Signal Frequency Research Articles

Specific Emitter Identification Algorithm Based on Time–Frequency Sequence Multimodal Feature Fusion Network

Specific emitter identification is a challenge in the field of radar signal processing. Its aims to extract individual fingerprint features of the signal. However, early works are all designed using either signal or time–frequency image and heavily rely on the calculation of hand-crafted features or complex interactions in high-dimensional feature space. This paper introduces the time–frequency multimodal feature fusion network, a novel architecture based on multimodal feature interaction. Specifically, we designed a time–frequency signal feature encoding module, a wvd image feature encoding module, and a multimodal feature fusion module. Additionally, we propose a feature point filtering mechanism named FMM for signal embedding. Our algorithm demonstrates high performance in comparison with the state-of-the-art mainstream identification methods. The results indicate that our algorithm outperforms others, achieving the highest accuracy, precision, recall, and F1-score, surpassing the second-best by 9.3%, 8.2%, 9.2%, and 9%. Notably, the visual results show that the proposed method aligns with the signal generation mechanism, effectively capturing the distinctive fingerprint features of radar data. This paper establishes a foundational architecture for the subsequent multimodal research in SEI tasks.

EMPIRICAL STUDY OF SIGNAL STRENGTH PATH LOSS IN A UNIVERSITY CAMPUS ENVIRONMENT

Conducting performance analysis can be a valuable tool for mobile network operators (MNO) in optimizing network performance, improving user experience, and ensuring services, such as voice calls, data transmission, and video streaming, as well as to adhere to quality standards. This approach facilitates capacity planning, resource allocation, and the prompt identification and resolution of network congestion, hardware failures, or security breaches. In this paper, the performance of three MNOs around Papua New Guinea University of Technology (PNGUoT) is examined. The downlink frequency signal was measured and collected using the MST207T spectrum analyzer. The allocated frequency spectrum for each mobile network evolution from 3G to 4G-LTE was analyzed and calculated for signal strength path loss utilizing Free Space Path Loss (FSPL) and the Cost 231 Hata Model methods. 5G is not discussed in this paper as the technology is awaiting PNG market entry approval. The findings indicate that Digicel towers exhibit a decrease in downlink signal intensity and quality in areas with poor signal reception. Certain technical issues were observed to be responsible for the identified poor performances, including overcrowding of base stations (BS). The signal strength qualities of Bmobile Vodafone and Telikom exhibit variations, suggesting a moderate to poor level of quality. The data results from the path loss calculation also show an increase in fading signal strength quality. The findings of this empirical study would provide valuable insights for enhancing the efficiency and coverage of mobile communication networks around PNGUoT.

Test and evaluation of radar systems operating in the modern electromagnetic spectrum

AbstractModern radar systems are critical infrastructure in defence and civilian applications. In the defence domain, modern radars operate in a highly contested, congested, and constrained electromagnetic environment. In the context of operating in this challenging environment, the challenges across the system life cycle are examined in testing modern radar systems which are characterised by advanced features, such as multi‐function capability, advanced digital signal processing, high resolution, wideband, and frequency agile systems that incorporate phase array and active electronically scanned array capabilities. The challenges and key objectives in testing these sophisticated modern radar systems are highlighted, as well as the enabling technologies that can support Test & Evaluation of such systems. An architecture for Hardware‐in‐the‐Loop testing, which leverages modular and flexible commercial‐of‐the‐shelf hardware, is proposed together with an application software that allows the testing of radar with a radar target generator.

An Adaptive RF Front-End Architecture for Multi-Band SDR in Avionics.

This study introduces a reconfigurable and agile RF front-end (RFFE) architecture that significantly enhances the performance of software-defined radios (SDRs) by seamlessly adjusting to varying signal requirements, frequencies, and protocols. This flexibility greatly enhances spectrum utilization, signal integrity, and overall system efficiency-critical factors in aviation, where reliable communication, navigation, and surveillance systems are vital for safety. A versatile RF front-end is thus indispensable, enhancing connectivity and safety standards. We explore the integration of this flexible RF front-end in SDRs, focusing on the detailed design of essential components, such as receivers, transmitters, RF switches, combiners, and splitters, and their corresponding RF pathways. Comprehensive performance evaluations confirm the architecture's reliability and functionality, including an extensive analysis of receiver gain, linearity, and two-tone test results. These assessments validate the architecture's suitability for aviation radios and address considerations of size, weight, and power-cost (SWaP-C), demonstrating significant gains in operational efficiency and cost-effectiveness. The introduction of the new RF front-end on a single SDR board not only substantially reduces size and weight but also adds up to 18 dB gain to the received signal. It also allows for a high level of design flexibility, enabling seamless software transitions between different radios and the capacity to manage three times more radios with the same hardware, thereby significantly boosting the system's ability to handle multiple radio channels efficiently.

Implementation of fuzzy logic controller for positive output LUO converter

This paper describes the fuzzy logic controller (FLC) for a positive output LUO converter (POLC), which uses a voltage controller to achieve the output voltage regulation. The fourth-order POLC DC–DC converter produces positive output voltage utilizing the voltage lift technique. Due to the converter’s switching functions and its inherently time-varying nature, it exhibits a highly nonlinear dynamic response. To enhance the dynamic performance of the converter, controllers are designed using small signal model and frequency response analysis. The closed loop is used to evaluate converter performance in the presence of variations in reference voltage, source voltage and load disturbances. The power loss analysis of the converter is performed. The comparative results show that FLC gives good dynamic response for wide range of variations than the proportional–integral (PI) controller. The prototype model is built using dsPIC (30F4011) microcontroller, and the experimental results are presented to validate the simulation results.

Comparison of sEMG activity of submental muscles between the head-lift exercise, tongue-press exercise and the head extension tongue-press exercise in healthy young adults.

Swallowing is a complex function that requires precise coordination between different muscles. Weakness in submental muscles can lead to complications such as pharyngeal residue and aspiration. Therefore, exercise interventions targeting these muscles, such as the Head-Lift Exercise (HLE), are clinically important but pose challenges. This study aimed to compare the myoelectric activity of submental muscles during traditional Head-Lift Exercise (HLE), Tongue-Press Exercise (TPE) and Head Extension Tongue-Press Exercise (HETPE), providing potential alternatives to HLE. Thirty healthy volunteers (22 females and 8 males, mean age 25.63 ± 5.27 years) participated in this cross-sectional study. Surface electromyography (sEMG) recorded activity of the submental muscles bilaterally during HLE, TPE and HETPE. Participants performed each manoeuvre for 14 s, repeated three times in a counterbalanced order. Statistical analyses assessed differences in mean, maximum and median frequency of the sEMG signals among manoeuvres using repeated-measures analysis of variance (RM-ANOVA). The experienced irritation levels by participants during manoeuvres were also compared by the Friedman test. HETPE exhibited significantly higher maximum sEMG activity compared to HLE and TPE (p < .05). Mean sEMG activity was significantly increased during HETPE compared to TPE. Median frequency was significantly lower during HETPE compared to the two other exercises, indicating greater muscle fatigue. Participants reported similar levels of irritation for HLE and HETPE, and significantly lower levels during TPE. These results indicate that HETPE is effective in activating and strengthening submental muscles, potentially serving as a viable alternative to HLE without added difficulty. Further research is recommended to assess the long-term impacts on swallowing physiology in patients with dysphagia.

Insights into the Mechanisms of Single-Photon and Two-Photon Excited Surface Enhanced Fluorescence by Submicrometer Silver Particles.

Surface enhanced fluorescence (SEF) based on noble metal nanoparticles is an effective means to achieve high sensitivity in fluorescence detection. Currently, the physical mechanism behind enhanced fluorescence is not fully understood. This paper measures the fluorescence signals of Dihydroporphyrin f methyl ether (CPD4) under both single-photon and two-photon excitation based on submicrometer silver particles with rough morphologies, achieving enhancement factors of 34 and 45 times, respectively. On this basis, by combining the radiative field characteristics produced by the silver particles, a stimulated radiation model of molecules is established to elucidate the changes in the molecular photophysical process when influenced by silver particles. Moreover, the fluorescence lifetime of the molecules was measured, showing that the presence of silver particles induces an increase in the molecular radiative decay rate, causing the fluorescence lifetime to decay from 3.8 ns to 3 ns. The results indicate that the fluorescence enhancement primarily originates from the submicrometer silver particles' enhancement effect on the excitation light. Additionally, the fluorescence signal emitted by the molecules couples with the silver particles, causing the local surface plasmon resonances generated by the silver particles to also emit light signals of the same frequency. Under the combined effect, the fluorescence of the molecules is significantly enhanced. The findings provide a theoretical foundation for understanding the fluorescence enhancement mechanism of silver particles, adjusting the enhancement effect, and developing enhanced fluorescence detection devices based on submicrometer silver particles, holding significant practical importance.

Wavelet-based generation of fully non-stationary random processes with application to seismic ground motions

Assessing the performance of earthquake-resistant structural and geotechnical systems is crucial for achieving a desired reliability level and enhancing the resilience of the built environment in seismic-prone regions. Nonlinear dynamic analyses are widely used to quantify structural and geotechnical performance. Still, they require an accurate representation of nonlinear behaviours and proper modelling of the expected seismic events. Stochastic approaches are popular strategies for modelling dynamic actions to account for the uncertain nature of ground shaking. In this framework, joint time–frequency signal representations of seismic records are powerful tools to analyze signals’ time-varying amplitude and frequency content. This paper presents a new method for the stochastic generation of artificial accelerograms using the circular harmonic wavelet transform, which possesses joint time–frequency localization capabilities and offers the engineers a clear and transparent interpretation of the results. The proposed approach adopts a new exponential auto-correlation structure for generating the random phases in the “child (generated) signals” starting from the deterministic ones in the “parent record”. The effects of the correlation structure and different subdivisions of earthquake records in frequency bands are investigated and discussed, leading to practical considerations for identifying an effective trade-off between localization in time and frequency domains. The method can be used for seismic assessment and design purposes, and numerical applications illustrate its potency.

A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.

Entangling gates on degenerate spin qubits dressed by a global field

Semiconductor spin qubits represent a promising platform for future large-scale quantum computers owing to their excellent qubit performance, as well as the ability to leverage the mature semiconductor manufacturing industry for scaling up. Individual qubit control, however, commonly relies on spectral selectivity, where individual microwave signals of distinct frequencies are used to address each qubit. As quantum processors scale up, this approach will suffer from frequency crowding, control signal interference and unfeasible bandwidth requirements. Here, we propose a strategy based on arrays of degenerate spins coherently dressed by a global control field and individually addressed by local electrodes. We demonstrate simultaneous on-resonance driving of two degenerate qubits using a global field while retaining addressability for qubits with equal Larmor frequencies. Furthermore, we implement SWAP oscillations during on-resonance driving, constituting the demonstration of driven two-qubit gates. Significantly, our findings highlight how dressing can overcome the fragility of entangling gates between superposition states and increase their noise robustness. These results constitute a paradigm shift in qubit control in order to overcome frequency crowding in large-scale quantum computing.

Development and experimental evaluation of an acoustic flow meter prototype for measuring air flow

This study outlines the development and testing of a flow meter prototype specifically designed for use in nuclear reactor environments. The meter uses flow-induced vibration to measure flow rates and is designed for remote monitoring in harsh environments where radiation can damage electronics. The device generates a vibration signal produced by a feedback system when fluid flows out of an orifice and interacts with a downstream wedge. The frequency of the vibration signal corresponds directly to flow rate and velocity. The study investigates geometric parameters intrinsic to the prototype, such as orifice height, width, edge distance, and chamber length. Different configurations were also tested, including a bypass loop with variable cross-sections around the device. The investigation results reveal the impact of these geometric parameters on the vibration signal output, providing valuable insights for future design improvements and enhancing the effectiveness of flow metering systems used in nuclear reactor applications.

An Anatomical and Physiological Basis for Flexible Coincidence Detection in the Auditory System.

Animals navigate the auditory world by recognizing complex sounds, from the rustle of a predator to the call of a potential mate. This ability depends in part on the octopus cells of the auditory brainstem, which respond to multiple frequencies that change over time, as occurs in natural stimuli. Unlike the average neuron, which integrates inputs over time on the order of tens of milliseconds, octopus cells must detect momentary coincidence of excitatory inputs from the cochlea during an ongoing sound on both the millisecond and submillisecond time scale. Here, we show that octopus cells receive inhibitory inputs on their dendrites that enhance opportunities for coincidence detection in the cell body, thereby allowing for responses both to rapid onsets at the beginning of a sound and to frequency modulations during the sound. This mechanism is crucial for the fundamental process of integrating the synchronized frequencies of natural auditory signals over time.

Application of the wavelet transform and Hilbert–Huang transform in stratigraphic sequence division of Jurassic Shaximiao Formation in Southwest Sichuan Basin

Abstract In the southwestern Sichuan Basin, the Jurassic Shaximiao Formation encompasses a multitude of working areas, displaying intricate sedimentary traits. Traditional methods of stratigraphic division based on sequence suffer from inherent subjectivity and limitations. This study employs a combined mathematical approach to use the wavelet transform (WT) and the Hilbert–Huang transform (HHT). It decomposes the natural gamma ray (GR) logging curve into energy spectrum plots and wavelet coefficients at different scales, high and low frequency signals at different frequencies, and a set of intrinsic mode function components and residual functions. The study conducted a detailed stratigraphic division of the Jurassic Shaximiao Formation in the southwestern Sichuan Basin using these methods. The WT offers greater resolution for the periodic changes in the base level, whereas the HHT demonstrates a superior correlation with the positions of stratigraphic interfaces. The combined utilization of the continuous wavelet transform, the discrete wavelet transform, and HHT methods has demonstrated encouraging outcomes in the stratigraphic division of the Jurassic Shaximiao Formation. These methods have been shown to enhance the accuracy of stratigraphic division and to reduce the influence of subjective factors. This study presents new insights and approaches for geological data processing, offering significant theoretical and practical implications and novel technical means for oil and gas exploration and development.

Addressing Imbalanced EEG Data for Improved Microsleep Detection: An ADASYN, FFT and LDA-Based Approach

Microsleep, brief lapses in consciousness lasting less than 15 seconds, are often accompanied by feelings of fatigue and are detectable through a deceleration in electroencephalogram (EEG) signal frequencies. Accurate identification of microsleep is critical for assessing driver alertness and preventing accidents. This paper introduces a novel approach to detecting driver microsleep by leveraging EEG signals and advanced machine learning techniques. The methodology begins with preprocessing raw EEG data to improve quality and balance, utilizing the ADASYN algorithm to address dataset imbalances. After preprocessing, features are extracted using Fast Fourier Transform (FFT), which provides a comprehensive frequency domain analysis of the EEG signals. For classification, Linear Discriminant Analysis (LDA) is employed to effectively distinguish between microsleep events and normal wakefulness based on the extracted features. The proposed framework was rigorously validated using a well-established publicly available EEG dataset, which included recordings from 76 healthy individuals. The validation results revealed a high testing accuracy of 92.71% in detecting microsleep episodes, demonstrating the effectiveness of the proposed approach. These results underscore the potential of combining EEG signal analysis with machine learning models for practical applications in monitoring driver alertness. The framework could significantly enhance driver safety by providing an effective tool for detecting microsleep and thereby reducing the risk of accidents caused by drowsy driving. This research highlights the promising application of advanced signal processing and machine learning techniques in the field of driver alertness monitoring.

Evolution characteristics of an induced electric charge signal and identification method of charge for instability failure in loaded coal

A coal-loaded charge induction monitoring system is developed to effectively forecast the dynamic disasters caused by coal failure. Specifically, a digital finite impulse response (FIR) filter is designed to denoise and filter the signal, and the time-frequency domain evolution of induced charge signals is analyzed during coal failure experiments. The quantitative relationships between the induced electric charge and stress-strain energy, and ultimately, between induced electric charge and coal deformation/failure, are revealed. Ultimately, the electric charge sensor exhibits high signal collection frequency and high sensitivity, and the FIR low-pass filter constructed in MATLAB effectively denoises and filters induced charge signals. The main frequency range of the white noise is 50–500 Hz, and the main frequency of the charge signal induced by coal deformation and failure is concentrated in the range of 0–50 Hz. The optimal distances for monitoring cubic and cylindrical raw coal samples using this sensor are 9 mm and 11 mm, respectively. Notably, strain energy is released faster when it can dissipate more readily, and induced charge pulses become denser when more intense signals produce large fluctuations. A method is proposed to identify coal deformation and failure based on changes in the induced electric charge. This study provides a new means of monitoring the early warning signs of dynamic coal mine disasters. Based on our experimental results and conclusions, a new method is proposed to identify coal deformation and failure based on changes in the induced electric charge. The precursor to the moment of coal failure can be identified by monitoring the amplitude of the induced charge, the dynamic trend of fluctuation, and the cumulative number of induced electric charge pulses during the process of coal deformation.

Improvement Underwater Acoustic Signal De-Noising Based on Dual-Tree Complex Wavelet Transform

Underwater Acoustic signal denoising is encountering high demand due to the extensive use of acoustic in a lot of underwater applications. Underwater acoustic noise (UWAN) has a high effect on the quality of the acoustic signal therefore, it is always preferred to use a de-noising filter to remove it. In this paper, we propose a filter that utilizes a Complex wavelet transform (CWT) to remove UWAN and help improve the signal-to-noise ratio (SNR) of the detected acoustic signal. CWT is nearly shift-invariant and offers a good directionality in contrast to normal wavelet transform (DWT). The proposed method was tested using a real recorded UWAN for three depths from the Tigris River. The proposed method was compared with a more conveniently used discrete wavelet transform. The test included using Two signals: fixed frequency and linear modulation signal. De-noising was performed using a soft thresholding technique based on level-dependent threshold estimation. The proposed method showed supreme performance in terms of SNR and root mean square error (RMSE). When the input signal was 5.9 dB and -13.2 dB for SNR and RMSE respectively, the results were 10.9 dB for SNR and -15.7 dB for RMSE in the case of fixed frequency.

Black phosphorus film as Q-switcher in neodymium-doped fiber laser

We present the successful development of a 1089.4 nm Q-switched laser employing a neodymium-doped fiber (NDF) as the active fiber and black phosphorus (BP) as the saturable absorber (SA). The BP SA was fabricated by inserting BP compound into a polyvinyl alcohol (PVA) host polymer, exhibiting a saturable absorption of 2.8 %. Integrated into an NDFL ring cavity, the SA modified the cavity loss, enabling the production of Q-switched pulses. With an increase in the 808 nm pumping power from 108.6 to 155.9 mW, the laser output pulse duration decreased from 3.74 to 3.54 μs, while the repetition rate improved from 40.6 to 51.0 kHz. The laser demonstrated a stable pulse train output, with a fundamental frequency signal to background noise ratio of 45.78 dB. The highest pulse energy of 1.3 nJ was recorded at 155.9 mW pump power. To the best of our knowledge, this represents the first utilization of BP as a SA or Q-switcher within an NDFL cavity.

Full-duplex two-way no-relay cross-media communication method based on acoustic wave conversion

Abstract Due to the difference in acoustic impedance between water and air, the water interface becomes a natural barrier. Underwater sound waves will bounce when they propagate to the water surface. Electromagnetic waves and light waves in the air will rapidly attenuate after passing through the water surface and going underwater, making it difficult to complete air-water cross-media information transmission. In this article, we first analyze the theoretical characteristics and vibration model of the water surface after medium collision, and develop a new full-duplex communication method across the air-ocean medium, which does not rely on relay equipment and only uses three transmission media in the air. Propagate signals in the sea channel physical field to achieve two-way communication, using the mutual conversion between laser, acoustic, and radio frequency signals to fill the gap in cross-media communication technology that can simultaneously achieve two-way, full-duplex and non-relay. It has been verified through experimental installations carried out in laboratory water tanks and large comprehensive anechoic pools that it is able to achieve full-duplex communication links across the air-sea surface, indicating that the combination of laser acousticization and radio frequency-acoustic wave conversion can be used across medium full-duplex wireless communication provides a feasible method for communication that breaks through the water interface.

Acoustic Metasurfaces for Efficient Modulation and Demodulation of OAM Multiplexed Signals in Acoustic Communication

Abstract Orbital angular momentum (OAM) multiplexing has been suggested as a potential solution to increase the data capacity of acoustic communication systems. A method of modulation and demodulation of OAM multiplexed signals using acoustic metasurfaces is proposed in this manuscript. The amplitude and phase of independent acoustic orbital angular momentum (AOAM) multiplexed signal provide orthogonal degrees of freedom. By designing appropriate acoustic metasurfaces and combining them with binary differential phase coding techniques, we demonstrated that efficient modulation and fast demodulation of AOAM multiplexed signals can be achieved. Orthogonal acoustic spiral modes prevent mode coupling and crosstalk, enhancing acoustic multiplexing. The resonance-based structure provides signal frequency selectivity, boosting efficiency and simplifying terminal equipment for practical applications, which leads to the creation of high-capacity acoustic communication systems.

Equivalent tensile elastic modulus measurement of cross-ply composite plates using S1 mode of Lamb wave

Abstract This paper proposes a simple and effective Lamb wave-based measurement method for the elastic moduli of cross-ply composite plates with large thickness, considered equivalent to a single-layer orthotropic plate with nine independent elastic constants. Due to the low-frequency signals required for measuring large thickness plates and the large wavelength of the low-frequency signals, the size of composite plates is highly required. Therefore, it is considered to use the first order symmetric (S1) mode wave to increase the signal frequency and reduce the wavelength. The effects of elastic constants on the phase velocities of the S1 mode are investigated. The phase velocity of the S1 mode depends only on the equivalent tensile elastic modulus and in-plane Poisson’s ratio in the low-dispersive frequency-thickness product regime. Moreover, the in-plane Poisson’s ratio of cross-ply laminates changes sufficiently small to neglect its effect on the phase velocities of the S1 mode. Therefore, the tensile elastic moduli of the cross-ply laminated plates can be estimated using the S1 mode, and the mapping relations between the phase velocities of S1 mode and the elastic moduli are established. The proposed approach has been numerically and experimentally tested on cross-ply glass fiber reinforced laminates with large thickness, and validated by theoretical values and conventional tensile tests.