Signal Spectrum Research Articles

Where is the hidden intramolecular H-bonding vibrational signal in the proline matrix IR spectrum?

The assignment of the hydrogen bonded O-H stretch vibration in the proline matrix IR spectrum has sparked controversy. Employing constrained nuclear electronic orbital methods, we provide a clear assignment that the vibrational frequency drops to near 3000cm-1 as a result of the interplay between electronic effects, nuclear quantum effects, and matrix effects.

Potholes Detection in Doppler Radar Signal’s Power Spectral Density using Decision Tree

Potholes are defects on the surface of roads, streets, or pavements brought on by depressions or holes, which are hazardous for vehicles and pedestrians, whether small divots or huge craters. Various methods have been explored to improve the accuracy of potholes detection. Existing approaches have advantages and disadvantages. This paper presents the proposed method of pothole detection utilising Doppler Radar signal's Power Spectrum Density (PSD) together with the Decision Tree classification algorithm. While continuous waveform (CW) radar is able to identify moving targets, it cannot localise the exact depth of the reflector, which is the prominent characteristic of potholes. In addition, the target's reflected signal is likely to be masked by nearby harmonics. Since the radar is moving despite the target of interest, mounting it on a moving vehicle offers a different perspective. This paper explores the potential of Doppler radar's signal for pothole detection while comparing two Machine Learning (ML) techniques. A commercially over-the-shelf (COTS) K-LC2 Doppler radar was employed to acquire pothole and non-pothole raw datasets. Doppler signal was hardly distinguished between pothole and non-pothole, either in the time or frequency domain. Hence, Doppler signals were converted to power spectral density (PSD), and PSD's features were extracted. Extracted features were applied with the coarse Decision Tree (DT) and K-Nearest Neighbours (KNN) classification algorithms. The result exhibits a better accuracy of 91.2% for 80:20 distribution by using the Decision Tree.

Research of the efficiency of Kravchenko weighting functions and combinations based on them in the problem of narrow band interference rejection

The influence of weight functions (WFs) of preliminary weighting on the quality of narrowband interference rejection based on direct and inverse discrete Fourier transforms has been studied. Classical and modern WFs are considered: Kravchenko, Kravchenko–Dolph–Chebyshev, Kravchenko–Gauss, Kravchenko–Bernstein–Rogozinsky. Quantitative estimates of efficiency were obtained – the coefficients of interference suppression and signal transmission, as well as their product – the total efficiency coefficient. Graphic examples of the behavior of the total efficiency coefficient depending on the interference frequency are shown. Estimates of the indicated quality indicators are presented when a fixed number of frequency samples are removed from the spectrum. The families of dependences of the probabilities of correctly performing a search for a spread spectrum signal on the “interference/signal” ratio when using modern WFs to weigh the implementations of an additive mixture of signal, interference and noise are presented. The significant advantage of modern WFs formed through combinations with Kravchenko functions in the problem of interference rejection has been demonstrated and confirmed.

Delayed-X Harmonics Synthesizer algorithm for low frequency noise removal

As various technologies on noise reduction have improved their performances, slight low frequency noise has become more prominent in quiet environments. Since the traveling of low noise is difficult to be isolated by walls and earplugs, we aim an active noise control embedded in unoccluded headphones. In this case, both desired sounds (e.g., speech) and unwanted noises pass through the opened ears, so the algorithm is required to adapt only the noise separating from the speech (selective cancelation). Therefore, our selected procedure was Delayed-X Harmonics Synthesizer algorithm which produces pure tones in the opposite phase, because the generated pure tones in the low frequency can minimize the damage for the speech quality. To select the frequencies of pure tones, we focused on the fact that noise is more stationary than speech. The targets frequencies were determined by a small frame-variation on the spectra of the error signals. Our simulations demonstrated that the low noise (< 100 Hz) could be reduced by 10dB even in the slight noise condition (SNR = 5dB), while the speech intelligibility (PESQ) was maintained. When our system is embedded in the unoccluded headphones, the users will take daily conversation without any troubles of low noise.

Dual-Activated Photoacoustic Probe for Reliably Detecting Hydroxyl Radical in Ischemic Cardiovascular Disease in Mouse and Human Samples.

Cardiovascular disease (CVD) is a chronic disease characterized by the accumulation of lipids and fibrous tissue within the arterial walls, potentially leading to vascular obstruction and an increased risk of heart disease and stroke. Hydroxyl radicals play a significant role in the formation and progression of CVD as they can instigate lipid peroxidation, resulting in cellular damage and inflammatory responses. However, precisely detecting hydroxyl radicals in CVD lesions presents significant challenges due to their high reactivity and short lifespan. Herein, we present the development and application of a novel activatable optical probe, Cy-OH-LP, designed to detect hydroxyl radicals in lipid-rich environments specifically. Built on the Cy7 molecular skeleton, Cy-OH-LP exhibits near-infrared absorption and fluorescence characteristics, and its specific response to hydroxyl radicals enables a turn-on signal in both photoacoustic and fluorescence spectra. The probe demonstrated excellent selectivity and stability in various tests. Furthermore, Cy-OH-LP was successfully applied in an in vivo model to detect hydroxyl radicals in mouse models, providing a potential tool for diagnosing and monitoring AS. The biosafety of Cy-OH-LP was also verified, showing low cytotoxicity and no significant organ damage in mice. The findings suggest that Cy-OH-LP is a promising tool for the specific detection of hydroxyl radicals in lipid-rich environments, providing new possibilities for research and clinical applications in the field of oxidative stress-related diseases.

Research on a High-Dynamics Acquisition Algorithm for New Binary Offset Carrier Signal in UAV Communication

As unmanned aerial vehicles (UAVs) are widely used in various fields, there is an increasing demand for UAV anti-jamming, multipath mitigation, and covert secrecy. Frequency-hopping binary offset carrier (FH-BOC) signals possess higher anti-jamming and multipath mitigation capabilities than direct-sequence spread spectrum (DSSS) and binary offset carrier (BOC) signals. A prerequisite for constructing communication links between UAVs using FH-BOC signals is the design of efficient acquisition algorithms to capture the signals successfully. In this paper, the modulation and characteristics of the FH-BOC signal are introduced. The maximum relative velocity between UAVs is 5.5 km/s, the maximum acceleration is 50 g, and the maximum plus acceleration is 20 g/s. In this high dynamic environment, the parameters for the parallel code phase and Partial Matched Filter–Fast Fourier Transform (PMF-FFT) acquisition algorithms targeting FH-BOC(10,1) signals are designed, and the acquisition performance of these algorithms is comparatively analyzed. The acquisition time for the first and second algorithms is 4.3317 s and 6.137 s. The number of real additions required by the first and second algorithms is approximately 10.9×109 and 8.9×109, and the number of real multiplications is approximately 7.6×109 and 6.7×109. This helps in selecting the acquisition algorithm when FH-BOC signals are used to build inter-UAV communication links.

Novel CRM cosine similarity mapping strategy for simultaneous in-situ visual profiling lignocellulose in plant cell walls

Confocal Raman microscopy (CRM) is a promising in-situ visual technique that provides detailed insights into multiple lignocellulosic components and structures in plant cell walls at the micro-nano scale. In this study, we propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls. The main stages of this strategy include: 1) a modified Otsu algorithm for extracting the regions of interest (ROI); 2) a modified subtraction method for cleaning the background signals in the ROI spectra; 3) a lignin signal subtraction method based on the pixel correction factor for eliminating the interference of strong lignin signals with weak cellulose and hemicellulose signals in the Raman full spectra of the cell walls; 4) second-order derivative spectral preprocessing for enhancing the discrimination between the characteristic peaks of cellulose and hemicellulose; 5) a CS mapping algorithm for simultaneous in-situ profiling of lignin, cellulose, and hemicellulose in plant cell walls. The effectiveness of the strategy is verified by characterizing the Brittle Culm1 (BC1) gene-mutant rice stem (IL349-BC1-KO) with known bioinformatics. This approach provides methodological support for in-situ visualization and analysis in fields such as plant or crop science at the micro-nano scale.

Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis

Varying components and operating conditions in industrial machines lead to different distribution characteristics and fault states of monitoring data for critical rotating machinery. Adequate and accurate labelling of monitoring data is challenging to achieve. Existing data-driven unsupervised domain adaptation methods exhibit poor interpretability and low confidence in the target domain’s pseudo-labelling. Therefore, a physics-informed unsupervised domain adaptation framework is proposed. Data and physical information are combined to enhance the reliability and interpretability of the model while ensuring its generalization capability. First, physical information is extracted by merging the knowledge of the mechanical component fault characteristics with indicators like the signal spectrum’s energy value. Subsequently, the physical label and loss are created. The physical loss is then embedded in the loss function of the source domain model. Next, by aligning the physical labels and the clustering results of the spectral clustering, the target domain samples’ hard pseudo-labels are obtained. The probability values of the sample belonging to each category as determined by the Wasserstein criterion comprise the soft pseudo-label of the sample. Furthermore, the confidence of hard pseudo-labels is continually increased by iteratively updating the probability values of soft pseudo-labels using the multi-label as a guide and the inverse improved sigmoid function as a gradient. Finally, unsupervised cross-machine diagnostic models with high robustness can be obtained. Three datasets are employed in the experimental section, and five sub-datasets are created and crossed for cross-machine experiments to confirm the suggested approach’s efficacy and superiority.

Characteristic analysis and diagnosis method optimization of scroll compressor pressure pulsation signal under voltage fluctuation

Under off-design conditions, scroll compressors can lead to reduced efficiency, motor damage, and even cause safety problems such as leaks or explosions. To solve the above problems, this paper analyzes the influence mechanism of different voltages on the spectrum of pressure pulsation signal and modulation signal and provides theoretical support for fault diagnosis and enhances the interpretability of the model. A voltage fault diagnosis method of scroll compressor based on Time-frequency Principal component Convolutional Network (TPCN) model is proposed. By demodulation analysis of the pressure pulsation signal of the low-pressure inlet and high-pressure outlet of the refrigerant in the scroll compressor, the spectrum information of the principal component modulation signal under different voltages is obtained. The pooling strategy is used to accurately identify and extract the fault information in the modulated signal spectrum as the input data of the model. The input data is divided into the training set and the test set according to the ratio of 8:2 to complete the training and testing of the fault diagnosis model. The experimental results show that the accuracy of TPCN model for the diagnosis of 5 types of faults reaches 100 %. The average accuracy of the model is 100 %, which indicates that the model has good stability.

Localization With the Fingerprints of Multipath Angle Delay Spectrum of OFDM Signal

Localization With the Fingerprints of Multipath Angle Delay Spectrum of OFDM Signal

Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection

Automatic epilepsy seizure detection has high clinical value since it can alleviate the burden of manual monitoring. Nevertheless, it remains a technically challenging task to achieve a reliable system. In this study, we investigated the significance of the phase information in EEG signals in seizure detection using machine learning. We used the Stockwell transform (S-transform) to extract both phase and power spectra of the EEG signal in epilepsy patients. A dual-stream convolution neural network (CNN) model was adopted as the classifier, which takes both spectra as inputs. We demonstrated that the phase input allows the CNN model to capture the heightened phase synchronization among EEG channels in seizure and add network attention to both the low- and high-frequency features of the inputs in the CHB-MIT and Bonn databases. We improved the detection AUC-ROC by 6.68% on the CHB-MIT database when adding phase inputs to the power inputs. By incorporating a channel fusion post-processing to the outputs of this CNN model, it achieves a sensitivity and specificity of 79.59% and 92.23%, respectively, surpassing some of the state-of-the-art methods. Our results show that the phase inputs are useful features in seizure detection. This discovery has significant implications for improving the effectiveness of automatic seizure detection systems.

High-strength self-healing multi-functional hydrogels with worm-like surface through hydrothermal-freeze-thaw method

Soft self-healing materials are promising candidates for flexible electronic devices due to their exceptional compatibility, extensibility, and self-restorability. Generally, these materials suffer from low tensile strength and susceptibility to fracture because of the restricted microstructure design. Herein, we propose a hydrothermal-freeze-thaw method to construct high-strength self-healing hydrogels with even interconnected networks and distinctive wrinkled surfaces. The integration of the wrinkled outer surface with the three-dimensional internal network confers the self-healing hydrogel with enhanced mechanical strength. This hydrogel achieves a tensile strength of 223 kPa, a breaking elongation of 442%, an adhesion strength of 57.6 kPa, and an adhesion energy of 237.2 J m-2. Meanwhile, the hydrogel demonstrates impressive self-repair capability (repair efficiency: 93%). Moreover, the density functional theory (DFT) calculations are used to substantiate the stable existence of hydrogen bonding between the PPPBG hydrogel and water molecules which ensures the durability of the PPPBG hydrogel for long-term application. The measurements demonstrate that this multifunctional hydrogel possesses the requisite sensitivity and durability to serve as a strain sensor, which monitors a spectrum of motion signals including subtle vocalizations, pronounced facial expressions, and limb articulations. This work presents a viable strategy for healthcare monitoring, soft robotics, and interactive electronic skins.

Optical resonant cavities carving pathways in tunable wavelength sensitive visible-NIR organic photodetectors

Enhancing the photodetection capabilities of organic photodetectors (OPDs) is crucial for advancing applications in medical monitoring, optical communications, image sensing, and robotics, where a strong, focused peak response at a specific designed wavelength is essential for improving sensitivity, wavelength selectivity, and resolution in imaging systems. By controlled integration of ZnO layers within PBDBT:BTP-4F-based OPDs to form a Fabry-Pérot optical cavity, we developed a cost-effective approach to fabricating highly sensitive OPDs by utilizing PBDBT:BTP-4F organic bulk heterojunctions, and extended its detection wavelengths into the near-infrared (NIR) range. Our design integrates a single silver (Ag) layer that significantly enhances peak detection at a wavelength of 830 ​nm, resulting in a remarkably narrow full-width at half maximum (FWHM) wavelength of 30 ​nm and yielding a photoresponse ten times greater than that of non-resonant devices. Furthermore, by varying the thickness of the ZnO layer from 77 ​nm to 620 ​nm, we achieve high spectral tunability, allowing fine adjustments of the resonant peak across a spectrum ranging from ultraviolet (UV) and visible to NIR wavelengths. This sensitive photodetector is also well-suited for applications in photoplethysmography (PPG), effectively detecting pulse signals in the NIR spectrum which has significant potential in medical diagnostics. This work advances the integration of cost-effective, wavelength-selective spectroscopic visible-NIR OPDs, paving the way for the next generation of sensitive photodetectors.

Enhancing Wireless Communication Efficiency Using Dynamic Nonlinear Distortion Adaptive Optimization Analysis

Abstract Modern communication networks require improved signal quality and spectrum efficiency. Modern wireless communication relies heavily on the multiple input multiple output (MIMO) technology to improve spectrum efficiency and achieve faster data rates. However, the performance of MIMO systems is significantly hampered by non-linear distortions in power amplifiers (PA), especially under dynamic operating conditions. Traditional methods to reduce these distortions often lack the adaptability required for effective optimization. The proposed dynamic nonlinear distortion adaptive optimization analysis (DNDAOA) therefore introduces an adaptive approach that dynamically adjusts the pre-distortion signals using real-time feedback mechanisms. DNDAOA effectively reduces non-linear distortions and thus improves the performance of MIMO systems. Simulation results show that DNDAOA significantly increases spectrum efficiency, reduces error rates, and improves signal quality. This method is widely used in areas such as wireless telecommunications, internet of things (IoT), autonomous vehicles, and smart infrastructure, driving advances in connectivity, reliability, and data integrity.

Re-configurable digital bandwidth interleaved sampling system based on fast spectrum sensing.

The complexity of systems with parallel acquisition architectures has increased sharply with the continuous growth of signal bandwidth, and sampling precision has been greatly challenged. Considering that the signal to be measured usually exhibits sparsity in the frequency domain, this paper proposes a re-configurable bandwidth interleaved acquisition architecture to maximize test flexibility and accuracy. The sampling process is divided into two stages: sensing and re-configurable acquisition. In the sensing stage, the signal spectrum distribution is roughly understood using a sparse Fourier transform. Based on the spectrum sensing results, the subband selection and the adaptive adjustment of the mixing local oscillator in the acquisition system are performed. Ultimately, these steps enhance sampling accuracy. This paper verifies the effectiveness of this method on a 10GHz acquisition system, demonstrating that it can significantly reduce data redundancy. In addition, it improves acquisition accuracy compared to traditional bandwidth interleaved systems. The experimental results indicate that re-configurable sampling can significantly improve the quality of sampling results. This is evidenced by a signal-to-noise ratio improvement of over 7.4 dB and a spurious-free dynamic range improvement of over 4.7 dB compared to traditional sampling results.

The Origins of Narrow Spectra of Fast Radio Bursts

Observations find that some fast radio bursts (FRBs) have extremely narrowband spectra, i.e., Δν/ν 0 ≪ 1. We show that, when the angular size of the emission region is larger than the Doppler beaming angle, the observed spectral width (Δν/ν 0) exceeds 0.58 due to the high-latitude effects for a source outside the magnetosphere, even when the spectrum in the source’s comoving frame is monochromatic. The angular size of the source for magnetospheric models of FRBs can be smaller than the Doppler beaming angle, in which case this geometric effect does not influence the observed bandwidth. We discuss various propagation effects to determine if any could transform a broad-spectrum radio pulse into a narrow spectrum signal at the observer’s location. We find that plasma lensing and scintillation can result in a narrow bandwidth in the observed spectrum. However, the likelihood of these phenomena being responsible for the narrow observed spectra with Δν/ν 0 < 0.58 in the fairly large observed sample of FRBs is exceedingly small.

Image quality improvement in single plane-wave imaging using deep learning

In ultrasound image diagnosis, single plane-wave imaging (SPWI), which can acquire ultrasound images at more than 1000 fps, has been used to observe detailed tissue and evaluate blood flow. SPWI achieves high temporal resolution by sacrificing the spatial resolution and contrast of ultrasound images. To improve spatial resolution and contrast in SPWI, coherent plane-wave compounding (CPWC) is used to obtain high-quality ultrasound images, i.e., compound images, by coherent addition of radio frequency (RF) signals acquired by transmitting plane waves in different directions. Although CPWC produces high-quality ultrasound images, their temporal resolution is lower than that of SPWI. To address this problem, some methods have been proposed to reconstruct a ultrasound image comparable to a compound image from RF signals obtained by transmitting a small number of plane waves in different directions. These methods do not fully consider the properties of RF signals, resulting in lower image quality compared to a compound image. In this paper, we propose methods to reconstruct high-quality ultrasound images in SPWI by considering the characteristics of RF signal of a single plane wave to obtain ultrasound images with image quality comparable to CPWC. The proposed methods employ encoder–decoder models of 1D U-Net, 2D U-Net, and their combination to generate the high-quality ultrasound images by minimizing the loss that considers the point spread effect of plane waves and frequency spectrum of RF signals in training. We also create a public large-scale SPWI/CPWC dataset for developing and evaluating deep-learning methods. Through a set of experiments using the public dataset and our dataset, we demonstrate that the proposed methods can reconstruct higher-quality ultrasound images from RF signals in SPWI than conventional method.

Local Mean Decomposition and Weighted Kurtosis Index for Bearing Defect Detection

Vibration signal analysis is an effective technique for detecting bearing defects, and it proposes an approach that involves signal processing methods to extract information about the defects. The initial step in the proposed approach involves dividing the signal into several components (PF) using the LMD algorithm. Subsequently, the weighted kurtosis index (WKI) values are computed, and the summation of components having WKI values higher than the average of WKI leads to the formation of a new signal containing multiple pulses that are very similar to the original signal. Then we can observe peaks at the frequencies of the bearing defects in the envelope spectrum of the new signal. Applying the proposed approach to the vibration signal available in the XJTU database shows a peak at the fault frequency of the outer ring.

Cationic fluorinated micelles for cell labeling and 19F-MR imaging

Magnetic resonance imaging (MRI) relies on appropriate contrast agents, especially for visualizing transplanted cells within host tissue. In recent years, compounds containing fluorine-19 have gained significant attention as MRI probe, particularly in dual 1H/19F-MR imaging. However, various factors affecting probe sensitivity, such as fluorine content and the equivalency of fluorine atoms, must be considered. In this study, we synthesized fluorinated micelles with adjustable surface positive charge density and investigated their physicochemical properties and MRI efficacy in phantoms and labeled cells. While the micelles exhibited clear signals in 19F-MR spectra and imaging, the concentrations required for MRI visualization of labeled cells were relatively high, adversely affecting cell viability. Despite their favourable physicochemical properties, achieving higher labeling rates without compromising cell viability during labeling remains a challenge for potential in vivo applications.

Potential to Identify the Neutrino Mass Ordering with Reactor Antineutrinos in JUNO

Abstract The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South of China. This paper presents an updated estimate of JUNO's sensitivity to the neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power plants. This measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5~km and is completely independent of the CP violating phase and the neutrino mixing angle θ23. The sensitivity is obtained through a joint analysis of JUNO and TAO detectors utilizing the best available knowledge to date about the location and overburden of the JUNO experimental site, the local and global nuclear reactors, the JUNO and TAO detectors responses, the expected event rates and spectra of signal and backgrounds, and the systematic uncertainties of the analysis inputs. It is found that a 3σ median sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure of about 6.5 years × 26.6~GW thermal power.Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.