Signal-to-noise Ratio Threshold Research Articles

In Vivo Demonstration of a Real-Time Temporal SNR Acoustic Output Adjustment Method.

This work proposes a novel method of temporal signal-to-noise ratio (SNR)-guided adaptive acoustic output adjustment and demonstrates this approach during in vivo fetal imaging. Acoustic output adjustment is currently the responsibility of sonographers, but ultrasound safety studies show recommended as low as reasonably achievable (ALARA) practices are inconsistently followed. This study explores an automated ALARA method that adjusts the mechanical index (MI) output, targeting imaging conditions matching the temporal noise perception threshold. A 28-dB threshold SNR is used as the target SNR, following prior work showing relevant noise quantities are imperceptible once this image data quality level is reached. After implementing adaptive output adjustment on a clinical system, the average MI required to achieve 28-dB SNR in an 11-volunteer fetal abdomen imaging test ranged from 0.17 to 0.26. The higher MI levels were required when imaging at higher frequencies. During tests with 20-s MI adjustment imaging periods, the degree of motion impacted the adaptive performance. For stationary imaging views, target SNR levels were maintained in 90% of SNR evaluations. When scanning between targets the imaging conditions were more variable, but the target SNR was still maintained in 71% of the evaluations. Given the relatively low MI recommended when performing MI adjustment and the successful adjustment of MI in response to changing imaging conditions, these results encourage adoption of adaptive acoustic output approaches guided by temporal SNR.

Distribution of Signal to Noise Ratio and Application to Leakage Detection

In the context of side-channel attacks, the Signal to Noise Ratio (SNR) is a widely used metric for characterizing the information leaked by a device when handling sensitive variables. In this paper, we derive the probability density function (p.d.f.) of the signal to noise ratio (SNR) for the byte value and Hamming Weight (HW) models, when the number of traces per class is large and the target SNR is small. These findings are subsequently employed to establish an SNR threshold, guaranteeing minimal occurrences of false positives. Then, these results are used to derive the theoretical number of traces that are required to remain below pre-defined false negative and false positive rates. The sampling complexity of the T-test, ρ-test and SNR is evaluated for the byte value and HW leakage model by simulations and compared to the theoretical predictions. This allows to establish the most pertinent strategy to make use of each of these detection techniques.

Machine learning assisted adaptive LDPC coded system design and analysis

AbstractThis paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

Comparison of quick speech-in-noise test and pure tone audiometry in noise-induced hearing loss

Objective Complaints of speech understanding in situations where background noise exists, can be seen from early stages of Noise-Induced Hearing Loss (NIHL); therefore, speech-in-noise tests including Quick speech-in-noise (Q-SIN) tests may be a suitable method for screening NIHL. The Q-SIN test uses the Signal to Noise Ratio (SNR) to evaluates speech perception in the presence of noise. This study aimed to compare pure tone audiometry and Q-SIN tests in patients with NIHL and normal hearing subjects. Methods In this cross-sectional study, according to the result of audiometry, subjects were classified into two groups: 45 individuals with audiograms suggestive of NIHL as a case group and 57 normal-hearing individuals as controls. The Persian version of the Quick-SIN test was performed for two study groups. The Mann-Whitney U Test was used for the comparison of mean SNR and hearing thresholds between the study groups. Spearman test was implemented for evaluation of the correlation between mean SNR and mean hearing thresholds in high and low frequencies in both ears. Results The median SNR loss was significantly higher in the NIHL group [0.75 (IQR: −0.75, 2.75)] than in the normal hearing group [−1.25 (−0.25, 1.25)] (p < 0.0001). It was also found that in the NIHL group, SNR loss had a direct and significant correlation with the high frequencies of both ears and the low frequency of the left ear, as the average hearing threshold increases at high frequencies and low frequencies of the left ear, the SNR loss mean increases as well. Conclusion The present study showed that the Q-SIN test as a rapid, easily applicable test has a positive correlation with pure tone audiometry results, especially at higher frequencies.

Parameter estimation of LFM signals based on time reversal

In this paper, parameter estimation of linear frequency modulation (LFM) signals containing additive white Gaussian noise is studied. Because the center frequency estimation of an LFM signal is affected by the error propagation effect, resulting in a higher signal to noise ratio (SNR) threshold, a parameter estimation method for LFM signals based on time reversal is proposed. The proposed method avoids SNR loss in the process of estimating the frequency, thus reducing the SNR threshold. The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform (DPT) method, and the root-mean-square error (RMSE) of the proposed estimator is close to the Cramer-Rao lower bound (CRLB).

On the Physical-Layer Security of a Dual-Hop UAV-Based Network in the Presence of Per-Hop Eavesdropping and Imperfect CSI

In this paper, the physical layer security of a dual-hop unmanned aerial vehicle-based wireless network, subject to imperfect channel state information (CSI) and mobility effects, is analyzed. Specifically, a source node (S) communicates with a destination node (D) through a decode-and-forward relay (R), in the presence of two wiretappers (E1,E2) independently trying to compromise the two hops. Furthermore, the transmit nodes (S,R) have a single transmit antenna, while the receivers (R,D,E1,E2) are equipped with multiple receive antennas. Based on the per-hop signal-to-noise ratios (SNRs) and correlated secrecy capacities’ statistics, a closed-form expression for the secrecy intercept probability (IP) metric is derived, in terms of key system parameters. Additionally, asymptotic expressions are revealed for two scenarios, namely (i) mobile nodes with imperfect CSI and (ii) static nodes with perfect CSI. The results show that a zero secrecy diversity order is manifested for the first scenario, due to the presence of a ceiling value of the average SNR, while the IP drops linearly at high average SNR in the second one, where the achievable diversity order depends on the fading parameters and number of antennas of the legitimate links/nodes. Furthermore, for static nodes, the system can be castigated by a 15 dB secrecy loss at IP=3×10-3, when the CSI imperfection power raises from 0 to 10-3. Lastly, the higher the legitimate nodes’ speed, carrier frequency, delay, and/or relay’s decoding threshold SNR, the worse is the system’s secrecy. Monte Carlo simulations endorse the derived analytical results.

False alarms induced by Gaussian noise in gravitational wave detectors

Gaussian noise is an irreducible component of the background in gravitational wave (GW) detectors. Although stationary Gaussian noise is uncorrelated in frequencies, we show that there is an important correlation in time when looking at the matched filter signal to noise ratio (SNR) of a template, with a typical autocorrelation time that depends on the template and the shape of the noise power spectral density (PSD). Taking this correlation into account, we compute from first principles the false alarm rate (FAR) of a template in Gaussian noise, defined as the number of occurrences per unit time that the template's matched filter SNR goes over a threshold $\rho$. We find that the Gaussian FAR can be well approximated by the usual expression for uncorrelated noise, if we replace the sampling rate by an effective sampling rate that depends on the parameters of the template, the noise PSD and the threshold $\rho$. This results in a minimum SNR threshold that has to be demanded to a given GW trigger, if we want to keep events generated from Gaussian noise below a certain FAR. We extend the formalism to multiple detectors and to the analysis of GW events. We apply our method to the GW candidates added in the GWTC-3 catalog, and discuss the possibility that GW200308\_173609 and GW200322\_091133 could be generated by Gaussian noise fluctuations.

DOA Estimation of Indoor Sound Sources Based on Spherical Harmonic Domain Beam-Space MUSIC

The Multiple Signal Classification (MUSIC) algorithm has become one of the most popular algorithms for estimating the direction-of-arrival (DOA) of multiple sources due to its simplicity and ease of implementation. Spherical microphone arrays can capture more sound field information than planar arrays. The collected multichannel speech signals can be transformed from the space domain to the spherical harmonic domain (SHD) for processing through spherical modal decomposition. The spherical harmonic domain MUSIC (SHD-MUSIC) algorithm reduces the dimensionality of the covariance matrix and achieves better DOA estimation performance than the conventional MUSIC algorithm. However, the SHD-MUSIC algorithm is prone to failure in low signal-to-noise ratio (SNR), high reverberation time (RT), and other multi-source environments. To address these challenges, we propose a novel joint spherical harmonic domain beam-space MUSIC (SHD-BMUSIC) algorithm in this paper. The advantage of decoupling the signal frequency and angle information in the SHD is exploited to improve the anti-reverberation property of the DOA estimation. In the SHD, the broadband beamforming matrix converts the SHD sound pressure to the beam domain output. Beamforming enhances the incoming signal in the desired direction and reduces the SNR threshold as well as the dimension of the signal covariance matrix. In addition, the 3D beam of the spherical array has rotational symmetry and its beam steering is decoupled from the beam shape. Therefore, the broadband beamforming constructed in this paper allows for the arbitrary adjustment of beam steering without the need to redesign the beam shape. Both simulation experiments and practical tests are conducted to verify that the proposed SHD-BMUSIC algorithm has a more robust adjacent source discrimination capability than the SHD-MUSIC algorithm.

Constrained radar waveform optimization for a cooperative radar-communication system

The coexistence of radar-sensing and communication systems research has received a surge of interest in recent times to tackle the issue of spectrum inadequacy. Designing an optimized radar waveform for a coexistence scenario has been a challenging task for accomplishing the convergence of radar-sensing and communication functionalities, without degrading the performance at either end. This paper proposes a novel global optimization-based Spatial Branch and Bound (SBnB) approach to optimize the phase coefficients of a Non-Linear Frequency Modulated (NLFM) waveform in a CRCS framework. In addition, the Modified-Power Ratio Constraint-Cramér–Rao Lower Bound (M-PRC-CRLB), a local optimization-based approach is proposed to optimize the phase coefficients of an NLFM waveform. The spectral energy distribution and auto-correlation characteristics of an NLFM waveform are comprehensively investigated for various values of polynomial order (N) and at different threshold Signal-to-Noise-Ratio (SNR) values. To compare the proposed waveform design approaches (M-PRC-CRLB, SBnB) with the existing waveform design approaches namely, Minimum Estimation Error Variance (MEEV) and PRC- CRLB, a Peak-to-Side-Lobe-Ratio (PSLR), and Integrated-Side-Lobe-Ratio (ISLR) are evaluated at various polynomial orders and threshold SNR values. Furthermore, the performance of a CRCS is assessed using the radar estimation rate and communication data rate. The simulation results reveal that the proposed optimized radar waveform design approaches provide improved performance compared to the existing radar waveform design approaches in terms of radar estimation rate. Further, the proposed global optimization-based SBnB approach achieves a comparable performance of the communication data rate. In addition, the proposed approaches accomplish enhanced spectral utilization, controlled side-lobe energy levels, reduced range-domain ambiguities, and a higher information rate in a CRCS.

SNR Threshold-Based Relay Association and Random Phase Rotation for Cooperative Communication

In this study, a cooperative communication system that employs multiple decode-and-forward relay nodes (RNs), in which the associated/active RNs perform phase rotation of the regenerated signals before retransmitting them to a destination node (DN), is examined. In the first phase, i.e., communication from a source node to RNs, a received signal-to-noise ratio (SNR) threshold-based RN association method is proposed. The optimal SNR thresholds are designed to maximize the bit-error-rate (BER) performance at the DN under various communication environments, such as modulation types and channel code rates. Furthermore, the number of phase rotations (PRs) in a frame is examined. Intensive numerical results show that more PRs in a frame provide better BER performance at the DN, irrespective of the communication environments. This study provides a valuable guideline for designing practical cooperative networks with multiple decode-and-forward RNs with PRs.

Vehicle-to-roadside-unit-to-vehicle communication system under different amplify-and-forward relaying schemes

A vehicular Rayleigh fading communication system in which a vehicular transmitter (VT) is communicating with a vehicular receiver (VR) only through a dedicated stationary amplify-and-forward (AF) relaying roadside unit (RSU) is proposed in this paper (i.e., V2RSU2V system). To capture the effect of VT and VR motion, system's fading channels from VT to RSU and from RSU to VR are modeled to be time-varying in accordance with the first order autoregressive model. For the sake of performance comparison, the three common AF relaying sub-schemes are adopted at the RSU relay in separate scenarios; namely, the optimal channel-state-information-and-noise-assisted variable-gain (CSIN-VG) sub-scheme, the blind fixed-gain (B-FG) sub-scheme, and the semi-blind fixed-gain (SB-FG) sub-scheme. Under these AF relaying sub-schemes, closed-form expressions for outage probabilities are derived. The derived expressions are given in terms of variant vehicular system and channel parameters and can be directly considered for the special scenario of stationary VT and VR. Numerical and simulation results are reported to validate the analytical work and to reveal new insightful guidelines into vehicular systems outage performance. For example, results reveal that, for low average signal-to-noise-ratio (SNR) per symbol values, the SB-FG sub-scheme is superior in terms of outage performance for all vehicular scenarios and all threshold SNR values. As well, at high per-symbol average SNR values, results illustrate that the CSIN-VG sub-scheme is superior for high threshold SNR values, while the SB-FG is superior for low threshold SNR values. The study in this paper is timely due to the rapid advancements towards autonomous vehicles and smart transportation networks.

Coverage Analysis and Chance-Constrained Optimization for HSR Communications With Carrier Aggregation

This paper investigates coverage analysis and performance optimization for a high-speed railway (HSR) communication system with carrier aggregation (CA). Initially, the system model is established. By using CA technology, the statistical characteristic of the received signal-to-noise ratio (SNR) is obtained. Then, for the systems with or without CA, unified theoretical expressions for the edge coverage probability (ECP) and the percentage of cell coverage area (CCA) are obtained, respectively. After that, a chance-constrained coverage optimization is proposed to improve the coverage performance, which is effectively solved by using a heuristic algorithm. Numerical results show that the derived expressions of the ECP and the percentage of CCA are accurate and can be directly used to evaluate the coverage performance without time-intensive simulations. Moreover, the impacts of CA, transmit power, cell radius, received SNR threshold, HSR scenario on coverage performance have been discussed. Furthermore, the effectiveness of the proposed coverage optimization scheme is also verified.

Research on Distributed Cooperative Intelligent Spectrum Sensing of UAV Cluster

Spectrum sensing is important to improving the survivability of unmanned aerial vehicles (UAVs) in complex electromagnetic environments. At a low signal-to-noise ratio (SNR), a UAV cluster has more prominent advantages in cluster distributed cooperative sensing than a single UAV. Aiming at this urgent need, joint optimal design is carried out for adaptive spectrum sensing algorithm and distributed estimation algorithm to realize the distributed cooperative intelligent spectrum sensing of UAV cluster. In this paper, the adaptive theory is analyzed first, and the performance of the conventional energy-aware sensing method and the least mean square (LMS) spectrum sensing algorithm is compared. In a complex electromagnetic environment, it is proposed to replan the real-time network by deleting erroneous data nodes in order to eliminate parameter estimation deviations caused by data errors. Under the condition of ensuring detection probability, the fast spectrum sensing algorithm based on SNR estimation is optimized by adaptively selecting and setting the SNR threshold to solve the problem of complex and slow calculation. The superiority of distributed spectrum estimation algorithm without erroneous data nodes is verified at a low SNR, showing that the algorithm has a good steady-state error curve and avoids the impact of data errors on detection results. In addition, the effectiveness of optimizing the fast spectrum sensing algorithm by selecting and setting the SNR threshold is verified to improve the distributed cooperative intelligent spectrum sensing rate of UAV cluster.

On the Noise Estimation in Super Dual Auroral Radar Network Data

AbstractThe Super Dual Auroral Radar Network (SuperDARN) currently consists of more than thirty high‐frequency (HF, 3–30 MHz) radars covering mid‐latitude to polar regions in both hemispheres. Their major task is to map ionospheric plasma circulation which provides information about the interactions between the solar wind and the near‐Earth's space plasma environment. One of the major factors defining radar data quality is the signal‐to‐noise ratio (SNR), which requires an accurate characterization of the HF noise. The standard SuperDARN data analysis software uses the SNR as part of a set of empirical procedures designed to remove low‐quality data from further analysis. In this study we found that the currently used empirical algorithm systematically underestimates the noise level by up to 40%. Based on comparison of theoretical and observational noise statistics, we resolve this issue by designing and validating a procedure for accurate background noise level estimation. We then propose a simple SNR threshold to replace the existing criteria for excluding low‐quality data. In addition, we show that several aspects of the radar operational regime design, as well as short‐lived anthropogenic radio interference, can adversely affect the quality of the noise estimates at selected radar sites, and we propose ways to mitigate these problems.

Efficient models for enhancing the link adaptation performance of LTE/LTE-A networks

Link adaptation (LA) is the ability to adapt the modulation scheme (MS) and the coding rate of the error correction in accordance with the quality of the radio link. The MS plays an important role in enhancing the performance of LTE/LTE-A, which is typically dependent on the received signal to noise ratio (SNR). However, using the SNR to select the proper MSs is not enough given that adaptive MSs are sensitive to error. Meanwhile, non-optimal MS selection may seriously impair the system performance and hence degrades LA. In LTE/ LTE-A, the LA system must be designed and optimized in accordance with the characteristics of the physical (e.g., MSs) and MAC layers (e.g., Packet loss) to enhance the channel efficiency and throughput. Accordingly, this study proposes using two LA models to overcome the problem. The first model, named the cross-layer link adaptation (CLLA) model, is based on the downward cross-layer approach. This model is designed to overcome the accuracy issue of adaptive modulation in existing systems and improve the channel efficiency and throughput. The second model, named the Markov decision process over the CLLA (MDP-CLLA) model, is designed to improve on the selection of modulation levels. Besides that, our previous contribution, namely the modified alpha-Shannon capacity formula, is adopted as part of the MDP-CLLA model to enhance the link adaptation of LTE/LTE-A. The effectiveness of the proposed models is evaluated in terms of throughput and packet loss for different packet sizes using the MATLAB and Simulink environments for the single input single output (SISO) mode for transmissions over Rayleigh fading channels. In addition, phase productivity, which is defined as the multiplication of the total throughput for a specific modulation with the difference between adjacent modulation SNR threshold values, is used to determine the best model for specific packet sizes in addition to determine the optimal packet size for specific packet sizes among models. Results generally showed that the throughput improved from 87.5 to 89.6% for (QPSK rightarrow 16-QAM) and from 0 to 43.3% for (16-QAM rightarrow 64-QAM) modulation transitions, respectively, using the CLLA model when compared with the existing system. Moreover, the throughput using the MDP-CLLA model was improved by 87.5–88.6% and by 0–43.2% for the (QPSK rightarrow 16-QAM)and (16-QAM rightarrow 64-QAM) modulation transitions, respectively, when compared with the CLLA model and the existing system. Results were also validated for each model via the summation of the phase productivity for every modulation at specific packet sizes, followed by the application one-way analysis of variance (ANOVA) statistical analysis with a post hoc test, to prove that the MDP-CLLA model improves with best high efficiency than the CLLA model and the existing system.

On the Capacity Performance of Hybrid FSO/RF System With Adaptive Combining Over Generalized Distributions

This paper investigates the ergodic capacity performance of hybrid free space optics (FSO)/ radio frequency (RF) system by employing an adaptive-combining-based switching scheme. In the adaptive combining scheme, the FSO and RF links are combined using the maximal-ratio combining (MRC) at the output, when the instantaneous signal-to-noise ratio (SNR) of the FSO link drops below a predefined threshold SNR value. The FSO link experiences the atmospheric turbulence, which is modeled using the generalized Malaga distribution, non-zero boresight misalignment or pointing errors, and atmospheric attenuation, under two types of detection techniques, i.e. heterodyne detection (HD) and intensity modulation/direct detection (IM/DD). Similarly, the fading of the RF link is characterized using the generalized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> distribution. In particular, we derive the probability density function (PDF) of the instantaneous SNR of the adaptive combining scheme. Capitalizing on the SNR statistics, we obtain the unified closed-form ergodic capacity expressions for the adaptive-combining-based hybrid FSO/RF system. In addition, the asymptotic expressions for the ergodic capacity of the system at the high-SNR region are presented. All the derived analytical expressions are validated by using Monte-Carlo simulations. Numerical results and discussions are provided to compare the ergodic capacity performance of the adaptive-combining-based hybrid FSO/RF system with the existing system models such as single-link FSO, MRC-based hybrid FSO/RF, and hard-switching-based hybrid FSO/RF systems.

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in "Smart Cities" under Various External Influences.

The paper proposes an approach to assessing the allowed signal-to-noise ratio (SNR) for light detection and ranging (LiDAR) of unmanned autonomous vehicles based on the predetermined probability of false alarms under various intentional and unintentional influencing factors. The focus of this study is on the relevant issue of the safe use of LiDAR data and measurement systems within the “smart city” infrastructure. The research team analyzed and systematized various external impacts on the LiDAR systems, as well as the state-of-the-art approaches to improving their security and resilience. It has been established that the current works on the analysis of external influences on the LiDARs and methods for their mitigation focus mainly on physical (hardware) approaches (proposing most often other types of modulation and optical signal frequencies), and less often software approaches, through the use of additional anomaly detection techniques and data integrity verification systems, as well as improving the efficiency of data filtering in the cloud point. In addition, the sources analyzed in this paper do not offer methodological support for the design of the LiDAR in the very early stages of their creation, taking into account a priori assessment of the allowed SNR threshold and probability of detecting a reflected pulse and the requirements to minimize the probability of “missing” an object when scanning with no a priori assessments of the detection probability characteristics of the LiDAR. The authors propose a synthetic approach as a mathematical tool for designing a resilient LiDAR system. The approach is based on the physics of infrared radiation, the Bayesian theory, and the Neyman–Pearson criterion. It features the use of a predetermined threshold for false alarms, the probability of interference in the analytics, and the characteristics of the LiDAR’s receivers. The result is the analytical solution to the problem of calculating the allowed SNR while stabilizing the level of “false alarms” in terms of background noise caused by a given type of interference. The work presents modelling results for the “false alarm” probability values depending on the selected optimality criterion. The efficiency of the proposed approach has been proven by the simulation results of the received optical power of the LiDAR’s signal based on the calculated SNR threshold and noise values.

On detecting stellar binary black holes via the LISA-Taiji network

The detection of gravitational waves (GWs) by ground-based laser interferometer GW observatories (LIGO/Virgo) reveals a population of stellar binary black holes (sBBHs) with (total) masses up to ∼ 150M ⊙, which are potential sources for space-based GW detectors, such as LISA and Taiji. In this paper, we investigate in details on the possibility of detecting sBBHs by the LISA-Taiji network in future. We adopt the sBBH merger rate density constrained by LIGO/VIRGO observations to randomly generate mock sBBHs samples. Assuming an observation period of 4 years, we find that the LISA-Taiji network may detect several tens (or at least several) sBBHs with signal-to-noise ratio (SNR) > 8 (or > 15), a factor 2 − 3 times larger than that by only using LISA or Taiji observations. Among these sBBHs, no more than a few that can merge during the 4-year observation period. If extending the observation period to 10 years, then the LISA-Taiji network may detect about one hundred (or twenty) sBBHs with SNR> 8 (or > 15), among them about twenty (or at least several) can merge within the observation period. Our results suggest that the LISA-Taiji network may be able to detect at least a handful to twenty or more sBBHs even if assuming a conservative SNR threshold (15) for “detection”, which enables multi-band GW observations by space and ground-based GW detectors. We also further estimate the uncertainties in the parameter estimations of the sBBH systems “detected” by the LISA-Taiji network. We find that the relative errors in the luminosity distance measurements and sky localization are mostly in the range of 0.05 − 0.2 and 1 − 100deg2, respectively, for these sBBHs.

A new Duffing detection method for underwater weak target signal

On the basis of the double coupled Duffing oscillator, a differential double coupled Duffing oscillator method, called the proposed method 1, is proposed. The large scale state is judged by the differential timing sequence diagram, and its detection result is more intuitive. To overcome the frequency limitation of differential double coupled Duffing oscillator in detecting unknown frequency periodic signal, according to the proposed method 1, based on the intermittent chaos phenomenon, the differential double coupled Duffing oscillator variable step size detection method, called the proposed method 2, is proposed. This method can be used to detect Gaussian noise background periodic signal and K-distributed noise background periodic signal. Compared with the double Duffing method and the double coupled Duffing method, its average signal-to-noise ratio (SNR) threshold is reduced by 4.94 dB and 1.68 dB respectively, and the detection result is more intuitive to judge through the differential timing sequence diagram. The accuracy and universality of the proposed method 2 are proved by the detection of three measured underwater weak target signals. Experiments show that it has some characteristics such as intuitive detection result, high sensitivity, low SNR threshold, few oscillators, low system complexity and so on.

Probability of Detecting the Deep Defects in Steel Sample Using Frequency Modulated Independent Component Thermography

Active thermography is a widely used non-destructive testing and evaluation technique (NDT&E) for evaluating the properties of materials without impairing its future usefulness. In this work, a mild steel sample made of artificial flat-bottom holes at varied depths, was examined with the emerging non-stationary thermal wave imaging (NSTWI) technique, i.e. frequency modulated thermal wave imaging (FMTWI). The pulse compression favorable of NSTWI technique is eminent for compressing the applied thermal energy into a narrow-compressed pulseto enhance the depth resolution and sensitivity. In this work, pulse compressed thermographic data generated from FMTWI experimentation is analyzed with the unsupervised learning approach independent component analysis (ICA) to test their mutual return in the detection of the deep defects in a mild steel sample and this proposed technique was referred to as frequency modulated independent component thermography (FMICT). In comparison, the effect of FMICT was contrasted with othermethodsi.e. pulse compression of time domain and ICA of feature space by considering the signal-to-noise ratio (SNR) as a figure of merit. Furthermore, a probability of detection (POD) analysis framework based on the minimum threshold SNR criteria for apparent visibility of the defects has been presented to assess the probability of identifying defects at various depths using such approaches. The influence of the SNR threshold value for the above strategies on the POD curves has also been presented.