Enhanced Noise Immunity Research Articles

Enhancing Noise Immunity in OFDM Communication Channels with Two-Stage Strobing and I /Q Demodulation

Enhancing Noise Immunity in OFDM Communication Channels with Two-Stage Strobing and I /Q Demodulation

Multiscale fluctuation-based symbolic dynamic entropy: a novel entropy method for fault diagnosis of rotating machinery

Health monitoring has garnered significant and increasing attention from the research community and industrial practices thanks to its critical role in ensuring the safe operation of machinery and maintenance schedule. With regard to this, this paper introduces a novel diagnostic approach called fluctuation-based symbolic dynamic entropy (FSDE), which can enhance noise immunity and computational efficiency by utilizing fluctuation-based entropy algorithm and symbolic dynamic filtering. Moreover, the noise immunity and computational efficiency of FSDE, permutation entropy, fuzzy entropy, and sample entropy are compared by simulation signals. The simulation results show that FSDE has a more robust anti-noise performance and higher computational efficiency than the other three entropy methods. In order to extract fault features more thoroughly, multiscale analysis is applied to the entropy method, and multiscale FSDE (MFSDE) is proposed. MFSDE divides the measurement data into several scale series by coarse-grained technology, and then, FSDE is used to process each scale series separately. A series of experiments verify the effectiveness of MFSDE in fault feature extraction of fault diagnosis. Furthermore, the experimental results substantiate that MFSDE outperforms the other three currently used entropy-based approaches in terms of accuracy in classifying different health conditions of transmission systems.

ENHANCING NOISE IMMUNITY IN OFDM COMMUNICATION CHANNELS WITH TWO-STAGE STROBING AND I/Q DEMODULATION

ENHANCING NOISE IMMUNITY IN OFDM COMMUNICATION CHANNELS WITH TWO-STAGE STROBING AND I/Q DEMODULATION

A robust high-resolution potential-field edge detection filter with its application in the Indo-China Peninsula

Potential-field edge detection techniques are effective tools for identification of large-scale tectonic boundaries and geologic target bodies. Although a series of edge detectors have been developed, most of them suffer from a trade-off between edge resolution and noise immunity. To this end, we integrate multiple edge detectors such as normalized Harris filter (NHF) and ILTHG filter to propose a novel edge detector called ILGNH. The ILGNH edge detector takes advantage of both the enhanced noise immunity of NHF and the high resolution of ILTHG. Model experiments without/with noises both show that it is an alternative robust high-resolution edge detector. Afterwards, we apply this edge detection method to the Indo-China Peninsula and its adjacent areas to identify multi-phase tectonic boundaries. Combining the edge detection, residual crustal gravity anomalies (RCGA), and active faults, we identify four major NS transverse suture zones associated with microplates. These suture/boundary zones, together with deep faults, enclose 14 microplates with different interior feature of RCGA, while most of the boundaries are located in the gradient zones or the dislocation/distortion zones of the RCGA. Notably, inside the West Burma microplate, the ILGNH edge detection shows a distinct near NS-oriented arc-shaped deep boundary zone, which indicates that the underlying slab beneath the Indo-Burma Ranges was blocked during its eastward advance and shifted downward to form a high-angle deep subduction. Further, multi-scale edge detection at different depths confirms that the microplate boundary under the western Indo-China Peninsula vertically extends deeper than the eastern part. In addition, the seismicity below 50 km is concentrated to the west of the identified arc-shaped deep boundary zone and near the Naga-Kalaymyo-Arakan-Andaman Suture Zone.

Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment.

The low light conditions, abundant dust, and rocky terrain on the lunar surface pose challenges for scientific research. To effectively perceive the surrounding environment, lunar rovers are equipped with binocular cameras. In this paper, with the aim of accurately detect obstacles on the lunar surface under complex conditions, an Improved Semi-Global Matching (I-SGM) algorithm for the binocular cameras is proposed. The proposed method first carries out a cost calculation based on the improved Census transform and an adaptive window based on a connected component. Then, cost aggregation is performed using cross-based cost aggregation in the AD-Census algorithm and the initial disparity of the image is calculated via the Winner-Takes-All (WTA) strategy. Finally, disparity optimization is performed using left-right consistency detection and disparity padding. Utilizing standard test image pairs provided by the Middleburry website, the results of the test reveal that the algorithm can effectively improve the matching accuracy of the SGM algorithm, while reducing the running time of the program and enhancing noise immunity. Furthermore, when applying the I-SGM algorithm to the simulated lunar environment, the results show that the I-SGM algorithm is applicable in dim conditions on the lunar surface and can better help a lunar rover to detect obstacles during its travel.

An INN-Based Solution for the Underdetermined Image Reconstruction Problem in EIT

Electrical impedance tomography (EIT) has been widely concerned in online nondestructive testing of carbon fiber-reinforced polymers (CFRPs) due to its significant advantages of high real-time performance, zero radiation, simple structure, and low cost. However, its inverse problem is highly underdetermined and nonlinear, thus limiting its imaging quality and application scope. Based on the architecture of an invertible neural network (INN), an INN image reconstruction algorithm with latent output variables is proposed in this article. With latent output variables, the information lost during training is reused, and a unique input can be determined by sampling latent output variables and predicted outputs. In addition, the forward and reverse processes are combined together to weaken the dependency of effective output and latent output variables, reduce the errors between input and output domains, and solve the underdetermination and nonlinearity problems of EIT. The simulation and experimental results showed that compared with the traditional algorithms and the convolution neural network (CNN) network algorithm, the INN can accurately reconstruct the damage location, yield a clearer damaged edge, and reduce artifacts. Noise-added simulations and prototype experiments showed that the INN achieves both good imaging quality and enhanced noise immunity, to improve the performance of the EIT system in CFRP laminate inspection.

Development of High Accuracy Classifier for the Speaker Recognition System.

Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), K-nearest neighbour (KNN), and support vector machine (SVM).

Investigation of ultra-lift Luo-converter with peak, average and hysteresis current-mode control

The Renewable energy system, electric vehicle, and telecommunication applications require relatively stable power converters with a high gain and enhanced noise immunity. A study of three different types of current-mode controllers for high-gain ultra-lift Luo-converter (ULC) is discussed in this paper. The stability of a constant frequency peak current-mode controller (PCM), an average current-mode controller (ACM) and a variable frequency hysteresis current-mode controller (HCM) are analyzed based on small-signal characteristics. Using mathematical modeling of the controllers, the closed-loop transfer functions such as control voltage to output voltage, current loop gain, inductor current to control voltage, and audio susceptibility are obtained. These transfer functions along with MATLAB simulation results for PCM, ACM and HCM controllers are compared. Then, the component losses of the ULC converter using PCM, ACM and HCM controllers are calculated and an efficiency comparison of different control techniques is done. Investigations of the voltage and current stresses of the switch and diodes are also carried out. Finally, a prototype is fabricated to validate the performance of the converter.

Structural Damage Identification Based on Integrated Utilization of Inverse Finite Element Method and Pseudo-Excitation Approach.

The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give rise to a novel structural health monitoring (SHM) framework showing various advantages, particularly in aspects of enhanced adaptability and robustness. As the key component of the method, the inverse finite element method (iFEM) enables precise reconstruction of vibration displacements based on measured dynamic strains, which, as compared to displacement measurement, is much more adaptable to existing on-board SHM systems in engineering practice. The PE, on the other hand, is applied subsequently, relying on the reconstructed displacements for the identification of structural damage. Delamination zones in a carbon fibre reinforced plastic (CFRP) laminate are identified using the developed method. As demonstrated by the damage detection results, the iFEM-PE method possesses apparently improved accuracy and significantly enhanced noise immunity compared to the original PE approach depending on displacement measurement. Extensive parametric study is conducted to discuss the influence of a variety of factors on the effectiveness and accuracy of damage identification, including the influence of damage size and position, measurement density, sensor layout, vibration frequency and noise level. It is found that different factors are highly correlated and thus should be considered comprehensively to achieve optimal detection results. The application of the iFEM-PE method is extended to better adapt to the structural operational state, where multiple groups of vibration responses within a wide frequency band are used. Hybrid data fusion is applied to process the damage index (DI) constructed based on the multiple responses, leading to detection results capable of indicating delamination positions precisely.

Studying of the scrambling coding sequence performance based on the ninth order primitive polynomial in telecommunication networks of information systems

Scrambling coding sequences find wide application in telecommunication networks to improve noise immunity and information transmission concealment. There are many different scrambling coding sequences with different autocorrelation properties from which one can be chosen. In the previous paper the authors researched a possibility of using scrambling coding sequences built on the base of the primitive eighth degree polynomial in telecommunication networks of information systems to enhance noise immunity and concealment of information transmission. This sequence, consisting of two hundred and fifty five chips, showed some good performance in terms of information transmission quality indicators. Nevertheless, some of its limitations were also revealed, which are mostly linked to the number of used chips. This paper aims to overcome the problems by proposing to use scrambling coding sequences based on the ninth degree primitive polynomial. The required polynomial was selected and the needed scrambling sequence generated. This scrambling coding sequence includes five hundred and eleven chips. Computer simulation helped to establish that scrambling coding sequence synthesized using this polynomial permits to substantially improve information transmission quality indicators as compared to the case of the scrambling coding sequence based on the eighth degree polynomial. For example, the interfering signal and internal noise to desired signal ratio, for which reception of transmitted bit sequence is still possible, increased by three decibels. It is clearly linked to the doubling of the number of the used chips. To preserve the transmission rate, the occupied frequency spectrum needs to be doubled. An inference is made as to a possibility of using the synthesized coding sequence in practical realizations of telecommunication channels.

Improved powered bus communication technique based on PD‐TDMT for distributed power system (DPS)

For many distributed power systems (DPSs) such as light-emitting diode lighting and battery management, communication is essential. This study presents an improved method of integrating power transfer and communication through a common bus by applying the concept of power/data time division multiplexing transmission (PD-TDMT). The proposed method employs power electronic topologies as the basic circuits to create the power transfer system and utilises the power transmission intervals for communication. Compared with conventional communication techniques in DPS, the proposed method has the advantages of simple implementation, enhanced noise immunity and higher output power capacity. The general structure of PD-TDMT systems is presented, which includes power sourcing equipment (PSE) and powered devices (PDs). A series of PSE and PD circuits are derived based on dc–dc topologies. The timing sequence of power transfer and communication is given. To suppress the common-mode interferences under the high-power condition, a balanced power transmission system is also proposed. Besides, the termination circuits are discussed in detail under the long-distance transmission condition. Finally, the proposed method and theoretical analysis are verified by experiment.

A Noise-Immunity-Enhanced Analog Front-End for $36\times64$ Touch-Screen Controllers With 20-$\text{V}_{\text{PP}}$ Noise Tolerance at 100 kHz

This paper describes the design of an analog front-end (AFE) for capacitive touch-screen controllers that provide highly enhanced noise immunity. The proposed AFE mitigates severe noise interference using a multi-channel-driving (MCD) TX scheme and a noise-filtering RX structure. The MCD scheme with a frequency hopping technique modulates mutual capacitances in a touch-screen panel (TSP) using low-offset orthogonal sequences, spreading the noise interference. In the RX sensing block, a pre-filtering differential sensing method is employed to improve touch sensitivity and to reject common-mode noise. An incorporated RX modulation scheme performs the down-conversion of the capacitance signal and the multiplexing of subsequent blocks, which enables an efficient high-order noise filtering. An incremental $\Delta \Sigma $ analog-to-digital converter is adopted to provide a high resolution and a scalable frame rate. The proposed AFE was fabricated in a 0.18- $\mu \text{m}$ CMOS process. The prototype achieves 20-VPP noise immunity against the external noise interference and provides 54-dB SNR and 120-Hz frame rate with a $36\times 64$ TSP. The prototype supports a supply voltage ranging from 2.7 to 3.3 V and consumes 94.5 mW from a 3.3-V supply.

Adaptive Filtering to Enhance Noise Immunity of Impedance and Admittance Spectroscopy: Comparison with Fourier Transformation

The time-domain technique for impedance spectroscopy consists of computing the excitation voltage and current response Fourier images by fast or discrete Fourier transformation and calculating their relation. Here we propose an alternative method for excitation voltage and current response processing for deriving a system impedance spectrum based on a fast and flexible adaptive filtering method. We show the equivalence between the problem of adaptive filter learning and deriving the system impedance spectrum. To be specific, we express the impedance via the adaptive filter weight coefficients. The noise-canceling property of adaptive filtering is also justified. Using the RLC circuit as a model system, we experimentally show that adaptive filtering yields correct admittance spectra and elements ratings in the high-noise conditions when the Fourier-transform technique fails. Providing the additional sensitivity of impedance spectroscopy, adaptive filtering can be applied to otherwise impossible-to-interpret time-domain impedance data. The advantages of adaptive filtering are justified with practical living-cell impedance measurements.

On a hybrid use of structural vibration signatures for damage identification: a virtual vibration deflection (VVD) method

A damage identification method named virtual vibration deflection (VVD) was developed, the principle of which was formulated based on the “weak” modality of the pseudo-excitation (PE) approach previously established. In essence, VVD is based on locating structural damage within a series of “sub-regions” divided from the entire structure under inspection, and each sub-region was considered as a “virtual” structure undergoing independent vibration. The corresponding vibration deflection of the “virtual” structure was then used to derive the damage index of VVD. Besides various advantages inheriting from the PE approach, for example, capability of detecting damage without baseline signals and pre-developed benchmark structures, VVD exhibits improved detection accuracy and particularly enhanced noise immunity compared with the PE approach, attributed to a hybrid use of multi-types of vibration signatures (MTVS). As a proof-of-concept investigation, a beam model was used in a numerical study to examine the philosophy of VVD. And the influences from different factors (i.e., level of measurement noise and measurement density) on the detection accuracy of VVD were discussed based on the numerical model. An experiment was carried out subsequently to identify the locations of multiple defects contained in an aluminum beam-like structure. Identification results constructed by the PE approach, VVD using single-type of vibration signatures, and VVD using MTVS, were presented, respectively, for the purpose of comparison.

Discovering data flow discords for enhancing noise immunity of acoustic-emission testing

A new approach has been suggested to revealing acoustic-emission data-flow discords that are related to the formation and growth of flaws. The discords were discovered using a statistical criterion and the Caterpillar SSA principal components method. The approach has proved its consistency when tested on realistic acoustic-emission data-flow models.

SAR Image Change Detection Based on Iterative Label-Information Composite Kernel Supervised by Anisotropic Texture

Kernel methods with specifically designed kernel function are suitable for dealing with practical nonlinear problems. However, kernel methods have found limited applications to synthetic aperture radar (SAR) image change detection in that their performances are affected by the inherent multiplicative speckle noise of SAR images. It is known that the spatial-contextual information is helpful in suppressing the degrading effects of the noise. Therefore, a label-information composite kernel ( LIC kernel ) constructed on the basis of the spatial-contextual information is proposed in this paper for SAR image change detection. A typical spatial information, the output-space label-neighborhood information that is extracted using all labels in the neighborhood of each pixel, may enhance noise immunity, but with inaccurate edge locations simultaneously. Consequently, the anisotropic Gaussian kernel model is utilized for analyzing anisotropic textures of the bitemporal images, and then, a comparison scheme acting on the input-space textures of the bi-temporal images is proposed to supervise the extraction of the output-space label-neighborhood information in the construction of the LIC kernel . The constructed LIC kernel is of good preservation of edge locations of changed areas as well as strong noise immunity. The LIC kernel is updated iteratively with the newest change map outputted from the support vector machine, until the change map converges. Experiments on real SAR images demonstrate the effectiveness of the LIC kernel method and illustrate that it has both strong noise immunity and good preservation of edge locations of changed areas for SAR image change detection.

37.2: A 10.4‐in. On‐Cell Touch‐Panel LCD with Correlated Noise Subtraction Method

AbstractWe demonstrated a 10.4‐in on‐cell touch‐panel LCD using an ITO layer formed on a color filter substrate as a capacitive sensor. To enhance noise immunity for larger capacitive sensors, we developed a correlated noise subtraction method, which improves the signal‐to‐noise ratio even under an environment where noise frequency coincides with signal frequency.

Ultra-low voltage implicit multiplexed differential flip-flop with enhanced noise immunity

An ultra-low voltage 22T implicit multiplexed differential (IMD) flip-flop (FF) is proposed. An implicit multiplexer is designed to simplify the differential FF complexity, while its control data path is able to enhance the FF noise immunity as well. So, the fully static IMD-FF with modified differential topology provides a sufficient noise margin under voltage scaling. On the other hand, the IMD-FF operation avoids considerable DC current dissipation to save active power and suppresses the idle leakage by stacked transistors. The post-layout simulation in 90 nm CMOS process with 400 mV supply voltage shows that the IMD-FF achieves 56% active power and 42.2% leakage power reduction. The tolerable noise energy is enhanced by 18.9% on average. Finally, this work provides 93% function yield rate under the effect of process-voltage-temperature variations and 40 pJ input noise energy.

Common-path self-referencing interferometer for carrier-envelope offset frequency stabilization with enhanced noise immunity

A nonlinear interferometer design for stabilizing the carrier-envelope offset frequency of a Ti:sapphire frequency comb with superior immunity to air currents and acoustic noise is presented. The scheme uses a pair of Wollaston prisms for group-delay dispersion compensation, providing an all-common-path optical configuration. Out-of-loop phase noise measurements for an unshielded interferometer setup showed up to 15 dB improvement compared to a Michelson interferometer based system. Further simplification of the self-referencing scheme providing a compact single-Wollaston-prism design has been demonstrated.

Generalized asymmetrical bidirectional associative memory for multiple association

A classical bidirectional associative memory (BAM) suffers from low storage capacity and abundance of spurious memories though it has the properties of good generalization and noise immunity. In this paper, Hamming distance in recall procedure of usual asymmetrical BAM is replaced with modified Hamming distance by introducing weighting matrix into connection matrix. This generalization is validated to increase storage capacity, to lessen spurious memories, to enhance noise immunity, and to enable multiple association using simulation work.