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Noise has always been an un-favorable performer in any form of communication. Because noise is a random process that changes over time, it is estimated at each instant to cancel out the original signal. Unless a perfect noise cancellation system is used in voice communication, any recorded or live speech data signal transmitted will be useless. There are several noise cancellation schemes, but the adaptive filter is the most effective. By implementing the three conventional adaptive algorithms [LMS, JAYA, PSO] noise was eliminated significantly. The Simulation results indicate a higher quality of noise cancellation.

The minimum output variance filtered reference least mean square (MOV-FxLMS algorithm is a effective algorithm that utilizes the penalty mechanism to help the active noise control (ANC system achieve noise cancellation with constrained output variance or power. As it can constrain output power, the MOV-FxLMS algorithm can freely determine the ANC system's control effort, avoid output saturation, and improve system stability. However, its performance is determined by a penalty factor, which is normally chosen by trial and error. Hence, this work proposes an optimal penalty factor and its feasible estimation that does not require any assumptions of Gaussian reference signal or input independence. This factor assists MOV-FxLMS in achieving the optimal solution under the target output-variance constraint. Numerical simulations on measure paths demonstrate its effectiveness for various types of noise.

This article presents a fractional-N digital multiplying delay-locked loop (MDLL) that employs a digital-to-time converter (DTC) to control the reference injection for the fractional-N operation. The presented MDLL features a background two-point DTC calibration that simultaneously corrects the DTC gain and offset errors to achieve a low-jitter and low-spur architecture. The DTC errors are sensed using an embedded time-to-digital converter (TDC) and extracted in the digital domain for reduced implementation overhead. A new TDC dithering and dither noise cancellation technique is used to improve the estimation accuracy of the DTC errors at the presence of TDC quantization error and differential nonlinearity (DNL). In addition, the adaptive dither noise cancellation scheme uses a comb filter to decouple the dither noise and DTC error estimations, allowing the two schemes to operate simultaneously. The proof-of-concept MDLL prototype fabricated in 65-nm CMOS achieves -60-dBc fractional spur and 1.67-ps rms jitter at around 20-MHz offset. With the proposed calibration scheme, a >25-dB spur reduction in reference and fractional spurs is demonstrated.

Active noise control (ANC) systems are widely used to cancel unwanted noise. However, for high-level noise, the residual error signal cannot be fully eliminated because of the nonlinearity of the secondary path, resulting in the diverging of the adaptive filter. In this letter, we propose a secondary path-decoupled ANC (SPD-ANC) algorithm based on deep learning. Specifically, the secondary path decoupled module consisting of two time-domain convolutional recurrent networks, one for modeling the nonlinear secondary path and the other for modeling the reverse process, is employed to calculate the secondary path-decoupled (SPD) error signal. The control signal is then generated by an adaptive filter that is optimized towards minimizing the SPD error signal. Simulation results indicate that the proposed method outperforms the conventional ANC methods under different conditions.

To design noise-shaping successive approximation register (NS-SAR) analog-to-digital converters (ADCs) with high resolution and good power efficiency, two key bottlenecks need to be addressed. One is to realize a high-order loop filter with low circuit overhead, and the other is to mitigate the thermal noise. This article presents an NS-SAR ADC that synergistically addresses both challenges. To achieve high-order efficiency, it proposes an innovative error feedback-cascaded integrator feedforward (EF-CIFF) structure that realizes third-order noise shaping using only a single amplifier. It combines the merits of both structures, showing improved robustness, and is free of dc offset concern. On reducing the kT/C noise, this work features a sampling kT/C noise cancellation (SNC) technique that reuses the native hardware of the EF-CIFF structure. An open-loop self-quenching floating-inverter dynamic amplifier (FIDA) is used to support all amplification with low noise and power. Prototyped in 65-nm CMOS, this work achieves 84.8-dB signal-to-noise-distortion ratio (SNDR) with 625-kHz bandwidth (BW) (OSR = 8) and 119 $\mu \text{W}$ , leading to 182-dB Schreier Figure of Merit (FoM). It uses only 0.8-pF input capacitance, which is $5\times $ smaller than prior NS-SAR ADCs with similar oversampling ratio (OSR) and SNDR.

Laser diagnostics, particularly the Thomson scattering technique, is one of the methods used to measure the electron temperature of plasma, which is highly contaminated by noise. In this work, novel preprocessing methods with adaptive filters have been proposed to suppress unwanted noise from the plasma signal. Simulations have been carried out on the real data captured in the laser diagnostic laboratory. The hardware architectures of novel preprocessing methods and adaptive filter-based noise cancellation systems to acquire filtered signals to a remote computer for signal monitoring are presented. Moreover, field-programmable gate array (FPGA)-based hardware simulation result for real-time noise cancellation is demonstrated with a developed graphical user interface (GUI). The proposed noise cancellation system occupies 4.09% lookup tables (LUTs) when implemented on the KC705 FPGA board and consumes 2.86-W power when operated at 250-MHz frequency.

To improve the performance of frequency-locked loops (FLLs) under distorted grid conditions, a series of pre-filtering techniques have been added to remove harmonics. One of them is the FLL-based comb filter (COMB-FLL), which offers a high disturbance rejection capability. However, it has some disadvantages, such as approximating a fractional delay and compensating an accumulated round-off error in the digital implementation of COMB-FLL. To alleviate these problems, this study proposes an active noise cancellation (ANC) FLL with a notch filter (NF), which incorporates an NF and an ANC to improve the frequency-locking ability. In this research, the structure of a comb filter is simplified into an NF, and an ANC is creatively introduced to eliminate harmonics from the frequency signal obtained by FLL. Furthermore, ANC has been improved to make it suitable for electricity-related applications. The proposed FLL features a unique cascade structure, which has excellent frequency-locking ability and dynamic characteristics under normal, abnormal, and harmonically distorted grid conditions. The simulations and experimental results verify the validity and reliability of the proposed FLL.

Active audio noise cancellation technology using piezoelements is fairly new and emerging technology on the market in recent years and is still gaining in popularity. The ability to use piezoelectric devices to gather information about vibration spectrum and to create interfering waves, to cancel out the noise, allows for the reduction in passive vibration methods and gives more flexibility in terms of space and application. The idea of using this technique on larger scales such as industrial equipment is the focus of ongoing research conducted by the authors of this article. This work is intended as the first part of a larger project and focuses solely on theoretical approach to the problem of modelling mechatronic systems capable of creating complex vibration spectrums and using piezoelectric components. The authors have focused on creating a mix of classical and non-classical methods to synthesize model systems based on input resonance frequencies. A classical calculation through matrix equations was also done to validate the accuracy of obtained results through the structural number method. The resulting model is still awaiting the empirical verification through extensive testing on real-life models, and that part of the research is still being developed. When validated and checked, this technology could bring new solutions in the vibration damping of industrial equipment, potentially increasing its flexibility and effectiveness.

Phase noise of PLLs is one of the critical issues in high-performance transceivers. Especially, in-band noise is troublesome because the phase noise inside a loop bandwidth is hard to be filtered out due to low-pass characteristic of most PLL components. The in-band noise of PLLs is mainly caused by charge-pumps (CPs), so that large-size transistors are used to improve in-band noise of CPs. Due to this trade-off between inband noise and occupied area, a large area is needed for CPs in high-performance transceivers. In this paper, we propose a noise-canceling CP to improve in-band noise. The prototype of the proposed CP embedded in a 28-GHz LC-PLL was fabricated using 16nm FinFET process and 1.2-dB improvement of single sideband (SSB) integrated phase noise is achieved. As the results of in-band noise improvement, occupied area for the CP can be reduced to 22%.

In many practical multi-channel active noise control (ANC) applications, various constraints need to be satisfied, such as stability, enhancement, etc. One way to enforce these constraints is to add a regularization term to the Wiener filter formulation, which, by tuning only a single parameter, can over satisfy many constraints and degrade the ANC performance. Another approach for non-adaptive ANC filter design that can produce better ANC performance is to directly solve the constrained optimization problem formulated based on the H2/H∞ control framework. However, such a formulation does not result in a convex optimization problem and its practicality can be limited by the significant computation time required in the solving process. In the presented work, the H2/H∞ formulation is convexified and a global minimum is guaranteed. It is then further reformulated into a cone programming problem which can be solved using specialized algorithms. Results show that the proposed method can produce better noise control performance than the regularization method. Compared with the traditional H2/H∞ formulation, the proposed method is more reliable and the computation time can be reduced by several orders, which, practically, provides a potential to extend its application to adaptive control.

Partial discharge (PD) detection plays a vital role in on-line condition monitoring of electrical apparatus in the power systems. However, the noise of PD measurements significantly degrades the performance of detection algorithms. In this paper, we focus on developing an adaptive filtering technique for the PD denoising problem. Heretofore, there are just a few literature reviews addressing the PD denoising based on such a method. The proposed recursive continuous S-shaped (RCSS) algorithm integrates the advantages of recursive strategy and continuous S-shaped function into adaptive noise cancellation (ANC) system, yielding enhanced filtering performance. The proposed algorithm can tackle PD noises in both Gaussian and impulsive scenarios, which is easy to implement in practical applications. The convergence behavior is also analyzed. Extensive simulation and experimental results confirm that the proposed algorithm can address polluted PD pulses, even in excessively noisy conditions, resulting in smaller mean square error (MSE) as compared to other state-of-the-art algorithms.

Active noise control (ANC) requires a controller with a small delay. However, in most ANC applications, this delay is significant due to the typically low sample rate, the anti-aliasing (AA), and reconstruction filters (RC) of the analog to digital (AD) and digital to analog (DA) converters. This delay can be reduced by increasing the sampling frequency at the expense of a significant increase in computational complexity. Sigma-delta AD and DA converters work at high sampling frequencies, but their use in ANC is limited due to the delay of AA and RC filters. This work proposes removing the AA and RC filters of the sigma-delta converters and using the oversampled signals directly in an ANC system. This proposal allows the ANC system to be implemented at a large sample frequency but using signals with fewer bits per sample (word-length). This is the first short-word length implementation of the least mean squares (LMS) algorithm to the authors’ knowledge. Gains of up to 6 times may be achieved in the computational complexity when compared with a long-word length implementation. Theoretical and simulation results validate the current solution.

Traditional active noise control (ANC) methods are based on adaptive signal processing with the least mean square algorithm as the foundation. They are linear systems and do not perform satisfactorily in the presence of nonlinear distortions. In this paper, we formulate ANC as a supervised learning problem and propose a deep learning approach, called deep ANC, to address the nonlinear ANC problem. The main idea is to employ deep learning to encode the optimal control parameters corresponding to different noises and environments. A convolutional recurrent network (CRN) is trained to estimate the real and imaginary spectrograms of the canceling signal from the reference signal so that the corresponding anti-noise can eliminate or attenuate the primary noise in the ANC system. Large-scale multi-condition training is employed to achieve good generalization and robustness against a variety of noises. The deep ANC method can be trained to achieve active noise cancellation no matter whether the reference signal is noise or noisy speech. In addition, a delay-compensated strategy is introduced to solve the potential latency problem of ANC systems. Experimental results show that deep ANC is effective for wideband noise reduction and generalizes well to untrained noises. Moreover, the proposed method can achieve ANC within a quiet zone and is robust against variations in reference signals.

This paper provides a detailed overview of active impulsive noise control (AINC) technology of α-stable distribution noise. The second-order moment of the non-Gaussian impulsive noise does not exist, so the adaptive algorithms based on second-order statistics would lead to poor noise reduction performance. On account of this defect, a series of AINC algorithms have been proposed over the past 25 years. In this paper, we briefly classified the AINC algorithms into the classic algorithms and the expanded algorithms depending on different criteria, and both the classic AINC algorithms and the expanded AINC algorithms are further divided. Afterward, we introduced the principles and characteristics of different AINC algorithms and listed the weights update formulas of those algorithms. Finally, the computational complexity comparisons of common AINC algorithms and further recommendations are given, and a brief review about the experimental testing of AINC systems is provided in order to assist related scholars to carry out AINC researches more conveniently.

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 optimization of secondary source configuration for an active noise control (ANC) system in its enclosed space generally focuses on noise reduction requirements at discrete points only. This may lead to the poor noise reduction performance in the whole spatial region, and it is necessary to know the information on error sensor positions in advance. To address this problem, a cost function for spatial-region-oriented noise reduction is proposed. The plane wave decomposition of the enclosed sound field is used to obtain the primary field plane waves and the unit secondary field plane wave of each candidate secondary source as the prior knowledge for configuration optimization, so as to formulate a wave-domain ANC cost function. The optimization method adopts the simulated annealing search. Taking a rigid-walled rectangular cavity as an example, the optimization method is firstly compared with two space-domain methods by using analytic values of the wave-domain prior knowledge. The comparison results show that the better reduction of spatial acoustic potential energy can be achieved independent of the error sensor configuration information. Then the estimated values of the wave-domain prior knowledge through measuring randomly distributed microphones are used to optimize the configuration of the ANC system. The optimization results suggest that the noise reduction of spatial acoustic potential energy of the optimized configuration can be better than that of the space-domain method, but the microphone positions have a great influence on the noise reduction performance.

Active noise control (ANC) has been intensively studied for decades. The most classical ANC algorithm should be the filtered-x least mean square (FxLMS) algorithm, which needs the model of the secondary path to work. Thus, the residual error of the ANC system is closely related to the preciseness of the secondary path model. In many applications, the secondary path is often time-varying. Therefore, off-line identification of the secondary path is not applicable. However, on-line identification often requires an additional white noise as a stimulating signal of the secondary path, which will deteriorate the final noise reduction effect. This paper proposes an improved artificial bee colony (ABC) algorithm for ANC system, which does not require identification of the secondary path. In order to guarantee the convergence of the algorithm and accelerate the convergence speed, this paper introduces a variable forgetting factor into the fitness function, and improves the traditional ABC algorithm by integrating LMS algorithm into the ABC algorithm. A single channel ANC system equipped with an FPGA hardware platform is set up in an anechoic chamber, and experiments show that the proposed algorithm can produce a satisfactory noise reduction effect without modeling the secondary path.

Active noise cancellation (ANC) headphones are becoming increasingly important as they can effectively attenuate perceived ambient noise. Fixed filters are commonly applied in commercially available ANC headphones due to their robustness. However, they are not capable of adapting to changes that occur in dynamic environments, resulting in degraded ANC performance. In contrast, adaptive filters are able to update the ANC filters to compensate for noise in dynamic environments, but large estimation errors can occur due to a sudden change in direction/type of noise or secondary path. Some studies have suggested an ANC system by combining fixed and adaptive filters. Based on this mechanism, we propose a semi-adaptive ANC system in which the fixed and adaptive filters are weighted in real-time. Initially, the weighting for the fixed filter dominates the whole system to ensure the robustness of the ANC system. Then, the residual error provided by the adaptive filter is simulated and compared to the real measured one to determine the relative weighting between the fixed and adaptive filters. In this study, this approach is applied to a feedback ANC system. Simulation results show that our proposed approach achieves high noise attenuation performance while maintaining robustness with time-varying secondary paths.

The key issue of three-dimensional active noise control (3D ANC) problems is that global control is generally difficult, given limited number of discrete sensors. In this paper, feedforward multi-channel ANC approach is proposed to circumvent this difficulty. In view of the model-matching principle and multiple secondary sources, an underdetermined multi-channel inverse filtering (UMIF) system is formulated. With this UMIF system as a design constraint, a cost function is introduced to minimize the noise energy at a large number of control points. This linearly constrained minimum variance (LCMV) proves effective in broadening the controlled area in a 3D space. Optimal deployment of control points and the regularization terms of LCMV approach are also examined. To implement the proposed ANC system in a non-freefield environment, sensor interpolation can be used to find the frequency response between control points and loudspeakers, with plane wave decomposition and some room response measurements. The proposed ANC system has been implemented on a six-element linear loudspeaker array. Simulation and experiment results have demonstrated that the propose approach has yielded significant noise reduction performance in a large control area.

Active noise control (ANC) headphone is widely used to attenuate the noise around human' s ear. The microphone mounted on the conventional ANC headphones collected the mixed reference signals when more than one noise sources are often present in the surrounding. In this case, the uncorrelated noise sources involved in the mixed reference usually deteriorate the noise reduction performance of the ANC headphones. To solve this problem, wireless microphones are proposed to install close to each potential noise source in the environment. The microphones pick up the clean reference signals and transmit them to the ANC controller embedded in the headphones with time-advance wirelessly. Every reference signal selected by a coherence-based-selection algorithm is provided individual control filter in each ear. Each control filter updated by using a single clean reference offers better noise reduction performance for ANC headphones. Furthermore, numerical simulations and real-time experiment results in this paper demonstrate the improvement of the proposed method compared with conventional ANC headphones.

In recent years, more attention has been paid to the performance of algorithm in active noise control (ANC). Compared with filtered-x LMS (FxLMS) algorithm based on stochastic gradient descent, filtered-x RLS (FXRLS) algorithm has faster convergence speed and better tracking performance at the cost of high computational complexity. In order to reduce the computation, fast transversal filter (FTF) algorithm can be used in ANC system. In this paper, simplified multi-channel FXFTF algorithms are presented, and the convergence speed and noise reduction performance of different multichannel algorithms are simulated and compared, and the numerical stability of the algorithms are analyzed.

EHONs (Energy Harvesting Overlay Networks) satisfy stringent design requirements such as high energy-and-spectrum utilization efficiencies. However, due to open access nature of these networks, eavesdroppers can emulate cognitive radios to wire-tap legitimate information, inducing information security to become a great concern. In order to protect legitimate information against eavesdroppers, this paper generates artificial noise transmitted simultaneously with legitimate information to interfere eavesdroppers. Nonetheless, artificial noise cannot be perfectly suppressed at legitimate receivers as for its primary purpose of interfering only eavesdroppers. Moreover, channel information used for signal detection is hardly estimated at receivers with absolute accuracy. As such, to quickly evaluate impact of channel estimation-and-artificial noise cancellation imperfection on secrecy performance of secondary/primary communication in ANaEHONs (Artificial Noise-aided EHONs), this paper firstly proposes precise closed-form formulas of primary/secondary SOP (Secrecy Outage Probability). Then, computer simulations are provided to corroborate these formulas. Finally, various results are illustrated to shed insights into secrecy performance of ANaEHON with key system parameters from which optimum parameters are recognized. Notably, secondary/primary communication can be secured at different levels by flexibly adjusting various parameters of the proposed system model.

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, we propose a baseband noise-canceling receiver architecture to increase in-band linearity. A key feature of the architecture is that all active circuits are in baseband, including the low-noise transconductance amplifier (LNTA). The LNTA operating at baseband frequencies allows the use of feedback to increase the linearity. This article analyzes a tradeoff that exists between in-band linearity and noise in mixer-first receivers and shows how the proposed architecture breaks such tradeoff. The receiver targets high IF bandwidths, enabled by a transimpedance amplifier (TIA) composed of an OpAmp using only inverters. This article describes the stabilization mechanism of this OpAmp with a unity-gain bandwidth (UGB) of 7.6 GHz. The receiver is fabricated in 22-nm FDSOI CMOS. The measured results show an in-band IIP3 of > 9 dBm for an IF bandwidth of 175 MHz with sub-5-dB noise figure (NF) across 1-6-GHz local oscillator (LO) frequencies.

In this letter, we propose a pilot-assisted receiver scheme based on learnable successive interference cancellation (PA-LSIC) for uplink single-input multiple-output (SIMO) non-orthogonal multiple access (NOMA) systems. The PA-LSIC combines the successive interference cancellation (SIC) structure with the model-driven deep learning network. Considering the noise impact of channel estimation and the incomplete detection and cancellation in SIC process, we introduce some new parameters, such as noise cancellation factor and interference cancellation factor, which are optimized by using the back-propagation algorithm and random gradient descent algorithm. Numerical results show that the PA-LSIC has superior bit error rate (BER) performance and lower complexity during training and implementation.

Hearing protection devices reduce cast-saw noise. It would be helpful to identify the devices that are both effective and economical. Prior studies have shown that expensive noise-reduction headphones significantly reduced the anxiety associated with cast removal with a powered oscillating saw. The cost of such headphones, however, is a drawback for some practices and hospitals. It would be helpful to determine whether lower cost ear protection can provide effective cast-saw noise reduction. In addition, it is unclear whether the noise reduction ratings (NRRs), the average sound-level reduction provided by a hearing protection device in a laboratory test, provided by manufacturers accurately characterize the effectiveness for reducing cast-saw noise. Note that noise-cancelling devices do not carry an NRR because they are not designed as hearing protectors. Five ear protection devices with different NNRs were tested. The devices varied greatly in cost and included earplugs, low-cost earmuffs, and noise-cancelling headphones. To standardize the evaluation, each device was fitted to an acoustic mannequin with high-fidelity ear microphones while a fiberglass spica cast was cut. An additional test was run without hearing protection as a control. The low-cost devices significantly reduced the saw noise, with the exception of earplugs, which had highly variable performance. The noise reduction was similar between low-cost earmuffs and the high-cost earphones when the noise-cancelling feature was not active. Active noise cancelling provided further reductions in the noise level. Patients can experience high anxiety during cast removal. The current study shows that low-cost earmuffs significantly reduce cast-saw noise. Patient care settings may be more likely to offer hearing protection that is one twenty-fifth the cost of noise-cancelling headphones. An NRR appears to be a reliable guide for selecting hearing protection that reduces cast-saw noise.

A modified noise/nonlinearity cancellation low-noise transconductance amplifier (LNTA) based on an inductively degenerated common-source stage is proposed to improve linearity. The linearity of the proposed LNTA is analyzed using the Volterra series yielding closed-form equations for the third-order input intercept point (IIP3) that shows excellent matching with the simulation results. The derived equations for noise figure (NF) and IIP3 are simple enough to be used for design optimization. Calculations and simulation results show that the proposed LNTA is more linear than a conventional one, while NF approximately remains unchanged. Another feature of the proposed noise-canceling LNTA is that no additional baseband recombination stage is needed, leading to reduced complexity in the receiver baseband stage. The NF and out-of-band (OOB) IIP3 of the receiver is about 3.3 dB and more than 20 dBm, respectively. The receiver consumes 10.5 mA from 1.8-V supply voltage. The current consumption of the LO divider is 6.5 mA from a dedicated 1-V supply voltage.

Sound field reconstruction technology is used to provide accurate primary reference signals for active noise control systems by reconstructing the interior sound field. Traditionally, time-domain noise signal-based reconstruction modeling has certain deficiencies, such as large data volume, noise reconstruction model complexity and considerable time consumption. Hence, a novel Signal compression optimisation-based BP network for passenger head position signal reconstruction (CBHSR) algorithm is proposed. Based on compressed sensing, the proposed algorithm converts raw multi-source signals into the compressed domain to implement compressed sampling. The signal reconstruction model is created by regarding the optimal fitness value as the initial weight and the threshold of the signal reconstruction BP network, and training with the compressed multi-source data. The recovery compression signal method realizes the time-domain signal reconstruction of the passenger head position. The effectiveness of the proposed CBHSR algorithm is validated using noise signal sources collected from a vehicle. Compared with the reconstruction model of the BP algorithm, the proposed algorithm is superior in reconstruction accuracy and time consumption.

Common-mode (CM) noise cancellation method is an attractive approach to reduce the original CM noise in power converters, which helps to shrink the size of electromagnetic interference filter. Traditional CM noise cancellation method cancels the CM noise current by adding a branch consisted of a compensation voltage source and a compensation capacitor, which generates a cancellation current and nullifies the noise current. As the duality, the voltage cancellation method is also feasible for reducing the CM noise, which has not been investigated in dc–dc converters. In this article, a common-mode voltage cancellation (CMVC) method is proposed, which cancels the CM voltage of the converter by inserting compensation voltage source into the input power cord. The requirement for achieving CMVC is that the cancellation voltage and the parasitic capacitances in the circuit are matched. The applications of CMVC method in basic nonisolated and isolated dc–dc converters are presented, and the considerations for practical applications of this method are discussed. Finally, a buck converter and a half-bridge LLC resonant converter prototype are built, and the proposed method is verified by experiment, respectively.

Automatic Speech Recognition (ASR) is a self-governing, computer-based spoken language transcript for real-time applications. It is used in various real time applications and it listens the speech signals through a microphone, identifies the words, and assists a network in converting the written text. When we use the ASR system in multiple environments there is a possibility of ambient noise captured by a microphone unit and ASR system doesn’t predicting correct words. The Non-linear Acoustic Noise Cancellation (NANC) approach based automatic speech recognition method focused on the properties of non-linear sound noise cancellation. There are several distinct small segments in this approach, such as speech signal sounds, syllables, and so on. As an acrylic symbol associated with organs, these units analyze syllables to find acoustic properties of speech signals. This experimental study has adopted Convolutional Neural Network (CNN) based noise reduction in the speech recognition system with an accuracy of 98.5%. Finally, a speech signal has been identified through the ASR's vocabulary, which has been obtained with correct words after all phonetic signs are present.

Active noise control (ANC) is a successful technique to reduce unwanted noise based on the superposition principle. In a conventional ANC system, the extent of unwanted noise can be attenuated at the error microphone location, thus making a silent zone at the desired location. However, in certain situations, it is impossible to place the error microphone at the desired location. In that case, the error microphone cannot assure adequate noise suppression at the desired location. To overcome that, the virtual sensing technique (VST) has been implemented in ANC to shift the zone of silence to the desired position. This paper proposes a supplemental filter in VST to avoid interference issues between secondary and virtual paths..In the virtual ANC, the supplemental filter H provides the required information to measure optimal noise control filter and also increases noise reduction in the ANC system at the virtual location. A Real-Time Simulink model is designed for the proposed virtual sensing ANC and evaluate its efficiency. The computer simulation findings showed that the ANC system performance with the proposed virtual sensing method had good noise reduction at the virtual location compared to the physical location. The proposed supplemental filter preserves reasonable noise reduction efficiency in the ANC system if the physical microphone is positioned far from the location of the silence zone.

The noise is one of the important factors that affect negatively the operation of circuits and systems. There are different methods and techniques for cancelling/reducing/de-noising these unwanted signals that mix with the original signal. In this study, an application/simulator that performs noise cancellation operations with least mean-squares algorithms was designed. The application, which has powerful visual support and a user-friendly interactive interface, can make single or comparative noise cancellation operations and performance analyzes with different algorithms on noisy signals. The effects of the algorithm parameters can be observed, information about the related algorithms can be obtained via the developed application that can also be used for educational purpose., Devre ve sistemlerin işleyişlerini olumsuz yönde etkileyen önemli faktörlerden birisi de gürültülerdir. Orijinal işarete karışan bu istenmeyen işaretlerin arındırılması/azaltılması/temizlenmesi için farklı yöntem ve teknikler mevcuttur. Gerçekleştirilen çalışmada, en küçük karesel ortalama algoritmalarıyla gürültü arındırma işlemlerini gerçekleştiren bir uygulama/simülatör tasarlanmıştır. Güçlü görsel desteğe ve kullanıcı dostu etkileşimli arayüze sahip, eğitim amaçlı da kullanılabilen uygulama ile gürültülü işaretler üzerinde, farklı algoritmalarla tekli veya karşılaştırmaları gürültü arındırma işlemleri ve performans analizleri yapılabilmekte, algoritma parametrelerinin etkileri gözlemlenebilmekte ve ilgili algoritmalar hakkında bilgiler edinilebilmektedir.

The timing errors and especially the timing skew mismatch errors are one of the main errors that cause substantial degradation in the resolution of high-speed Time-Interleaved analog-to-digital converters (TIADCs). These errors cannot be completely corrected using blind methods such as ANC, BSS etc., because of the correlation between the desired signal and the noise as both are sampled from the same input signal. In this brief, a new algorithm using oversampling is proposed to guarantee independence between the signal and the noise. The compensation structure is a typical Adaptive Noise Cancelling (ANC) block which uses special cost function gained from the oversampling concept. To estimate the noise signal caused by the timing skew error, a new structure using modulation concept is derived from the reformulation of the timing skew error effect on the TIADC signal and oversampling assumptions. The simulation results are presented for a four-channel 10-bit TIADC in order to verify the effectiveness of the proposed method. Low computational complexity and fast convergence speed are the main advantages of the proposed algorithm.

Active noise control systems are generally application-specific, and an appropriate algorithm with an optimal configuration is desirable in the first stage of active noise control system design and deployment. This study presents a design of the subband active noise control system with optimal parameters for a practical broadband active noise control. Although the delayless subband active noise control has gained wide attention for broadband noise cancellation, an optimal design remains a challenge because of the complex interplay between multiple factors such as spectral leakage, delay and weight stacking distortion subject to a number of configurable parameters, and weight stacking method. The configurable parameters can hardly be optimized independently because the active noise control performance depends on the combined configuration. A simple near black box active noise control algorithm optimization model is thus established by incorporating the genetic algorithm optimization into the parametric design of the delayless subband algorithm. The automated process does not require an understanding of the performance characteristics for different parameters. The significance of applying the automated genetic algorithm optimization to the complex multiparameter nonlinear active noise control design is revealed by numerical simulations, particularly for the multichannel low-frequency broadband active noise control system configured with the delayless subband algorithms. This provides a way for the optimal parametric design of subband active noise control before being used in a practical complex scenario.

A wireless capsule endoscopy is a noninvasive medical device designed for human gastrointestinal tract diagnosis. The localization function of the capsule provides a real‐time monitoring on the capsule movement, which can detect the retention at an early stage. The static magnetic localization system requires little power consumption, and it is easy to transform the system structure into a wearable system for capsule endoscopy. This chapter discusses the challenges and possible solutions of the magnetic localization. The DC signal measured in the static magnetic localization suffers from the static noise interference in the environment. One of the methods to avoid DC interference is to modulate the information to a certain frequency, where the alternating magnetic field is adopted. The chapter also discusses the aspects of challenges and corresponding solutions, including the model of the target magnet, the adaptive noise cancellation, the calculation of the position retrieval algorithm, and the impact of the placement of sensors.

Active noise control technique was used to reduce noise generated by a grass cutting machine. Grass cutting machine running on a diesel engine generates loud noise of about 105 dBA. Based on the spectral analysis of engine noise, it was observed that frequencies in the range of 440–5000 Hz were having more noise power. An active noise control circuit was designed and fabricated using operational amplifiers. The active noise control circuit was tested with the help of a duct made of thermocol. Results show that reduction in noise up to 10 dBA was obtained when the active noise control circuit was used with a duct made of thermocol, while a reduction up to 5 dBA was obtained when used on a grass cutting machine. The active noise control system developed may be used to reduce noise generated by a grass cutting machine.

A stable feedback active noise control (FBANC) system with an improved performance in a broadband disturbance environment is proposed in this article. This is achieved by using a Steiglitz-McBride adaptive notch filter (SM-ANF) and robust secondary path identification (SPI) both based on variable step size Griffiths least mean square (LMS) algorithm. The broadband disturbance severely affects not only FBANC input synthesized but also the SPI.TheSM-ANFestimated signal has narrowband component that is utilized for the FBANC input synthesis. Further, the SM-ANF error has broadband component utilized to get the desired signal for SPI. The use of variable step size Griffiths gradient LMS algorithm for SPI enables the removal of broadband disturbance and non-stationary disturbance from the available desired signal for better SPI. For a narrowband noise field, the proposed FBANC improves the convergence rate significantly (20 times) and the noise reduction from 10 dB to 15 dB (50%improvement) over the conventional FBANC (without SM-ANF and variable step size Griffiths LMS adaptation for SPI).

Automotive Ethernet is considered to be the backbone of future in-vehicle data communication. The IEEE 802.3bu standard specifies the use of one unshielded twisted pair of copper cables to simultaneously carry data and energy via Power over Data Lines (PoDL). One main challenge in combined data and energy transfer is the distortion of the data channel due to wideband interference from power signals present in the Automotive environment. This paper proposes the design of a novel enhanced coupling network for PoDL that features a noise cancelling current source controlled by an adaptive finite impulse response (FIR) filter for the active attenuation of interfering power signals. The concept of active attenuation was proven for a resistive network, an average attenuation of 92 dB was measured up to 120 MHz. Further analyses show that the cancellation performance heavily relies on low latency of the active filter. It is derived that the provided attenuation decreases exponentially with the frequency for a given time delay and a novel latency management concept called compressed summation is proposed. The approach maximizes the cancellation performance of the proposed filter architecture by exploiting the knowledge of the ideal filter function and effective latency to define the FIR-filter weights.

Magnetic resonance imaging generates unwanted acoustic noise. This review describes the work characterizing the acoustic noise, and the various solutions to control and attenuate the acoustic noise. There are also discussions about the permissible limits, and guidance regarding acoustic noise exposure for staff, patients, and volunteers. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.

The phase-shifting based active noise control (ANC) algorithm without secondary path modeling is appealing due to its simple implementation and high computational efficiency. However, the algorithm suffers from the influence of an implicit circular convolution caused by directly applying the most appropriate phase shift to the reference signal in the frequency domain. The implicit circular convolution results in a poor linear relationship between the phase-shifted reference signal and the error signal, which considerably degrades the convergence performance of the ANC system. In this paper, two methods are proposed to deal with this problem. In the first method, the circular wraparound artifacts are removed by the time-domain constraining operation. In the second method, a noncausal phase-shifting filter is used to regularize the phase of the reference signal. Compared with the standard frequency-domain implementation, the two proposed algorithms both eliminate the influence caused by the wraparound artifacts of the circular convolution and improve the linear relationship between the phase-shifted reference signal and the error signal, leading to a higher level of noise attenuation with a tolerable increase in computational complexity. Simulation results validate the better noise attenuation ability of the proposed algorithms.

Optimization by definition is the action of making most effective or the best use of a resource or situation and that is required almost in every field of engineering. In this work, the optimization of Least Mean square (LMS) algorithm is carried out with the help of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Efforts have been made to find out the advantages and disadvantages of combining gradient based (LMS) algorithm with Swarm Intelligence SI (ACO, PSO). This optimization of LMS algorithm will help us in further extending the uses of adaptive filtering to the system having multi-model error surface that is still a gray area of adaptive filtering. Because the available version of LMS algorithm that plays an important role in adaptive filtering is a gradient based algorithm, that get stuck at the local minima of system with multi-model error surface considering it global minima, resulting in an non-optimized convergence. By virtue of the proposed method we have got a profound solution for the problem associated with system with multimodal error surface. The results depict significant improvements in the performance and displayed fast convergence rate, rather stucking at local minima. Both the SI techniques displayed their own advantage and can be separately combined with LMS algorithm for adaptive filtering. This optimization of LMS algorithm will further help to resolve serious interference and noise issues and holds a very important application in the field of biomedical science.

Linear-in-the-parameters nonlinear controllers are widely used for active noise cancellation (ANC) in the presence of nonlinearities in the system. However, the performance of such controllers deteriorates significantly in highly nonlinear systems and also in the presence of nonlinear harmonic distortion. To overcome these issues, a kernel filtered-x least mean square (KFxLMS) algorithm based narrowband ANC system has been recently reported in the literature. We observe that the large memory size and high computational complexity of the KFxLMS algorithm hinder its implementation in real-time ANC systems. To alleviate these issues, a single channel random Fourier filter based FxLMS (RFFxLMS) algorithm is proposed in this brief. The above formulation is then extended to the multichannel scenario, and an RFF based multichannel narrowband ANC system is developed to achieve noise reduction over a broader area. The computational load of the proposed multichannel approach is further reduced by using a partial updating method. The simulation study shows that the overall performance of the proposed schemes is significantly better than the existing nonlinear methods.

All spatial active noise control (ANC) systems, when deployed in typical room environments, have time-varying acoustic channels between the secondary sources and the error microphones. The conventional online secondary path modeling techniques, which introduces additive auxiliary random noise to estimate the secondary paths, become challenging especially in a multichannel setup. In this work, we propose to use higher-order variable-directivity sound sources as secondary sources for spatial ANC, in which both the interior residual noise field within the control region and exterior sound field due to secondary source radiation are jointly controlled. The aim of controlling the exterior sound field is to minimize room reverberation generated by the secondary sources so that the secondary paths in the proposed algorithm can be approximated as free-field propagation and thus can be pre-calibrated. The system is implemented in an adaptive manner to track noise variations. The results show that the proposed method can effectively cancel spatial noise field and control exterior sound field at an acceptable low level in time-varying room environments.

Power line interference is the main noise source that contaminates Electrocardiogram (ECG) signals and measurements. In recent years, adaptive filters with different approaches have been investigated to eliminate power line interference in ECG waveforms. Adaptive line enhancement filter is a special type of adaptive filter that, unlike other adaptive filters, does not require a reference signal and has potential application in ECG signal filtering. In this paper, a selflearning filter based on an adaptive line enhancement (ALE) filter is proposed to remove power line interference in ECG signals. We simulate the adaptive filter in MATLwith a noisy ECG signal and analyze the performance of algorithms in terms of signal-to-noise ratio (SNR) improvement. The proposed algorithm is validated with Physikalisch-Technische Bundesanstalt (PTB) ECG signals database. Additive white gaussian noise is added to the raw ECG signal. Influential parameters on the ALE filter performance such as filter delay, the convergence factor, and the filter length are analyzed and discussed.

Output saturation, mainly caused by the power amplifier, is a critical issue influencing the performance and stability of an adaptive system, such as in active noise control. In this paper, a quadratically constrained quadratic program (QCQP) is defined to achieve optimal control under the averaging-output-power constraint, which ensures the output of the system operates linearly and hence, avoids the output saturation. To solve this QCQP problem recursively in practice, this paper utilizes one of the leaky-based filtered-x least mean square algorithm with an optimal leak factor. However, this method only can be applied when the statistical feature of the control signal with maximum output-power is known, which is difficult to obtain in practice. Hence, by incorporating the adaptive inverse modeling technique, we can derive a practical estimation of the optimal leaky factor, which is applicable to different noise types. Furthermore, as the optimal output-constraint control forces the output to operate linearly, the nonlinear amplifier model is not required for the leak factor estimate. The simulation of the proposed algorithm is carried out on measured nonlinear paths to validate its efficacy.