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This article studies the distributed average tracking (DAT) problem pertaining to a discrete-time linear time-invariant multiagent network, which is subject to, concurrently, input delays, random packet drops, and reference noise. The problem amounts to an integrated design of delay and a packet-drop-tolerant algorithm and determining the ultimate upper bound of the tracking error between agents' states and the average of the reference signals. The investigation is driven by the goal of devising a practically more attainable average tracking algorithm, thereby extending the existing work in the literature, which largely ignored the aforementioned uncertainties. For this purpose, a blend of techniques from Kalman filtering, multistage consensus filtering, and predictive control is employed, which gives rise to a simple yet comepelling DAT algorithm that is robust to the initialization error and allows the tradeoff between communication/computation cost and stationary-state tracking error. Due to the inherent coupling among different control components, convergence analysis is significantly challenging. Nevertheless, it is revealed that the allowable values of the algorithm parameters rely upon the maximal degree of an expected network, while the convergence speed depends upon the second smallest eigenvalue of the same network's topology. The effectiveness of the theoretical results is verified by a numerical example.

An accurate performance evaluation of jitter-power figure-of-merit (FOM) for multiplying delay-locked loop (MDLL) is presented. For a typical MDLL employing a single-ended multiplying-delay ring voltage-controlled oscillator (MDVCO), it can be tuned via the capacitive loads to uphold a constant normalized phase noise (PN) across different frequencies for better jitter-power performance. Linear approximation and z-domain PN model are utilized to simplify the analysis with excellent agreement between the time-domain simulation and z-domain approximation. The influences of the asymmetric waveform, flicker noise corner, frequency error and reference noise are also discussed and then ignored based on the reasonable approximation. Under a given process, reference clock frequency and supply voltage, the ideal FOM can be derived theoretically. Based on these insights, the predicted FOM can be the benchmark metric in the design of MDLL for the possible best jitter-power performance.

Linear active noise control (ANC) systems have been used in the past to effectively suppress Gaussian noise. A practical ANC system must consider nonlinearities in the secondary path with a non-minimum phase. For an ANC system to effectively operate in a modern-day acoustic environment with multiple electrical/electronic systems operating in the vicinity, the reference noise source is taken as a non-Gaussian stochastic process. Linear systems have shown unacceptable performance in countering the disturbances that are impulsive in nature. Considering these issues, we propose a family of adaptive algorithms for ANC systems that employ a second-order Volterra filter for accurate modeling of the impulsive disturbances. We utilize the maximum correntropy criterion as the cost function to improve the adaptive filtering process, which generates the most appropriate output signal in the ANC system’s successive iterative stages. The proposed algorithms feature dynamic learning-rate parameters, which improve the tracking performance of the algorithms. Also, a careful selection of the Volterra filter’s kernel size in the proposed algorithms ensures a balance between system stability and convergence rate. These parameters are made automatically adjustable, based on the residual error and the reference input signals, to optimize performance in non-Gaussian environments. A comparison of the proposed algorithms with its counterparts is presented through computer simulations in terms of average noise reduction for different levels of impulsive noise. The proposed algorithms are also compared with each other to find the best performing algorithm in a non-Gaussian environment. Achieved results exhibit the effectiveness of the proposed algorithms in their ability to attenuate impulsive noise in comparison with the existing adaptive algorithms for ANC systems. Later, the proposed algorithm has been implemented for speech enhancement, where the noise from the speech sample is identified and removed using an adaptive filter algorithm. Simulations reveal that the devised algorithm can effectively denoise speech signals. Further, in the last part of the paper, the VF-MCCRMC is modified in accordance with the energy of the error signal, and the simulation results demonstrated the improvement in stability and error performance.

The paper aims to design and analyse Active Noise Control (ANC) performance in PVC Duct experimentally for reducing periodic background noises. In the ANC system, different adaptive methods are preferred to reduce the unwanted noise, of which the most preferred one is the famous Filtered cross Least Mean Square (F×LMS) algorithm. On the other hand, since it is structured with acoustic feedback issues, the ANC's efficacy is degraded and becomes unstable. This problem could be overcome by implementing a Feedback Neutralization (FN) concept in the ANC system based on the F×LMS algorithm's step-size parameter to control the unwanted noise. The small step-size with the ANC system is robust compared to the large step-size because the large size is susceptible to any random noise change. In this article, a Harmonic Mean dependent Variable Step-Size (HMVSS) method is proposed and developed in the feedback neutralization F×LMS algorithm to continuously change the algorithm's step-size corresponding to the reference noise and error signals from the sensors. The proposed method improves the convergence rate of the ANC method toward the desired response. The simulation results demonstrated that the proposed AHMVSS method could achieve better noise reduction and convergence speed compared to F×LMS, FN F×LMS algorithms. Besides, ANC is implemented in the PVC Duct noise control application using the proposed F×LMS feedback neutralization algorithm to produce a real-time anti-noise signal experimentally to limit PVC Duct noise. The experimental findings show that reducing noise in PVC Duct holds good, approximately 23–29 dB for different noises.

In this article, the performance limitations problem are investigated for a class of signal-input, signal-output networked control systems with repeated zeros and poles. The additive white noise is adopted in the communication channel. The tracking performance limitations are investigated based on a two-degree of freedom (2DOF) controller. The results demonstrate that the multiplicity of nonminimum phase (NMP) zeros and unstable poles of the plant can affect the tracking performance limitations. The explicit quantitative relationship is characterized for the tracking performance limitations, which is derived based on NMP zeros, unstable poles of the plant with corresponding multiplicity, as well as the statistical characteristics of the reference noise and communication noise. The admissible infimal signal-to-noise ratio (SNR) is also obtained for the tracking system by using a 2DOF controller structure, where the admissible infimal SNR can satisfy the stability conditions while achieving tracking performance limitations. Finally, an illustrative example is presented to validate the effectiveness of our proposed scheme.

Purpose Assessment of image quality directly in clinical image data is an important quality control objective as phantom-based testing does not fully represent image quality across patient variation. Computer algorithms for automatically measuring noise in clinical computed tomography (CT) images have been introduced, but the accuracy of these algorithms is unclear. This work benchmarks the accuracy of the global noise (GN) algorithm for automatic noise measurement in contrast-enhanced abdomen CT exams in comparison to precise reference noise measurements. The GN algorithm was further optimized compared to the previous report in the literature. Methods Reference values of noise were established in a public image dataset of 82 contrast-enhanced abdomen CT exams. The reference noise values were obtained by manual regions-of-interest measurements of pixel standard deviation in the liver parenchyma according to an instruction protocol. Noise measurements taken by six observers were averaged together to improve reference noise statistical precision. The GN algorithm was used to automatically measure noise in each image set. The accuracy of the GN algorithm was determined in terms of RMS error compared to reference noise. The GN algorithm was optimized by conducting 1000 trials with random algorithm parameter values. The trial with the lowest RMS error was used to select optimum algorithm parameters. Results The range of noise across CT image sets was 8.8-28.8 HU. Reference noise measurements were made with a precision of ±0.78 HU (95% confidence interval). The RMS error of automatic noise measurement was 0.93 HU (0.77-1.19 HU 95% confidence interval). The automatic noise measurements were equally accurate across image sets of varying noise magnitude. Optimum GN algorithm parameter values were: a kernel size of 7 pixels, and soft tissue lower and upper thresholds of 0 and 170 HU, respectively. Conclusions The performance of automatic noise measurement was benchmarked in a large clinical CT dataset. The study provides a framework for thorough validation of automatic clinical image quality measurement methods. The GN algorithm was optimized and validated for automatic measurement of soft-tissue noise in abdomen CT exams.

This article presents a configurable high directivity radio frequency reference noise source and its radiated emission (RE) simulation model. This noise source is a representative of actual programmable logic controller (PLC) and input output (IO) products with a high-speed gigabit backplane. It is designed to produce a reference noise signature from 30 MHz to 6 GHz with RE levels close to the class A electromagnetic radiation disturbance limits of CISPR 11 and CISPR 22. An RE model f this noise source was established to investigate the design factors. Multiple emissions’ scans were performed in two separate semianechoic chambers located in different facilities in order to test the repeatability of this noise source. The measurement results indicated that the noise source has very good repeatability and that it can serve as a reference noise source. The results were also used to evaluate the maturity of the simulation technology as well as to quantify the difference between chambers.

PurposeThis paper aims to describe a new microwave radiometer designed for sensing natural mediums to solve various applied scientific problems. The research findings enable to make assertions about high efficiency of the described microwave radiometer being a part of mobile sensor systems with self-contained power supplies.Design/methodology/approachA new microwave radiometer is based on the modification of the null method. Modification of the null method has been implemented by using two reference noise generators. The first reference noise generator is passive and its implementation is based on the matched load. A low-noise amplifier is used as the second reference noise generator. The use of the low-noise amplifier as the reference noise generator is based on the noise wave generation effect at its input whereby the waves form low-temperature noise.FindingsThe use of the low-noise amplifier as the reference noise generator in the modified microwave radiometer has made it possible to simplify the device design at the system level while reducing the weight and power consumption and increasing sensitivity.Originality/valueThe novelty of the modified radiometer lies in the modification of the null method and the removal of high-temperature reference noise generators based on avalanche transit-time diodes. Further, the novelty lies in the invariance of measurement results toward changes in the receiver’s own noise and transmission factor while the design of the device has been simplified.

The Wasserstein distance originated from the optimal transport theory is a general and flexible statistical metric in a variety of image processing problems. In this paper, we propose a novel Wasserstein driven low-dimensional manifold model (W-LDMM), which tactfully embraces the Wasserstein distance between two noise histograms and low-dimensional manifold regularization for noisy image restoration. Specifically, with the Wasserstein distance, the discrepancy between the estimated noise histogram and the reference noise histogram is minimized to improve the noise estimation accuracy. Moreover, based on the fact that the patch manifold structures of many natural images are low dimensional, the dimension of the image patch maniflod is utilized as regularization constraint. The key idea of W-LDMM is that the Wasserstein distance for noise constraint and the low-dimensional manifold for image regularization are complementary to each other, rather than isolated and uncorrelated. Together, they promote the W-LDMM to exhibit excellent restored results in Gaussian denoising and noisy image inpainting. Finally, extensive experiments confirm that our W-LDMM can greatly improve visual and quantitative performance compared to several popular image restoration models.

This research presents a modeling framework that allows checking the statistical requirements for producing noise maps based on mobile measurements. First, a sound field of reference is created based on a micro-simulation traffic modeling coupled with acoustic modeling, which outputs sound levels each second on a grid of receivers. The aggregated indicators ( L Aeq ) calculated from this sound field serve then as reference. Mobile targets performing measurements evolve within the simulation, aiming to estimate these indicators. The difference between the reference noise map and the one generated by the moving receivers, characterized by the Root Mean Square Error (RMSE), is computed for different aggregation radius of mobile receivers, and as a function of the number of passes-by and to the distance to its nearest cross street. It is observed that the mobile sampling is actually possible and the RMSE can be reduced by setting an optimal aggregation radius and a minimum number of passes-by. With the optimal parameters, 95% of the mobile samples fall within an estimation error interval of [−3.0, 2.2] dBA from the reference. It is also shown that the distance to the nearest cross street affects the estimation error depending on the traffic flow, producing a RMSE greater than 2 dB for distances lower than 30 m.

The adaptive feedforward active noise control strategy can effectively attenuate the broad-band noise. However, many feedforward active noise control systems have the internal acoustical feedback coupling between the secondary noise source and the measurement of the image of the reference noise, which may lead to instability of the system. In this paper, a central controller is first designed to stabilize the internal acoustical feedback loop. Then the central controller will be augmented into a Youla(Q) parameterized adaptive FIR regulator to attenuate the noise. Finally, the simulation results show that the proposed adaptive feedforward active noise control system can effectively attenuate the unknown broad-band noise in the presence of acoustic coupling.

PURPOSE The global noise (GN) algorithm has been previously introduced as a method for automatic noise measurement in clinical CT images. The accuracy of the GN algorithm has been assessed in abdomen CT examinations, but not in any other body part until now. This work assesses the GN algorithm accuracy in automatic noise measurement in head CT examinations. METHODS A publicly available image dataset of 99 head CT examinations was used to evaluate the accuracy of the GN algorithm in comparison to reference noise values. Reference noise values were acquired using a manual noise measurement procedure. The procedure used a consistent instruction protocol and multiple observers to mitigate the influence of intra- and interobserver variation, resulting in precise reference values. Optimal GN algorithm parameter values were determined. The GN algorithm accuracy and the corresponding statistical confidence interval were determined. The GN measurements were compared across the six different scan protocols used in this dataset. The correlation of GN to patient head size was also assessed using a linear regression model, and the CT scanner's X-ray beam quality was inferred from the model fit parameters. RESULTS Across all head CT examinations in the dataset, the range of reference noise was 2.9-10.2 HU. A precision of ±0.33 HU was achieved in the reference noise measurements. After optimization, the GN algorithm had a RMS error 0.34 HU corresponding to a percent RMS error of 6.6%. The GN algorithm had a bias of +3.9%. Statistically significant differences in GN were detected in 11 out of the 15 different pairs of scan protocols. The GN measurements were correlated with head size with a statistically significant regression slope parameter (p

Ship radiated noise denoising is an important step before underwater target detection. To obtain better denoising effect, adaptive fuzzy inference system (ANFIS) is used to denoise ship radiated noise. Experimental results show that there is a nonlinear component in ship radiated noise signal, and ANFIS can deal with this nonlinear relationship. Firstly, the received signal is composed of the random noise with a certain signal to noise ratio (SNR) and the expected signal with a certain SNR, and then the random noise with a certain SNR is added to the mechanical noise to form the reference noise. Finally, the received signal and reference noise are used as the input of ANFIS to get the denoised signal. The experiment is carried out by Monte Carlo method, and the results are evaluated by SNR. The results show that the method has good denoising effect on ship radiated noise.

A 1mW −261dB FOM PLL with 91fs jitter is presented that combines a low-noise, high-frequency FBAR reference with the unity-gain harmonic-mixing (HM) PLL to minimize reference noise gain and avoid entirely the amplification of sigma-delta modulator quantization noise and fractional spurs, which, in turn, allows for a substantially larger loop bandwidth to suppress the VCO noise and speed settling.

In most of the existing approaches for neutralizing the effects of acoustic feedback in feed-forward active noise control (FF-ANC) systems, three adaptive filters have been employed. Besides the noise control and feedback neutralization filters (FBNF), a third disturbance removal filter has been exploited to obtain suitable signals for (apposite) adaptation of the other two (adaptive filters). In this paper, we develop an effective acoustic feedback neutralization method which does not require any supplementary third adaptive filter. The proposed method incorporates a probe signal with a time-varying gain and a novel variable step-size strategy for adaptation of the FBNF. During the transient state, the FBNF is excited by a large variance probe signal and a large valued step-size. This enhances the convergence rate of the FBNF. As the FF-ANC system converges, the level of probe noise is decreased to achieve good residual error attenuation performance (at steady-state). Furthermore, the step-size is also reduced to a small value to improve the steady-state performance of the acoustic FBNF. Simulations results show that the proposed two adaptive filters-based method outperforms the existing methods for broad-band reference noise sources and yields better steady-state residual error attenuation performance for narrow-band noise signals.

Surface and underground nuclear magnetic resonance (UNMR) method has the advantage of direct and quantitative detection of groundwater and has been widely used in water resource survey and advance detection of water sources causing disaster in underground space. However, the UNMR signal is extremely weak in tunnels or mines. The signal-to-noise ratio (SNR) of received data at one single record is even less than −20 dB because of the strong environmental noise. A nearby reference noise cancellation (NRNC) method is proposed in this study to improve SNR. The method calculates the transfer function between the reference coil and the detection coil by using the second half of the received data to determine the noise estimation in the first half of the detection coil. The non-linear fitting method is used to estimate and remove the UNMR signal in the noise estimation and avoid the loss of the UNMR signal in the detection coil. We compare the nearby reference coil layouts of coaxial, non-separation tri-axial, and separation tri-axial types, as well as the NRNC results of the number and distance of different reference coils, through a large number of experiments. The non-separation tri-axial reference coil is optimal. The experimental results show that the SNR can be increased significantly by 18.1 dB, and the uncertainty in UNMR signal parameter estimation is evidently decreased. We prove that the NRNC algorithm is superior to the existing remote reference noise cancellation and power frequency modeling algorithms, and discuss that the improvement in SNR will be beneficial to improve the accuracy of inversion results.

Noise power estimation is central to efficient radio resource allocation in modern wireless communication systems. In the literature, there exist many noise power estimation methods that can be classified based on underlying theoretical principle; the most common are spectral averaging, eigenvalues of sample covariance matrix, information theory, and statistical signal analysis. However, how these estimation methods compare against each other in terms of accuracy, stability, and complexity is not well studied, and the focus instead remains on the enhancement of individual methods. In this paper, we adopt a common simulation methodology to perform a detailed performance evaluation of the prominent estimation techniques. The basis of our comparison is the signal-to-noise ratio estimation in the simulated industrial, scientific and medical band transmission, while the reference noise signal is acquired from an industrial production plant using a software-defined radio platform, USRP-2932. In addition, we analyze the impact of different techniques for noise-samples’ separation on the estimation process. As a response to defects in the existing techniques, we propose a novel noise-samples’ separation algorithm based on the adaptation of rank-order filtering. Our analysis shows that the proposed solution, apart from its low complexity, has a very good root-mean-squared error of 0.5 dB and smaller than 0.1-dB resolution, thus achieving a performance comparable with the methods exploiting information theory concepts.

A method for leak monitoring of noisy industrial pipelines is proposed in this work. This method is based on evaluating a single feature, the root mean square of the acquired acoustic signal of a pipeline and relies on defining updateable thresholds based on the previously acquired values. If at any point, these values increase above the active threshold, a check routine is performed to identify if that event was transient and short-term. The presence of a leak can be modeled through this procedure as a long-term increase in the acquired rms values. The algorithm behind the operation is associated with a set of parameters, that are adjustable in such a way that the system can adapt to the transient noise characteristics of the pipeline under inspection. These parameters can be fine-tuned based only on noise measurements without the need for a training phase with artificially generated leaks. The algorithm can begin operating after only a reference noise measurement during its initialization. The proposed algorithm's overall accuracy was evaluated with measurements performed on an operating pipeline in the facilities of an oil refinery.

The aim of this study is to investigate the impact of CT acquisition parameter setting on organ dose and its influence on image quality metrics in pediatric phantom during CT examination. The study was performed on 64-slice multidetector CT scanner (MDCT) Siemens Definition AS (Siemens Sector Healthcare, Forchheim, Germany) using various CT CAP protocols (P1–P9). Tube potential for P1, P2, and P3 protocols were fixed at 100 kVp while P4, P5, and P6 were fixed at 80 kVp with used of various reference noise values. P7, P8, and P9 were the modification of P1 with changes on slice collimation, pitch factor, and tube current modulation (TCM), respectively. TLD-100 chips were inserted into the phantom slab number 7, 9, 10, 12, 13, and 14 to represent thyroid, lung, liver, stomach, gonads, and skin, respectively. The image quality metrics, signal to noise ratio (SNR) and contrast to noise ratio (CNR) values were obtained from the CT console. As a result, this study indicates a potential reduction in the absorbed dose up to 20% to 50% along with reducing tube voltage, tube current, and increasing the slice collimation. There is no significant difference (p &gt, 0.05) observed between the protocols and image metrics.

Active noise control (ANC) provides an efficient way for eliminating low-frequency noise, whereas accurate controlling non-stationary noise of rotating machinery under time-variant secondary path is still a major challenge. To address this, a novel ANC method based on signal decomposition and simultaneous perturbation is developed in this paper. In the proposed method, an indicator of periodic characteristic (IPC) with the degree of cyclostationarity is first presented to evaluate reference noise. Subsequently, a real-time strategy of the IPC combined with fast empirical mode decomposition is designed to adaptively decompose reference noise into a series of intrinsic mode functions, which are stationary and further controlled individually. Besides, the simultaneous perturbation is applied to update the controller weights for having a better tracking ability under the time-variant secondary path. Experiments were conducted to evaluate the capacity of the proposed method for controlling non-stationary reference noise under the time-variant secondary path. The results demonstrate that proposed method is capable of performing better than commonly used algorithms.

Background Noise pollution is increasingly recognised as a public health hazard, yet limited evidence is available from low- and middle-income countries (LMIC), particularly for specific sources. Here, we investigated the association between day-night average (Ldn) aircraft noise and the risk of death due to cardiovascular disease (CVD), stroke and coronary heart disease (CHD) at small-area level around São Paulo‘s Congonhas airport, Brazil during the period 2011–2016. Methods We selected 3259 census tracts across 16 districts partially or entirely exposed to ≥50 dB aircraft noise levels around the Congonhas airport, using pre-modelled 5 dB Ldn noise bands (≤50 dB to > 65 dB). We estimated the average noise exposure per census tract using area-weighting. Age, sex and calendar year-specific death counts for CVD, stroke and CHD were calculated by census tract, according to the residential address at time of death. We fitted Poisson regression models to quantify the risk associated with aircraft noise exposure, adjusting for age, sex, calendar year and area-level covariates including socioeconomic development, ethnicity, smoking and road traffic related noise and air pollution. Results After accounting for all covariates, areas exposed to the highest levels of noise (> 65 dB) showed a relative risk (RR) for CVD and CHD of 1.06 (95% CI: 0.94; 1.20) and 1.11 (95%CI: 0.96; 1.27), respectively, compared to those exposed to reference noise levels (≤50 dB). The RR for stroke ranged between 1.05 (95%CI: 0.95;1.16) and 0.91 (95%CI: 0.78;1.11) for all the noise levels assessed. We found a statistically significant positive trend for CVD and CHD mortality risk with increasing levels of noise (p = 0.043 and p = 0.005, respectively). No significant linear trend was found for stroke. Risk estimates were generally higher after excluding road traffic density, suggesting that road traffic air and noise pollution are potentially important confounders. Conclusions This study provides some evidence that aircraft noise is associated with increased risk of CVD and CHD mortality in a middle-income setting. More research is needed to validate these results in other LMIC settings and to further explore the influence of residual confounding and ecological bias. Remarkably, 60% of the study population living near the Congonhas airport (~ 1.5 million) were exposed to aircraft noise levels > 50 dB, well above those recommended by the WHO (45 dB), highlighting the need for public health interventions.

An optimization process for the horizontally polarized HF-band receive antenna based on an extended concept of the International Telecommunication Union (ITU) noise model "ITU-R P.372-14 on radio noise" is presented. Considering the ambient noise (or sky noise), a receiving antenna has been optimized based on the concept of equivalent antenna system noise figure. Furthermore, the ambient noise is evaluated with the optimized antenna and the results are compared with the reference noise curves.

Summary Surface NMR is a competitive method in hydro-geophysical investigation due to its directive sensitivity to the water content, permeability and hydraulic conductivity of the aquifer. One of the main obstacles to expand its application in many area of interest is the low signal-to-noise ratio (SNR) of measurement. To improve the SNR by mitigating noise, numerous approaches has been proposed, for example remote reference noise cancellation (RNC) and model-based powerline harmonics subtraction (MB). In this abstract, the question whether MB should be applied before RNC is investigated. First, the theoretical length of Wiener filter (WF) in RNC is analysed by observing harmonics relationship between primary and reference channels. Then the benefits of applying MB before RNC is given within multiple noise sources are present. Last, the noise level of field measurements processed by RNC and MB followed by RNC are compared.

Measurements are often contaminated with large amounts of non-stationary noise which require effective noise reduction techniques. We present a new deep learning algorithm which learns continuously in real-time while filtering the data. The algorithm requires a reference noise signal alongside the noisy signal itself. As a poof of concept we demonstrate its performance by reducing noise in electroencephalogram (EEG) with the help of a custom 3D printed compound electrode using flexible materials. With this setup we achieved an average of 20dB improvement of the signal to noise ratio of the EEG for both removing eye-blink and muscle-artefacts. This concept has the potential to not only improve the signal to noise ratio of EEG but can be applied to a wide range of industrial and consumer applications such as industrial sensing or noise cancelling headphones.

This work introduces an accurate linearized model and phase noise spectral analysis of digital bang-bang PLLs, that includes both the reference and the digitally-controlled oscillator (DCO) noise contributions. A time-domain analysis of bang-bang PLLs is leveraged to derive closed-form expressions for the integrated jitter, leading to a precise estimation of the binary phase detector (BPD) equivalent gain. The theoretical predictions differ by less than 1% from the simulation results obtained using a behavioral model, in all typical cases: dominant reference noise, dominant DCO noise, and comparable contributions. An accurate discrete-time model that takes into account the time-variant effect arising from the multirate nature of a digital phase-locked loop (DPLL) is used, along with the provided estimation of the jitter, to predict the output and input-referred phase noise spectra. An excellent match with the simulated spectra is achieved for all the different operating conditions.

Post-filtering is an effective method for further reducing noise components at a beamformer output. Existing techniques either are inefficient at suppressing highly non-stationary noise, or possess complex calculations. This paper proposes a novel dual-microphone sub-band post-filtering algorithm using a simplified transient beam to reference ratio (TBRR), applicable to adaptive beamformer, which is named SS-TBRR (simplified sub-band-TBRR). The sub-band signal processing approach is extended to the post-filtering for reducing computational complexity and smoothing the spectrum of the processed signal to eliminate musical noise. The relation between the observed signal, beamformer primary output, and the reference noise signal is exploited to differentiate non-stationary noise components from speech components. Based on speech presence probability and combined with an appropriate spectral enhancement approach, non-stationary noise is reduced significantly without canceling desired signal. Experimental results verify the effectiveness and robustness of the proposed algorithm.

This paper presents a 5-order low-pass filter (LPF) structure based on the operational transconductance amplifier(OTA) structure that is designed with low noise and HD3. To ensure that the filter has a lower power dissipation, noise, and third harmonic distortion(HD3), the OTA operates in weak inversion region and uses technology including current division, current cancellation, source degeneration. The LPF is simulated by the 0.18 µm CMOS process, and the results show that the LPF locates within 250Hz and consumes 65.49 µW, the ripple is less than 0.2dB, and the HD3 is less than -70.21dB (@50mV). The maximum input reference noise (IRN) is $71.86\ \mu V/\sqrt{Hz}$ The proposed LPF is suitable for Electrocardiograph (ECG) signal detection analog front circuit.

This paper presents behavioural modelling and analysis of Error Feedback (EF) SAR ADCs. The capacitor array model proposed here is based in charge equations, which allows accurate modelling of the feedback in EF-SARs. It also introduces models for the feedback amplifier, including offset, non-linear gain, thermal and flicker noise, as well as sampled kT/C noise for switches and reference noise. The proposed models are implemented and validated in Matlab¯ Simulink¯, showing results with accuracy comparable to transistor-level simulations and simulation times several orders of magnitude faster.

The surface Nuclear Magnetic Resonance method is gaining momentum as an efficient geophysical method for the detection and characterization of groundwater. However, the method still suffers from a low signal to noise ratio mostly due to electromagnetic noise of anthropic origin.To solve this problem, signal processing in surface nuclear magnetic resonance surveys often relies on the reference-based noise cancellation technique. This method consists of capturing the main characteristics of the noise through a secondary loop ideally located and obtaining an estimate of the noise affecting the primary loop which can be subtracted from the noisy sNMR signal.The main problem associated with the method occurs when the spatial distribution of the noise is heterogeneous, which can result in a low correlation between the reference loop and the primary loop, and hence in a poor noise reduction. Difficulties may also arise when the field survey location prevents the display of a reference loop for logistics or physical reasons.To remediate these situations we have investigated the possibility of recording noise-only signals through the primary loop, prior to the sNMR measurement, and use those signals as references for subsequent calculation of the local transfer function. The correlation between a series of noise-only signal recorded on a primary loop was analyzed through the computation of the magnitude squared coherence function, and comparison was made with noise records from a secondary loop. The analysis demonstrates that temporal reference noise cancellation (TRNC) can provide more efficient noise reduction results than the classical spatial reference noise cancellation if the temporal noise-only database is large enough.Such a technique would be particularly suited for the development of long-term sNMR monitoring systems, where noise records could be acquired for long periods without any difficulty. In addition to two field survey examples, we present a synthetic statistical analysis to estimate the minimal volume of the signal database required for optimal noise reduction.

A recent trend in high-performance systems is distribution of computing across many chips and packages to sustain performance scaling while achieving high yield and alleviating power delivery. High-end data center systems and new applications like deep neural network (DNN) accelerators with scalable architecture [1] may extend the system from large chip-scale computing not just to package-scale multi-chip modules (MCM), but also to PCB-scale computing systems. An essential requirement for these distributed systems is a highly scalable low-power and high-bandwidth interconnect system, that can cover a wide range of integration and channel distances. Ideally, the same low-power links designed for ultra-short reach interconnects in MCM should be used in more challenging board-level environments.

In active noise control (ANC) systems, the primary path may exhibit nonlinear impulse responses. Conventional linear ANC controllers based on a filtered-x least mean square (FxLMS) algorithm exhibit performance degradation when compensating for nonlinear distortions of the primary path. Several nonlinear active noise control algorithms, including Volterra filtered-x least mean square (VFxLMS) and filtered-s least mean square (FsLMS), have been utilized to overcome this nonlinear effect. However, the performance still needs to be improved when the reference noise is mixed with multiple narrowband signals and additional Gaussian white noise. Over the last several years, kernel adaptive filters have exhibited powerful capabilities in multiple signal processing domains. When kernel adaptive filters are introduced into the ANC system, a great challenge is to compensate for the inherent delay caused by the secondary path. Due to the implicit mapping of the kernel method, it is difficult to filter the reference signal in the high-dimensional feature space. In this paper, an approximate method is proposed in which the filtered reference signal is mapped to the high-dimensional feature space. In addition, a kernel filtered-x least mean square (KFxLMS) algorithm is developed for an ANC system with a nonlinear primary path. Simulation experiments demonstrate that the performance of the proposed KFxLMS algorithm is better than that of the FxLMS, VFxLMS, and FsLMS algorithms.

A conventional single-channel feedforward active noise control (ANC) system encounters noise from multiple sources. In many cases, there is a reference signal that is independently available which is correlated with the ambient noise. However, in some situations, a portion of the noise generated is uncorrelated with the reference signal. In this paper, a new hybrid ANC (HANC) system is proposed using a convex combination of time and frequency domain Filtered-X LMS (FXLMS) algorithms. The feedforward part uses a time domain FXLMS (TDFXLMS) algorithm to control disturbances that are correlated with the reference noise and the feedback part uses a multiresolution analysis-based frequency domain wavelet packet FXLMS (WPFXLMS) algorithm to control the uncorrelated disturbances encountered during the operation of an ANC system. An extra adaptive filter is used to smoothen the error signal in the HANC system. An effort is made to exploit the benefits of the structural design and time–frequency domain signal processing techniques for active control of disturbances that are both correlated and uncorrelated with the reference noise. As a result of the convex combination of the TDFXLMS and WPFXLMS algorithms in a HANC system, the proposed method is successful in canceling the disturbances encountered by the ANC system from multiple noise sources.

Background: Labeled noise reduction (NR) data presented by manufacturers are considered one of the main challenging issues for occupational experts in employing hearing protection devices (HPDs). This study aimed to determine the actual NR data of typical HPDs using the objective fit testing method with a microphone in real ear (MIRE) method. Methods: Five available commercially earmuff protectors were investigated in 30 workers exposed to reference noise source according to the standard method, ISO 11904-1. Personal attenuation rating (PAR) of the earmuffs was measured based on the MIRE method using a noise dosimeter (SVANTEK, model SV 102). Results: The results showed that means of PAR of the earmuffs are from 49% to 86% of the nominal NR rating. The PAR values of earmuffs when a typical eyewear was worn differed statistically (p < 0.05). It is revealed that a typical safety eyewear can reduce the mean of the PAR value by approximately 2.5 dB. The results also showed that measurements based on the MIRE method resulted in low variability. The variability in NR values between individuals, within individuals, and within earmuffs was not the statistically significant (p > 0.05). Conclusion: This study could provide local individual fit data. Ergonomic aspects of the earmuffs and different levels of users experience and awareness can be considered the main factors affecting individual fitting compared with the laboratory condition for acquiring the labeled NR data. Based on the obtained fit testing results, the field application of MIRE can be employed for complementary studies in real workstations while workers perform their regular work duties. Keywords: fit testing, hearing protection device, microphone in real ear, noise reduction rating

A method of transmitting information with the expansion of the spectrum based on spectral interference between the reference noise and the information noise signal with a delay of different time according to the stream of binary bits is proposed. The occurrence of intrasystem noises with a shift in evaluation of the autocorrelation effect at the output of a two-channel correlation receiver is found. The possibility of noise-resistant transmission of information during spectral modulation as a result of the superposition of completely incoherent ultrawideband noise chaotic signals is shown.

Unlike the conventional active noise control (ANC) technique that aims to minimize the sound pressure level (SPL) of target noise, the active sound quality control (ASQC) technique tends to balance the SPLs of certain frequency points or frequency bands to satisfy the requirements of human perception in some applications. In this paper, a feedforward hybrid ASQC (HASQC) algorithm is proposed for the situation where both narrowband and broadband noise components reside in primary and reference noise signals. By utilizing a narrowband ASQC (NASQC) subsystem, a sinusoidal noise canceller (SNC) subsystem, and a broadband ASQC (BASQC) subsystem, the presented HASQC algorithm may control both the spectral characteristics and SPLs of the target noise with both narrowband and broadband characteristics. The algorithm inherits the advantages of existing active noise equalization or active sound profiling strategies and has greater potential to meet practical sound quality requirements. Moreover, to improve the algorithm performance, we also introduce a variable step size updating method. Extensive simulations for various scenarios are performed to demonstrate the effectiveness of the algorithm. The results show that the proposed HASQC algorithm achieves satisfactory sound quality control of the target noise in steady, dynamic as well as multi-frequency situations.

This paper presents a configurable radio frequency reference noise source and its radiated emission (RE) simulation model. This noise source is representative of actual PLC and IO products with a High Speed Gigabit backplane. It is designed to produce a reference noise signature from 30 MHz to 6GHz with radiated emission levels close to the class A electromagnetic radiation disturbance limits of CISPR 11 and CISPR 22. A radiated emission model of this noise source was established to investigate design factors. Multiple emissions scans were performed in two separate semi-anechoic chambers located in different facilities in order to test the repeatability of this noise source. Measurement results indicated that the noise source has very good repeatability and that it can serve as a reference noise source. The results were also used to evaluate the maturity of the simulation technology, as well as, to quantify the difference between chambers.

In this paper, the tracking performance limitation of networked control systems (NCSs) is studied. The NCSs is considered as continuous-time linear multi-input multi-output (MIMO) systems with random reference noises. The controlled plants include unstable poles and non-minimum phase (NMP) zeros. The output feedback path is affected by multiple communication constraints. We focus on some basic communication constraints, including additive white noise (AWN), quantization noise, bandwidth, as well as encoder-decoder. The system performance is evaluated with the tracking error energy, and used a two-degree of freedom (2DOF) controller. The explicit representation of the tracking performance is given in this paper. The results indicate the tracking performance limitations rely to internal characteristics of the plant (unstable poles and NMP zeros), reference noises (the reference noise power distribution (RNPD) and its directions) and the characteristics of communication constraints. Moreover, the tracking performance limitations are also affected by the angles between the each transform NMP zero direction and RNPD direction, and these angles between each transform unstable poles direction and the direction of communication constraint distribution/allocation. In addition, for MIMO NCSs, bandwidth (there are not identical two channels) always can affects the direction of unstable poles, and the channel allocation of bandwidth and encode-decode may be used for a feasible method for the performance allocation of each channels. Lastly, a instance is given for verifying the effectiveness of the theoretical outcomes., Comment: This paper give a fundament performance limitation of networked control systems with multiple communication constraints

Wrist based devices, like smart-watches, fitness bands and health monitors all provide a common sensor called Photoplethysmography (PPG) to measure optical pulse signal. This is usually used to derive the instantaneous heart-rate (HR), which is useful while doing any exercise or to monitor on a regular basis for chronic patients. However, one major issue with the signal is that it is easily corrupted by ambulatory motion generated by hand movements of the subject. Since, these devices also come equipped with an independent motion sensor, namely a tri-axes accelerometer, researchers have taken interest in trying to correct the motion artifact in PPG using the accelerometer as a reference noise signal. However, it is not a trivial problem and hence, even after a substantial body of research, the problem remains unsolved, especially when considering on-premise estimation due to the resource-constrained nature of wearable devices. In this paper, we aim to solve this problem using subspace based learning approach. Though this approach has been utilized before, we have added some novel steps to the algorithm pipeline and also made modifications so that the algorithm can be possibly run on a typical wearable device. Our preliminary results show efficacy and promise of our proposed approach.