Signal Detection Research Articles

Resolving plasmon-mediated high-order multiphoton excitation pathways in dolmen nanostructures using ultrafast nonlinear optical interferometry.

The multiphoton excitation pathways of plasmonic nanorod assemblies are described. By using dolmen structures formed from the directed assembly of three gold nanorods, plasmon-mediated three-photon excitation is resolved. These high-order multiphoton excitation channels were accessed by resonantly exciting a hybrid mode of the dolmen structure that was resonant with the 800-nm carrier wavelength of an ultrafast laser system. Rotation of the exciting field polarization to a non-resonant configuration did not generate third-order responses. Hence, the multiphoton excitation and resultant non-equilibrium electron distributions were generated by structure- and mode-selective excitation. Correlation between high-order and resonant plasmon excitation was achieved through sub-cycle time-resolved interferometric detection of incoherent nonlinear emission signals. The results illustrate the advantages of nonlinear optical interferometry and Fourier analysis for distinguishing plasmon-mediated processes from those that do not require plasmon excitation.

Active Surface-Enhanced Raman Spectroscopy (SERS): A Novel Concept for Enhancing Signal Contrast in Complex Matrices Using External Perturbation.

Noninvasive detection of surface-enhanced Raman spectroscopy (SERS) signals from deep within tissue represents a common challenge in many biological and clinical applications including disease diagnosis and therapy monitoring. Such signals are typically weak and not readily discernible from often much larger Raman and fluorescence background signals (e.g., from surrounding tissue). Consequently, suboptimal sensitivity in the detection of SERS signals is often achieved in these situations. Similar issues can arise in SERS measurements in other diffusely scattering samples and complex matrices. Here, we propose a novel concept, active SERS, for the efficient retrieval of SERS signals from deep within complex matrices such as biological tissues that mitigates these issues. It relies on applying an external perturbation to the sample to alter the SERS signal from nanoparticles (NPs) deep inside the matrix. A measurement with and without, or before and after, such perturbation then can provide powerful contrasting data enabling an effective elimination of the matrix signals to reveal more clearly the desired SERS signal without the interfering background and associated artifacts. The concept is demonstrated using ultrasound (US) as an external source of perturbation and SERS NPs inserted deep within a heterogeneous tissue phantom mimicking a cluster of NPs accumulated within a small target lesion. The overall SERS signal intensity induced by the applied US perturbation decreased by ∼21% and the SERS signal contrast was considerably improved by eliminating subtraction artifacts present in a conventional measurement performed at a neighboring spatial location in a heterogeneous tissue sample. Although the technique was demonstrated with SERS gold NPs with a standard Raman label, it is envisaged that active SERS NPs (both the nanoscale metal geometry and Raman label) could be specifically designed to deliver an augmented response to the external stimulus to further enhance the achievable SERS signal contrast and yield even greater improvement in detection sensitivity. The method was demonstrated using transmission Raman spectroscopy; however, it is also applicable to other Raman implementations including spatially offset Raman spectroscopy and conventional Raman spectroscopy performed both at depth and at surfaces of complex matrices.

Signal of dementia with proton pump inhibitor after minimizing competition bias: an updated disproportionality analysis

ABSTRACT Objective The association between proton pump inhibitor (PPI) and dementia was controversial. The aim of the current study was to perform an updated pharmacovigilance analysis of the association between dementia event and PPI treatment after minimizing competition bias. Methods We gathered cases reported with PPI treatment based on the United States Food and Drug Administration Adverse Event Reporting System database from 2004 to 2023. We employed disproportionality algorithms, including reporting odds ratio (ROR) and the information component (IC), to detect the association between dementia event and PPI. We investigated the affection of event competition bias on the current disproportionality signal detection. Results We finally included a total of 776,191 PPI cases, and 1813 cases in the dementia group. Analyzing primary suspect PPIs, we detected a significant association between dementia and PPI (ROR = 1.38, 95%CI 1.22 to 1.56; IC = 0.46, 95%CI 0.04 to 0.86). After excluding the PPI case with renal injury events, the strength of the dementia signal increased. Omeprazole (589 cases), pantoprazole (514 cases), and esomeprazole (386 cases) were the top three PPI reported with dementia events. Conclusion The current pharmacovigilance study identified a significant association between dementia and PPIs, except vonoprazan and tegoprazan, especially taking competition bias into account. Further high-quality prospective study still needed.

Capacitive Low-Frequency Hydrophone Based on Micronanostructured Iontronic Hydrogel for Underwater Monitoring.

Hydrophones play a crucial role in underwater target detection within sonar systems. However, existing hydrophones often encounter challenges such as low sensitivity and poor signal-to-noise ratio (SNR) in the detection of low-frequency acoustic signals. This work introduces a capacitive hydrophone (CH) designed for highly sensitive detection of low-frequency underwater sound signals. Comprising a latex film/silver electrode and a structured hydrogel as the electrolyte layer, the CH is enclosed in a cylindrical casing. By strategically integrating a carbon nanotube (CNT) topology network within a pyramid microarray in the hydrogel, the sensor efficiently forms the electric double layer (EDL), enhancing sensitivity and precision. The CH showcases exceptional low-pressure sensitivity across a wide frequency spectrum (20 to 800 Hz), achieving a receiving sensitivity of up to -159.7 dB in the critical low-frequency band (20 to 125 Hz), surpassing the performance of the commercial hydrophone (RHC-14) by a substantial margin of 33.29 dB. Furthermore, the CH maintains a superior SNR, enabling the detection of sound waves as faint as 0.3 Pa. This study demonstrates the capabilities of the CH in detecting maritime vessels and underwater sounds, underscoring the potential of the CNT-enhanced EDL sensing mechanism for future low-frequency hydrophone design.

Improved echo signal detection for pseudo-random single-photon counting laser ranging

In this paper, a new echo signal detection method, to the best of our knowledge, for pseudo-random single-photon counting ranging (PSPCR) LiDAR systems is proposed, which is applied for long distances, low repetition rates, and system cost reduction. First, in order to achieve a comparable temporal resolution as that in time-correlated single-photon counting (TCSPC) systems, we extend the pseudo-random code to discriminate the minimal time slot in time correlation. Second, we use the full width at half maxima (FWHM) in the duration of each pseudo-random code for correlation to reduce the impact of pulse width variation and timing jitters on ranging accuracy. Third, we study the bias and errors caused by using synchronous signals as the “START” signal, and propose to use the time of flight (ToF) at half energy to reduce the walk error. Simulation results show that, compared with existing PSPCR methods, the proposed method improves ranging accuracy with a lower repetition rate and lower peak and average power—centimeter-level ranging accuracy over tens of kilometers can be achieved using a laser with a repetition rate of 400 kHz, peak power of up to 1 kW, and average power of up to 1 W.

Data mining and safety analysis of dual orexin receptor antagonists (DORAs): a real-world pharmacovigilance study based on the FAERS database

ObjectiveUsing the Food and Drug Administration Adverse Event Reporting System (FAERS) database, four signal detection methods were applied to mine adverse drug events (ADEs) related to use of dual orexin receptor antagonists (DORAs) to provide reference for safe clinical use.Research design and MethodsData collected from Q3rd 2014 to Q4th 2023 were obtained from the FAERS database. According to the preferred terminology (PT) and systematic organ classification (SOC) of MedDRA v.26.0, the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN) were used to detect ADE signals.ResultsA total of 11,857 DORAs-related adverse reactions were detected, reported with suvorexant, lemborexant, and daridorexant as the main suspected drugs was 8717584, and 2556, respectively. A higher proportion of females than males were reported (57.27% vs. 33.04%). The top 20 positive PT signals from three DORAs showed that “sleep paralysis” ranked first. “Brain fog” was stronger following daridorexant but was not detected for the other two drugs, and “sleep sex” and “dyssomnia” were stronger in suvorexant but not in the other two drugs. Additionally, some PTs occurred that were not included in drug instructions, such as “hangover” and “hypnagogic hallucination.”ConclusionIn this study, four algorithms (ROR, PRR, BCPNN, and MGPS) were used to mine the safety signals of DORAs. We identified some potential ADE signals that can promote the rational use of DORAs and improve their safety.

Early warning signals for bifurcations embedded in high dimensions

Recent work has highlighted the utility of methods for early warning signal detection in dynamic systems approaching critical tipping thresholds. Often these tipping points resemble local bifurcations, whose low dimensional dynamics can play out on a manifold embedded in a much higher dimensional state space. In many cases of practical relevance, the form of this embedding is poorly understood or entirely unknown. This paper explores how measurement of the critical phenomena that generically precede such bifurcations can be used to make inferences about some properties of their embeddings, and, conversely, how prior knowledge about the mechanism of bifurcation can robustify predictions of an oncoming tipping event. These modes of analysis are first demonstrated on a simple fluid flow system undergoing a Hopf bifurcation. The same approach is then applied to data associated with the West African monsoon shift, with results corroborated by existing models of the same system. This example highlights the effectiveness of the methodology even when applied to complex climate data, and demonstrates how a well-resolved spatial structure associated with the onset of atmospheric instability can be inferred purely from time series measurements.

WSe2/chitosan-based wearable multi-functional platform for monitoring electrophysiological signals, pulse rate, respiratory rate, and body movements.

Acleanroom free optimized fabrication of a low-cost facile tungsten diselenide (WSe2) combined with chitosan-based hydrogel device is reportedfor multifunctional applications including tactile sensing, pulse rate monitoring, respiratory rate monitoring, human body movements detection, and human electrophysiological signal detection. Chitosan being a natural biodegradable, non-toxic compound serves as a substrate to the semiconducting WSe2 electrode which is synthesized using a single step hydrothermal technique. Elaborate characterization studies are performed to confirm the morphological, structural, and electrical properties of the fabricated chitosan/WSe2 device. Chitosan/WSe2 sensor with copper contacts on each side is put directly on skin to capture human body motions. The resistivity of the sample was calculated as 26 kΩ m-1. The device behaves as an ultrasensitive pressure sensor for tactile and arterial pulse sensing with response time of 0.9s and sensitivity of around 0.02kPa-1. It is also capable for strain sensing with a gauge factor of 54 which is significantly higher than similar other reported electrodes. The human body movements sensing can be attributed to the piezoresistive character of WSe2 that originates from its non-centrosymmetric structure. Further, the sensor is employed for monitoring respiratory rate which measures to 13 counts/min for healthy individual and electrophysiological signals like ECG and EOG which can be used later for detecting numerous pathological conditions in humans. Electrophysiological signal sensing is carried out using a bio-signal amplifier (Bio-Amp EXG Pill) connected to Arduino. The skin-friendly, low toxic WSe2/chitosan dry electrodes pave the way for replacing wet electrodes and find numerous applications in personalized healthcare.

Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring

Swallowing is a complex neuromuscular activity regulated by the autonomic central nervous system, and impairment can lead to dysphagia, which is difficulty in swallowing. This research presents a novel approach that utilizes wireless, wearable technology for the continuous mechano-acoustic tracking of respiratory activities and swallowing. To address the challenge of accurately tracking swallowing amidst potential confounding activities or significant body movements, we employ two accelerometers. These accelerometers help distinguish between genuine swallowing events and other activities. By monitoring movements and vibrations through the skin surface, the developed device enables non-intrusive monitoring of swallowing dynamics and respiratory patterns. Our focus is on the development of both the wireless skin-interfaced device and an advanced algorithm capable of detecting swallowing dynamics in conjunction with respiratory phases. The device and algorithm demonstrate robustness in detecting respiratory patterns and swallowing instances, even in scenarios where users exhibit periodic movements due to disease or daily activities. Furthermore, peak detection using an adaptive threshold automatically adjusts to an individual’s signal strength, facilitating the detection of swallowing signals without the need for individual adjustments. This innovation has significant potential for enhancing patient training and rehabilitation programs aimed at addressing dysphagia and related respiratory issues.

Dynamic potential stochastic resonance for weak signal detection

Abstract To suppress the low-frequency interference noise, a dynamic potential stochastic resonance (DPSR) model is proposed in this paper for weak signal detection. The DPSR model introduces a single dynamic parameter k that simplifies parameter optimization. Its dynamic potential function can adaptively adjust to match noisy input signals. This model provides a new nonlinear model for triggering the SR phenomenon. Experimental results indicate that compared to the traditional methods that rely on clear interference frequency distributions to filter low-frequency components directly, the SR model offers greater flexibility and convenience. Unlike classical SR models, the proposed DPSR model demonstrates a 1.5dB improvement in output performance for suppressing low-frequency interference. Therefore, the DPSR model not only robustly suppresses interference but also effectively enhances and detects characteristic signals in variant-noise environments. Application to sea trial signals highlights the superior performance of the DPSR model in significantly reducing low-frequency interference and improving target signal recognizability compared to other models.

Enhancement of Signal‐to‐Background Ratio in Molecular Vibrational Signal Extraction by Stimulated Emission Depletion Mechanism

Herein, a novel approach is presented to mitigate the fluorescence interference during the detection of vibrational signal via the stimulated emission depletion (STED). STED is the mechanism commonly employed in optical imaging; however, its application should not be confined solely to this field. To explore additional possibilities, a novel application of STED in vibrational spectroscopy detection is introduced. Vibrational spectroscopy is a widely used technique for the material detection and identification, but its sensitivity is influenced by impurity signals, especially the fluorescence. The proposed method is capable of suppressing fluorescence without influencing vibrational signal. At the low concentration of fluorescent impurities, the signal‐to‐background ratio of vibrational spectroscopy is 2.6 times as high as that without this method. The introduction of depletion light can enhance the detection of vibrational signals, resulting in more optimal signal detection. A promising new application of STED other than super‐resolution imaging is investigated.

Simultaneous Confidence Intervals for Signal Detection and Ascertaining Precision of Adverse Event Rates in Clinical Trials

The marketing authorization of a medicinal product is contingent upon demonstration of safety and efficacy in support of the product’s labeled conditions of use. To demonstrate safety, one group of adverse events that requires detailed consideration is common adverse reactions (ARs). ARs and their frequency are reported in prescription drug labeling in the US and EU. The determination of these adverse reactions generally takes a simple approach - usually, inclusion is through the frequency of reporting and whether the adverse event (AE) rate for the drug exceeds the placebo rate. This standard method does not account for confounders or multiplicity. To overcome these limitations, we propose a Monte-Carlo approach to detect drug safety signals in clinical trials. We fit regression models incorporating covariates to assess the drug effect on the rate of AEs. Adjustment for multiplicity is carried out through the construction of simultaneous confidence intervals accounting for arbitrary correlations. A computationally efficient multiplier bootstrap approach using the Rademacher sequences is developed to generate random samples from the joint distribution of the estimators for all the AE rates. Compared to Bonferroni-based methods, the proposed method leads to narrower simultaneous confidence intervals and is more powerful in detecting potential safety signals.

A functional renormalization group for signal detection and stochastic ergodicity breaking

Signal detection is one of the main challenges in data science. As often happens in data analysis, the signal in the data may be corrupted by noise. There is a wide range of techniques that aim to extract the relevant degrees of freedom from data. However, some problems remain difficult. This is notably the case for signal detection in almost continuous spectra when the signal-to-noise ratio is small enough. This paper follows a recent bibliographic line, which tackles this issue with field-theoretical methods. Previous analysis focused on equilibrium Boltzmann distributions for an effective field representing the degrees of freedom of data. It was possible to establish a relation between signal detection and Z2 -symmetry breaking. In this paper, we consider a stochastic field framework inspired by the so-called ‘model A’, and show that the ability to reach, or not reach, an equilibrium state is correlated with the shape of the dataset. In particular, by studying the renormalization group of the model, we show that the weak ergodicity prescription is always broken for signals that are small enough, when the data distribution is close to the Marchenko–Pastur law. This, in particular, enables the definition of a detection threshold in the regime where the signal-to-noise ratio is small enough.

Host-imposed control mechanisms in legume-rhizobia symbiosis.

Legumes are ecologically and economically important plants that contribute to nutrient cycling and agricultural sustainability, features tied to their intimate symbiosis with nitrogen-fixing rhizobia. Rhizobia vary dramatically in quality, ranging from highly growth-promoting to non-beneficial; therefore, legumes must optimize their symbiosis with rhizobia through host mechanisms that select for beneficial rhizobia and limit losses to non-beneficial strains. In this Perspective, we examine the considerable scientific progress made in decoding host control over rhizobia, empirically examining both molecular and cellular mechanisms and their effects on rhizobia symbiosis and its benefits. We consider pre-infection controls, which require the production and detection of precise molecular signals by the legume to attract and select for compatible rhizobia strains. We also discuss post-infection mechanisms that leverage the nodule-level and cell-level compartmentalization of symbionts to enable host control over rhizobia development and proliferation in planta. These layers of host control each contribute to legume fitness by directing host resources towards a narrowing subset of more-beneficial rhizobia.

Stressful life events and tinnitus : Reflections and speculations.

The role of life events has not been extensively studied in the context of tinnitus. There is, however, evidence about the psychological impact of tinnitus and about the influence of psychological processes as mediators of the tinnitus experience. On the basis of this evidence, the possibility that stressful life events can act as atrigger for tinnitus is discussed; although the evidence is fragmentary and indirect, the possibility cannot be discounted. It is argued that the onset of tinnitus and the associated clinical encounters can constitute an acute stressful life event, and the ongoing experience of tinnitus can be regarded as achronic stressful life event. Interactions between ongoing tinnitus and other life events are discussed. Possible mechanisms in terms of stress influencing predictive processing and signal detection judgments are considered.

Signal detection in degree corrected ERGMs

Signal detection in degree corrected ERGMs

Temperature imaging during the hydrogen release reaction from a liquid organic hydrogen carrier (LOHC) system using phosphor thermometry

Liquid organic hydrogen carrier (LOHC) systems offer a particularly interesting option for chemical hydrogen storage. In order to characterize and understand the endothermal hydrogen release from the carrier liquid and to evaluate suitable catalyst materials, knowledge of the temperature fields in the dehydrogenation reactor is important. One suitable technique for planar temperature sensing in reacting systems is phosphor thermometry. It is based on the excitation of a luminescent material by a laser pulse and detection of the subsequent phosphorescence signal. We investigated the luminescence of the thermographic phosphor (Sr,Ca)SiAIN3:Eu2+ (“SCASN:Eu2+”) dispersed in the H0-DBT / H18-DBT LOHC system in a temperature range from 400 to 600 K. A measurement cell enables repeatable and homogeneous measurement conditions of the hydrogen release reaction. A catalytic plate was put inside the heated LOHC. Temperature fields during the hydrogen release reaction were measured for the first time using the phosphorescence decay time (PDT) and the phosphorescence intensity ratio method (PIR). As expected, a strong cooling at the catalyst surface during the endothermal hydrogen release reaction could be observed, which was quantified to be in the range of 40 K.Graphical abstract

Efficient traffic signal detection with tiny YOLOv4: enhancing road safety through computer vision

As decades go by, technology advances and everything around us becomes smarter, such as televisions, mobile phones, robots, and so on. Artificial intelligence (AI) is applied in these technologies where AI assists the computer in making judgments like humans, and this intelligence is artificially fed to the model. The self-driving technique is a developing technology. Autonomous driving has been a broad and fast-expanding technology over the last decade. This model is carried out using the tiny you only look once (YOLO) algorithm. YOLO is mainly used for object detection classification. Tiny YOLO model is explored for the traffic signal detection. ROBI FLOW dataset is used for object detection which contains 2000+ image data to train the tiny YOLO model for traffic signal detection in real time. This model gives an improved accuracy and lightweight implementation compared to other models. Tiny YOLO is fast and accurate model for real-time traffic signal detection.

The political (a)symmetry of metacognitive insight into detecting misinformation.

Political misinformation poses a major threat to democracies worldwide, often inciting intense disputes between opposing political groups. Despite its central role for informed electorates and political decision making, little is known about how aware people are of whether they are right or wrong when distinguishing accurate political information from falsehood. Here, we investigate people's metacognitive insight into their own ability to detect political misinformation. We use data from a unique longitudinal study spanning 12 waves over 6 months that surveyed a representative U.S. sample (N = 1,191) on the most widely circulating political (mis)information online. Harnessing signal detection theory methods to model metacognition, we found that people from both the political left and the political right were aware of how well they distinguished accurate political information from falsehood across all news. However, this metacognitive insight was considerably lower for Republicans and conservatives-than for Democrats and liberals-when the information in question challenged their ideological commitments. That is, given their level of knowledge, Republicans' and conservatives' confidence was less likely to reflect the correctness of their truth judgments for true and false political statements that were at odds with their political views. These results reveal the intricate and systematic ways in which political preferences are linked to the accuracy with which people assess their own truth discernment. More broadly, by identifying a specific political asymmetry-for discordant relative to concordant news-our findings highlight the role of metacognition in perpetuating and exacerbating ideological divides. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Feedback exercises boost discernment of misinformation for gamified inoculation interventions.

Gamification is a promising approach to reducing misinformation susceptibility. Previous research has found that "inoculation" games such as Bad News and Harmony Square help build cognitive resistance against misinformation. However, recent research has offered two important nuances: a potentially inadvertent impact of such games on people's evaluation of non-misinformation ("real news") and exponential decay over time if no memory-strengthening exercise is provided. We address these issues in two preregistered lab experiments (N1 = 191, N2 = 321) and four quasi-experimental in-game surveys implemented in Harmony Square (N3 = 559) and Bad News (N4 = 2,558, N5 = 419, N6 = 882). In Experiments 1 and 2, we test if providing different types of feedback after playing Bad News enhances discriminative ability of misinformation and real news 1 week postgameplay and find that doing so resulted in homogeneously better accuracy at identifying both misinformation and non-misinformation compared with a control condition, which played Bad News without feedback. In Experiments 3-6, we implemented two different types of feedback exercises in the Harmony Square and Bad News games and find that this significantly boosts discernment compared with playing the game without a feedback exercise, primarily by improving accuracy at detecting real news. We confirm these results using signal detection theory. We conclude that feedback exercises boost the effectiveness of gamified misinformation interventions, likely due to an improved learning environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).