Signal Detection Research Articles

Clutter evalution of unmanned surface vehicles for maritime traffic monitoring

A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments.

Self-Powered Hybrid Motion and Health Sensing System Based on Triboelectric Nanogenerators.

Triboelectric nanogenerator (TENG) represents an effective approach for the conversion of mechanical energy into electrical energy and has been explored to combine multiple technologies in past years. Self-powered sensors are not only free from the constraints of mechanical energy in the environment but also capable of efficiently harvesting ambient energy to sustain continuous operation. In this review, the remarkable development of TENG-based human body sensing achieved in recent years is presented, with a specific focus on human health sensing solutions, such as body motion and physiological signal detection. The movements originating from different parts of the body, such as body, touch, sound, and eyes, are systematically classified, and a thorough review of sensor structures and materials is conducted. Physiological signal sensors are categorized into non-implantable and implantable biomedical sensors for discussion. Suggestions for future applications of TENG-based biomedical sensors are also indicated, highlighting the associated challenges.

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.

A Fully Self‐Healing Patch of Integrated Bio‐Signal Monitoring Sensors with Self‐Healing Microporous Foam and Au Nanosheet Electrodes

AbstractA fully self‐healing sensor patch consisting of an in‐plane integrated dual‐mode sensor and a pressure sensor with a vertically integrated electrocardiogram (ECG) electrode is reported. For a full‐device self‐healing, interlayer self‐bonding by using self‐healing oxime‐carbamate bond‐based polyurethane and room temperature self‐healing TUEG3 capped Au nanosheet (T‐Au NS) electrodes are devised. Via the use of a self‐healing sensor foam prepared by a coating of a self‐healing composite of polyurethane and polyaniline onto a microporous graphene foam, a self‐healing dual‐mode sensor is fabricated to detect the pressure and temperature simultaneously without interference. Furthermore, the interdigitated self‐healing electrode of T‐Au NS is applied to the pressure sensor to enhance the sensitivity up to 208.62 kPa−1 (<1 kPa), enabling the detection of small pulse signals even after multiple self‐healing events. By using the common self‐healing T‐Au NS electrode vertically integrated onto the sensor patch, ECG signals are also detected. With this sensor patch, skin temperature, wrist pulse, and ECG signals are successfully detected after a simultaneous full‐device self‐healing from complete bisection. This study demonstrates the facile fabrication of a high‐performance, fully self‐healing patch of multi‐sensors via the deliberate selection of sensor design and the associated functional materials, confirming its high potential applicability to highly durable health monitoring systems.

Stretchable Unsymmetrical Piezoelectric BiO2-x Deposited-Hydrogel as Multimodal Triboelectric Nanogenerators for Biomechanical Motion Harvesting.

Enhancing the output performance of triboelectric nanogenerators (TENGs) is essential for increasing their application in smart devices. Oxygen-vacancy-rich BiO2-x nanosheets (BiO2-x NSs) are advanced-engineered nanomaterials with excellent piezoelectric properties. Herein, a stretchable unsymmetrical BiO2-x NSs deposited-hydrogel made of polyacrylamide (PAM) as a multimodal TENG is rationally fabricated, and the performance of TENG can be tailored by controlling the BiO2-x NSs deposition amount and spatial distribution. The alteration of resistance caused by the Poisson effect of PAM/BiO2-x composite hydrogel (H-BiO2-x) can be used as a piezoresistive sensor, and the piezoelectricity of BiO2-x NSs can effectively enhance the density of transfer charge, thus improving the output performance of the H-BiO2-x-based TENG. In addition, the chemical cross-linking between the BiO2-x NSs and the PAM polymer chain allows the hydrogel electrode to have a higher tensile capacity (867%). Used for biomechanical motion signal detection, the sensors made of H-BiO2-x have high sensitivity (gauge factor = 6.93) and can discriminate a range of forces (0.1-5.0 N) at low frequencies (0.5-2.0Hz). Finally, the prepared TENG can collect biological energy and convert it into electricity. Consequently, the improved TENG shows a good application prospect as multimodal biomechanical sensors by combining piezoresistive, piezoelectric, and triboelectric effects.

A hierarchical signal detection model with unequal variance for binary responses.

Gaussian signal detection models with equal variance are commonly used in simple yes-no detection and discrimination tasks whereas more flexible models with unequal variance require additional information. Here, a hierarchical Bayesian model with equal variance is extended to an unequal-variance model by exploiting variability of hit and false-alarm rates in a random sample of participants. This hierarchical model is investigated analytically, in simulations and in applications to existing data sets. The results suggest that signal variance and other parameters can be accurately estimated if plausible assumptions are met. It is concluded that the model provides a promising alternative to the ubiquitous equal-variance model for binary data.

Design of Lidar Receiving Optical System with Large FoV and High Concentration of Light to Resist Background Light Interference

Lidar has the advantages of high accuracy, high resolution, and is not affected by sunlight. It has been widely used in many fields, such as autonomous driving, remote sensing detection, and intelligent robots. However, the current lidar detection system belongs to weak signal detection and generally uses avalanche photoelectric detector units as detectors. Limited by the current technology, the photosensitive surface is small, the receiving field of view is limited, and it is easy to cause false alarms due to background light. This paper proposes a method based on a combination of image-side telecentric lenses, microlens arrays, and interference filters. The small-area element detector achieves the high-concentration reception of echo beams in a large field of view while overcoming the interference of ambient background light. The image-side telecentric lens realizes that the center lines of the echo beams at different angles are parallel to the central axis, and the focus points converge on the same focal plane. The microlens array collimates the converged light beams one by one into parallel light beams. Finally, a high-quality aspherical focusing lens is used to focus the light on the small-area element detector to achieve high-concentration light reception over a large field of view. The system achieves a receiving field of view greater than 40° for a photosensitive surface detector with a diameter of 75 μm and is resistant to background light interference.

Weak Signal Detection Application Based on Incommensurate Fractional-Order Duffing System

Weak Signal Detection Application Based on Incommensurate Fractional-Order Duffing System

Treefrog-inspired Flexible Electrode with High Permeability, stable Adhesion, and Robust Durability.

Long-term continuous monitoring (LTCM) of physiological electrical signals is an effective means for detecting several cardiovascular diseases. However, the integrated challenges of stable adhesion, low impedance, and robust durability under different skin conditions significantly hinder the application of flexible electrodes in LTCM. This paper proposes a structured electrode inspired by the treefrog web, comprising dispersed pillars at the bottom and asymmetric cone holes at the top. Attachment structures with a dispersed pillar improve the contact stability (adhesion increases 2.79/13.16 times in dry/wet conditions compared to an electrode without structure). Improved permeable duct structure provides high permeability (12 times compared to cotton). Due to high adhesion and permeability, the electrode's durability is 40 times larger than commercial Ag/AgCl electrodes. The treefrog web-like electrode has great advantages in permeability, adhesion, and durability, resulting in prospects for application in physiological electrical signal detection and a new design idea for LTCM wearable dry electrodes. This article is protected by copyright. All rights reserved.

The Estimation of the Stress State of the Iron Alloy Strip Material by the Barkhausen Noise Method

This paper presents the effect of the complex strain state resulting from the asymmetric rolling of TRB products on the changes and distribution of the stress state in the material. The evaluation of the stress state in the material was based on measurements of the magnetoelastic parameter (MP) using the Barkhausen magnetic noise method. The key characteristics of the material under study that enabled the use of changes in the MP parameter to assess the stress state were ferromagnetism and a lack of texture. The first of these enabled the detection of the magnetic signals produced when a magnetic field is applied to the material, causing magnetic domains to align and sudden changes in magnetization. On the other hand, the absence of texture in the material precluded the occurrence of magnetocrystalline anisotropy, which could disturb the results of measurements of the magnetoelastic parameter in the material. In order to determine these features in the material under study, its chemical composition was determined, and a phase analysis was carried out using the X-ray diffraction method. The results of these tests showed the possibility of determining the stress state of the material by means of changes in the values of the MP parameter. On this basis, it was shown that in the TRB strips studied, there is a complex state of stress, the values of which and the nature of the changes depending on the direction of the measurements carried out, as well as on the amount of rolling reduction in the studied area of the strip.

Enhancing spectrum sensing efficiency in multi‐channel cognitive device‐to‐device networks: Medium Access Control layer strategies and analysis

AbstractThe detection and characterisation of electromagnetic signals within a specific frequency range, known as spectrum sensing, plays a crucial role in Cognitive Radio Networks (CRNs). The CRNs aim to adapt their communication parameters to the surrounding radio environment, thereby improving the efficiency and utilisation of the available radio spectrum. Spectrum sensing is particularly important in device‐to‐device (D2D) communication when operating independently of the cellular network infrastructure. The Medium Access Control (MAC) protocol coordinates device communication and ensures interference‐free operation of the CRN coexisting with the primary cellular network. A spectrum sensing strategy at the MAC layer for cognitive D2D communication. The strategy focuses on reducing the overall sensing period allocated at the MAC layer by having each Cognitive D2D User (cD2DU) sense a smaller subset of available channels while maintaining the same sensing time for cellular user detection at the physical layer. To achieve this, the concept of concurrent groups of D2D devices is introduced in proximity, which are formed by using unique IDs of cD2DUs during the device discovery stage. Each concurrent group senses a specific portion of the cellular user band in a shorter time, resulting in a reduced overall sensing period. In addition to mitigating traffic congestion through data diversion from the cellular network, the proposed strategy facilitates the concurrent sensing of multiple channels by cD2DUs within the underutilised cellular user band. This leads to extended data transmission periods, increased network throughput, and effective offloading of the cellular network. The effectiveness of the proposed work is evaluated by considering factors, such as network throughput and transmission time. Simulation results confirm the effectiveness of the approach in improving spectrum utilisation and communication efficiency in multi‐channel Cognitive D2D Networks (cD2DNs).

Exploration of peak frequency mapping for ventricular tachycardia ablation. Identification of critical isthmus zones during substrate mapping

Abstract Background/Introduction The ability to classify and differentiate between near field and far-field components of intracardiac signals is a key element in identifying critical arrhythmogenic substrate during ventricular tachycardia (VT) ablation. Near Field algorithms based on omnipolar electrograms enable detection and automation of true localised signals providing unique insights into substrate characteristics. This novel feature identifies localised signal peak frequency (PF) which can be displayed across different voltage ranges. Purpose/Aims The significance of PF mapping in the context of VT ablation is limited and its utility in the identification of the critical diastolic isthmus zone (IZ) is not known. The aim of this study is to explore the utilisation of PF mapping during VT ablation and evaluate whether significant differences in PF occur within areas of interest and critical components of the VT circuit during substrate mapping. Methods All patients underwent VT ablation using the EnSite™ X mapping system with the HDGrid omnipolar mapping catheter. Only cases with both high density sinus rhythm (SR) substrate maps and VT activation maps with clear identification of the IZ were included. VT activation and propagation maps were generated and the diastolic IZ was superimposed onto the SR substrate map. PF analysis was performed retrospectively. Surface area, omnipolar voltage and PF of the maps were measured in the SR substrate maps and co-localised with the critical IZ of the VT circuit as identified in the VT activation maps. Analysis of PF was conducted across different voltage ranges: healthy myocardium (>1.5mV), scar (1.5-0.5mV) and dense scar (0.5-0.1mV). Sensitivity and specificity measurements were made within segments of the IZ and non-isthmus zones and depicted as ROC curves. Results 55 patients underwent VT ablation over a 12-month period, with a total of 18 fulfilling the inclusion criteria. Mean age was 70 (±12.5years) with an average ejection fraction of 36%. The overall PF of the SR substrate maps was 121.85±26.85 Hz, compared to 162.22±46.22Hz within the IZ (p=0.002). Within the low voltage scar region, the overall PF was 120.86±33.24Hz compared to 188.32±81.36Hz within the IZ (p=0.003). No significant difference in PF was observed within healthy myocardium (112.70±52.06 Hz) compared to components of the IZ within the >1.5mV range (157.61±136.11Hz), p=0.207. The AUC for PF within the overall substrate map and co-localisation with the IZ was 0.67, and 0.73 within low voltage zones (<1.5mV). Conclusions PF mapping may identify critical components of the VT circuit during SR substrate mapping and its utilisation may compliment established substrate mapping approaches. PF guided ablation strategies require further evaluation in future prospective studies.

Frequency multiplexed dual-pulse fiber-optic Φ-OTDR for a fading-free distributed fiber sound sensor

Distributed fiber-optic sensor utilizing optical interference signal detection of the Rayleigh backscattering (RBS) light along the fiber under test (FUT) has greatly progressed over the past 20 years. It can now realize real-time sound signal recovery along the FUT with a good fidelity and high sensitivity. However, the performance of the practical distributed fiber-optic sound sensor is still limited by the fading problem, as invalid spatial channels at unknown positions arise due to the low signal intensity at these positions and the resulting near-zero signal-to-noise ratio (SNR). In this paper, we propose a novel, to the best of our knowledge, fading-free fiber sensor structure by incorporating an acousto-optic frequency shift (AOFS) loop into the light source in a dual-pulse phase optical time domain reflectometry (Φ-OTDR). As a result of stable frequency shifting and feedback within the loop, the loop source can output a high-repetition-rate narrow pulse train consisting of multiple separated frequencies. This pulse train is further shaped into two pulse bursts by an acousto-optic modulator (AOM) and an in-balanced fiber-optic Mach–Zehnder interferometer (MZI). The pulse bursts are then sent into the sensing fiber, while the RBS light is detected using an Avalanche photodiode after an erbium-doped fiber amplifier. The experimental results show that the proposed structure is capable of vibration signal recovery with a good SNR all along the FUT. The fading-free performance is evaluated under different AOFS loop gains, showing significant potential in practical fiber sensor network applications.

Self-Tuning of Signal Detection Level for Energy Detection-Based Carrier Sense in Low-Power Wide-Area Networks

Carrier sense allows end devices to improve the communication quality through autonomous decentralization by consuming power. In particular, energy detection-based carrier sense can improve communication quality compared with peak detection-based carrier sense. To improve the trade-off between communication quality and energy consumption in low-power wide-area networks (LPWANs), this study proposes a self-tuning method for the signal detection level of an energy detection-based carrier sense, that is, the carrier sense level in sub-GHz band LPWANs. In the proposed method, the carrier sense level of each end device is determined based on the reception success probability of the acknowledgment packet, such that they become low carrier sense levels for an end device with low probability and high carrier sense levels for an end device with high probability. The proposed method enables autonomous decentralized derivation of the carrier sense level using only existing protocols. Numerical examples show that the proposed method can improve the performance of end devices with a high path loss to a gateway.

Instability Compensation of Recording Interferometer in Phase-Sensitive OTDR

In the paper, a new method of phase measurement error suppression in a phase-sensitive optical time domain reflectometer is proposed and experimentally proved. The main causes of phase measurement errors are identified and considered, such as the influence of the recording interferometer instabilities and laser wavelength instability, which can cause inaccuracies in phase unwrapping. The use of a Mach–Zender interferometer made by 3 × 3 fiber couplers is proposed and tested to provide insensitivity to the recording interferometer and laser source instabilities. It is shown that using all three available photodetectors of the interferometer, instead of just one pair, achieves significantly better accuracy in the phase unwrapping. A novel compensation scheme for accurate phase measurements in a phase-sensitive optical time domain reflectometer is proposed, and a comparison of the measurement signals with or without such compensation is shown and discussed. The proposed method, using three photodetectors, allows for very good compensation of the phase measurement errors arising from common-mode noise from the interferometer and laser source, providing a significant improvement in signal detection. In addition, the method allows the tracking of slow temperature changes in the monitored fiber/object, which is not obtainable when using a simple low-pass filter for phase unwrapping error reduction, as is customary in several systems of this kind.

Periodic Signal Recovery and Detection from 1-Bit Measurements Using Convolutional Neural Network

Periodic Signal Recovery and Detection from 1-Bit Measurements Using Convolutional Neural Network

Modern models for predicting bankruptcy to detect early signals of business failure: Evidence from Montenegro.

This paper explores predicting early signals of business failure using modern models for bankruptcy prediction. It reviews how continuous operations enhance market value, strengthening competitiveness and reputation among stakeholders. The study involves medium and large companies in the Montenegrin market from 2015 to 2020, comprising 30 bankrupt and 70 financially stable firms. Logistic regression is also employed to create a logit model for early detection of bankruptcy signals in companies. This research establishes the empirical validity of modern models in predicting business failure in the Montenegrin market, particularly through logistic regression. Significant indicators, such as the Degree of Indebtedness (DI) and turnover ratio of business assets (TR), exhibit strong predictive power with a p-value less than 0.001 according to Likelihood ratio tests. The paper underscores the potential benefits of bankruptcy prediction for both internal and external stakeholders, especially investors, in enhancing the competitiveness of Montenegro's large and medium-sized companies. Notably, the research contributes by bridging the gap between theory and practice in Montenegro, as bankruptcy prediction models have not been extensively applied in the market. The authors suggest the possible applicability of the created logit model to neighboring countries with similar economic development levels. In that sense, the concept of predicting bankruptcy is positioned as integral to corporate strategy, impacting the overall reduction of bankruptcies. The paper concludes by highlighting its role as a foundation for future research, addressing the literature gap in the application of bankruptcy prediction models in Montenegro. The created logit model, tailored to the specific needs of Montenegrin companies, is presented as an original contribution, emphasizing its potential to strengthen the competitiveness of companies in the market.

Bifurcation investigation, phase synchronization and FPGA implementation of a new photosensitive Fitz Hugh Nagumo neuron based meminductor

Abstract This paper focuses on the estimation of the nonlinear encoding and responses of a photosensitive Fitz Hugh Nagumo (FHN) neuron-based-meminductor and provides a relevant analysis of its phase synchronization. In the pattern, a Fitz Hugh–Nagumo neuron connected to a meminductor is activated by a photocell, and a time-varying current source is generated by the presence of external optical signals. This coupling is a benchmark circuit with memory similar to artificial eyes with the ability to capture and encode external signals. It is designed with the aim of studying the dynamics of a neuron with a memory effect highlighted through the meminductor. An appropriate dynamical analysis is provided using standard indicators such as bifurcations to point out thorough transitions and the nature of the electrical neuronal activities. Some couplings between two FHN neurons with meminductor using hybrid synapses composed of passive electronic components are achieved. Relevant tools are used to analyze the stability of the synchronization which gives prominent details on the selection of the appropriate coupling. The energy balance of the external system is evaluated which gives the effort to achieve encoding signals and also proves the feasibility of the proposal in real-time implementation. Simulations are performed on FPGA. The results present a good agreement. In many engineering applications, the detection of optical signals is inescapable as well as the synchronization of its signals for the transmission of the stimuli. These results could be useful for the designer.

Two-sided automatic censoring CFAR detector based on the interquartile range in a heterogeneous Weibull clutter

As it is well known, CFAR (Constant False Alarm Rate) detectors are intended to regulate the PFA (Probability of False Alarm) in the presence of a clutter background whose mean power is unknown and/or varying. In heterogeneous clutter backgrounds, CFAR detectors false alarm control and detection efficiency rely upon their ability of clutter edge resilience and/or interfering targets rejection. Aiming to improve radar signal detection in a heterogeneous Weibull clutter, the two-sided automatic censoring IQR-CFAR (InterQuartile Range-CFAR) detector is introduced in this paper. This new detector first resorts to a logarithmic amplifier to dynamically achieve a Weibull to Gumbel transformation of the reference samples. Then, working on a cell-by-cell basis, the CFCR (Constant False Censoring Rate) algorithm retains and rejects samples based on the IQR within the reference window. Upon removal of the unwanted samples, the remaining data set is utilized to estimate the unknown biparametric linear detection threshold through the BLU (Best Linear Unbiased) estimates of the Gumbel distribution’s location and scale parameters. Extensive MC (Monte Carlo) simulations with both simulated and real data show that the suggested detector outperforms its challenging automatic censoring CFAR detectors found in the literature.

Molecular recognition between volatile molecules and odorant binding proteins 7 by homology modeling, molecular docking and molecular dynamics simulation.

Odorant-binding proteins (OBPs) in insects are key to detection and recognition of external chemical signals associated with survival. OBP7 in Spodoptera frugiperda's larval stage (SfruOBP7) may search for host plants by sensing plant volatiles, which are important sources of pest attractants and repellents. However, the atomic-level basis of binding modes remains elusive. SfruOBP7 structure was constructed through homology modeling, and complex models of six plant volatiles ((E)-2-hexenol, α-pinene, (Z)-3-hexenyl acetate, lauric acid, O-cymene and 1-octanol) and SfruOBP7 were obtained through molecular docking. To study the detailed interactions between the six plant volatile molecules and SfruOBP7, we conducted three 300 ns molecular dynamics simulations for each study object. The correlation coefficients between binding free energy obtained by molecular mechanics/generalized Born surface area together with solvated interaction energy methods and experimental values are 0.90 and 0.88, respectively, showing a good correlation. By comparing binding free energy along with interaction patterns between SfruOBP7 and the six volatile molecules, hotspot residues of SfruOBP7 when binding with different volatile molecules were determined. Hydrophobic interactions stemming from van der Waals interactions play a significant role in SfruOBP7 and these plant volatile systems. The optimized three-dimensional structure of SfruOBP7 and its binding modes with six plant volatiles revealed their interactions, thus providing a means for estimating the binding energies of other plant volatiles. Our study will help to guide the rational design of effective and selective insect attractants. © 2024 Society of Chemical Industry.