Signal Detection Research Articles

Tear-Based Ocular Wearable Biosensors for Human Health Monitoring.

Wearable tear-based biosensors have garnered substantial interest for real time monitoring with an emphasis on personalized health care. These biosensors utilize major tear biomarkers such as proteins, lipids, metabolites, and electrolytes for the detection and recording of stable biological signals in a non-invasive manner. The present comprehensive review delves deep into the tear composition along with potential biomarkers that can identify, monitor, and predict certain ocular diseases such as dry eye disease, conjunctivitis, eye-related infections, as well as diabetes mellitus. Recent technologies in tear-based wearable point-of-care medical devices, specifically the state-of-the-art and prospects of glucose, pH, lactate, protein, lipid, and electrolyte sensing from tear are discussed. Finally, the review addresses the existing challenges associated with the widespread application of tear-based sensors, which will pave the way for advanced scientific research and development of such non-invasive health monitoring devices.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

Mitigating calibration errors from mutual coupling with time-domain filtering of 21 cm cosmological radio observations

Abstract The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k∥ ≈ 0.5 h Mpc−1.

Prediction of Energy Resolution in the JUNO Experiment

Abstract This paper presents the energy resolution study in the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The study reveals an energy resolution of 2.95% at 1 MeV. Furthermore, the study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data taking. Moreover, it provides a guideline in comprehending the energy resolution characteristics of liquid scintillator-based detectors. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

Enhanced neural activity detection with microelectrode arrays modified by drug-loaded calcium alginate/chitosan hydrogel

Microelectrode arrays (MEAs) are pivotal brain-machine interface devices that facilitate in situ and real-time detection of neurophysiological signals and neurotransmitter data within the brain. These capabilities are essential for understanding neural system functions, treating brain disorders, and developing advanced brain-machine interfaces. To enhance the performance of MEAs, this study developed a crosslinked hydrogel coating of calcium alginate (CA) and chitosan (CS) loaded with the anti-inflammatory drug dexamethasone sodium phosphate (DSP). By modifying the MEAs with this hydrogel and various conductive nanomaterials, including platinum nanoparticles (PtNPs) and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT: PSS), the electrical properties and biocompatibility of the electrodes were optimized. The hydrogel coating matches the mechanical properties of brain tissue more effectively and, by actively releasing anti-inflammatory drugs, significantly reduces post-implantation tissue inflammation, extends the electrodes' lifespan, and enhances the quality of neural activity detection. Additionally, this modification ensures high sensitivity and specificity in the detection of dopamine (DA), displaying high-quality dual-mode neural activity during in vivo testing and revealing significant functional differences between neuron types under various physiological states (anesthetized and awake). Overall, this study showcases the significant application value of bioactive hydrogels as excellent nanobiointerfaces and drug delivery carriers for long-term neural monitoring. This approach has the potential to enhance the functionality and acceptance of brain-machine interface devices in medical practice and has profound implications for future neuroscience research and the development of strategies for treating neurological diseases.

A signal processing system for infrasound-based detection and tracking of tornadoes

It has been established that tornadic storms emit infrasound at infrasonic frequencies and that this sound can be detected from practically significant distances exceeding 100 km in some cases. This work describes a signal processing system that is used to ingest data from a network of infrasound arrays and produce near real-time detections and ground tracks of multiple, simultaneously active tornadic storms. Specifically, a brief review of a particular array network and corresponding hardware is presented followed by detailed descriptions of the array signal processing architecture and source geolocation system. Emphasis is placed on algorithms for multiple moving source acoustic source bearing tracking, low SNR signal detection, mitigation of errors created by anomalous acoustic events, and multiple-array data association. Performance results corresponding to simulated and recorded data are presented.

Non-contact photoacoustic imaging with a silicon photonics-based Laser Doppler Vibrometer

Photoacoustic imaging has emerged as a powerful, non-invasive modality for various biomedical applications. Conventional photoacoustic systems require contact-based ultrasound detection and expensive, bulky high-power lasers for the excitation. The use of contact-based detectors involves the risk of contamination, which is undesirable for most biomedical applications. While other non-contact detection methods can be bulky, in this paper, we demonstrate a proof-of-concept experiment for compact and contactless detection of photoacoustic signals on silicone samples embedded with ink-filled channels. A silicon photonics-based Laser Doppler Vibrometer (LDV) detects the acoustic waves excited by a compact pulsed laser diode. By scanning the LDV beam over the surface of the sample, 2D photoacoustic images were reconstructed of the sample.

An ultrasensitive flexible force sensor with nature-inspired minimalistic architecture to achieve a detection resolution and threshold of 1 mN for underwater applications

Highly sensitive flexible force sensors enable precise detection of underwater signals for monitoring biological activity, environmental conditions, and vehicle movement. Multilayer stack assembly and micro/nano structure array are often seen in most force/pressure sensors which are toughly hard to control the interlayer spacing and micro/nano structures alignment precisely, resulting in poor consistency and stability. Herein, we first reported a new force sensor with a nature-inspired minimalistic architecture, addressing such issues in an elegant and surprising approach by using a single-layer arched functional membrane with one microgroove. Inspired by the scorpions’ slit sensilla and mantis’ campaniform sensilla, a highly sensitive and waterproof flexible force sensor was fabricated. It is demonstrated that the force sensor has a sensitivity of 27.6 N−1, a high force resolution (1 mN), a fast response time of 70 ms, excellent stability over 5000 cycles and linearity (0.996), and a small force detection limit (≤ 1 mN), showing great potential in underwater environment sensing and motion monitoring of vehicles. This novel but minimalistic architecture provides a new direction in the development of sensors with advanced performance.

Post-marketing safety of tralokinumab: a real-world pharmacovigilance study based on the FDA adverse event reporting system

ABSTRACT Background Tralokinumab is a fully human IgG4 monoclonal antibody targeting IL-13, used for treating atopic dermatitis. This study analyzed tralokinumab-related adverse drug events by mining the Food and Drug Administration Adverse Event Reporting System (FAERS) database to provide a safety reference for clinical application. Methods Adverse drug event reports from Q1 2022 to Q2 2024 were extracted from the FAERS database. After standardizing the data, various signal detection methods were used for analysis, including ROR, PRR, BCPNN, and MGPS. Results A total of 1,820 reports of adverse events (AEs) with tralokinumab as the primary suspected drug were identified. 70 preferred terms (PTs) met the criteria across four signal detection methods, involving 11 system organ classes (SOCs). These included known adverse reactions like conjunctivitis and injection site reactions, and signals not previously reported in clinical trials, such as eye pruritus, dry eye, eye swelling, pneumonia pneumococcal, and cutaneous T-cell lymphoma. Most AEs occurred within one month of initiating tralokinumab treatment. Conclusions Based on the FAERS database, this study comprehensively and systematically analyzed AE signals in tralokinumab treatment. The results enhance the understanding of tralokinumab’s safety and serve as valuable references for reducing the risk of adverse reactions during clinical use.

Wafer-Sized CsPbBr3/CsPbCl3 Heterojunction: Breaking the Trade-Off between Sensitivity and Dark Current for Efficient X-ray Detector.

Polycrystalline lead halide perovskite finds promising use in fabricating X-ray detectors with a large lateral size, adjustable thickness, and diverse synthesis processes. However, a large dark current hinders its development for weak signal detection. Herein, we propose a multistep pressing strategy for manufacturing a CsPbBr3/CsPbCl3 heterojunction wafer for a reduced dark current X-ray detector, and the device keeps a high sensitivity value after the insertion of a barrier by heterojunction; thus, the trade-off between sensitivity and dark current can be broken. The X-ray detector with a metal-semiconductor-metal structure yields a sensitivity of 6.32 × 104 μC Gyair-1 cm-2 at a bias of 12 V, a 1/f noise of 1.02 × 10-13 A/Hz-1/2, and a detection limit of 66.58 nGy s-1. These performance parameters are considerably better than those of a similar X-ray detector based on the single-structure wafer. The improved device performance of the heterostructure X-ray detector is ascribed to the suppressed carrier recombination, enhanced carrier transportation of the heterojunction, and strong X-ray attenuation of the CsPbCl3 layer. The pixel array device is further used in imaging applications. Hence, this study provides an efficient strategy for fabricating heterostructure polycrystalline lead halide perovskite wafers for use in high-performance wafer-based X-ray detectors.

Signal detection of drugs associated with obstructive and central sleep apnoea

We aim to discover new safety signals of drug-induced sleep apnoea (SA), a global health problem affecting approximately 1 billion people worldwide.We first conducted a series of sequence symmetry analyses (SSA) in a cohort composed from all patients who received a first SA diagnosis or treatment between 2006 and 2018 in the Echantillon Généraliste des Bénéficaires (EGB), a random sample of the French healthcare database. We used two primary outcomes to estimate the sequence ratio (SR) for all drug classes available in France: a sensitive one (diagnosis or treatment of SA) and a specific one (Positive Airway Pressure (PAP) therapy). We then performed disproportionality analyses using the “Bayesian neural network method” on all cases of sleep apnoea (MedDRA high level term) reported up to November 2023 in the World Health Organisation (WHO) pharmacovigilance database.Among the 728,167 individuals, 46,193 had an incident diagnosis or treatment for SA and 17,080 had started an incident treatment by PAP therapy. Fifty-eight drug classes had a significant SR, with 7 considered highly plausible: opium alkaloids and derivatives, benzodiazepine derivatives, other centrally acting agents, other anxiolytics, carbamic acid esters, quinine and derivatives and antivertigo preparations; with consistent signals found for the first 3 drug classes in the disproportionality analysis.In this signal detection study, we found that opioids, benzodiazepines (but not Z-drugs) and myorelaxing agents are associated with the onset or aggravation of SA. Moreover, a new safety signal for antivertigo preparations such as betahistine emerged and needs to be further explored.

Detection of Alzheimer's Disease Using Hybrid Meta-ROI of MRI Structural Images.

The averaged cortical thickness of meta-ROI is currently being used for the diagnosis and prognosis of Alzheimer's disease (AD) using structural MRI brain images. The purpose of this work is to present a hybrid meta-ROI for the detection of AD. The AD detectability of selected cortical and volumetric regions of the brain was examined using signal detection theory. The top performing cortical and volumetric ROIs were taken as input nodes to the artificial neural network (ANN) for AD classification. An AD diagnostic accuracy of 91.9% was achieved by using a hybrid meta-ROI consisting of thicknesses of entorhinal and middle temporal cortices, and the volumes of the hippocampus and inferior lateral ventricles. Pairing inferior lateral ventricle dilation with hippocampal volume reduction improves AD detectability by 5.1%. Hybrid meta-ROI, including the dilation of inferior lateral ventricles, outperformed both cortical thickness- and volumetric-based meta-ROIs in the detection of Alzheimer's disease.

Auditory masking of tonal and conspecific signals by continuous active sonar, amplitude modulated noise, and Gaussian noise in killer whales (Orcinus orca).

Continuous active sonar is thought to mitigate severe acoustic impacts due to its lower sound pressure level compared to pulsed active sonar typically used by world navies. However, due to its almost continuous duty cycle, continuous active sonar could have a higher potential for auditory masking. Here, we evaluate the auditory masking potential of several noise types including a recording of continuous active sonar, amplitude modulated noise, and Gaussian noise, on signal detection in two killer whales. Signals were either a 1.5 kHz pure tone or a recording of a broadband burst-pulse killer whale call. For the 1.5 kHz tone, all noise types resulted in statistically significant masking, however, there was a release from masking of approximately 13 dB for the amplitude-modulated noise. When the killer whale call was the signal, the whales employed an off-frequency listening strategy where the whales were able to detect frequency components of the signal that did not directly overlap with the noise. However, this strategy was less useful for the continuous active sonar noise due to its broadband harmonic structure. Continuous active sonar has spectral features that considerably overlap with those of killer whale calls, making this type of noise an effective auditory masker.

Effect of autaptic synapse on signal transmission performance of dressed Hodgkin–Huxley neuron

Astrocytes, which have complex relationships with neurons, are the most important biological arguments that support neurons in the performance of cognitive functions. Another important biological argument in nervous systems is autapse, a special type of synapse through which the neuron provides feedback to itself. It is well known that both astrocyte and autapse modulate neuron dynamics and they affect memory functions. Considering the idea that some cognitive brain functions occur through chaotic firings, more complex models in which structures such as astrocytes and autaptic synapses are included in the system are needed to better understand the neural system. In this study, the signal detection abilities of astrocyte-dressed Hodgkin–Huxley neurons which have electrical, excitatory, and inhibitory chemical autaptic synapses are examined separately under chaotic environmental conditions. The results show that astrocyte-supported neurons, especially those with electrical and excitatory-chemical autapse, exhibit a strong chaotic resonance for appropriate parameters. After the autaptic synapse, the astrocyte is a second modulator of the neuron’s signal detection success. Additionally, it is observed that autaptic time delay is more effective than autaptic conductance in modulating the signal transmission success of the neuron. For all autapse types, the parameter values at which the supportive effect of the astrocyte in the model is obtained are presented. Furthermore, multiple chaotic resonance is detected in neurons with electrical and excitatory chemical autapse when the autaptic delay is equal to integer multiples of the weak signal period. This is observed for neurons with inhibitory chemical autapse when half of the weak signal period is added to whole multiples of the weak signal period. We hope that our findings will shed light on the chaotic environment behavior of complex nervous systems interacting with astrocytes and autapse.

Environmental complexity and regularity shape the evolution of cognition.

The environmental complexity hypothesis suggests that cognition evolves to allow animals to negotiate a complex and changing environment. By contrast, signal detection theory suggests cognition exploits environmental regularities by containing biases (e.g. to avoid dangerous predators). Therefore, two significant bodies of theory on cognitive evolution may be in tension: one foregrounds environmental complexity, the other regularity. Difficulty in reconciling these theories stems from their focus on different aspects of cognition. The environmental complexity hypothesis focuses on the reliability of sensors in the origin of cognition, while signal detection theory focuses on decision making in cognitively sophisticated animals. Here, we extend the signal detection model to examine the joint evolution of mechanisms for detecting information (sensory systems) and those that process information to produce behaviour (decision-making systems). We find that the transition to cognition can only occur if processing compensates for unreliable sensors by trading-off errors. Further, we provide an explanation for why animals with sophisticated sensory systems nonetheless disregard the reliable information it provides, by having biases for particular behaviours. Our model suggests that there is greater nuance than has been previously appreciated, and that both complexity and regularity can promote cognition.

Signal detection

Acoustic emission (AE) occurring in concrete fracture process is a combination of concrete fracture at local material level and the propagation of induced elastic waves at structural level. Simulation of the fracture-induced AE in concrete needs a model that is able to address the fracture behavior and the wave propagation at the same time. However, to date, no available modelling tools can simulate the whole fracture-induced AE process. Among others, the lattice models have the potential to achieve the simulation of the whole fracture-induced AE process. However, the lattice models are currently restricted to statistical analysis of AE events in fracture process. This paper investigates the feasibility and limitation of lattice model for the simulation of elastic wave propagation process. Several simulation cases are performed considering the propagation of both plane waves and point-source waves. The simulation results, in terms of wave velocity and geometric spreading loss of wave amplitudes, agree well with the analytical solutions. This study represents the first step for the simulation of complete fracture-induced AE waves in concrete.

Physically stressed bees expect less reward in an active choice judgement bias test.

Emotion-like states in animals are commonly assessed using judgment bias tests that measure judgements of ambiguous cues. Some studies have used these tests to argue for emotion-like states in insects. However, most of these results could have other explanations, including changes in motivation and attention. To control for these explanations, we developed a novel judgment bias test, requiring bumblebees to make an active choice indicating their interpretation of ambiguous stimuli. Bumblebees were trained to associate high or low rewards, in two different reward chambers, with distinct colours. We subsequently presented bees with ambiguous colours between the two learnt colours. In response, physically stressed bees were less likely than control bees to enter the reward chamber associated with high reward. Signal detection and drift diffusion models showed that stressed bees were more likely to choose low reward locations in response to ambiguous cues. The signal detection model further showed that the behaviour of stressed bees was explained by a reduction in the estimated probability of high rewards. We thus provide strong evidence for judgement biases in bees and suggest that their stress-induced behaviour is explained by reduced expectation of higher rewards, as expected for a pessimistic judgement bias.

Direction-dependent Effects on Global 21 cm Detection

Cosmic dawn represents a critical juncture in cosmic history when the first population of stars emerged. The astrophysical processes that govern this transformation need to be better understood. The detection of redshifted 21 cm radiation emitted from neutral hydrogen during this era offers a direct window into the thermal and ionization state of the Universe. This emission manifests as differential brightness between spin temperature and the cosmic microwave background. The SARAS experiment aims to detect the sky-averaged signal in the frequency range 40–200 MHz. SARAS’s unique design and operational strategy to float the antenna over a water body minimizes spectral features that may arise due to stratified ground beneath the antenna. However, the antenna environment can be prone to configuration changes due to variations in critical design parameters such as conductivity and antenna tilt. In this paper, we connect the variations in antenna properties to signal detection prospects. By using realistic simulations of a direction- and frequency-dependent radiation pattern of the SARAS antenna and its transfer function, we establish critical parameters and estimate bias in the detectability of different models of the global 21 cm signal. We find a correlation between the nature of chromaticity in antenna properties and the bias in the recovered spectral profiles of 21 cm signals. We also find stringent requirements for transfer function corrections, which can otherwise make detection prospects prohibitive. We finally explore a range of critical parameters that allow robust signal detection.

Wood derived conductive aerogel with ultrahigh specific surface area and exceptional mechanical flexibility for pressure sensing

Conductive nanocellulose aerogels possess durability and sensitivity by using as piezoresistive sensors. Those aerogels are mostly prepared via a “bottom-up” strategy by using nanocellulose or dissolved and regenerated cellulose as raw materials. However, there is a pressing need to further enhance their sustainability and economic viability. To alleviate these issues, cellulose aerogel can alternatively be made via a “top-down” technique that entails the direct removal of contaminants from naturally porous biomass. Herein, conductive aerogels with exceptional mechanical flexibility and ultrahigh specific surface area were created. Delignified wood was partially dissolved and regenerated to created nanocellulose fibrils with spider web structure inside the pores of wood. After modification with silane coupling agent and Carbon Nanotubes (CNTs), flexible conductive wood aerogel (C-WA) were obtained. The specific surface area of C-WA was as high as 333.82 m2/g, which was 45 times of that of wood. C-WA could maintain its good resilience even after 1000 cycles, showing high compression resilience and excellent fatigue resistance (stress retention rate was 87.22 %, height retention rate was 99.5 %). The utilization of C-WA as a pressure sensor demonstrated remarkable attributes, including ultra-fast response and stability within the designated range. Furthermore, it exhibited exceptional sensing performance even after enduring 2000 stable cycles. The system is capable of real-time, accurate, and efficient detection of human motion signals, showcasing immense potential for future applications in wearable electronic products.

Extracting parity-violating gravitational waves from projected tidal force tensor in three dimensions

Gravitational waves (GWs) may be produced by various mechanisms in the early universe. In particular, if parity is violated, it may lead to the production of parity-violating GWs. In this paper, we focus on GWs on the scale of the large-scale structure. Since GWs induce tidal deformations of the shape of galaxies, one can extract such GW signals by observing images of galaxies in galaxy surveys. Conventionally the detection of such signals is discussed by considering the three-dimensional power spectra of the E/B-modes. Here, we develop a complementary new technique to estimate the contribution of GWs to the tidal force tensor field projected on the celestial sphere, which is a directly observable quantity. We introduce two two-dimensional vector fields constructed by taking the divergence and curl of the projected tidal field in three dimensions. Their auto-correlation functions naturally contain contributions of the scalar-type tidal field. However, we find that the divergence of the curl of the projected tidal field, which is a pseudo-scalar quantity, is free from the scalar contribution and thus enables us to extract GW signals. We also find that we can detect parity-violating signals in the GWs by observing the nonzero cross-correlation between the divergence of the projected tidal field and the curl of it. It roughly corresponds to measuring the cross-power spectrum of E and B-modes, but these are complementary to each other in the sense that our estimator can be naturally defined locally in position space. Finally we present expressions of the correlation functions in the form of Fourier integrals, and discuss the properties of the kernels specific to the GW case, which we call the overlap reduction function, borrowing the terminology used in the pulsar timing array experiments.