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The characterising sound required for the Acoustic Scene Classification (ASC) system is contained in the ambient signal. However, in practice, this is often distorted by e.g., foreground speech of the speakers in the surroundings. Previously, based on the iVector framework, we proposed different strategies to improve the classification accuracy when foreground speech is present. In this paper, we extend these methods to deep-learning (DL)-based ASC systems, for improving foreground speech robustness. ResNet models are proposed as the baseline, in combination with multi-condition training at different signal-to-background ratios (SBRs). For further robustness, we first investigate the noise-floor-based Mel-FilterBank Energies (NF-MFBE) as the input feature of the ResNet model. Next, speech presence information is incorporated within the ASC framework obtained from a speech enhancement (SE) system. As the speech presence information is time-frequency specific, it allows the network to learn to distinguish better between background signal regions and foreground speech. While the proposed modifications improve the performance of ASC systems when foreground speech is dominant, in scenarios with low-level or absent foreground speech, performance is slightly worse. Therefore, as a last consideration, ensemble methods are introduced, to integrate classification scores from different models in a weighted manner. The experimental study systematically validates the contribution of each proposed modification and, for the final system, it is shown that with the proposed input features and meta-learner, the classification accuracy is improved in all tested SBRs. Especially for SBRs of 20 dB, absolute improvements of up to 9% can be obtained. [ABSTRACT FROM AUTHOR]

Background: Heterodyne interferometry offers an exciting solution for achieving sub-nanometer or ten-picometer-order mechanical displacement measurements because of its high resolution and noise immunity. Objective: In this paper, we investigate the stability and resolution of a conventional displacement-measuring heterodyne interferometer using a single phase-locked loop (PLL) at a sub-nanometer or 10-pm level over an µm-scale measurement regime in normal air. Methods: In the experiment, we perform the beat frequency reduction of the heterodyne signals to take advantage of a low-cost frequency sampling data acquisition (DAQ). The primary calculations of the single phase-locked loop (PLL) algorithm are implemented in a point-by-point (pt-pt) construction, providing pt–pt low-pass filters (LPFs) with high noise suppression. The output is appended into an array of N points and averaged to reduce the noise of the outgoing signal again. Using the PLL phase meter with the beat frequency reduction can ensure a mdeg-order phase measurement with Allan deviation stability of ~ 10 µdeg over 1000 s. We consider a combination of the miniaturization of optical paths in air and the PLL phase meter integrated with low-cost devices while ensuring mdeg-order measurement stability to achieve the interferometer's pm-order measurement resolution and stability. Results: The measurement results show that the interferometer can achieve a resolution of 26 pm over a 5 μm-range mechanical displacement measurement. The Allan deviation stability of the interferometer reaches ~ 10 pm over a sampling time of 30 s, and the displacement noise floor is 10 pm/Hz1/2 above 1 Hz. Conclusions: The new signal-processing PLL phase meter provides higher noise reduction than the old one presented in our previous work. With low cost but high resolution and stability, the PLL phase meter is incredibly suitable for pm-order displacement-measuring heterodyne interferometry. Combining a well-reduced PLL phase meter and a conventional heterodyne interferometer with a stable, isolated configuration and the minimization of optical paths in the air can give the measurement system a high resolution and stability. The measurement system can have a more significantly improved resolution of a few tens of pm than interferometers presented in previous publications and stability comparable to the modified heterodyne interferometers shown in earlier works over the sampling time of 30 s (possibly up to 100 s). A conventional heterodyne interferometer with a single PLL is expected to perform large-range displacement measurements with resolution at ten picometer levels in air. [ABSTRACT FROM AUTHOR]

The characterising sound required for the Acoustic Scene Classification (ASC) system is contained in the ambient signal. However, in practice, this is often distorted by e.g., foreground speech of the speakers in the surroundings. Previously, based on the iVector framework, we proposed different strategies to improve the classification accuracy when foreground speech is present. In this paper, we extend these methods to deep-learning (DL)-based ASC systems, for improving foreground speech robustness. ResNet models are proposed as the baseline, in combination with multi-condition training at different signal-to-background ratios (SBRs). For further robustness, we first investigate the noise-floor-based Mel-FilterBank Energies (NF-MFBE) as the input feature of the ResNet model. Next, speech presence information is incorporated within the ASC framework obtained from a speech enhancement (SE) system. As the speech presence information is time-frequency specific, it allows the network to learn to distinguish better between background signal regions and foreground speech. While the proposed modifications improve the performance of ASC systems when foreground speech is dominant, in scenarios with low-level or absent foreground speech, performance is slightly worse. Therefore, as a last consideration, ensemble methods are introduced, to integrate classification scores from different models in a weighted manner. The experimental study systematically validates the contribution of each proposed modification and, for the final system, it is shown that with the proposed input features and meta-learner, the classification accuracy is improved in all tested SBRs. Especially for SBRs of 20 dB, absolute improvements of up to 9% can be obtained.

Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been widely applied in in situ and real-time monitoring of trace gas concentrations. In this paper, an advanced TDLAS-based optical gas sensing system with laser linewidth analysis and filtering/fitting algorithms is proposed and experimentally demonstrated. The linewidth of the laser pulse spectrum is innovatively considered and analyzed in the harmonic detection of the TDLAS model. The adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm is developed to process the raw data and could significantly eliminate the background noise variance by about 31% and signal jitters by about 12.5%. Furthermore, the Radial Basis Function (RBF) neural network is also incorporated and applied to improve the fitting accuracy of the gas sensor. Compared with traditional linear fitting or least squares method (LSM), the RBF neural network brings along the enhanced fitting accuracy within a large dynamic range, achieving an absolute error of below 50 ppmv (about 0.6%) for the maximum 8000 ppmv methane. The proposed technique in this paper is universal and compatible with TDLAS-based gas sensors without hardware modification, allowing direct improvement and optimization for current optical gas sensors. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) attenuation imaging is a technique that uses the optical attenuation coefficient (OAC) to distinguish the types or pathological states of tissues and has been increasingly used in basic research and clinical diagnosis. With the increasing application of swept-source OCT, scholars are increasingly inclined to explore deep tissues. Unfortunately, the accuracy of OAC calculation when exploring deep tissues has yet to be improved. Existing methods generally have the following problems: overestimation error, underestimation error, severe fluctuation, or stripe artifacts in the OAC calculation of the OCT tail signal. The main reason for this is that the influence of the noise floor on the OCT weak signal at the tail-end is not paid enough attention. The noise floor can change the attenuation pattern of the OCT tail signal, which can lead to severe errors in the OAC. In this paper, we proposed a Kalman filter-based OAC optimal algorithm to solve this problem. This algorithm can not only eliminate the influence of the noise floor, but can also effectively protect the weak signal at the tail-end from being lost. The OAC of deep tissues can be calculated accurately and stably. Numerical simulation, phantom, and in vivo experiments were tested to verify the algorithm's effectiveness in this paper. This technology is expected to play an essential role in disease diagnosis and in the evaluation of the effectiveness of treatment methods. [ABSTRACT FROM AUTHOR]

A stable and reliable time keeping system depends on the integrity monitoring of the atomic frequency standard. This paper reports a scheme for autonomous integrity monitoring of multiple atomic clocks, which combines the frequency standard comparison method and the frequency jump detection method. The frequency standard comparison method uses multi-channel synchronous acquisition technology and digital frequency measurement technology to realize the precise measurement of multiple atomic frequency standards. The frequency jump detection method uses adaptive filtering to predict the relative frequency difference and give an accurate and timely alarm for the abnormal of frequency jump. The results show that the noise floor frequency standard comparator is better than 6.5×10−15 s. For a relative frequency deviation of 2.0×10−6 Hz, the probability of anomaly detection is almost 100 %. The system has high frequency measurement resolution and fast alarm of frequency jump, which can meet the real-time requirements of a time keeping system for the integrity monitoring of multiple atomic clocks.

This paper proposes a prototype of micro-electromechanical frequency modulation electrometers based on pre-bias charge mechanism with a single-anchor circular beam (SACB) resonator. The SACB can overcome the uneven energy distribution in the traditional axial-extended tuning forks structure to weaken the geometric nonlinearity. The charge sensitivity is directly related to the pre-bias charge proven by both theorical and experimental results. With increase of pre-bias charge, the resonator works from the clumsy region to the sensitive region. The SACB electrometer has sensitivity of 5.14 ppm fCâˆ'1 under 1.416 pC bias in open-loop measurement and 4.52 ppm fCâˆ'1 in closed-loop measurement. Real-time dynamic modulation detection is completed with 0.354 fC step variation. Through the analysis of the noise power spectral density, increasing the pre-bias charge can suppress the noise floor of the resonator. As the bias is increased from 0.708 pC to 1.416 pC, the charge resolution is increased by almost 20 times, and the dynamic range is enlarged by 131%. The pre-bias mechanism can be also used in other resonant sensing applications for improvement of performance. [ABSTRACT FROM AUTHOR]

Delayed self-heterodyne/homodyne measurements based on an unbalanced interferometer are the most used methods for measuring the linewidth of narrow-linewidth lasers. They typically require the service of a delay of six times (or greater) than the laser coherence time to guarantee the Lorentzian characteristics of the beat notes. Otherwise, the beat notes are displayed as a coherent envelope. The linewidth cannot be directly determined from the coherence envelope. However, measuring narrow linewidths using traditional methods introduces significant errors due to the 1/f frequency noise. Here, a short fiber-based linewidth measurement scheme was proposed, and the influence of the noise floor on the measurement of the laser linewidth using this scheme was studied theoretically and experimentally. The results showed that this solution and calibration process is capable of significantly improving the measurement accuracy of narrow linewidth. [ABSTRACT FROM AUTHOR]

Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been widely applied in in situ and real-time monitoring of trace gas concentrations. In this paper, an advanced TDLAS-based optical gas sensing system with laser linewidth analysis and filtering/fitting algorithms is proposed and experimentally demonstrated. The linewidth of the laser pulse spectrum is innovatively considered and analyzed in the harmonic detection of the TDLAS model. The adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm is developed to process the raw data and could significantly eliminate the background noise variance by about 31% and signal jitters by about 12.5%. Furthermore, the Radial Basis Function (RBF) neural network is also incorporated and applied to improve the fitting accuracy of the gas sensor. Compared with traditional linear fitting or least squares method (LSM), the RBF neural network brings along the enhanced fitting accuracy within a large dynamic range, achieving an absolute error of below 50 ppmv (about 0.6%) for the maximum 8000 ppmv methane. The proposed technique in this paper is universal and compatible with TDLAS-based gas sensors without hardware modification, allowing direct improvement and optimization for current optical gas sensors.

Optical coherence tomography (OCT) attenuation imaging is a technique that uses the optical attenuation coefficient (OAC) to distinguish the types or pathological states of tissues and has been increasingly used in basic research and clinical diagnosis. With the increasing application of swept-source OCT, scholars are increasingly inclined to explore deep tissues. Unfortunately, the accuracy of OAC calculation when exploring deep tissues has yet to be improved. Existing methods generally have the following problems: overestimation error, underestimation error, severe fluctuation, or stripe artifacts in the OAC calculation of the OCT tail signal. The main reason for this is that the influence of the noise floor on the OCT weak signal at the tail-end is not paid enough attention. The noise floor can change the attenuation pattern of the OCT tail signal, which can lead to severe errors in the OAC. In this paper, we proposed a Kalman filter-based OAC optimal algorithm to solve this problem. This algorithm can not only eliminate the influence of the noise floor, but can also effectively protect the weak signal at the tail-end from being lost. The OAC of deep tissues can be calculated accurately and stably. Numerical simulation, phantom, and in vivo experiments were tested to verify the algorithm’s effectiveness in this paper. This technology is expected to play an essential role in disease diagnosis and in the evaluation of the effectiveness of treatment methods.

In this work, it is proposed that the use of statistical percentage of errors correlated to the Received Signal Strength (RSS) and to both the output power and noise bandwidth, together with the data rate ratio, can give a clear indication of the communication channel quality in the presence of a noise floor. Simulation of a WSN network with variable Data Rate to Noise Band Width ratio (R), different output power (Ptran), and variable Noise Floor Power (Pnoise), showed that each one of these parameters affects error probability distribution and values, which in turn affect channel utilization. The work also proved that these are critical parameters that, if correlated and optimized, can achieve a high utilization of WSN channels. The largest effect realized is related to Pnoise and interference, which, if present at high values, would reduce the probability of exchanging data by 25%. The work also showed that as the ratio R increases, the probability and magnitude of errors increases, which in turn contributes to lower utilization, as depicted in the tables and plots. The 10-nodes WSN is simulated in a uniform topology setting. [ABSTRACT FROM AUTHOR]

Delayed self-heterodyne/homodyne measurements based on an unbalanced interferometer are the most used methods for measuring the linewidth of narrow-linewidth lasers. They typically require the service of a delay of six times (or greater) than the laser coherence time to guarantee the Lorentzian characteristics of the beat notes. Otherwise, the beat notes are displayed as a coherent envelope. The linewidth cannot be directly determined from the coherence envelope. However, measuring narrow linewidths using traditional methods introduces significant errors due to the 1/f frequency noise. Here, a short fiber-based linewidth measurement scheme was proposed, and the influence of the noise floor on the measurement of the laser linewidth using this scheme was studied theoretically and experimentally. The results showed that this solution and calibration process is capable of significantly improving the measurement accuracy of narrow linewidth.

Heterodyne architecture has been frequently chosen to resolve the problem of dc offset in frequency modulated continuous wave (FMCW) radar. However, heterodyne FMCW radars use different local oscillators, resulting in uncorrelated phase noise (UPN) in beat signals. Therefore, the inherent leakage signal in the heterodyne FMCW radar also has UPN and raises the noise floor in the power spectrum. In this letter, we propose a strategic decision method for parameter values in the heterodyne FMCW radar to achieve a low noise floor over the middle‐long range. In addition, we experimentally discover the relation between the UPN and the processing gain for the first time. Based on this relation, we devise an exact formula for the degree of improvement achieved due to the proposed strategic decision method. Experimental results confirm the devised formula and show that the proposed method significantly reduces the noise floor over the middle‐long range. [ABSTRACT FROM AUTHOR]

With the increase in threats posed by small drones, the development of radars to detect such drones has become a key necessity. In earlier studies, we proposed the stationary point concentration (SPC) technique for small drone detection using frequency-modulated continuous-wave (FMCW) radar. The SPC technique is a novel approach for mitigating leakage, which is an inherent problem in FMCW radar. The SPC technique improves the signal-to-noise ratio of small drones by reducing the noise floor and provides accurate distance and velocity information of such drones. However, this technique has shortcomings in its realization. In this article, we present the drawbacks of this technique and propose an advanced SPC (A-SPC) technique. The proposed technique can overcome the drawbacks of the SPC technique while implementing the benefits of the SPC technique. The experimental results verify the proposed A-SPC technique and demonstrate its robustness and usefulness. [ABSTRACT FROM AUTHOR]

Multiple radio occultation (RO) missions are currently providing observations that are assimilated by the world’s leading numerical weather prediction centers. These RO missions use the same signals originating from the Global Navigation Satellite Systems (GNSS), but they have different satellite designs and sizes with different antennas and receivers. This results in different noise levels for different missions. Although the amplitude data are characterized by the Signal-to-Noise Ratio (SNR), the noise, to which they are normalized, is not the real Noise Floor (NF) of the RO observations. We study the statistical distributions of the SNR and NF for RO missions including COSMIC, COSMIC2, METOP-A, METOP-B, METOP-C, and Spire. We demonstrate that different missions have different NF values and different NF and SNR distributions, sometimes multimodal. We propose to use the most probable NF value as an SNR normalization constant in order to compare the SNR values from different RO missions.

Given the rapidly growing drone market, the development of drone detection radars has become highly important to prevent drones from damaging lives and property. Frequency-modulated continuous-wave (FMCW) radars have been frequently used for drone detection. Among the architectures of FMCW radar, a heterodyne architecture that involves different local oscillators and produces beat signals at the IF stage has often been chosen. The FMCW radar, however, has a chronic problem called leakage. In an earlier study, we proposed the stationary point concentration (SPC) technique to improve the signal-to-noise ratio (SNR) of small drones by reducing the increase in noise floor caused by leakage. We demonstrated that the proposed technique increases the SNR of small hovering drones in the 1-D power spectrum. In this article, we newly analyze the practical problems in the heterodyne FMCW radar system. One is the 2-D noise floor increase in the range-Doppler (r-D) map due to the leakage, and the other is unwanted Doppler shifts. Then, we propose an upgraded theory and realization method of the SPC technique for the detection of the small moving drone by improving the r-D map. We prove that the proposed method not only reduces the 2-D noise floor by mitigating the leakage but also removes the unwanted Doppler shifts by the phase calibration. Consequently, we show that the proposed method increases the SNR of the small moving drone and corrects its velocity information at the same time in the r-D map. [ABSTRACT FROM AUTHOR]

A high-speed x-ray imaging system was built to investigate the fundamentals of laser material processing, such as laser welding. The system allows visualizing and analyzing the laser-induced process dynamics occurring also inside optically nontransparent workpieces. The details of the setup are presented together with the resulting performance with regard to edge sharpness, contrast, contrast transfer, and spatial as well as temporal noise. For voids with a depth (in respect to the x-ray radiation) of 100–500 μm, which is a typical range for focal diameters used in laser beam welding, a maximum contrast of 3%–19%, respectively, can be achieved. Due to the edge blurring, caused by the imaging system, the contrast is further decreased by 20% for voids with a lateral size of 100 μm and less than 10% for voids with a lateral size of 500 μm, when a magnification of 5.4 is used. The image intensifier, needed to increase the number of photons to saturate the camera, induces a noise floor of ± 15 % in respect to the mean gray value and results in a maximum achievable signal to noise ratio for a 100 μm step of 1.2. [ABSTRACT FROM AUTHOR]

A novel differential configuration of a high-resolution mode-localized resonant SOI-MEMS accelerometer is presented in this paper. An effective maximum scale factor of 11/g and a bias-stability of $2.96\mu g$ is demonstrated. A minimum noise floor of $3\mu g/\sqrt {Hz}$ is shown for device operation in an optimum working region. A large bandwidth of 350Hz is reported and methods to electronically tune the working bandwidth are described. These results benchmark mode-localized accelerometers favourably with respect to other high-end low-g commercial MEMS accelerometers. [ABSTRACT FROM AUTHOR]

To prevent potential hazards posed by fast-evolving drones, it is of importance to develop a radar system for drone detection. Frequency modulated continuous wave (FMCW) radars are widely used for that purpose. Heterodyne architectures are preferred for them to mitigate dc offset errors. Having said that, FMCW radars suffer from permanent leakage from the transmitter into the receiver. The leakage phase noise raises the total noise floor and limits the radar sensitivity. Here, we propose a stationary point concentration (SPC) technique in order to overcome the challenges. The SPC technique concentrates the leakage phase noise on a stationary point to alleviate the impact of the noise. The technique can be realized using digital signal processing without additional hardware. The results show that the proposed technique significantly lowers the noise floor. [ABSTRACT FROM AUTHOR]

Abstract Purpose To assess intrascanner repeatability and cross-scanner comparability for diffusion tensor imaging (DTI) metrics in a multicenter clinical trial. Methods DTI metrics (including longitudinal diffusivity [LD], fractional anisotropy [FA], mean diffusivity [MD], and transverse diffusivity [TD]) from pyramidal tracts for healthy controls were calculated from images acquired on twenty-seven 3T MR scanners (Siemens and GE) with 6 different scanner models and 7 different software versions as part of the NN102/SPRINT-MS clinical trial. Each volunteer underwent two scanning sessions on the same scanner. Signal-to-noise ratio (SNR) and signal-to-noise floor ratio (SNFR) were also assessed. Results DTI metrics showed good scan-rescan repeatability. There were no significant differences between scans and rescans in LD, FA, MD, or TD values. Although the cross-scanner coefficient of variation (CV) values for all DTI metrics were <5.7%, significant differences were observed for LD (p < 3.3e-5) and FA (p < 0.0024) when GE scanners were compared with Siemens scanners. Significant differences were also observed for SNR when comparing GE scanners and Siemens Skyra scanners (p < 1.4e-7) and when comparing Siemens Skyra scanners and TIM Trio scanners (p < 1.0e-10). Analysis of background signal also demonstrated differences between GE and Siemens scanners in terms of signal statistics. The measured signal intensity from a background noise region of interest was significantly higher for GE scanners than for Siemens scanners (p < 1.2e-12). Significant differences were also observed for SNFR when comparing GE scanners and Siemens Skyra scanners (p < 2.5e-11), GE scanners and Siemens Trio scanners (p < 7.5e-11), and Siemens Skyra scanners and TIM Trio scanners (p < 2.5e-9). Conclusions The good repeatability of the DTI metrics among the 27 scanners used in this study confirms the feasibility of combining DTI data from multiple centers using high angular resolution sequences. Our observations support the feasibility of longitudinal multicenter clinical trials using DTI outcome measures. The noise floor level and SNFR are important parameters that must be assessed when comparing studies that used different scanner models. [ABSTRACT FROM AUTHOR]