Pure Signal Research Articles

(Don't) look where you are going: Evidence for a travel direction signal in humans that is independent of head direction.

We often assume that travel direction is redundant with head direction, but from first principles, these two factors provide differing spatial information. Although head direction has been found to be a fundamental component of human navigation, it is unclear how self-motion signals for travel direction contribute to forming a travel trajectory. Employing a novel motion adaptation paradigm from visual neuroscience designed to preclude a contribution of head direction, we found high-level aftereffects of perceived travel direction, indicating that travel direction is a fundamental component of human navigation. Interestingly, we discovered a higher frequency of reporting perceived travel toward the adapted direction compared to a no-adapt control-an aftereffect that runs contrary to low-level motion aftereffects. This travel aftereffect was maintained after controlling for possible response biases and approaching effects, and it scaled with adaptation duration. These findings demonstrate the first evidence of how a pure travel direction signal might be represented in humans, independent of head direction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Design and Implementation of a DC to AC Power Electronics-Based Inverter that Produces Pure Sine Wave Output for Critical Engineering Applications

Power inverters play a crucial role in the field of engineering, particularly in applications where power stability is imperative. In devices such as Uninterruptible Power Supplies (UPS), the conversion of raw power to DC, subsequent filtering, and inversion to AC are executed through pure sine wave inverters. These inverters exhibit remarkable stability, making them ideal for powering sensitive equipment like data switches and Remote Terminal Units (RTUs). This study delves into the intricate process of converting DC power into a pristine sine wave signal. The heart of this power conversion lies in the utilization of the KA3525A integrated circuit (IC) in conjunction with MOSFETs of the PN55 series, supported by capacitors and resistors for effective power filtration. The KA3525A, a monolithic IC, encompasses all essential control circuits for a pulse width modulating regulator. Within this IC, a voltage reference, error amplifier, pulse width modulator, oscillator, under-voltage lockout, soft start circuit, and output driver collaborate seamlessly. The MOSFETs function as switches, synchronized with the oscillating signal from the KA3525A IC. This coordination, combined with the filter and other signal conditioning units, enables the conversion process. The design achieves the conversion of raw power into a stable pure sine wave signal of 170V AC at the H-bridge terminals, demonstrating the success of the designed approach.

Using clustering as pre-processing in the framework of signal unmixing for exhaustive exploration of archaeological artefacts in Raman imaging

Analytical chemistry on archaeological material is an essential part of modern archaeological investigations and from year to year, instrumental improvement has made it possible to generate data at a high spatial and temporal frequency. In particular, Raman spectral imaging can be successfully applied in archaeological research by its simplicity of implementation to study past human societies through the analysis of their material remains. This technique makes it possible to simultaneously obtain spatial and spectral information by preserving sample integrity. However, because of the inherent complexity of the samples in Archaeology (e.g. seniority, fragility, lack or full absence of any information about its composition), chemical interpretation can be difficult at first glance. Indeed, specific problems of spectral selectivity related to unexpected chemical compounds could appear due to their state of conservation. Furthermore, detecting minor compounds becomes challenging as major components impose their contributions in the acquired spectra. Therefore, a relevant chemometric approach has been introduced in this context to characterize distinct spectral sources in a Raman imaging dataset of an archaeological specimen – a mosaic fragment. The fragment was unearthed during the Ruscino archaeological dig on the outskirts of Perpignan, France. It dates back to the oppidum period. The aim is to extract selective spectral information from pixel clustering analysis in order to enhance the initial optimisation step within the Multivariate Curve Resolution and Alternating Least-Squares (MCR-ALS) algorithm, a well-known signal unmixing technique. The underlying principle of the MCR-ALS is that the acquired spectra can be expressed as linear combinations of pure spectra of all individual components present in the chemical system under study. Sometimes it can be difficult to obtain the desired results through the algorithm, particularly if initial estimates of spectral or concentration profiles are inaccurate due to complex signals, noise or lack of selectivity, resulting in rank deficiency (i.e. a poor estimation of the total number of pure signals). For this reason, an innovative threshold-based clustering algorithm, combined with multiple Orthogonal Projection Approaches (OPA), has been developed to improve matrix rank investigation and thus the initialisation step of the MCR-ALS approach before optimisation. The effective analysis of Raman imaging data for an archaeological mosaic played a crucial role in uncovering significant chemical information about a particular biogenic material. This insight sheds light on the origins of mortar manufacture during the oppidum period.

Comparison of In Silico Signal Sequence-Phospholipid Results with Described In Vitro and In Vivo Protein Translocation Studies Seems to Underscore the Significance of Phospholipids

The precise role of protein–lipid interactions in protein translocation is, after almost four decades of research, still a matter of debate. The experimental evidence, as described in the literature, indicates that (anionic) phospholipids play a role in numerous events in protein translocation; however, its meaning and relevance are still a matter of debate. This study tries to fill some missing links in the experimental evidence by means of in silico experiments. The study presented here indicates not only that there is a direct signal sequence–phospholipid interaction but also that the corresponding signal peptides can translocate additional amino acids across a pure lipid membrane. Furthermore, results are presented when it comes to the extent of anionic phospholipids’ dependence on this process. The correlations between the in silico results of pure signal peptide–phospholipid interactions and the observed experimental trends in the overall protein translocation effects are at least remarkable. The results emphasize that new models for protein translocation will have to be developed to take all these and previous experimental data into account.

Star help and knowledge transfer: an event study analysis of star interactions observed from acknowledgement texts

AbstractThis paper contributes to the growing literature on the impact of connections to star scientists on the productivity of academic scientists. The existing literature generally focuses on larger economies and specific scientific fields in evaluating star-connection effects. It has rarely examined the particular channels through which stars have their effects. Using natural language processing (NLP) techniques to explore the acknowledgement texts of a broad corpus of published papers from three small open economies, we examine the effects of star help revealed by the acknowledgement texts published in articles. Using an event-study framework with matched data, we find evidence of an economically and statistically significant effect on scientist productivity in the year of acknowledgement of star help. However, there is only evidence of an enduring productivity effect if scientists maintain their acknowledgement of ties to the star over time. A similar pattern is evident across different types of acknowledgements, except for acknowledgements of star help with access to materials, which shows an enduring effect even after a single acknowledgement. The largest estimated star-help effects are found for authors in lower quartiles of the field-specific productivity distribution measured in the year before the help is acknowledged. The results are robust to using a raw-publications-based measure of scientist productivity in place of our preferred citation-weighted publications measure of productivity, suggesting that the observed productivity effect is unlikely to be due to a pure signalling effect. We discuss the implications of these findings for the design of star recruitment and integration policies.

Bearing Fault Diagnosis Based on VMD-WPT-ViT for Wind Turbine

As an important part of wind turbine, the fault diagnosis of rolling bearings is helpful to maintain the stable operation of the fan. However, most of the existing fault diagnosis methods focus on one-dimensional time series data, which is not only difficult to extract multi-level feature information, but also fails to consider the noise problem in the signal. Therefore, to solve the above problems, a noise reduction algorithm combining variational mode decomposition (VMD) and wavelet packet threshold noise reduction (WPT) is proposed, and Vision-Transformer (ViT) model is combined to realize the fault diagnosis of rolling bearings of wind turbines. VMD is used to decompose the original signal and find out the noisy component. WPT is used to de-noise the component, and the de-noised component and other components are reconstructed to form a pure signal and converted into a two-dimensional image. Finally, a diagnostic model is built based on Vision-transformer. The experimental results show that the proposed method can effectively improve the diagnostic accuracy by extracting the advanced global features of the signal.

Adopting a reductionist approach to advance acoustic deterrents in fish conservation

IntroductionDeterrents that use acoustics to guide fish away from dangerous areas (e.g., water intakes) depend on the elicitation of avoidance in the target species. Background noise is often neglected when testing acoustic deterrents, but it is important to account for its effects as freshwater environments present a wide variety of ambient soundscapes.MethodsUsing the widely studied goldfish (Carassius auratus) as a suitable experimental model, this study adopted a reductionist approach to investigate the relationship between the startle response to a pure tone signal and background noise. Under laboratory conditions, the startle responses of individual goldfish exposed to 120 ms tones at 250 Hz and four Sound Pressure Levels (SPLs: 115, 125, 135, 145 dB re 1 μPa) were quantified in the presence (treatment) and absence (ambient - control) of band-limited random noise (105 dB re 1 μPa).Results and discussionWhen observing the dose-response relationship, the proportion of fish that startled to the signal increased with SPL in both the treatment and control, although there was no difference between them, suggesting that the signal-to-noise ratio was not influential under the conditions tested. However, further analysis using Signal Detection Theory indicated that the was higher in the noisy treatment than the control when responding to both false alarms (startle during a pre-signal period) and hits (startle to an external stimulus during the pre-signal period). Furthermore, fish were better able to discriminate (d?) external stimuli over time (during the pre-signal period) in the treatment than control. There is a need to consider the role of background noise when designing acoustic fish deterrents that depend on the exhibition of avoidance behaviors.

Advances in using ultrasound to regulate the nervous system.

Ultrasound is a mechanical vibration with a frequency greater than 20kHz. Due to its high spatial resolution, good directionality, and convenient operation in neural regulation, it has recently received increasing attention from scientists. However, the mechanism by which ultrasound regulates the nervous system is still unclear. This article mainly explores the possible mechanisms of ultrasound's mechanical effects, cavitation effects, thermal effects, and the rise of sonogenetics. In addition, the essence of action potential and its relationship with ultrasound were also discussed. Traditional theory treats nerve impulses as pure electrical signals, similar to cable theory. However, this theory cannot explain the phenomenon of inductance and cell membrane bulging out during the propagation of action potential. Therefore, the flexoelectric effect of cell membrane and soliton model reveal that action potential may also be a mechanical wave. Finally, we also elaborated the therapeutic effect of ultrasound on nervous system disease such as epilepsy, Parkinson's disease, and Alzheimer's disease.

Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU.

The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based convolutional neural network framework with adaptively parametric ReLU (APtrans-CNN) for ECG signal denoising. The proposed APtrans-CNN architecture combines the strengths of transformers in global feature learning and CNNs in local feature learning to address the inadequacy of learning with long sequence time-series features. By fully exploiting the global features of ECG signals, our framework can effectively extract critical information that is necessary for signal denoising. We also introduce an adaptively parametric ReLU that can assign a value to the negative information contained in the ECG signal, thereby overcoming the limitation of ReLU to retain negative information. Additionally, we introduce a dynamic feature aggregation module that enables automatic learning and retention of valuable features while discarding useless noise information. Results obtained from two datasets demonstrate that our proposed APtrans-CNN can accurately extract pure ECG signals from noisy datasets and is adaptable to various applications. Specifically, when the input consists of ECG signals with a signal-to-noise ratio (SNR) of -4 dB, APtrans-CNN successfully increases the SNR to more than 6 dB, resulting in the diagnostic model's accuracy exceeding 96%.

Orthogonal retrieval algorithm of atmospheric temperature profiles from pure rotational Raman lidar signals.

Aimed at the stability of calibration coefficients in a general non-orthogonal retrieval algorithm (NRA) of pure rotational Raman lidars (PRRLs), an orthogonal retrieval algorithm (ORA) of atmospheric temperature profiles based on the orthogonal basis function is proposed. This algorithm eliminates the correlation between the calibration coefficients in the NRA to reduce the influence of the number of calibration points and the selection scheme on the calibration coefficients. In this paper, the stabilities of calibration coefficients in the NRA and ORA are compared and analyzed, and the data analysis for atmospheric temperature profiles with a time resolution of minute-level are given, based on the developed Cloud Precipitation Potential Evaluation (CPPV) lidar data and the parallel radiosonde temperature data. The analysis results show that coefficients of variation (CVs) of ORA calibration coefficients are one order of magnitude smaller than those of NRA coefficients. The mean deviation of the ORA retrieval results is roughly reduced by 16.1% compared with the NRA, and the root-mean-square deviation is roughly reduced by 15.0% compared with the NRA. Therefore, the temperature retrieval performance of the ORA is better than that of the NRA.

Signal detection and material identification method for loose particles inside aerospace relays based on overlapping signals

Loose particle is an important factor affecting the reliability of aerospace relays. Particle Impact Noise Detection method is a commonly used loose particle detection method, but it can generate interference signals. Accurately identifying loose particle signals and component signals becomes the key to accurately detecting loose particles. Meanwhile, deeply exploring the properties of loose particle signals and further provide material information can provide guidance for manufacturing processes. However, the existing loose particle signal and component signal identification research, as well as the loose particle material identification research, has problems such as limited research objects to pure signals, independent identification results, and failure to refer to the detection requirements in real application scenarios. It is difficult to apply and its practicality is not high. Based on this, the authors proposed a signal detection and material identification method for loose particles based on overlapping signals. Specifically, referring to the latest loose particle detection information, aerospace relay samples were made, and the component identification model and material identification model based on parameter-optimized random forest were trained, respectively. They can achieve good classification effects on data sets created from pure loose particle signals, pure component signals, and pure loose particle signals corresponding to different materials, respectively. On this basis, confidence coefficient was proposed to quantify the degradation degree of the classification effects of the two models on the data set created from overlapping signals, thus the component confidence coefficient and the loose particle confidence coefficient were obtained, respectively. They can be used to determine valid pulses from pure loose particle signals, pure component signals, and mixed signals in overlapping signals, completing loose particle detection. Valid pulses from pure loose particle signals were screened for material identification. In this way, for the aerospace relay to be tested, first, the loose particle detection results can be obtained by a comprehensive judgment of the component identification model, component confidence coefficient, and loose particle confidence coefficient. Second, the material identification results can be obtained by combining the material identification model with the majority voting processing. In addition, the definition of identification accuracy applicable to the loose particle detection was proposed to meet the engineering application requirements in real application scenarios. Multiple experiments verified the feasibility, practicality, and stability of the proposed method.

Does education prevent job loss during downturns? Evidence from exogenous school assignments and COVID-19 in Barbados

Canonical human capital theories posit that education, by enhancing worker skills, reduces the likelihood that a worker will be laid-off during times of economic change. Yet, this has not been demonstrated causally. We link administrative education records from 1987 through 2002 to nationally representative surveys conducted before and after the COVID-19 onset in Barbados to explore the causal impact of improved education on job loss during this period. Using a regression discontinuity design, we show that females (but not males) who score just above the admission threshold for more selective secondary schools attain more years of education than those who scored just below (essentially holding initial ability fixed). We then find that these same females are much less likely to have lost a job after the onset of COVID-19. We show that these effects are not driven by labor supply decisions, selection into more resilient sectors and occupations, the ability to telework, job seniority, health status, fertility or access to child care, or improved social networks. Because employers observe incumbent worker productivity, these patterns are inconsistent with pure education signaling and suggest that education enhances worker skill.

A new underwater acoustic signal denoising method based on modified uniform phase empirical mode decomposition, hierarchical amplitude-aware permutation entropy, and optimized improved wavelet threshold denoising

Underwater acoustic signal (UAS) denoising is base and prerequisite for UAS detection, recognition and classification. In order to perform UAS denoising effectively, a new UAS denoising method based on modified uniform phase empirical mode decomposition (MUPEMD), hierarchical amplitude-aware permutation entropy (HAAPE), and improved wavelet threshold denoising (IWTD) method optimized by sand cat swarm optimization (SCSO) (SIWTD), named MUPEMD-HAAPE-SIWTD, is proposed. Firstly, decompose original signal into a battery of intrinsic mode functions (IMFs) by MUPEMD. Secondly, determine the double threshold by HAAPE to classify signal into pure signal, mixed signal and noisy signal. Then, optimize IWTD by SCSO, so that it can adaptively select the optimal wavelet basis function to achieve the denoising of mixed signal. Finally, the denoised mixed signal is reconstructed with pure signal to obtain ultima denoised signal. The denoising experiments on Lorenz signal and Duffing signal demonstrate that signal-to-noise ratio can be improved by 9 dB–13 dB by the proposed method. The denoising experiments on simulating ship radiated noise signals and four kinds of actual ship radiated noise signals (https://www.aigei.com/, accessed on June 1, 2023) demonstrate that the proposed method makes phase diagram smoother and clearer, and makes the ability of suppressing noise higher.

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring.

Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.

Simultaneous compensation of distorted DC bus and AC side voltage using enhanced virtual synchronous generator in Islanded DC microgrid

ABSTRACT There are many effective techniques for virtual inertia emulation in DC microgrids that can help DC bus voltage stability through power exchange with virtual inertia injection. But one of the vexingly complicated challenges in virtual inertia emulation is the connection of unbalanced loads on the AC side of a DC microgrid. Unbalanced AC loads connected to a DC microgrid may cause severe fluctuations in DC bus voltage and battery power, as well as distorting AC side voltage. The need to solve this issue is very important because it can be a threat to the microgrid DC bus voltage stability and feed sensitive loads. One effective method to mimic the real inertia feature and dampen the unfavourable unbalanced conditions is to employ a virtual synchronous generator (VSG) equipped with a decoupled double synchronous reference frame (DDSRF) approach. The DDSRF can extract positive and negative components with high precision and create pure DC signals for the control system to improve accuracy and controllability. Hence, this paper investigates a combination of a VSG structure enhanced with a DDSRF technique to attenuate the fluctuations of DC bus voltage, battery power, and AC-side voltage caused by an unbalanced AC load in an islanded DC microgrid. The simulation results confirm that the unbalanced loads connected to the AC side of the microgrid are destructive for DC bus voltage, battery power, and also create voltage imbalances for AC loads. Furthermore, the proposed DDSRF-based VSG control system that has been implemented on the AC side of the microgrid can strongly dampen the fluctuations on the DC bus, battery, and AC loads.

Deep Learning Based Noise Identification for CSAMT Data Processing

Controlled-source audio-frequency magnetotellurics (CSAMT) has been seriously affected by strong electromagnetic interferences including large-scale drift, durative outbreak interference, and impulsive outliers. To improve the efficiency of noise reduction, a deep learning strategy was proposed to identify the type of noise interference, so as to select the appropriate noise reduction method. First, a CSAMT time series simulation algorithm was developed based on current decomposition and one-dimensional (1D) forward modeling. Three kinds of noise interferences were also generated by simulation and randomly added to the pure signals. A total of 30000 groups of simulated noisy electromagnetic signals were generated. Together with 210 sets of practically measured data, these samples were used to train a long-short-term memory network (LSTM) noise classifier. Then, three targeted de-noising algorithms were adopted to separate the three interferences in the CSAMT time series according to the identifications. The test results by simulated data showed that the identification accuracy of LSTM for noise interferences can reach more than 95%. Finally, the noise identification and suppression methods were applied to a practical CSAMT dataset and the effect was further verified.

A PRPD-Based UHF Filtering and Noise Reduction Algorithm for GIS Partial Discharge.

The online detection of partial discharge (PD) in gas-insulated switchgear (GIS) is a crucial and powerful tool for maintaining their reliability. However, extracting weak discharge signals from strong disturbances is a significant challenge. The presence of noise can hamper the identification and localization of PD types, making the extraction of pure PD signals the focus of current research. This paper proposes a PRPD-based PD filtering algorithm that analyzes interference using the output information from PRPD and sets threshold parameters for noise reduction processing. This method is mainly used for secondary noise reduction at a later stage, without analyzing the noise source, to achieve effective signal acquisition while retaining the characteristics of the PD signals, thereby improving the system's sensitivity and the signal's purity.

Chirality-induced spin selectivity in functionalized carbon nanotube networks: The role of spin-orbit coupling.

Spin-orbit coupling in a chiral medium is generally assumed to be a necessary ingredient for the observation of the chirality-induced spin selectivity (CISS) effect. However, some recent studies have suggested that CISS may manifest even when the chiral medium has zero spin-orbit coupling. In such systems, CISS may arise due to an orbital polarization effect, which generates an electromagnetochiral anisotropy in two-terminal conductance. Here, we examine these concepts using a chirally functionalized carbon nanotube network as the chiral medium. A transverse measurement geometry is used, which nullifies any electromagnetochiral contribution but still exhibits the tell-tale signs of the CISS effect. This suggests that CISS may not be explained solely by electromagnetochiral effects. The role of nanotube spin-orbit coupling on the observed pure CISS signal is studied by systematically varying nanotube diameter. We find that the magnitude of the CISS signal scales proportionately with the spin-orbit coupling strength of the nanotubes. We also find that nanotube diameter dictates the supramolecular chirality of the medium, which in turn determines the sign of the CISS signal.

Hemagglutination Assay via Optical Density Characterization in 3D Microtrap Chips.

Hemagglutination assay has been used for blood typing and detecting viruses, thus applicable for the diagnosis of infectious diseases, including COVID-19. Therefore, the development of microfluidic devices for fast detection of hemagglutination is on-demand for point-of-care diagnosis. Here, we present a way to detect hemagglutination in 3D microfluidic devices via optical absorbance (optical density, OD) characterization. 3D printing is a powerful way to build microfluidic structures for diagnostic devices. However, mixing liquid in microfluidic chips is difficult due to laminar flow, which hampers practical applications such as antigen-antibody mixing. To overcome the issue, we fabricated 3D microfluidic chips with embedded microchannel and microwell structures to induce hemagglutination between red blood cells (RBCs) and antibodies. We named it a 3D microtrap chip. We also established an automated measurement system which is an integral part of diagnostic devices. To do this, we developed a novel way to identify RBC agglutination and non-agglutination via the OD difference. By adapting a 3D-printed aperture to the microtrap chip, we obtained a pure absorbance signal from the microchannels by eliminating the background brightness of the microtrap chip. By investigating the underlying optical physics, we provide a 3D device platform for detecting hemagglutination.

Sensorless microcontroller-based zero-crossing detection system for AC signals using a rounding function

Microcontroller-based methods for zero-crossing detection (ZCD) presented in the literature employ external integrated circuits (ICs) to sense electrical signals, and their algorithms are sophisticated. Therefore, this paper proposes a simple open-loop sensorless microcontroller-based algorithm for determining zero-crossing (ZC) instants of sinusoidal signals that can be used for phase detection (PD) in phase-locked loops (PLLs). The proposed algorithm employs a built-in rounding function in the Arduino software environment. The developed system was simulated using MATLAB Simulink® to demonstrate its prominent features in recognizing zero-crossing points in two cases: a pure sine signal at a grid frequency of 50 Hz, and distorted signals with total harmonic distortions (THDs) of 10 %, 20 %, 30 %, and 40 %. In the simulation, the proposed system exhibited outstanding results in terms of simplicity, accuracy, and robustness. However, in practice, accuracy and robustness are relative terms, because they depend on the specifications of the microcontroller board. In this study, a low-cost microcontroller board, Arduino Uno, was used. The Uno is an 8-bit board with a 16-MHz clock speed. Therefore, an error in terms of the phase shift emerged in the output signal, which was measured to be 0.0314 rad at 50 Hz. Moreover, the proposed system detected the ZC points of distorted signals with a THD of up to 30 % without using filters and failed at a THD of 40 %. Finally, the ZC points were identified in a three-phase system, and the validity of applying the proposed method to polyphase systems was demonstrated.