Noise Cancellation Research Articles

Optimization of hybrid microperforated panel and nonuniform space-coiling channels for broadband low-frequency acoustic absorption

Broadband noise cancellation at low frequencies (<1000 Hz) with thin absorbers has been a long-standing demand in acoustic engineering. Here, a design method is proposed for a subwavelength hybrid absorber, consisting of a microperforated panel (MPP) backed by nonuniform space-coiling channels. The low-frequency absorption performance is analyzed by impedance theory and complex frequency method. The sensitivity of the absorption band to the parameters of the MPP and the coiled channels is investigated, based on which a genetic algorithm is employed to costume quasi-perfect absorption at the desired frequency. Furthermore, a parallel integration of multiple inhomogeneous units is developed to broaden the absorption spectrum. An optimization strategy is proposed to effectively mitigate the performance degradation caused by the impedance coupling effect. The influence of the number of combined units and the objective function is analyzed for the optimization results. Numerical simulations, experiments, and theoretical predictions verify the effectiveness of the design method. Furthermore, the as-designed combination absorber achieves continuous absorption from 305 Hz to 1088 Hz. The proposed design approach may offer a promising solution for practical applications in broadband noise reduction.

Analysis of Audio Filtering Algorithms for Noise Cancellation

Real-time signal noise reduction poses a significant challenge in communication systems. To combat intrusive background noise during portable device conversations, an active noise cancellation (ANC) is employed. ANC generates anti-noise signals using techniques such as Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) algorithms, Recursive Least Squares (RLS) and modified Recursive Least Squares (M-RLS) for effective reduction of unwanted sound and enhancing the output signal quality. The study includes various real-time noise signals to create anti-noise responses, successfully negating corresponding noise in input signals, which include sine waves, live voices, and music. Through a dedicated micro-controller device and electronic network, an "Anti-noise" wave is introduced to achieve active noise control (ANC). This research identifies ANC module specifications and effectively transmits them through the ANC library, demonstrating the efficiency of the proposed noise reduction technique.

A Proof of Concept Balanced Mixer with the use of a Digital IF Power Combiner to Improve LO Noise Rejection

In this work we present a novel digital technique, that allows local oscillator (LO) noise cancellation using a digital power combiner in a balanced mixer receiver architecture. A theoretical analysis of the noise cancellation using the proposed technique is derived and a proof of concept experiment is made for the Ku-Band. This experiment includes the design and construction of a custom balanced mixer and an artificial noise source. Experimental results show a consistent noise temperature reduction in comparison with a full analog mixer, and in some cases reaching noise temperature levels similar to the receiver operating without the artificial LO noise.

Series Motor Four Quadrants Drive DC Chopper Controllers Noise Cancellation

DC Series motor armature reaction causes EMI. EMI generates noise signals that affect feedback sensor readings, feedback controller function, and controller halting. This paper discusses software discrete filter and RLC hardware filter noise cancellation and filtering. The filter eliminates EMI-induced IGBT gate firing noise. Matlab/Simulink tests the filter circuit and digital filter control method, confirming software and hardware filter implementation.

Active noise control performance in the high-frequency range

Noise control for vehicle mainly uses a reduction method through sound absorbing and insulating materials but has disadvantages of increasing cost and weight. It is known that the noise reduction performance of Active Noise Control (ANC) technology that can be considered as an alternative is limited to the low frequency range. However, there could be a case when the use of active noise cancellation is required at higher frequency range. Therefore, the purpose of this paper is to find out how much high-frequency noise can be controlled by applying active noise cancellation and the performance of ANC according to the equipment used. For this end, an ANC experimental environment was installed at the passenger's seat interior vehicle. By using Filtered-x Least Mean Square (FxLMS) algorithm based on adaptive control, ANC was conducted to reduce the high frequency noise in the 2kHz~4kHz and its noise reduction performance was compared according to the following variables: Filter length, step size, sampling frequency, etc. As a result of selecting the audio DSP board as the control system and applying appropriate filter length, step size, and sampling frequency, noise reduction of up to 40 dB(A) was obtained at the position of both ears of the occupant.

The complex landscape of TMS devices: A brief overview.

The increasing application of TMS in research and therapy has spawned an ever-growing number of commercial and non-commercial TMS devices and technology development. New CE-marked devices appear at a rate of approximately one every two years, with new FDA-approved application of TMS occurring at a similar rate. With the resulting complex landscape of TMS devices and their application, accessible information about the technological characteristics of the TMS devices, such as the type of their circuitry, their pulse characteristics, or permitted protocols would be beneficial. We here present an overview and open access database summarizing key features and applications of available commercial and non-commercial TMS devices (http://www.tmsbase.info). This may guide comparison and decision making about the use of these devices. A bibliometric analysis was performed by identifying commercial and non-commercial TMS devices from which a comprehensive database was created summarizing their publicly available characteristics, both from a technical and clinical point of view. In this document, we introduce both the commercial devices and prototypes found in the literature. The technical specifications that unify these devices are briefly analysed in two separate tables: power electronics, waveform, protocols, and coil types. In the prototype TMS systems, the proposed innovations are focused on improving the treatment regarding the patient: noise cancellation, controllable parameters, and multiple stimulation. This analysis shows that the landscape of TMS is becoming increasingly fragmented, with new devices appearing ever more frequently. The review provided here can support development of benchmarking frameworks and comparison between TMS systems, inform the choice of TMS platforms for specific research and therapeutic applications, and guide future technology development for neuromodulation devices. This standardisation strategy will allow a better end-user choice, with an impact on the TMS manufacturing industry and a homogenisation of patient samples in multi-centre clinical studies. As an open access repository, we envisage the database to grow along with the dynamic development of TMS devices and applications through community-lead curation.

Impact Behaviour Modelling of Magnetorheological Elastomer Using a Non-Parametric Polynomial Model Optimized with Gravitational Search Algorithm

This paper presents an approach to model the impact behaviour of a dual-acting magnetorheological elastomer (MRE) damper using 4th-order polynomial functions optimized with a gravitational search algorithm. MRE is a type of smart material that can change its mechanical properties in response to an injected current, making it well-suited for a wide range of applications such as vibration absorption, noise cancellation, and shock mitigation. The proposed model uses a combination of polynomial functions designed to predict the nonlinearity of MRE during compression and extension stages. The model is tuned and validated using experimental data from impact tests conducted on the MRE damper under various currents. The results indicate that the developed model can accurately track the impact behaviour of MRE with minimum error. Additionally, an interpolation model is proposed to estimate the appropriate forces for median currents. The interpolation model predicts the force between the upper and lower currents, demonstrating the model's ability to predict MRE behaviour accurately. The main contribution of this study is proposing a non-parametric model of MRE that is able to identify the hysteretic behaviour of the MRE based on specific current applied. In addition, an interpolation model is introduced in this study to cover not only the input current starting from 0 A to 2 A but also the intermediate current such as 0.3 A, 0.7 A, 1.3 A and 1.7 A.

A Nonlinear Functional Link Multilayer Perceptron Using Volterra Series as an Adaptive Noise Canceler for the Extraction of Fetal Electrocardiogram.

Uninterrupted monitoring of fetal cardiac health is essential for the timely diagnosis of congenital diseases. The maternal Electrocardiogram (mECG), which has the most significant impact, always tampers with the signals collected from the pregnant woman's abdomen. So, an efficient nonlinear filtering network based on artificial neural network (ANN) is required to eliminate the maternal part from the abdominal Electrocardiogram (aECG) that is traveled from the thoracic of the mother to the abdomen following nonlinear dynamics. In this work, we have presented an adaptive noise canceler (ANC) using 3-layer perceptron architecture where the inputs are expanded by the functional link expansion using the second-order Volterra series, and the weights are updated using backpropagation. The adaptive filter approximates the nonlinear mapping between the thoracic Electrocardiogram (tECG) and the maternal component present in the aECG. Here the thoracic signal is the reference signal, and the abdominal signal is the desired signal to the adaptive filter. The proposed methodology uses the advantages of both multilayer perceptron (MLP) as well as functional link neural network (FLNN) in mapping the nonlinearity and effectively determining the fetal Electrocardiogram (fECG) from the aECG. For the detailed analysis, we have used the real Daisy database, the Non-invasive Fetal ECG database, and the fetal ECG synthetic database from Physionet. The results show that the nonlinear functional link MLP using the Volterra series gives a high-level performance compared to other classical adaptive filtering techniques, as all the evaluation metrics are above 90%.

E-DPNCT: an enhanced attack resilient differential privacy model for smart grids using split noise cancellation

High frequency reporting of energy consumption data in smart grids can be used to infer sensitive information regarding the consumer’s life style and poses serious security and privacy threats. Differential privacy (DP) based privacy models for smart grids ensure privacy when analysing energy consumption data for billing and load monitoring. However, DP models for smart grids are vulnerable to collusion attack where an adversary colludes with malicious smart meters and un-trusted aggregator in order to get private information from other smart meters. We first show the vulnerability of DP based privacy model for smart grids against collusion attacks to establish the need of a collusion resistant privacy model. Then, we propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) which not only provides resistance against collusion attacks but also protects the privacy of the smart grid data while providing accurate billing and load monitoring. We use differential privacy with a split noise cancellation protocol with multiple master smart meters (MSMs) to achieve collusion resistance. We propose an Enhanced Differential Private Noise Cancellation Model for Load Monitoring and Billing for Smart Meters (E-DPNCT) to protect the privacy of the smart grid data using a split noise cancellation protocol with multiple master smart meters (MSMs) to provide accurate billing and load monitoring and resistance against collusion attacks. We did extensive comparison of our E-DPNCT model with state of the art attack resistant privacy preserving models such as EPIC for collusion attack. We simulate our E-DPNCT model with real time data which shows significant improvement in privacy attack scenarios. Further, we analyze the impact of selecting different sensitivity parameters for calibrating DP noise over the privacy of customer electricity profile and accuracy of electricity data aggregation such as load monitoring and billing.

Practical Considerations for Implementing Adaptive Acoustic Noise Cancellation in Commercial Earbuds

Active noise cancellation has become a prominent feature in contemporary in-ear personal audio devices. However, due to constraints related to component arrangement, power consumption, and manufacturing costs, most commercial products utilize fixed-type controller systems as the basis for their active noise control algorithms. These systems offer robust performance and a straightforward structure, which is achievable with cost-effective digital signal processors. Nonetheless, a major drawback of fixed-type controllers is their inability to adapt to changes in acoustic transfer paths, such as variations in earpiece fitting conditions. Therefore, adaptive-type active noise control systems that employ adaptive digital filters are considered as the alternative. To address the increasing system complexity, design concepts and implementation strategies are discussed with respect to actual hardware limitations. To illustrate these considerations, a case study showcasing the implementation of a filtered-x least mean square-based active noise control algorithm is presented. A commercial evaluation board accommodating a low-cost, fixed-point digital signal processor is used to simplify operation and provide programming access. The earbuds are obtained from a commercial product designed for noise cancellation. This study underscores the importance of addressing hardware constraints when implementing adaptive active noise cancellation, providing valuable insights for real-world applications.

Coherent optical frequency transfer via 972-km fiber link

We demonstrate coherent optical frequency dissemination over a distance of 972 km by cascading two spans where the phase noise is passively compensated for. Instead of employing a phase discriminator and a phase locking loop in the conventional active phase control scheme, the passive phase noise cancellation is realized by feeding double-trip beat-note frequency to the driver of the acoustic optical modulator at the local site. This passive scheme exhibits fine robustness and reliability, making it suitable for long-distance and noisy fiber links. An optical regeneration station is used in the link for signal amplification and cascaded transmission. The phase noise cancellation and transfer instability of the 972-km link is investigated, and transfer instability of 1.1 × 10−19 at 104 s is achieved. This work provides a promising method for realizing optical frequency distribution over thousands of kilometers by using fiber links.

Adaptive noise cancelling for an AUV-mounted passive inverted USBL array

Noise interference of an autonomous underwater vehicle (AUV) is a critical factor affecting the performance of AUV-mounted sonar payloads, especially for passive acoustic systems. Adaptive noise cancellers (ANCs) are used to eliminate noise in many scenarios for signal enhancement. However, a conventional ANC uses a primary sensor to get the data and a secondary sensor to get correlated reference noise input, which adds complexity to the AUV-mounted sonar systems. In this paper, a modified ANC is proposed to improve the performance of an AUV-mounted passive inverted ultra-short baseline (piUSBL) positioning system receiver. Unlike the conventional ANC, the reference input of this ANC is not the noise obtained using a secondary sensor; instead, the transmitted source signal is used as the reference signal, independent of the noise characteristics. The performance of the ANC is thoroughly evaluated using the field navigation data collected from an AUV sea trial. Post-processing results indicate that the ANC can effectively improve the beamforming performance of the piUSBL array, suppressing the beamformed power of the AUV noise by up to 13.8 dB. To our knowledge, this work is the first ANC study for piUSBL, which is of great significance for the further development of piUSBL.

Denoising complex background radar signals based on wavelet decomposition thresholding

Abstract The echo signals of the radar in complex backgrounds are often very unstable and thus require effective noise cancellation. In this paper, according to the characteristics of continuous wavelet variation and discrete wavelet variation, the decomposition effect of multi-resolution analysis and orthogonal Mallat algorithm on low-frequency and high-frequency non-smooth signals is studied, and the selection method of wavelet bases is explored. Then, the noise characteristics affecting the pulsed LIDAR system are analyzed, and the LIDAR pulse signal is simulated by MATLAB, while Gaussian white noise is introduced to obtain the noise-added echo signal, and then multiple wavelet threshold denoising methods are applied to denoise the echo signal. For the input signal-to-noise ratio of −10.57 dB, the output signal-to-noise ratios of db8, db9, db10, and bior3.5 wavelet bases under forced thresholding are −1.971, −2.178, −2.173, and −1.032, respectively. For different input signal-to-noise ratios, the average root mean square error of db8, db9, db10, and bior3.5 wavelet bases under default thresholding is 1.51. The denoising methods for radar signals using the properties of wavelet decomposition have obvious superiority compared to traditional filters, and the wavelet transforms threshold denoising methods have wide adaptability.

A new particle swarm optimization aided evolutionary digital filter for noise cancellation in early fault diagnosis of rotating machinery

Noise cancellation plays a crucial role in enhancing the signal-to-noise ratio (SNR) of fault characteristics, making it an essential step in fault diagnosis, particularly during the early stages of a fault. Among various adaptive noise cancellation (ANC) methods, evolutionary digital filter (EDF) stands out as an effective approach. EDF employs an adaptive algorithm based on biological evolutionary computation to control its operations. The quality of noise cancellation performance directly relies on the global optimization ability of EDF. However, conventional EDF and its improved versions mainly rely on random search strategies involving cloning and mating, which lack an explicit search direction akin to gradient-based methods. Consequently, substantial computational resources are wasted. To address this problem, an improved algorithm called particle swarm optimization-based evolutionary digital filter (PSO-EDF) is newly proposed. The central concept of this method is to integrate the PSO algorithm into the global optimization process of EDF. By introducing PSO, explicit search directions are provided to individuals adopting the cloning strategy, thus promoting cooperation and information sharing among individuals. The PSO-aided EDF significantly enhances the global optimization capability of ANC filter parameters. Through the proposed PSO-EDF method, the ability to achieve global optimization of ANC filter parameters is substantially improved. This approach mitigates the resource wastage associated with traditional EDF techniques, offering a more efficient and effective solution for noise cancellation in fault diagnosis.

A 1.25-MHz-BW, 83-dB SNDR Pipelined Noise-Shaping SAR ADC With MASH 2-2 Structure and kT/C Noise Cancellation

A 1.25-MHz-BW, 83-dB SNDR Pipelined Noise-Shaping SAR ADC With MASH 2-2 Structure and kT/C Noise Cancellation

A novel adaptive noise cancellation method based on minimization of error entropy for electrocardiogram denoising

&lt;span&gt;In this paper, we use an adaptive method that conforms to the error entropy criterion in order to eliminate noise from cardiac signals electrocardiogram (ECG). In previous works, the mean squared error (MSE) criterion has been used to adaptive noise cancelation of ECG signals, which only has the ability to minimize the second moment of error. The MSE criterion only works optimally on systems with Gaussian noise and stationary signals, so this is not suitable for ECG signals that are non-stationary and have non-Gaussian noise. In contrast, the use of error entropy-based algorithms like minimum error entropy (MEE) is very useful in ECG noise cancelation. The results of the proposed algorithm indicate a significant advantage in terms of signal-to-noise ratio (SNR) and convergence value compared to the algorithm based on MSE criteria.&lt;/span&gt;

AI Based Tamil Palm Leaf Character Recognition

A brand-new character recognition method has been created especially for Tamil manuscripts written on palm leaves. A variety of preprocessing techniques are used to obtain reliable recognition because these manuscripts are complicated and have age-related deterioration. Using cutting-edge image processing techniques, manuscript photos are enhanced and preprocessed in the earliest stage to improve clarity. Then, in accordance with the distinctive arrangement of these historical artifacts, these photos are divided into three separate portions. After segmentation, these parts are converted to grayscale to create the ideal contrast needed for character detection. Noise canceling algorithms have been incorporated to prevent any distortions or degradations that may occur as a result of the age of the manuscripts or other environmental influences. After this mitigation, the photos are binarized to emphasize the isolation of the figures from their surroundings. This makes sure that each character stands out clearly, laying the groundwork for the crucial next step: segmenting individual characters. The foundation of the training phase is this segmentation. Convolutional neural networks (CNNs), which excel at handling image-based tasks, were incorporated into the deep learning architecture used for model building. Utilizing a sizable dataset compiled from numerous articles, the model underwent intensive training. As a result, the model was able to recognise characters from these texts with an astounding accuracy rate of 89%. With the help of Flask, the complete system has been easily integrated into a user-friendly web interface. This platform's goal is quite clear: fusing the elegance and profundity of ancient scripts with the effectiveness of contemporary technology. It is positioned as a crucial tool for both academic research and teaching purposes. By doing this, it guarantees that the priceless history and knowledge found in these manuscripts remain accessible and lays the groundwork for their preservation for future generations. Keywords— Character recognition, Tamil palm manuscripts, Preprocessing, Visual clarity, Grayscale conversion, Noise cancellation, Binarization, Segmentation, Training, Flask-based web interface, Research, Education.

Performance evaluation on a convolutional neural network with adaptive interference cancellation for detecting a buried person's voice in a disaster situation

In a disaster situation, rapid detection of victims is critical to prevent life-threatening accidents. However, it is not easy to detect the target's sound due to noise and interference which are difficult to predict. In addition, it is difficult to collect acoustic data in disaster situations. Therefore, this study proposes a target detection scheme in which untrained noise is added through a binary classification model trained only with a clean target signal and additive white Gaussian noise (AWGN). The feature uses the Mel spectrogram of the signal, and the model adopts a convolutional neural network (CNN). Two acoustic sensors are used in this study to adaptively remove environmental noise and a CNN to detect target signals. The first process trains a CNN using the target signal and AWGN data. In the next step, environmental noise is removed by compensating for the gain difference and delay characteristics between the two acoustic sensors through adaptive filtering. The input signal passes through preprocessing to eliminate noise through an adaptive filter. This signal is converted into a Mel spectrogram, which is then detected through a CNN model. The simulation results show that the model improves from 50% to 99% in detection rate at SNR 0 dB. In a catastrophe, there are many sounds that are difficult to discern. However, the model cannot learn from noise. Therefore, it shows that the target detection method using adaptive filter-based noise cancellation is effective in disaster situations.

Low-Noise Low-Power Ultrasound AFE With Continuous TGC Built in Both TIA and Beamformer.

This article presents a low-noise high-power-efficiency analog front-end (AFE) for capacitive-micromachined-ultrasonic transducers (CMUT). Implemented in 28-nm CMOS technology, the proposed AFE features three-stage continuous time-gain compensation (TGC) embedded in both trans-impedance amplifiers (TIAs) and an analog beamformer to provide a large compensation range with no extra power-consumption cost. The use of noise cancellation and capacitive feedback optimizes the noise performance of TIAs. The first stage of the TGC is built in the TIA by adjusting the positive and negative resistance loads, which are composed of voltage-controlled transistor arrays. An all-pass passive network is used as the delay unit of the analog beamformer, meanwhile achieving the second TGC stage. Phase shift for all frequency components in the ultrasound pass-band is manifested as a delay to the echoes. The third stage of the TGC is merged with a summing unit, which is a closed-loop amplifier with variable resistance feedback. The design takes into account the ability to handle large signals and power consumption, with TIA and beamforming operating at voltages of 2.5 V and 0.9 V, respectively. Experimental results show that the proposed AFE achieves a 2.11 pA/√Hz input-referred noise (IRN) at the 5 MHz center frequency of the echoes while consuming only 1.02 mW/channel. A total exponential TGC range of 60 dB with continuous ranges of 12 dB, 24 dB, and 24 dB assigned to three stages has been verified for this work.

Clipping Noise Cancellation Receiver for the Downlink of Massive MIMO OFDM System

Clipping Noise Cancellation Receiver for the Downlink of Massive MIMO OFDM System