Signal Detection Research Articles

Adaptive approach to spectrum monitoring in cognitive radio networks through signal detection optimization

The article considers the improvement of the adaptive algorithm of the spectral monitoring method for cognitive radio networks by introducing adaptive wavelet transforms and filters. The use of adaptive Morle and Dobechy wavelet transforms, as well as adaptive Kalman, LMS, and RLS filters is proposed, which allows dynamically changing parameters depending on the conditions of the radio environment. The comparative analysis with traditional methods showed that adaptive methods significantly increase the efficiency of signal detection in conditions of low SNR values, reducing the noise level, improving the accuracy of signal detection and reducing the probability of false alarms. The results of the study confirm the perspective of using adaptive methods to increase the reliability and efficiency of spectral monitoring in real operating conditions.

GNSS positioning enhancement based on NLOS signal detection using spatio-temporal learning in urban canyons

GNSS positioning enhancement based on NLOS signal detection using spatio-temporal learning in urban canyons

Multi-frequency weak signal detection based on Liu-like chaotic synchronization system and its hardware circuit implementation

Considering the shortcomings of traditional chaotic systems in weak signal detection methods, such as the high threshold sensitivity requirement and the narrow detection frequency domain. This study proposes a novel three-dimensional chaotic synchronization system, and the dynamical of the system are exhaustively characterized using equilibrium points, phase diagrams, Lyapunov exponential spectra, and bifurcation diagrams. This method involves weak signal detection by means of chaotic synchronization control. Synchronization of a chaotic system using a backstepping synchronization method is used to detect weak signals by analyzing the synchronization error after the introduction of weak signals in a strong noise background. The chaotic system is implemented by hardware circuits, and the simulation of chaotic synchronization control and detection of weak signals from the perspective of circuits is carried out by circuit simulation software. Additionally, the frequency range within which the system is capable of weak signal detection is tested through extensive simulation experiments. Finally, multi-frequency signals detection experiments are performed. The experimental results demonstrate that the system can accurately detect the frequency of weak signals address the limitations of narrow-band detection and multi-frequency signal detection is possible. Meanwhile, the circuit structure proposed in this paper is simple and has some value for engineering applications.

Enhancing protein signal detection in asexual and viviparous pea aphids: A guided protocol for tissue dissection and proteinase K treatment

Aphids, as hemipteran insects, reproduce via parthenogenesis and viviparity, resulting in rapid and exponential offspring production. To investigate the molecular mechanisms underlying parthenogenetic viviparity in asexual aphids, precise protein detection through immunostaining is essential. Our previous research demonstrated the need for proteinase K (PK) treatment to improve tissue permeability, enabling antibodies targeting the germ-cell marker Ap-Vas1 to access gastrulating and later-stage embryos. However, optimal PK digestion protocols have not been thoroughly explored. In this study, we propose strategies to optimize PK digestion conditions for early, middle, and late-stage pea aphid embryos, which have varying tissue thicknesses. Additionally, we extend the application of PK treatment to salivary glands, a representative somatic tissue, by optimizing conditions for antibody penetration against the salivary gland marker C002. To enhance spatial precision in signal detection, we provide a detailed protocol for tissue dissection specific to pea aphids, focusing on the preservation of tissue integrity. These comprehensive guidelines, covering tissue dissection and PK titration, are expected to improve the specificity and intensity of protein signals in pea aphids and other aphid species.•Provide aphid-specific dissection methods to obtain intact embryos and salivary glands.•Present strategies for optimizing PK treatment conditions across different tissue types.

Adverse reactions of immune checkpoint inhibitors combined with Proton pump inhibitors: a pharmacovigilance analysis of drug-drug interactions

BackgroundCombining immune checkpoint and proton pump inhibitors is widely used in cancer treatment. However, the drug-drug interactions of these substances are currently unknown. This study aimed to explore drug-drug interactions associated with concomitant immune checkpoint and proton pump inhibitors.MethodsData were obtained from the US Food and Drug Administration Adverse Event Reporting System from 2014 to 2023. Disproportionality analysis was used for data mining by calculating the reporting odds ratios (RORs) with 95% confidence intervals (95%Cls). The adjusted RORs (RORadj) were then analysed using logistic regression analysis, considering age, sex, and reporting year. Drug-drug interactions occur when a combination treatment enhances the frequency of an event. Further confirmation of the robustness of the findings was achieved using additive and multiplicative models, which are the two statistical methodologies for signal detection of DDIs using spontaneous reporting system.ResultsThe total number of reports on immune checkpoint combined with proton pump inhibitors was 4,276. Median patient age was 66 years (interquartile range [IQR]: 60–74 years). Significant interaction signals were observed for congenital, familial and genetic disorders (RORadj = 2.66, 95%CI, 1.38–5.14, additive models = 0.7322, multiplicative models = 3.5142), hepatobiliary disorders (RORcrude = 6.64, 95%CI, 5.82–7.58, RORadj = 7.10, 95%CI, 6.16–8.18, additive models = 2.0525, multiplicative models = 1.1622), metabolism and nutrition disorders (RORcrude = 3.27, 95%CI, 2.90–3.69, RORadj = 2.66, 95%CI, 2.30–3.08, additive models = 0.6194), and skin and subcutaneous tissue disorders (RORcrude = 1.41, 95%CI, 1.26–1.58, RORadj = 1.53, 95%CI, 1.34–1.75, additive models = 0.6927, multiplicative models = 5.3599). Subset data analysis showed that programmed death-1 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders; hepatobiliary disorders; and skin and subcutaneous tissue disorders. Programmed death ligand-1 combined with proton pump inhibitors was associated with adverse reactions of metabolism and nutrition disorders. Cytotoxic T-lymphocyte antigen-4 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders, and skin and subcutaneous tissue disorders.ConclusionsBased on real-world data, four Standardized MedDRA Query System Organ Class toxicities were identified as drug-drug interactions associated with combining immune checkpoint and proton pump inhibitors. Clinicians should be cautious when administering these drugs concomitantly. Preclinical trials and robust clinical studies are required to explore the mechanisms and relationships underlying interactions, thus improving understanding of drug-drug interactions associated with this combination therapy.

Improved polygenic risk prediction in migraine-first patients

BackgroundRecent meta-analyses estimated 14.6% and 11.2% SNP-based heritability of migraine, compared to twin-heritability estimates of 30–60%. This study aimed to investigate heritability estimates in “migraine-first” individuals, patients for whom G43 (migraine with or without aura) was their first medical diagnosis in their lifetime.FindingsUsing data from the UK Biobank (N = 199,929), genome-wide association studies (GWAS) were conducted on 6,139 migraine-first patients and 193,790 healthy controls. SNP-based heritability was estimated using SumHer, yielding 19.37% (± 0.019) for all SNPs and 21.31% (± 0.019) for HapMap3 variants, substantially surpassing previous estimates. Key risk loci included PRDM16, FHL5, ASTN2, STAT6/LRP1, and SLC24A3, and pathway analyses highlighted retinol metabolism and steroid hormone biosynthesis as important pathways in these patients.ConclusionsThe findings underscore that excluding comorbidities at onset time can enhance heritability estimates and genetic signal detection, significantly reducing the extent of “missing heritability” in migraine.

A high-entropy electrode material for electrobiochemical and eletrophysiological signals detection

Electrobiochemical and eletrophysiological signals indicate human physiology, health, and activity performance. It is still a big challenge to detect these biosignals due to the lack of sensing material. Here, we report a high-entropy material for electrobiochemical and eletrophysiological signals sensing. The high-entropy material consists of the conventional transition metallic elements of Fe, Co, Ni, Cu, and Zn. This high-entropy biosignal sensing material is able to detect the concentration of the biomarker of glucose and electrophysiological signals of Electromyography (EMG), Electroencephalography (EEG), Electrocardiography (ECG). The limitation of the glucose detection is 3.3 μM with the sensitivity of 2861 μA mM−1 cm−2. The HEA electrode also presents good linearship to the glucose concentration in PBS. The cell assay and skin assessment results also demonstrate the excellent biocompatibility of this HEA electrode, indicating good feasibility to the real biosamples. The contact impedance of this HEA electrode is lower than the conventional dry electrode, which leads to the good capability in electrophysiological signals detection. This report not only extends the application of high-entropy material in the field of biomedical engineering but also provides a new pathway for electrobiochemical and eletrophysiological signals detection.

Detecting emerald ash borer boring vibrations using an encoder-decoder and improved DenseNet model.

Forest ecosystems are under constant threat from wood-boring pests such as the Emerald ash borer (EAB), which remain elusive owing to their hidden life cycles within tree trunks. Early detection is vital to mitigate economic and ecological damage. The main current monitoring method is manual detection which is ineffective at early stages of infestation. This study introduces VibroEABNet, a deep learning-based joint recognition network designed to enhance the detection of EAB boring vibration signals, with a novel approach integrating denoising and recognition modules. The proposed VibroEABNet model demonstrated exceptional performance, achieving an average accuracy of 98.98% across multiple signal-to-noise ratios (SNRs) in test datasets and a remarkable 97.5% accuracy in real forest datasets, surpassing traditional models and other deep learning networks evaluated in this study. These findings were supported by rigorous noise resistance analysis and real dataset evaluation, indicating the model's robustness and reliability in practical applications. Furthermore, the model's efficiency was highlighted by its inference time of 26 ms and a compact model size of 8.43 MB, underscoring its suitability for deployment in resource-limited environments. The development of VibroEABNet marks a significant advancement in pest detection methodologies, offering a scalable, accurate and efficient solution for early monitoring of wood-boring pests. The integration of a denoising module within the network structure addresses the challenge of environmental noise, one of the primary limitations in acoustic monitoring of pests. Currently, this research is limited to a specific pest. Future work will focus on the applicability of this network to other wood-boring pests. © 2024 Society of Chemical Industry.

Low-Dose Oral Minoxidil and Associated Adverse Events: Analyses of the FDA Adverse Event Reporting System (FAERS) With a Focus on Pericardial Effusions.

Low-dose oral minoxidil (LDOM) is used to treat hair loss, but the literature on its safety profile is relatively sparse. Using the FDA Adverse Event Reporting System (FAERS) database, we determined signals for adverse events (AEs) with LDOM use. Four sets of case/noncase study disproportionality analyses were conducted to determine reporting odds ratio (ROR) for 10 AEs including pericardial effusion (PE). The oral minoxidil dose ranges were: (i) ≤1.25 mg (i.e., 0-1.25 mg), (ii) ≤2.5 mg (i.e., 0-2.5 mg), (iii) ≤5 mg (i.e., 0-5 mg), and (iv) ≤10 mg (i.e., 0-10 mg). For ≤1.25 mg, we detected a signal for PE (ROR = 16.41, 95% CI: 2.29, 117.37, p < 0.05). For ≤2.5 mg, the analyses detected a signal for PE (ROR = 13.30, 95% CI: 5.96, 29.68, p < 0.05); the ROR in the absence of cardiac impairment was 5.34 (95% CI: 1.33, 21.37, p < 0.05); in the presence of cardiac impairment, the ROR was 49.42 (95% CI: 18.27, 133.66, p < 0.05). A signal for PE was also detected at ≤5 and ≤10 mg. For PE, there was a significant (p < 0.05) association with a patient outcome of "life threatening" only at the ≤10 mg dose range. Our study, the first FAERS-based signal detection study for LDOM, found significant associations between LDOM use and several AEs. In the absence of causal evidence, these correlations warrant more attention regarding safe use of LDOM. Until more safety data are available, we recommend using LDOM at the lowest effective dose (≤5 mg/day).

Radiation Detectors and Sensors in Medical Imaging.

Medical imaging instrumentation design and construction is based on radiation sources and radiation detectors/sensors. This review focuses on the detectors and sensors of medical imaging systems. These systems are subdivided into various categories depending on their structure, the type of radiation they capture, how the radiation is measured, how the images are formed, and the medical goals they serve. Related to medical goals, detectors fall into two major areas: (i) anatomical imaging, which mainly concerns the techniques of diagnostic radiology, and (ii) functional-molecular imaging, which mainly concerns nuclear medicine. An important parameter in the evaluation of the detectors is the combination of the quality of the diagnostic result they offer and the burden of the patient with radiation dose. The latter has to be minimized; thus, the input signal (radiation photon flux) must be kept at low levels. For this reason, the detective quantum efficiency (DQE), expressing signal-to-noise ratio transfer through an imaging system, is of primary importance. In diagnostic radiology, image quality is better than in nuclear medicine; however, in most cases, the dose is higher. On the other hand, nuclear medicine focuses on the detection of functional findings and not on the accurate spatial determination of anatomical data. Detectors are integrated into projection or tomographic imaging systems and are based on the use of scintillators with optical sensors, photoconductors, or semiconductors. Analysis and modeling of such systems can be performed employing theoretical models developed in the framework of cascaded linear systems analysis (LCSA), as well as within the signal detection theory (SDT) and information theory.

MXene-based self-adhesive, ultrasensitive, highly tough flexible hydrogel pressure sensors for motion monitoring and robotic tactile sensing

With the rapid development of artificial intelligence and human–computer interaction fields, wearable electronic devices represented by flexible pressure sensors have a wider range of application scenarios. In this paper, we used a composite hydrogel made by one-pot polymerization using a combination of acrylamide (AM), sodium carboxymethyl cellulose (CMC), dopamine (DA), and MXene (Ti3C2Tx). The composite hydrogel has the advantages of self-adhesion, high sensitivity, large detection range (0–500 kPa), low detection limit, high toughness (compression > 90 %), stability (more than 6700 s) and other advantages of hydrogel flexible pressure strain sensor. Sodium carboxymethyl cellulose (CMC), as a multifunctional thickener and gelling agent, significantly improves rheological properties and viscosity in hydrogels, forms crosslinked structures to enhance mechanical properties and provides reliable toughness for pressure sensors. Dopamine (DA), which is rich in catechol groups, can adhere to the material surface through oxidative polymerization to enhance bond strength. Hydrogel pressure sensors for human motion detection can accurately track joint movement, respond sensitively to changes in knocks and touches, and display specific characters such as Morse code. The technology extends to robotic systems, where pressure-activated electrical signals are used to control robot movements. Therefore, it has a very promising application in the field of human–computer interaction, human-body movement signal detection, etc. PAM/CMC/DA/MXene hydrogel pressure sensors have excellent mechanical, electrical, and adhesion properties, which provide a more reliable solution for the fields of flexible wearable e-skin, human–computer interaction, and tactile sensing.

A novel anomaly detection method for magnetic flux leakage signals via a feature-based unsupervised detection network

High-precision anomaly detection, as the key technology of magnetic flux leakage (MFL) signal detection, is a challenging task. It is difficult to detect anomalies in MFL signals due to the variety of anomalies and the characteristics of the anomalies are easily submerged in the variation of the natural signals. To address the above issues, a feature-based unsupervised detection network (FUDet) is designed, which accomplishes the unsupervised anomaly detection task through feature discrimination and feature reconstruction. Firstly, a bidirectional discrimination module is proposed, which can input normal and anomaly feature distributions to mine the characteristics of samples, so as to enhance the ability of the model to recognize anomaly signals. Secondly, a dynamic noise generation module is designed to generate different feature distributions for each input that are consistent with the characteristics of MFL signals. This module creates an adversarial effect with the discriminator, allowing it to identify more subtle feature differences through training. Finally, a reconstruction classification module is designed to naturally reconstruct the non-normal features and normal features into normal signals, which can be used to detect anomalies by comparing the difference between the input signals and the reconstructed signals. Experimentally, the method is proved to outperform the P-AUROC of the state-of-the-art method by 3.1% under MFL signals and achieves outstanding results in MFL signal anomaly detection.

Radial velocity analysis of stars with debris discs

ABSTRACT This study aims to identify potential exoplanet signals from nearby stars with resolved debris discs. However, the high activity of many stars with debris discs limits the detection of periodic signals. Our study is constrained to a sample of 29 stars that have appropriate radial velocity data and debris disc measurements sufficient to resolve their inclination. Our results confirm and update previous findings for exoplanets around HD 10647, HD 115617, HD 69830, GJ 581, HD 22049, and HD 142091, and we identify long-term activity signals around HD 207129 and HD 202628. We utilize the inclination angles of the debris discs, assuming co-planarity between debris disc and exoplanet orbit, to determine the ‘disc-aligned’ masses of radial velocity exoplanets in this study. The ‘disc-aligned’ masses of HD 69830 b, HD 69830 c, and 61 Vir b suggests that they may be classified as ‘hot’ or ‘warm’ Jupiters and so might be nearby examples of planets that have undergone recent type-II disc migration.

Application of a vibration resonance-assisted enhanced feedforward cascaded stochastic resonance system in bearing diagnostics

Extracting fault features from strong background noise is a key challenge in bearing diagnostics. Stochastic resonance (SR) has been widely applied in bearing diagnostics by utilizing noise to enhance useful signals. This paper proposes a feedforward cascaded system based on vibration resonance (VR) to assist in enhancing SR from the perspective of multi-system synergy, aiming to improve the performance of the SR system. Firstly, a matching steady-state potential function is designed to effectively alleviate the output saturation phenomenon. Then, by introducing a feedback term and VR, a feedforward cascaded system with band-pass characteristics is constructed, significantly enhancing the ability to suppress low-frequency interference. Finally, the proposed system is analyzed in the detection of simulated signals and actual bearing fault signals from the German PADERBORN dataset. The results demonstrate that the system outperforms two other SR systems and the Fast-Kurtogram method (FK), validating its performance and practicality.

Achieving a Noise Limit with a Few-layer WSe2 Avalanche Photodetector at Room Temperature.

We engineered a two-dimensional Pt/WSe2/Ni avalanche photodetector (APD) optimized for ultraweak signal detection at room temperature. By fine-tuning the work functions, we achieved an ultralow dark current of 10-14 A under small bias, with a noise equivalent power (NEP) of 8.09 fW/Hz1/2. This performance is driven by effective dark barrier blocking and a record-long electron mean free path (123 nm) in intrinsic WSe2, minimizing dark carrier replenishment and suppressing noise under an ultralow electric field. Our APD exhibits a high gain of 5 × 105 at a modulation frequency of 20 kHz, effectively balancing gain and bandwidth, a common challenge in traditional photovoltaic-based APDs. By addressing the typical challenges of high noise and low gain and minimizing dependence on high electric fields, this work highlights the potential of 2D materials in developing efficient, low-power, and ultrasensitive photodetections.

Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones

The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value.

Signal Detection Theoretic Estimates of the Murine Absolute Visual Threshold Are Independent of Decision Bias.

Decision bias influences estimates of the absolute visual threshold. However, most psychophysical estimates of the murine absolute visual threshold have not taken bias into account. Here we developed a one-alternative forced choice (1AFC) assay to assess the decision bias of mice at the absolute visual threshold via the theory of signal detection and compared our approach with the more conventional high-threshold theoretic approach. In the 1AFC assay, mice of both sexes were trained to signal whether they detected a flash stimulus. We directly measured both hit and false alarm rates, which were used to estimate d' Using the theory of signal detection, we obtained absolute thresholds by interpolating the intensity where d' = 1 from d'-psychometric functions. This gave bias-independent estimates of the absolute visual threshold which ranged over sixfold, averaging ∼1 R* in 1,000 rods (n = 7 mice). To obtain high-threshold theoretic estimates of the absolute visual threshold from the same mice, we estimated threshold intensities from the frequency of seeing curves, corrected for guessing. This gave us thresholds that were strongly correlated with decision bias, ranging over 13-fold and averaged ∼1 R* in 2,500 rods. We conclude that the theory of signal detection uses false alarms to overcome decision bias and narrow the range of threshold estimates in mice, providing a powerful tool for understanding detection behavior near absolute visual threshold.

Transformer-empowered receiver design of OFDM communication systems

With deep learning, we perform channel estimation and signal detection in massive Multiple Input Multiple Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems in this paper. Specifically, we design and extend the basic framework of receivers for MIMO-OFDM systems in an end-to-end approach. A Transformer-based MIMO-OFDM receiver called TCD-Receiver is proposed, which introduces a multi-attention mechanism to learn the channel characteristics by introducing a generic and flexible Transformer network structure. The network parameters are updated based on the relationship between the received signal and the original signal, where the final signal information is obtained without explicit channel estimation and the predicted transmit bits are directly output. The experimental results show that the TCD-Receiver proposed can effectively solve the channel distortion and detect the transmitted signals compared with the traditional communication receivers, and its performance can be comparable to that of the traditional OFDM receivers, and it also has obvious advantages in combating the complex and difficult-to-model channel environment as well as the nonlinear interference factors.

Practical considerations for the amplification of electro-optically detected THz signals

Advances in THz methods and applications require the detection of weak THz pulsed signals. One solution to this problem is to amplify weak signals using optically biased electro-optical (EO) techniques. Several different EO amplification schemes are compared. The simplest of these involves using a non-polarizing beam splitter followed by two quarter-wave plates set to small positive and negative angles, respectively. The signals are then passed through polarizers perpendicular to the polarization of the 800 nm EO detector crystal gating beam. As long as sufficient intensity is present in the gate pulse for linear photodiode detection, amplification of up to 10 times with dynamic ranges for single sweep scans of &gt;104 is obtained.

A High-Precision, Ultra-Short Baseline Positioning Method for Full Sea Depth

To fulfill the demand for high-precision underwater acoustic positioning at full sea depth, an ultra-short baseline (USBL) positioning method with the square array based on the least squares estimating signal parameters via rotational invariance techniques (LS-ESPRIT) algorithm is presented in this paper. A combination of beam tracking and beamforming is employed to improve the accuracy of direction-of-arrival (DOA) estimation and, consequently, enhance overall positioning accuracy. In order to mitigate the issue of position jumping resulting from phase ambiguity in traditional four-element cross arrays, we have improved the stability of the positioning algorithm by utilizing a multi-element square array and employing the LS-ESPRIT algorithm for DOA estimation. Furthermore, the signal detection method integrating the correlation coefficient and ascending/descending chirp signals is employed to enhance the reliability of the location algorithm. Simulation analysis and experimental results demonstrate that the proposed algorithm effectively enhances positioning accuracy and improves the problem of jumping in positioning results.