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Trace NO 2 detection is essential for the production and life, where the sensing strategy is appropriate for rapid detection but lacks molecular specificity. This investigation proposes a sensing mechanism dominated by surface-scattering to achieve the molecularly-specific detection. Two-dimensional Bi 2 O 2 Se is firstly fabricated into a Schottky-junction-based gas-sensor. Applied with an alternating excitation, the sensor simultaneously outputs multiple response signals (i.e., resistance, reactance, and the impedance angle). Their response times are shorter than 200 s at room temperature. In NO 2 sensing, these responses present the detection limit in ppt range and the sensitivity is up to 16.8 %·ppb −1 . This NO 2 sensitivity presents orders of magnitude higher than those of the common gases within the exhaled breath. The impedance angle is involved in the principle component analysis together with the other two sensing signals. Twelve kinds of typical gases containing NO 2 are acquired with molecular characteristics. The change in dipole moment of the target molecule adsorbed is demonstrated to correlate with the impedance angle via surface scattering. The proposed mechanism is confirmed to output ultra-sensitive sensing responses with the molecular characteristic. Here, trace detection of NO2 is achieved with a 2D Schottky-junction Bi 2 O 2 Se gas sensor. Simultaneous signals allow for detection in the ppt range, and impedance angle analysis allows for molecular specificity.

Intrapartum asphyxia is responsible for approximately 900 000 deaths per year worldwide. These numbers show the urgency of investing in the quality of fetal health care. The heart rate signal is a complex signal and sometimes behaves unpredictably. Thus, it becomes relevant to study approaches that take into account their complexity, namely non-linear compression-based methods. In this work, feature extraction was based on two approaches: univariate and bivariate. The univariate approach is concerned with the extraction of fetal, maternal and maternal-fetal compression ratios and the bivariate approach aims to extract compression indices from maternal-fetal heart rate simultaneous signals and of each of the signals individually over time. To understand how the features calculated in this work can be useful in distinguishing acidemic and non-acidemic cases, a classifier was applied. Three different classifiers were tested, and the one that proved to be more effective was the Support-Vector Machine. Furthermore, it was also possible to conclude that the input set of variables that provides a better performance (f1-score = 0.793) of the classifier is composed of the variables of maternal-fetal compression ratio, maternal-fetal normalized relative compression and maternal-fetal normalized compression distance, obtained through trend and residual signal, which indicates that slow and fast fluctuations on the heart rate time series are important in acidemia assessment.

This paper presents a theoretical analysis in discrete time for a multi-tier weak radiofrequency (RF) signal estimation process with N simultaneous signals. Discrete time dynamic sampling is introduced and is shown to provide the capability to extract signal parameter values with increased accuracy compared with accuracy of estimates obtained in prior work. This paper advances phase measurement approaches by proposing discrete time dynamic sampling which our paper shows offers the desirable capability for more accurate weak signal parameter estimates. For N=2 simultaneous signals with a strong signal at 850 MHz and a weak signal at 855 MHz, the results show that dynamically sampling the instantaneous frequency at 24 times the Nyquist rate provides weak signal frequency estimates that are within 1.7×10-5 of the actual weak signal frequency and weak signal amplitude estimates that are within 428 PPM of the actual weak signal amplitude. Results are also presented for situations with N=2 simultaneous 5G signals. In one case, the strong signal is 3950 MHz, and the weak signal is 3955 MHz; in the other case the strong case is 5950 MHz, and the weak signal is 5955 MHz. The results for these cases show that estimates obtained with dynamic sampling are more accurate than estimates provided using a single sample rate of 65 MSPS. This work has promising applications for weak signal parameters estimation using instantaneous frequency measurements.

In light of the challenging conditions of exploration environments coupled with escalating exploration expenses, seismic data acquisition frequently entails the capturing of signals entangled amidst diverse noise interferences and instances of data loss. The unprocessed state of these seismic signals significantly jeopardizes the interpretative phase. Evidently, the integration of attention mechanisms and the utilization of generative adversarial networks (GANs) have emerged as prominent techniques within signal processing owing to their adeptness in discerning intricate global dependencies. Our research introduces a pioneering approach for reconstructing and denoising seismic signals, amalgamating the principles of self-attention and generative adversarial networks—hereafter referred to as SAGAN. Notably, the incorporation of the self-attention mechanism into the GAN framework facilitates an enhanced capacity for both the generator and discriminator to emulate meaningful spatial interactions. Subsequently, leveraging the feature map generated by the self-attention mechanism within the GAN structure enables the interpolation and denoising of seismic signals. Rigorous experimentation substantiates the efficacy of SAGAN in simultaneous signal interpolation and denoising. Initially, we benchmarked SAGAN against prominent methods such as UNet, CNN, and Wavelet for the concurrent interpolation and denoising of two-dimensional seismic signals manifesting varying levels of damage. Subsequently, this methodology was extended to encompass three-dimensional seismic data. Notably, performance metrics reveal SAGAN’s superiority over comparative methods. Specifically, the quantitative tables exhibit SAGAN’s pronounced advantage, with a 3.46% increase in PSNR value over UNet and an impressive 11.90% surge compared to Wavelet. Moreover, the RMSE values affirm SAGAN’s robust performance, showcasing an 11.54% reduction in comparison to UNet and an impressive 29.27% decrement relative to Wavelet, hence unequivocally establishing the SAGAN method as a preeminent choice for seismic signal recovery.

In a congested signal environment, it is difficult to obtain estimates of weak RF signal parameters. Determining signal parameter estimates in real time is a challenge for electronic warfare receivers that aim to receive multiple simultaneous signals. Prior work provided estimates of weak signal parameters (weak signal frequency and weak signal amplitude) without taking into account any error introduced by analog-to-digital converters that are inherently part of digital signal processing systems. In order to obtain realistic estimates, we need to take error introduced by an ADC into account. The primary aim of this paper is to quantify error introduced by a single ideal ADC as a function of angle. This paper presents a method to estimate angle resolution and quantization levels in N-bit analog-to-digital converters (ADCs) for use in a weak radiofrequency (RF) simultaneous signal estimation process. The paper quantifies the error in the angle quantization of an N-bit ADC for an input complex signal that is the instantaneous frequency obtained for the situation in which there are two simultaneous signals (with one strong signal and one weak signal) in a weak RF simultaneous signal estimation process. The presented method describes the process to determine the angle quantization range, angle quantization uncertainty, and angle quantization error. This approach has potential applications in electronic warfare (EW) systems. The approach also has potential for assessing ADC performance for measurements that approach the quantum limit. Results are presented for 1-bit, 2-bit, 3-bit, and 10-bit ADCs.

Neurotransmitters (NTs) play a crucial role in regulating the behavioral and physiological functions of the nervous system. Imbalances in the concentrations of NT have been directly linked to various neurological diseases (e.g., Parkinson’s, Huntington’s, and Alzheimer’s disease), in addition to multiple psychotic disorders such as schizophrenia, depression, dementia, and other neurodegenerative disorders. Hence, the rapid and real-time monitoring of the NTs is of utmost importance in comprehending neurological functions and identifying disorders. Among different sensing techniques, electrochemical biosensors have garnered significant interest due to their ability to deliver fast results, compatibility for miniaturization and portability, high sensitivity, and good controllability. Furthermore, the utilization of enzymes as recognition elements in biosensing design has garnered renewed attention due to their unique advantages of catalytic biorecognition coupled with simultaneous signal amplification. This review paper primarily focuses on covering the recent advances in enzymatic electrochemical biosensors for the detection of NTs, encompassing the importance of electrochemical sensors, electrode materials, and electroanalytical techniques. Moreover, we shed light on the applications of enzyme-based biosensors for NTs detection in complex matrices and in vivo monitoring. Despite the numerous advantages of enzymatic biosensors, there are still challenges that need to be addressed, which are thoroughly discussed in this paper. Finally, this review also presents an outlook on future perspectives and opportunities for the development of enzyme-based electrochemical biosensors for NTs detection.

Pedestrian positioning by surveillance video offers high accuracy and real-time performance in indoor scenes. However, pedestrian positioning based on surveillance video presents both challenges and promising application prospects. The practical application of existing surveillance video is hindered by the lack of tag information and incomplete video coverage. Although positioning methods with terminal devices provide tag information, simultaneous signal transmission from multiple tags to the base station can lead to collisions and mutual interference, such as with ultrawide-band (UWB) positioning. A positioning method that combines surveillance video and terminal devices is expected to solve this problem. This paper introduces a credibility scoring algorithm for matching surveillance video targets with terminal tags. The algorithm considers four factors: the distance between the obtained positioning points, the weighted average speed, the direction of motion, and the number of positioning points obtained from both the video and the UWB tag. The algorithm matches the surveillance video target with the UWB tag according to the highest score. The experimental results show that the proposed credibility scoring algorithm achieved accurate matching results in both unobstructed scenes (scores for the same tag are generally twice as high as scores for different tags) and obstructed scenes (scores for the same tag are 13% higher than scores for different tags). This approach improves the usability of pedestrian positioning via surveillance videos in indoor scenes, avoids the problem of tag misalignment, and will provide further technical support for pedestrian positioning and management based on surveillance videos.

Background Multiple testing of composite null hypotheses is critical for identifying simultaneous signals across studies. While it is common to incorporate external information in simple null hypotheses, exploiting such auxiliary covariates to provide prior structural relationships among composite null hypotheses and boost the statistical power remains challenging. Results We propose a robust and powerful covariate-assisted composite null hypothesis testing (CoCoNuT) procedure based on a Bayesian framework to identify replicable signals in two studies while asymptotically controlling the false discovery rate. CoCoNuT innovatively adopts a three-dimensional mixture model to consider two primary studies and an integrative auxiliary covariate jointly. While accounting for heterogeneity across studies, the local false discovery rate optimally captures cross-study and cross-feature information, providing improved rankings of feature importance. Conclusions Theoretical and empirical evaluations confirm the validity and efficiency of CoCoNuT. Extensive simulations demonstrate that CoCoNuT outperforms conventional methods that do not exploit auxiliary covariates while controlling the FDR. We apply CoCoNuT to schizophrenia genome-wide association studies, illustrating its higher power in identifying replicable genetic variants with the assistance of relevant auxiliary studies.

Raman spectroscopy is capable of precisely identifying and analyzing the composition and properties of samples collected from the lunar surface, providing crucial data support for lunar scientific research. However, in situ Raman spectroscopy on the lunar surface faces challenges such as weak Raman scattering from targets, alongside requirements for lightweight and long-distance detection. To address these challenges, time-gated Raman spectroscopy (TG-LRS) based on a passively Q-switched pulsed laser and a linear intensified charge-coupled device (ICCD), which enable simultaneous signal amplification and background suppression, has been developed to evaluate the impact of key operational parameters on Raman signal detection and to explore miniaturization optimization. The TG-LRS system includes a 40 mm zoom telescope, a passively Q-switched 532 nm pulsed laser, a fiber optic delay line, a miniature spectrometer, and a linear ICCD detector. It achieves an electronic gating width under 20 ns. Within a detection range of 1.1–3.0 m, the optimal delay time varies linearly from 20 to 33 ns. Raman signal intensity increases with image intensifier gain, while the signal-to-noise ratio peaks at a gain range of 800–900 V before declining. Furthermore, the effects of focal depth, telescope aperture, laser energy, and integration time were studied. The Raman spectra of lunar minerals were successfully obtained in the lab, confirming the system’s ability to suppress solar background light. This demonstrates the feasibility of in situ Raman spectroscopy on the lunar surface and offers strong technical support for future missions.

An optical phase‐sensitive amplifier (PSA) has the potential of low‐noise optical amplification. A frequency non‐degenerate PSA (ND‐PSA) can amplify arbitrary modulation formats including higher‐order QAM. The ND‐PSA requires a co‐propagating phase conjugated light (idler light) that has been conventionally created with an optical phase conjugator at the transmitter side. We propose a transmitter configuration using simultaneous signal generation of both a signal and its idler by double‐sideband modulation for transmission systems with ND‐PSAs. The proposed scheme provides a simple configuration with a single Mach–Zehnder amplitude modulator without optically creating the idler light. We performed experiments using 16QAM, 32QAM, and probabilistically shaped 256QAM signals. As a result, a phase‐sensitive amplification with each modulation format was successfully demonstrated using the proposed transmitter configuration.