Signal Detection System Research Articles

Empowering African Expertise: Enhancing Safety Data Integration and Signal Detection for COVID-19 Vaccines Through the African Union Smart Safety Surveillance Joint Signal Management Group.

The COVID-19 pandemic accelerated new vaccine development. Limited safety data necessitated robust global safety surveillance to accurately identify and promptly communicate potential safety issues. The African Union Smart Safety Surveillance(AU-3S) program established theJoint Signal Management (JSM) group to support identification of potential vaccine safety concerns in five pilot countries (Ethiopia, Ghana, Kenya,Nigeria, South Africa), accounting for approximately35% of the African population. Our objective was to provide an overview of the JSM group's role in supporting signal management activities for the AU-3S program during the COVID-19 pandemic. Spontaneous, electronically reported COVID-19 vaccine adverse events following immunization (AEFI)from each country'ssafety data were integrated into the interim Data Integration and Signal Detection system. Statistical disproportionality methods were used to identify and review vaccine-event combinations (VECs) for potential safety concerns. The JSM group-which comprised pharmacovigilance and subject matter experts from National Medicine Regulatory Authorities, Expanded Programs on Immunization, and vaccine safety committees-conducted signal detection activities on cross-country safety data and provided recommendations. From April 2021 to December 2023, a total of 48,294 spontaneously reported AEFIwere analyzed for six COVID-19 vaccines (NRVV Ad [ChAdOx1 nCoV-19]; Ad26.COV2.S; Elasomeran; Tozinameran; Covid-19 vaccine [Vero Cell], Inactivated; NRVV Ad26 [Gam-Covid-Vac]) administered in Ethiopia (34.6%), Nigeria (30.3%), South Africa (16.9%), Ghana (13.5%), and Kenya (4.7%). Overall, 2,742 VECs were validated. A causal association between theCOVID-19 vaccines and thereported AEFIcannot be inferred, as data were reported spontaneously. JSM group recommendations included monitoring for further evidence, no immediate action required, engaging marketing authorization holder(s) for additional information, or sensitizing healthcare providers and/or the public about events. Although no new safety signals were identified, nine safety-related recommendations were issued, including patient and healthcare provider education. The JSM group established a scalable and replicable model for future signal management of other priority health products in low- and middle-income countries, fostering ongoing collaboration and capacity building. Knowledge and experience gained from this pilot initiative will guide stakeholders in future safety surveillance initiativeswithin the African continent.

Статистики вищих порядків в задачах виявлення сигналів на фоні корельованих негаусових завад

Signal detection in noise is critical in telecommunications, navigation and radar systems, image processing and biomedical research, where noise often deviates from a normal distribution, and sample values exhibit statistical dependence. Traditional methods for analyzing and designing such systems face significant limitations, including algorithmic and computational complexity, severely restricting their practical application. An effective approach to developing signal detection systems involves using moment and cumulant descriptions of random variables, simplifying the design of detection systems for signals with various probability density functions. The authors propose a novel approach based on one-dimensional (1D) and two-dimensional (2D) moment-cumulant models to describe correlated non-Gaussian processes. This approach enables the modification of the moment-based criterion for statistical hypothesis testing and synthesising polynomial stochastic decision rules for detecting signals in correlated non-Gaussian noise. The study demonstrates that the nonlinear processing of sample values and the application of higher-order statistics to describe the investigated processes account for the structure of non-Gaussian noise and its statistical dependencies. This reduces the error probabilities of the synthesized decision rules compared to traditional Gaussian models. The study aims to enhance the efficiency of signal detection systems under additive interaction with correlated non-Gaussian noise by developing new moment-cumulant models of the investigated processes, modifying the moment-based criterion for hypothesis testing, and designing polynomial decision rules. The study's practical significance lies in creating simple-to-implement algorithms with high accuracy that achieve lower error probabilities in decision rules compared to existing methods.

Feature Extraction and Attribute Recognition of Aerosol Particles from In Situ Light-Scattering Measurements Based on EMD-ICA Combined LSTM Model

Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer’s probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification.

Extremely multi-stable grid-scroll memristive chaotic system with omni-directional extended attractors and application of weak signal detection

The extreme multi-stability and omni-directional expansion of the attractors in chaotic systems play an indispensable role in the chaos-based engineering applications. In this study, a novel extremely multi-stable grid-scroll memristive chaotic system with omni-directional extended attractors is proposed, and applies it to weak signal detection. Firstly, a flux-controlled non-volatile memristor with a typical 8-shaped pinched hysteresis loop, and three piecewise linear functions that can promote the position offset of the attractor are designed. On this basis, we construct a novel grid-scroll memristive chaotic system with infinite equilibrium points, which can generate n × n × n-scroll attractors. The piecewise linear functions lead to the infinite expansion of the chaotic attractor in the x, y, and z directions, which also indirectly causes the position offset in the w direction. This phenomenon has not been discovered in existing researches. The numerical analysis demonstrates that the memristive chaotic system has infinite coexisting attractors, and a built-in parameter that can achieve global amplitude control of the attractor has also been verified. The omni-directional expansion, coexistence and global amplitude control of the attractor illustrates the extreme multi-stability of the designed system. The analog circuit of the memristive chaotic system has been implemented to verify its feasibility and practicability. Finally, a weak signal detection system based on the grid-scroll memristive chaotic system is constructed. This system not only has excellent robustness to various noise, but also is more sensitive to weak periodic signals. The detection signal-to-noise ratio (SNR) threshold reaches -63 dB. The applicability of this method in practical engineering is verified through the detection of ship radiation signals submerged in ocean background noise.

Bi-LSTM Based Deep Learning Algorithm for NOMA-MIMO Signal Detection System

Bi-LSTM Based Deep Learning Algorithm for NOMA-MIMO Signal Detection System

Development of Cooperative-Intelligent Transport Systems (C-ITS) based Motorcycle Traffic Signal Detection System

Development of Cooperative-Intelligent Transport Systems (C-ITS) based Motorcycle Traffic Signal Detection System

Adaptive Measurement and Parameter Estimation for Low-SNR PRBC-PAM Signal Based on Adjusting Zero Value and Chaotic State Ratio

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator’s behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB.

ECG signal analysis and detection system based on deep learning

In view of the problems of large workload and easy error in traditional manual recognition of ECG signals, and the existing ECG monitoring equipment still has few ECG signal recognition types, low diagnostic accuracy, and excessive reliance on network services, in order to improve the performance of ECG monitoring equipment, an ECG (Electrocardiogram Signals) analysis and detection system is designed based on deep learning technology. The SENet-LSTM (Squeeze-and-Excitation Networks Long Short Term Memory) network model is built to realize automatic diagnosis of 7 categories of ECG signals. The model is built on an intelligent hardware platform that uses ADS1292R as ECG acquisition module, STM32F103 as data processing module, and Raspberry Pi as central processing module. The system uses the integrated high- performance microcomputer Raspberry Pi for calculation and analysis, providing users with offline artificial intelligence (AI: Artificial Intelligence) services. At the same time, the accuracy and precision of the model are and respectively 98.44%, 90.00%thereby realizing real-time detection and accurate classification of ECG, providing patients with accurate disease diagnosis.

AEEFCSR: an adaptive ensemble empirical feed-forward cascade stochastic resonance system for weak signal detection

Abstract A novel adaptive ensemble empirical feed-forward cascade stochastic resonance (AEEFCSR) method is proposed in this study for the challenges of detecting target signals from intense background noise. At first, we create an unsaturated piecewise self-adaptive variable-stable potential function to overcome the limitations of traditional potential functions. Subsequently, based on the foundation of a feed-forward cascaded stochastic resonance method, a novel weighted function and system architecture is created, which effectively addresses the issue of low-frequency noise enrichment through ensemble empirical mode decomposition. Lastly, inspired by the spider wasp algorithm and nutcracker optimization algorithm, the spider wasp nutcracker optimization algorithm is proposed to optimize the system parameters and overcome the problem of relying on manual experience. In this paper, to evaluate its performance, the output signal-to-noise ratio (SNR), spectral sub-peak difference, and time-domain recovery capability are used as evaluation metrics. The AEEFCSR method is demonstrated through theoretical analysis. To further illustrate the performance of the AEEFCSR method, Validate the adoption of multiple engineering datasets. The results show that compared with the compared algorithms, the output SNR of the AEEFCSR method is at least 6.2801 dB higher, the spectral subpeak difference is more than 0.25 higher, and the time-domain recovery effect is more excellent. In summary, the AEEFCSR method has great potential for weak signal detection in complex environments.

High-performance flexible pressure sensors with bionic dome-shaped fold structures inspired by crocodile skin

Flexible pressure sensors have drawn substantial community interest as a result of the advancement of contemporary technology. Inspired by the unique sensory organs of crocodiles, an ordered bionic dome-shaped fold structure array was prepared using a facile process in this paper, and electrical experiments confirmed the enhancement of the capabilities of piezoresistive flexible pressure sensors. The results revealed that this type of flexible pressure sensor presented a sensitivity of 8.87 kPa−1, could withstand a maximum pressure of 60 kPa, a minimum detection pressure of 10 Pa, a response time of 70 ms, and maintained stable performance under 5000 repeated presses. In addition, the flexible pressure sensors based on dome-shaped fold structure arrays have been successfully applied for human signal detection and plantar pressure monitoring system. The current research has significant implications for the manufacturing and practical applications of highly effective flexible pressure sensors.

Quantitative Signal Analysis of Sum-Frequency Scattering Experiments from Aerosol Surfaces.

Droplet interfaces are instrumental in processes of biology, engineering, production, and environmental systems. The chemical and physical properties of heterogeneous interfaces are known to be different from those of their underlying bulk phases, and different again when considering the curved surface of submicron aerosol droplets. The recently developed technique of vibrational sum-frequency scattering (VSFS) spectroscopy from airborne particles has emerged as an interface-specific method for the in situ analysis of this unique system. While the technique has shown promise in debut works, a quantitative analysis of the VSFS system has not yet been performed. Here we provide a comprehensive analysis of a VSFS spectrometer with reference to the well-documented planar analog. We decompose the VSFS signal into coherent and incoherent as well as resonant and nonresonant components as a function of incident pulse delay time. We then quantify and compare resonant and nonresonant VSFS and VSFG experimental data using the same laser and detection systems. Using the air/water interface as a guide, we show that the resonant and nonresonant contributions to the SF responses are comparable for the two systems by extracting second-order susceptibilities and hyperpolarizabilities, and using them to estimate single-particle susceptibilities. A quantitative analysis of the signal detection systems for the scattering and planar geometries is made, and conversion efficiencies for VSFG, VSFS, and other nonlinear scattering experiments are compared. Lastly, the possibility of a low-repetition (1 kHz) VSFS spectrometer is considered, determining that it may be possible with modern laser technology but is inevitably less efficient than a high-repetition (100 kHz) system. Though this multistep analysis we obtain a better understanding of the components of the VSFS signal from aerosol particles, further validate the feasibility of the experiments, and provide insight to those wishing to conduct similar experiments and how they may be improved.

Genetic algorithm optimized microstructure to enhance waveguide light coupling efficiency at normal incidences

The recent trend to place well-designed photonic structures on waveguides is capable of effectively enhancing waveguides properties. One typical example is a nanostructure-empowered waveguide targeted for efficient light coupling. However, conceiving the high-freedom structures is a time-consuming and labor-intensive process, where an ineffective workflow limits the development of photonic microstructures. To address this issue, we deploy a genetic algorithm to customize structures in order to improve the coupling coefficients under predetermined situations (i.e., normal incidence combined with two polarizations). Three types of micropatterns are first conceived in the periodical model and then fully characterized on the real waveguide sizes. The simulated data reveal that the 550–1650 nm average coupling efficiencies of structure-enabled waveguides are raised by about 2% in contrast to the bare case, and the lineshapes are also flattened thanks to the grating modifications. In short, our solution underlines the role of an algorithm-developed nanostructure to lift waveguide coupling coefficients. By integrating well-engineering patterns, the waveguide-based probes may find a multitude of usages for weak signal detection and communication systems.

Statistical Signal Detection Algorithm in Safety Data: A Proprietary Method Compared to Industry Standard Methods.

Several quantitative methods have been established, in pharmacovigilance, to detect signals of disproportionate reporting (SDRs) from databases containing reports of adverse drug reactions (ADRs). The signal detection algorithms (SDAs) and the source of the reporting per product vary, but it is unclear whether any algorithm can provide satisfactory performance using data with such large variance factors. Determine the appropriate SDA for Biogen's internal Global Safety Database (GSD) given the characteristics of the database including frequencies of events, data skewness, outliers, and missing information. Compare performance of standard approaches (EBGM, EB05, PRR, and ROR), well accepted by industry, to a Biogen-developed Machine Learning (ML) Regression Decision Tree (RDT) model, across several Biogen products, to determine a champion SDA. All data associated with seven marketed Biogen products were chosen and a historical subset of reported ADRs were considered. Six SDAs (five common industry disproportionality methods) and RDT were evaluated. The SDRs were calculated on training and test data composed of quarterly reporting intervals from 2004-2019. The performance measures used were sensitivity, precision, time to detect new events, and frequency of detected cases for each algorithm for each product. Outcomes in the test data are known a priori and easily compared to predicted outcomes. Validation was performed via rates of misclassification. This work solely represents Biogen's internal information, intentionally chosen to serve the performance review of its signal detection systems, and results will not necessarily be generalizable to other external sources. Several algorithms performed differently among products, but no one method dominated any other. Performance was dependent on the thresholds used to define a signal according to different criteria. However, those different statistics subtly influenced the achievable performance. The relative performance of RDT and Medicines and Healthcare products Regulatory Agency (MHRA) algorithms were superior and paired across products. A reduction in precision for all methods spanning the products was present. Hence, companies evaluating signal detection approaches, search for innovative methods to minimize this effect. In designing signal detection systems, careful consideration should be given to the criteria that are used to define SDRs. The choice of disproportionality statistics does not affect the achievable range of signal detection performance. These choices should consider mainly ease of implementation and interpretation. The implementation of a method is specific to its accuracy. The RDT attempted to take advantage of known methods and compare results on a per-product basis. Many factors influencing ADRs may improve RDT in future efforts. In this experiment, RDT demonstrated superiority in terms of quickest time to detect and capturing of the highest number of ADRs. Next steps include expansion of data for products representing other indications and testing models in external databases to investigate generalizability of estimates when comparing SDAs.

Raspberry Pi Based Real Time Lane, Obstacle and Traffic Signal Detection System

The project proposes a Raspberry Pi-based system for real-time detection of lanes, obstacles, and traffic signals to improve road safety and assist drivers. The system uses computer vision algorithms and sensor data to provide situational awareness for vehicles. The lane detection module uses image processing to identify lane markings and estimate vehicle position, while the obstacle detection module uses visual and distance sensors to identify obstacles in the vehicle’s path. The traffic signal detection module uses image processing to identify and interpret traffic signals, helping drivers adhere to traffic regulations. The integration of these modules on a Raspberry Pi platform allows for compact deployment within vehicles, ensuring timely response to dynamic road conditions. This project offers a promising solution for intelligent transportation systems aimed at reducing accidents and improving traffic flow in urban environments. Key Words: Spotting,hough transform,obstascle detection,rasp cam,traffic signal detection,lane.

Experimental demonstration of flexible information rate PON beyond 100 Gb/s with rate-compatible LDPC codes

The applications of rate-compatible low-density parity-check (RC-LDPC) codes are investigated for a 16 quadrature amplitude modulation (16QAM) signal and coherent detection system. With rate-compatible signals, we can provide the flexible net data rate between 135.5 Gb/s and 169.7 Gb/s in a passive optical network (PON) link. Based on the LDPC codes defined in the IEEE 802.3ca standard, we construct two sets of RC-LDPC codes with a fixed and variable information bit length. Since the puncturing operation may degrade the performance of LDPC codes, we apply the protograph-based extrinsic information transfer (PEXIT) technique to optimize the puncturing positions to mitigate the degradation. Additionally, we explore four low-complexity LDPC decoding algorithms (min sum, offset min sum, variable weight min sum, and relaxed min sum with 2 nd min emulation) to investigate the relationship between the computational complexity and decoding performance. Simulation results indicate that the constructed codewords exhibit good performance in the waterfall region across a range of code rates. Finally, we conduct an experimental setup in a dual-polarization 25 GBaud 16QAM coherent PON to verify the effectiveness of the constructed LDPC codes with four decoding algorithms. The experimental results show maximal 4.8 dB receiver sensitivity differences, which demonstrate the feasibility of the method for constructing RC-LDPC codes in future high-speed flexible coherent PON.

Design of Pulse and Respiratory Signals Detection System

A pulse and respiration synchronous detection system is designed to explore the changes of physiological signals in different situations. The system obtains the corresponding signal through STM32 control pulse and respiratory acquisition circuit, calculates and displays real-time parameters such as heart rate and respiratory rate, and transmits the data to the upper computer for storage in the database. The experimental test results show that the system can monitor pulse and respiratory waveform in different situations, and the waveform is in good condition. Compared with medical pulse oximeter, the error of measured heart rate and blood oxygen concentration is less than 3%, and the error of respiratory rate is less than 5% compared with the actual value, which verifies the accuracy of system signal acquisition. The system is small in size, low in cost, and comfortable to wear, and can be applied in experimental research related to pulse and respiratory signals.

Multiplexed fluorescence and scatter detection with single cell resolution using on-chip fiber optics for droplet microfluidic applications

Droplet microfluidics has emerged as a critical component of several high-throughput single-cell analysis techniques in biomedical research and diagnostics. Despite significant progress in the development of individual assays, multiparametric optical sensing of droplets and their encapsulated contents has been challenging. The current approaches, most commonly involving microscopy-based high-speed imaging of droplets, are technically complex and require expensive instrumentation, limiting their widespread adoption. To address these limitations, we developed the OptiDrop platform; this platform is a novel optofluidic setup that leverages the principles of flow cytometry. Our platform enables on-chip detection of the scatter and multiple fluorescence signals from the microfluidic droplets and their contents using optical fibers. The highly customizable on-chip optical fiber-based signal detection system enables simplified, miniaturized, low-cost, multiparametric sensing of optical signals with high sensitivity and single-cell resolution within each droplet. To demonstrate the ability of the OptiDrop platform, we conducted a differential expression analysis of the major histocompatibility complex (MHC) protein in response to IFNγ stimulation. Our results showed the platform’s ability to sensitively detect cell surface biomarkers using fluorescently labeled antibodies. Thus, the OptiDrop platform combines the versatility of flow cytometry with the power of droplet microfluidics to provide wide-ranging, scalable optical sensing solutions for research and diagnostics.

In situ self-referenced intracellular two-electrode system for enhanced accuracy in single-cell analysis

Since the emergence of single-cell electroanalysis, the two-electrode system has become the predominant electrochemical system for real-time behavioral analysis of single-cell and multicellular populations. However, due to the transmembrane placement of the two electrodes, cellular activities can be interrupted by the transmembrane potentials, and the test results are susceptible to influences from factors such as intracellular solution, membrane, and bulk solution. These limitations impede the advancement of single-cell analysis. Here, we propose a highly miniaturized and integrated in situ self-referenced intracellular two-electrode system (IS-SRITES), wherein both the working and reference electrodes are positioned inside the cell. Additionally, we demonstrated the stability (0.28 mV/h) of the solid-contact in situ Ag/AgCl reference electrode and the ability of the system to conduct standard electrochemical testing in a wide pH range (pH 6.0–8.0). Cell experiments confirmed the non-destructive performance of the electrode system towards cells and its capacity for real-time monitoring of intra- and extracellular pH values. Moreover, through equivalent circuits, finite element simulations, and drug delivery experiments, we illustrated that the IS-SRITES can yield more accurate test results and exhibit enhanced resistance to interference from the extracellular environment. Our proposed system holds the potential to enable the precise detection of intracellular substances and optimize the existing model of the electrode system for intracellular signal detection, thereby spearheading advancements in single-cell analysis.

Sensitive Methods to Detect Single-Stranded Nucleic Acids of Food Pathogens Based on Cell-Free Protein Synthesis and Retroreflection Signal Detection.

Cell-free protein synthesis (CFPS) has recently gained considerable attention as a new platform for developing methods to detect various molecules, ranging from small chemicals to biological macromolecules. Retroreflection has been used as an alternative signal to develop analytical methods because it can be detected by using a simple instrument comprising a white light source and a camera. Here, we report a novel reporter protein that couples the capability of CFPS and the simplicity of retroreflection signal detection. The design of the reporter was based on two pairs of protein-peptide interactions, SpyCatcher003-SpyTag003 and MDM2-PMI(N8A). MDM2-MDM2-SpyCatcher003 was decided as the reporter protein, and the two peptides, SpyTag003 and PMI(N8A), were immobilized on the surfaces of retroreflective Janus particles and microfluidic chips, respectively. The developed retroreflection signal detection system was combined with a previously reported CFPS reaction that can transduce the presence of a single-stranded nucleic acid into protein synthesis. The resulting methods were applied to detect 16S rRNAs of several foodborne pathogens. Concentration-dependent relationships were observed over a range of 10° fM to 102 pM, with the limits of detection being single-digit femtomolar concentrations. Considering the designability of the CFPS system for other targets, the retroreflection signal detection method will enable the development of novel methods to detect various molecules.

Nonparametric assessment of two-dimensional density of nonlinear location signal distribution for object recognition despite background noise

Problem statement Searching for objects in shelter environments is a challenge associated with the difficulty of obtaining and interpreting search results. Currently, within the statistical theory of detection and recognition, no models and methods have been developed that allow automatically identifying the detected object. This is due to the difficulty of describing signal and noise models in the form of known statistical distributions. The variety of signals leads to the inability to describe them by any universal law of distribution. Therefore, traditional detection and recognition criteria do not provide high reliability of object search and identification. The purpose of the study. To develop and test the method for nonparametric estimation of nonlinear location signals, assess the potential possibilities of applying the developed method for automatic (automated) signal detection (recognition) systems. Results. The model for recognizing nonlinear location signals based on nonparametric approximation of two-dimensional probability distribution densities is presented. The potential possibility of improving the efficiency of nonlinear locators is shown. Practical significance. For nonlinear location systems a toolkit for estimating the probability of the detected object belonging to a given class of objects is proposed.