellites to enable the implementation of secure and green IoRT. By combining the uplink signals received at collaborative LEO satellites, the Signal-to-Noise Ratio (SNR) can be significantly improved, so that relieving the transmit power requirement of the energy-limited terminal. Aiming at constructing a collaborative LEO satellite-based IoRT network, this paper introduces the system design principles regarding to frequency planning, waveform selection, collaboration strategies, and terminal design. In order to obtain optimal collaboration performance, we propose a signal coherent combining scheme to compensate Doppler shift, propagation delay, and phase differences. Furthermore, we propose a modified SUMPLE algorithm to estimate and compensate phase differences among satellites, which is applicable to Direct Sequence Spread Spectrum (DSSS) signal scheme. Simulation results demonstrate that our proposed algorithm outperforms the traditional SUMPLE algorithm in combining gain.

Online Bayesian Meta-Learning for Cognitive Tracking Radar

A key component of cognitive radar is the ability to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalize</i> , or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform selection approaches, since transmission policies which are effective in one scene may be highly suboptimal in another. We address this problem by strategically <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">biasing</i> a learning algorithm by exploiting high-level structure across tracking instances, referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meta-learning</i> . In this work, we develop an online meta-learning approach for waveform-agile tracking. This approach uses information gained from previous target tracks to speed up and enhance learning in new tracking instances. This results in sample-efficient learning across a class of finite state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics. We formulate the online waveform selection problem within the framework of Bayesian learning, and provide prior-dependent performance bounds for the meta-learning problem using Probability Approximately Correct (PAC)-Bayes theory. We present a computationally feasible meta-posterior sampling algorithm and study the performance in a simulation study consisting of diverse scenes. Finally, we examine the potential performance benefits and practical challenges associated with online meta-learning for waveform-agile tracking.

On the Value of Online Learning for Radar Waveform Selection

On the Value of Online Learning for Radar Waveform Selection

A vibration-driven locomotion robot excited by time-varying stiffness

This investigation is aimed to propose a vibration-driven friction-induced robot structure with time-varying stiffness. Different from the common vibration-driven robots with force excitation, a vibration-driven robot with sinusoidal stiffness excitation is proposed for the first time. The obtained results reveal the nonlinear driving mechanism of the vibration-driven system under the coupling action of time-varying stiffness and large deformation. First, the dynamic model of a vibration-driven robot excited by the periodic stiffness variation is introduced, and the equations of motion of the robot are discussed. On this basis, the dynamic response of the robot is evaluated via the incremental harmonic balance method, and the effects of the load mass and stiffness are methodically examined and discussed. Finally, an experimental test system based on the dielectric elastomer actuator is established to verify the dynamic performance of the vibration-driven robot excited by the periodic stiffness change. It is found that compared with the common vibration-driven robots, the vibration-driven robot with sinusoidal stiffness excitation can move in both directions avoiding anisotropic friction construction or cumbersome input waveform selection, by controlling the frequency of the input waveform. The result expands the application of the intellectual structure in the field of vibration-driven robots.

Universal Learning Waveform Selection Strategies for Adaptive Target Tracking

Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cramér–Rao lower bound on measurement error is used to select the optimal waveform for each tracking step. However, this approach is only valid in the high SNR regime, and requires a rather restrictive set of assumptions regarding the target motion and measurement models. Furthermore, due to computational concerns, many traditional approaches are limited to near-term, or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">myopic</i> , optimization, even though radar scenes exhibit strong temporal correlation. More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process, allowing for long-term planning. However, a major limitation of reinforcement learning is that the memory length of the underlying Markov process is often unknown for realistic target and channel dynamics, and a more general framework is desirable. This work develops a universal sequential waveform selection scheme which asymptotically achieves Bellman optimality in any radar scene, which can be modeled as a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$U{\text{th}}$</tex-math></inline-formula> -order Markov process for a finite, but unknown, integer <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$U$</tex-math></inline-formula> . Our approach is based on well-established tools from the field of universal source coding, where a stationary source is parsed into variable length phrases in order to build a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">context-tree</i> , which is used as a probabilistic model for the scene’s behavior. We show that an algorithm based on a multialphabet version of the context-tree weighting method can be used to optimally solve a broad class of waveform-agile tracking problems while making minimal assumptions about the environment’s behavior.

Acoustic Emission Source Characterisation during Fatigue Crack Growth in Al 2024-T3 Specimens.

While acoustic emission (AE) testing can be used as a valuable technique in structural health monitoring and non-destructive testing, little research has been conducted to establish its sources, particularly in 2024-T3 aluminium alloys. The major contribution of this work is that it provides a method to obtain a better linear relationship of count rate with crack growth rate based on waveform. This paper aims to characterise AE sources by synchronising the AE waveforms with load levels and then to propose possible dominant frequency ranges. The AE waveforms during fatigue crack growth in edge-notched 2024-T3 aluminium specimens, from an initial crack length of 10 mm to 70 mm, were collected at two different load ratios R = 0.125 and 0.5. At the same time, the crack growth rate was determined using thermal imaging and associated control software. The AE waveforms obtained were processed using the fast Fourier transform. It was shown that a significantly higher AE count rate was recorded at R = 0.125 compared to R = 0.5 when the maximum load was kept the same. This means that the R-ratio would affect the total amount of AE activities collected. It was also found that the dominant frequency range of the AE waveforms directly related to crack growth was 152-487 kHz, and the ranges due to crack closure were likely to be 310 kHz-316 kHz and 500-700 kHz. Based on the proposed frequency ranges, waveform selection was conducted and a better linear relationship between count rate and crack growth rate was observed. This study provides a better understanding of the AE sources and waveforms for future structural health monitoring applications.

Design of a Frequency Selective Rasorber With a Waveform Selective Passband

In this letter, a frequency selective rasorber with a waveform selective passband (FSR-WSP) is proposed based on the combination of frequency selective absorber and waveform selective surface techniques to achieve the multiple functions, including absorbing stealth, energy protection, and waveform selection. The structure achieves more than 90% absorptance in the range of 8.4–18.5 GHz (relative bandwidth reaches 75.1%) and a waveform selective transmission band around 3.8 GHz. For waveform selective function, the transmittance reaches more than 80% when a continuous wave is incident, while the transmittance is less than 25% when a high-power short pulse wave is incident. The absorption characteristics and waveform selective transmission mechanism are analyzed and validated by full-wave simulation and cosimulation. Finally, the FSR-WSP is fabricated and measured to verify its functionality.

Joint Transmit Resource Management and Waveform Selection Strategy for Target Tracking in Distributed Phased Array Radar Network

In this article, a joint transmit resource management and waveform selection (JTRMWS) strategy is put forward for target tracking in distributed phased array radar network. We establish the problem of joint transmit resource and waveform optimization as a dual-objective optimization model. The key idea of the proposed JTRMWS scheme is to utilize the optimization technique to collaboratively coordinate the transmit power, dwell time, waveform bandwidth, and pulse length of each radar node in order to improve the target tracking accuracy and low probability of intercept (LPI) performance of distributed phased array radar network, subject to the illumination resource budgets and waveform library limitation. The analytical expressions for the predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and subsequently adopted as the metric functions to evaluate the target tracking accuracy and LPI performance, respectively. It is shown that the JTRMWS problem is a nonlinear and nonconvex optimization problem, where the above four adaptable parameters are all coupled in the objective functions and constraints. Combined with the particle swarm optimization algorithm, an efficient and fast three-stage-based solution technique is developed to deal with the resulting problem. Simulation results are provided to verify the effectiveness and superiority of the proposed JTRMWS algorithm compared with other state-of-the-art benchmarks.

Optimizing Terahertz Waveform Selection of a Pharmaceutical Film Coating Process Using Recurrent Network

In-line terahertz pulsed imaging has been utilized to measure the film coating thickness of individual tablets during the coating process in a production-scale pan coater. A criteria-based waveform selection algorithm (WSA) was developed to select terahertz signals reflected from the surface of coating tablets and determine the coating thickness. Since the WSA uses many criteria thresholds to select terahertz waveforms of sufficiently high quality, it could reject some potential candidate tablet waveforms that are close but do not reach the threshold boundary. On the premise of the availability of large datasets, we aim to improve the efficiency of WSA with machine learning. This article presents a recurrent neural network approach to optimize waveform selection. In comparison with the conventional method of WSA, our approach allows more than double the number of waveforms to be selected while maintaining excellent agreement with offline thickness measurements. Moreover, the processing time of waveform selection decreases so that it can be applied for real-time coating monitoring in the pharmaceutical industry, which leads to more advancements in quality control for pharmaceutical film coating.

Joint waveform selection and power allocation algorithm in manned/unmanned aerial vehicle hybrid swarm based on chance-constraint programming

In this paper, we propose a joint waveform selection and power allocation (JWSPA) strategy based on chance-constraint programming (CCP) for manned/unmanned aerial vehicle hybrid swarm (M/UAVHS) tracking a single target. Accordingly, the low probability of intercept (LPI) performance of system can be improved by collaboratively optimizing transmit power and waveform. For target radar cross section (RCS) prediction, we design a random RCS prediction model based on electromagnetic simulation (ES) of target. For waveform selection, we build a waveform library to adaptively manage the frequency modulation slope and pulse width of radar waveform. For power allocation, the CCP is employed to balance tracking accuracy and power resource. The Bayesian Cramér-Rao lower bound (BCRLB) is adopted as a criterion to measure target tracking accuracy. The hybrid intelli gent algorithms, in which the stochastic simulation is integrated into the genetic algorithm (GA), are used to solve the stochastic optimization problem. Simulation results demonstrate that the proposed JWSPA strategy can save more transmit power than the traditional fixed waveform scheme under the same target tracking accuracy.