Middle Frequency Components Research Articles

Research on interference compensation methods for color image sensors based on iterative learning

PurposeFixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.Design/methodology/approachThrough an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.FindingsThe comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).Originality/valueIn this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

Rotor equivalent wind speed prediction based on mechanism analysis and residual correction using Lidar measurements

Efficient power generation and reduced operational cost are crucial to make wind energy a successful market player. To achieve satisfactory performance in wind turbine technology, it is essential to accurately predict the rotor equivalent wind speed (REWS). Since traditional wind transducers are difficult to measure multiple-point wind speed, related researches on predicting REWS are lacking. In this paper, a hybrid REWS prediction framework is proposed, which combines mechanism analysis and data-driven technique, aided by Lidar measurements. The framework employs the mechanism modeling and time delay analysis to achieve preliminary REWS prediction, but the accuracy is restricted by the quantity of Lidar measurement points. To enhance the prediction accuracy, a residual prediction model based on the data-driven technique is created to correct the mechanism prediction. Specifically, a novel combined modeling approach based on empirical mode decomposition and sample entropy algorithms is presented. Through reconstructing the input data of the combined model into high, middle, and low frequency components and exploring the prediction laws of different frequency components obtained by typical machine learning models, the combined residual prediction model is established. Additionally, an enhanced whale optimization algorithm named WOAmM is presented to optimize the parameters of the combined model. The proposed method is validated through a four-beam Lidar measurement scenario simulated by BLADED software. The experimental results indicate that the combined model outperforms each single model and the mechanism prediction, and improves the REWS prediction accuracy by up to75.04%, 60.92%, 51.85%, and 57.52% in the four datasets, respectively. The proposed method could facilitate the utilization of Lidar in improving the performance of wind turbines.

Generating Black-Box Adversarial Examples in Sparse Domain

Applications of machine learning (ML) models and convolutional neural networks (CNNs) have been rapidly increased. Although state-of-the-art CNNs provide high accuracy in many applications, recent investigations show that such networks are highly vulnerable to adversarial attacks. The black-box adversarial attack is one type of attack that the attacker does not have any knowledge about the model or the training dataset, but it has some input data set and their labels. In this paper, we propose a novel approach to generate a black-box attack in sparse domain whereas the most important information of an image can be observed. Our investigation shows that large sparse (LaS) components play a critical role in the performance of image classifiers. Under this presumption, to generate adversarial example, we transfer an image into a sparse domain and put a threshold to choose only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> LaS components. In contrast to the very recent works that randomly perturb <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> low frequency (LoF) components, we perturb <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> LaS components either randomly (query-based) or in the direction of the most correlated sparse signal from a different class. We show that LaS components contain some middle or higher frequency components information which leads fooling image classifiers with a fewer number of queries. We demonstrate the effectiveness of this approach by fooling six state-of-the-art image classifiers, the TensorFlow Lite (TFLite) model of Google Cloud Vision platform, and YOLOv5 model as an object detection algorithm. Mean squared error (MSE) and peak signal to noise ratio (PSNR) are used as quality metrics. We also present a theoretical proof to connect these metrics to the level of perturbation in the sparse domain.

Optimal blind colour image watermarking based on adaptive chaotic grasshopper optimization algorithm

ABSTRACT This paper proposes an effective optimal blind colour image watermarking based on Triangular Vertex Transform (TVT), Lifting Wavelet Transform (LWT) and Schur decomposition. Firstly, the RGB channels of the colour host image are processed with TVT to attain , and coefficients. The image quality is preserved by applying LWT on the coefficients. As the middle frequency (HL and LH) components of the LWT wavelet domain achieve much robustness, these two components are split into nonoverlapping blocks further it is processed with Schur decomposition for improving perceptual transparency. The optimal solution attained by an Adaptive Chaotic Grasshopper Optimization Algorithm (ACGOA) is used as the embedding factor to ensure the embedding strength of the watermark. The confidentiality of the watermark is improved by 2D Logistic-Modulated-Sine-Coupling-Logistic Chaotic Map (LSMCL). Subsequently, each bit of encrypted watermark image is inserted in the highest eigenvalue of upper triangular matrix of Schur decomposition using the embedding factor β obtained by ACGOA to accomplish the watermarked image. The watermark recovery is performed using the embedding factor β and the decryption process is done using LSMCL. The efficiency of the ACGOA based scheme is estimated and the highest Peak signal-to-noise ratio (PSNR) value achieved is 54.2987 dB. The Normalized Cross-Correlation (NCC) results show better reliability for different attacks including geometric attacks and the NCC value obtained is close to 1.

Changes in cardiovascular parameters in rats exposed to chronic widespread mechanical allodynia induced by hind limb cast immobilization.

To elucidate the relationship between chronic pain conditions with cast immobilization and autonomic function, we investigated the functional changes of the autonomic nervous system in conscious rats with chronic post-cast pain (CPCP) induced by a two-week cast immobilization of one hind limb. We telemetrically examined the time courses of systolic arterial blood pressure (SBP), heart rate (HR), and the middle-frequency (MF) component obtained from the power spectral analysis of SBP variability as a vasomotor sympathetic index. We also investigated the baroreflex sensitivity to phentolamine, an α-adrenoceptor antagonist, and the SBP and HR responses to a low ambient temperature (LT; 9.0 ± 0.2°C) exposure, a sympathetic stimulant. Rats exposed to cast immobilization exhibited mechanical allodynia lasting for at least 10 weeks after cast removal in the calf area (skin and muscle) of the bilateral hind limbs. Under resting conditions, the SBP, HR, and MF components were significantly increased during cast immobilization (all p < 0.001). Following cast removal, these parameters gradually decreased and within 1 week reached lower than baseline levels, lasting for over 10 weeks. Phentolamine administration (10 mg/kg, intraperitoneally) significantly decreased the SBP before and during cast immobilization (before, p < 0.001; during, p = 0.001) but did not lower the SBP after cast removal. The baroreflex gain after phentolamine administration, calculated as the HR increase divided by the SBP reduction, was significantly increased after cast removal (p = 0.002). The SBP increase on LT exposure was significantly greater after cast removal than that before cast immobilization, suggesting hypersensitivity to sympathetic neurotransmitters. These results revealed that, in the CPCP model, sympathetic activation was augmented during cast immobilization, which then decreased after cast removal and remained below normal levels with persisting pain behaviors. Additionally, the responsiveness of the autonomic nervous system was impaired in the CPCP model.

Fused spectral-decomposition seismic attributes and forward seismic modelling to predict sand bodies in meandering fluvial reservoirs

Abstract Understanding the hierarchical architectural elements of fluvial sand bodies is important for planning their development strategy and to enhance oil recovery. Red-Green-Blue (RGB) blending of multiple seismic attributes and forwarding seismic modelling are commonly used in the analysis of compound sand bodies. However, RGB blending of multiple seismic attributes can only qualitatively describe the boundaries and thickness of sand bodies. The forward seismic modelling techniques previously documented in the literature are not effective when depicting the geometry of, and stacking relationships between, sand bodies (i.e., reservoir compartmentalisation). Hence, we propose in this work a new workflow that combines fused spectral-decomposition seismic attributes (SDSAs) and forwarding seismic modelling to quantitatively predict sand thickness, and to characterise stacking relationships between sand bodies. First, we employ a Support Vector Machine (SVM) algorithm to fuse high, middle, and low frequency components (attributes) of seismic data so as to quantitatively predict the thickness of sand bodies. Second, we define the seismic waveform response patterns corresponding to the typical conceptual stacked sand bodies. With the constraints of waveform patterns and predicted sand thickness (fused SDSAs), the geometry and stacking relationships of the sand bodies are characterised by forward seismic modelling. To illustrate the effectiveness of our proposed workflow, we apply it to the Neogene Minghuazhen Formation (Nm) of the QHD 32–6 oil field, Bohai Bay Basin, China. We define five architectural elements of a meandering fluvial reservoir by analysing the hierarchy of sand bodies using our workflow. The predicted sand bodies in this workflow were further proven by horizontal drilling and production data.

Optimal Sizing and Control Strategies for Hybrid Storage System as Limited by Grid Frequency Deviations

Frequency deviations of power systems caused by grid-connected wind power fluctuations is one of the key factors which restrains the increase of wind penetration level. This paper examines a combined wind and hybrid energy storage system (HESS, supercapacitor, and battery) to smooth wind power fluctuations. A fuzzy-based wind-HESS system (FWHS) controller is proposed to suppress the wind power fluctuations. The proposed controller takes full advantage of the complimentary characteristics of the supercapacitor and battery with the supercapacitor and battery in charge of high and middle frequency components of wind fluctuations, respectively. A differential evolution (DE)-based optimal sizing method for HESS systems is introduced to evaluate the minimum capacity of HESS as being limited by grid frequency deviation. The efficiency of the proposed scheme in the paper for wind-HESS system is evaluated by a real Chinese power system.

Modulation of terahertz-spectrum generation from an air plasma by tunable three-color laser pulses

We theoretically demonstrate that the terahertz-spectrum generation in three-color laser-gas interactions can be effectively manipulated by tuning the laser wavelengths. We show that by positive and negative detuning of one of the color laser wavelengths in the three-color laser pulse, the terahertz spectral range can be greatly expanded, and in particular, the high spectral frequency components emerge. However, the total terahertz energy is maintained at a relatively high level and can even significantly increase. Moreover, we also show that the middle and high spectral frequency components of the terahertz spectrum can be effectively tuned by modulating the relative wavelength of the three-color laser pulses. Finally, our analysis shows that the ionization events play an important role in the intense and shaped terahertz generation.

The effect of intermediate frequency on sheath dynamics in collisionless current driven triple frequency capacitive plasmas

The Capacitively Coupled Plasma discharge featuring operation in current driven triple frequency configuration has analytically been investigated, and the outcome is verified by utilising the 1D3V particle-in-cell (PIC) simulation code. In this analysis, the role of middle frequency component of the applied signal has precisely been explored. The discharge parameters are seen to be sensitive to the ratio of the chosen middle frequency to lower and higher frequencies for fixed amplitudes of the three frequency components. On the basis of analysis and PIC simulation results, the middle frequency component is demonstrated to act as additional control over sheath potential, electron sheath heating, and ion energy distribution function (iedf) of the plasma discharge. For the electron sheath heating, effect of the middle frequency is seen to be pronounced as it approaches to the lower frequency component. On the other hand, for the iedf, the control is more sensitive as the middle frequency approaches towards the higher frequency. The PIC estimate for the electron sheath heating is found to be in reasonably good agreement with the analytical prediction based on the Kaganovich formulation.

Association analysis of accommodative microfluctuations and wavefront aberration in emmetropia and myopia in teenagers

Background When focusing on a target, the accommodative system exhibits small variations in power within a range, termed microfluctuations. Exploring the correlation between the characters of microfluctuations and the aberrations of the eyes through a critical period-teenager can deepen the understanding of the onset and development of myopia. Objective This study was to investigate the correlation between accommodative microfluctuations and wavefront aberrations of human eyes, and to explore the role of accommodative microfluctuations in the development of myopia. Methods A cross-sectional study was designed and performed. Fifty-four teenagers aged from 12 to 15 years were enrolled in this study in Eye Hospital, Wenzhou Medical University from 2010 to 2011, including 19 emmetropic subjects, 18 incipient myopic subjects and 17 progressive myopic subjects, who were chosen based on the refractive changes for the last two years. The accommodative response and microfluctuations were measured by WAM-5500 infrared autorefractor with RSVP at 25, 33 and 50 cm respectively, each RSVP distance was tested for 5 minutes. The accommodative values were divided into low frequency component (LFC) (0-0. 6 Hz), middle frequency component(MFC) (0. 6-0. 9 Hz) and high frequency component(HFC) (1. 0-1. 4 Hz) after Fourier spectrum analysis. The wavefront aberrations of the whole eye and corneal were recorded by i-Trace aberrometer and Humphrey corneal topography. The differences of accommodative response, microfluctuations and aberrations were evaluated and compared among the emmetropic group, incipient myopic group and progressive myopic group. The correlations of accommodation and aberrations were analyzed. This study followed Declaration of Helsinki, and informed consent was obtained from each subject. Results Under the 25 cm and 33 cm reading distance, the accommodative response values were (2. 78±0. 35)D and (2. 19±0. 27)D in the emmetropic group, which were significantly higher than (2. 44±0. 33)D and (1. 89±0. 35)D in the progressive myopic group (P=0. 002, 0. 003). LFC, MFC and HFC were remarkably increased as the reading distance decreased (Fdistance=8. 480, 14. 270, 21. 400; all at P 0. 05). Under the 25 cm and 33 cm reading distances, significant positive correlations were found between LFC and corneal coma, intraocular coma, whole ocular aberrations or intraocular higher order aberrations (all at P 0. 05). Conclusions In teenagers, the accommodative response of emmetropia is much stronger than that of progressive myopia, and the accommodative microfluctuation in emmetropia and incipient myopia is much less than that in progressive myopia. These shifts are possibly associated with the variability, and microfluctuations of accommodation is greater in progressive myopes than emmetropes. There are certain correlations between wavefront aberrations and accommodative microfluctuations. The refractive group differences in the accommodative microfluctuations may be related to differences in the wavefront between myopes and emmetropes. Under near reading distance, LFC is correlated with higher order aberrations. Key words: Accommodation, ocular/physiology; Myopia/physiopathology; Emmetropia; Refractive errors; Teenagers

Seismic energy dispersion compensation by multi-scale morphology

Seismic energy decays while propagating subsurface, which may reduce the resolution of seismic data. This paper studies the method of seismic energy dispersion compensation which provides the basic principles for multi-scale morphology and the spectrum simulation method. These methods are applied in seismic energy compensation. First of all, the seismic data is decomposed into multiple scales and the effective frequency bandwidth is selectively broadened for some scales by using a spectrum simulation method. In this process, according to the amplitude spectrum of each scale, the best simulation range is selected to simulate the middle and low frequency components to ensure the authenticity of the simulation curve which is calculated by the median method, and the high frequency component is broadened. Finally, these scales are reconstructed with reasonable coefficients, and the compensated seismic data can be obtained. Examples are shown to illustrate the feasibility of the energy compensation method.

Single Image Super-resolution Algorithm Based on Two-stage and Multi-frequency-band Dictionaries

The conventional super-resolution algorithms on sparse representation reconstruct the high resolution image using one-stage high/low resolution dictionary pairs with inadequate detail information. In order to recover detail information as much as possible, two-stage dictionaries are explored in this paper. Then we train jointly multiple-frequency-band dictionaries consisting of low frequency (LF) dictionaries, middle frequency (MF) dictionaries and high frequency (HF) dictionaries, and simultaneously exploit the prediction relation of LF component, MF component and HF component to recover middle and high frequency information. Considering that there are many repetitive structures in the natural image, nonlocal self-similarity information is combined properly with iterative back-projection procedure to post-process the image. Experimental results demonstrate that the proposed algorithm has remarkable improvement in peak signal-to-noise ratio, structural similarity and visual quality compared with the other learning-based algorithms.

Effect of Weight Factor on The Performance of Hybrid Column Wavelet Transform used for Watermarking under Various Attacks.

Digital image watermarking is aimed at copyright protection of digital images. Strength of embedded watermark plays an important role in robustness and invisibility of watermarking technique. In this paper, effect of two parameters namely, watermark strength and middle frequency coefficients of host image used for embedding watermark is studied. In the given watermarking technique, watermark is normalized before embedding. This reduces the strength of watermark so that there will be minimum possible distortion in watermarked image. However, it has been observed in our work proposed in previous paper that, such embedment responds poorly to various image processing attacks like compression, cropping, resizing, noise addition etc. Hence in this paper, an attempt has been made to increase the strength of embedded watermark by using suitable weight factor so that robustness of watermarking technique proposed in our previous paper is further increased with small acceptable decrease in imperceptibility. Also middle frequency elements of host image selected for embedding watermark are varied by selecting different rows of host such that slowly we move from middle frequency components towards high frequency components. For certain attacks like image cropping, selection of middle frequency coefficients affects the robustness achieved. Increase in weight factor significantly improves the performance of given watermarking technique by more than 50% as proposed in our previous paper where weight factor value was 25.

Fractional M-band dual tree complex wavelet transform for digital watermarking

In this paper, a novel digital watermarking scheme using fractional M-band dual tree complex wavelet transform (Fr-M-band-DT-CWT) is proposed. High frequency channels have wide bandwidth and low frequency channels have narrow bandwidth. These characteristics are suitable for analysing low frequency signal, but not for relatively high frequency signal. The images often contain many edges, which may cause rich middle and high frequency components in the 2-band wavelet domain. Therefore, the ordinary 2-band dual tree complex wavelet transform (DT-CWT) is not well-suited for analysing the image. So, the M-band DT-CWT with the FrFT called Fr-M-band-DT-CWT is proposed in this paper to address this problem. Further, we integrate the Fr-M-Band-DT-CWT with singular value decomposition (SVD) in order to enhance the performance. Experimental results of the proposed watermarking scheme are compared with the previously available watermarking algorithms, fractional Fourier transform (FrFT), fractional wavelet transform (FrWT). Further, the proposed watermark extraction scheme is also tested on different attacks. The results of the present investigations show that the proposed watermarking scheme is superior as compared to other existing watermarking schemes.

Fault Detection Enhancement in Rolling Element Bearings via Peak-Based Multiscale Decomposition and Envelope Demodulation

Vibration signals of rolling element bearings faults are usually immersed in background noise, which makes it difficult to detect the faults. Wavelet-based methods being used commonly can reduce some types of noise, but there is still plenty of room for improvement due to the insufficient sparseness of vibration signals in wavelet domain. In this work, in order to eliminate noise and enhance the weak fault detection, a new kind of peak-based approach combined with multiscale decomposition and envelope demodulation is developed. First, to preserve effective middle-low frequency signals while making high frequency noise more significant, a peak-based piecewise recombination is utilized to convert middle frequency components into low frequency ones. The newly generated signal becomes so smoother that it will have a sparser representation in wavelet domain. Then a noise threshold is applied after wavelet multiscale decomposition, followed by inverse wavelet transform and backward peak-based piecewise transform. Finally, the amplitude of fault characteristic frequency is enhanced by means of envelope demodulation. The effectiveness of the proposed method is validated by rolling bearings faults experiments. Compared with traditional wavelet-based analysis, experimental results show that fault features can be enhanced significantly and detected easily by the proposed method.

Factors influencing the porosity of gas hydrate bearing sediments

Porosity is a key parameter in calculating the velocity of gas hydrate bearing sediments and quantifying the amount of gas hydrate. The variation of porosity is affected by many factors. The influences of different factors on porosity are distinct. The purpose of this paper is to analyze the main factors that affect the overall and local change of porosity in marine sediments where gas hydrate was sampled. Porosity logs were collected from ODP Leg 164, Blake Ridge, ODP Leg 204, Hydrate Ridge, and IODP expedition 311, Cascadia Margin. Based on the characteristic of porosity variation in depth, porosity was divided into three components: low frequency component, middle frequency component, and high frequency component. The factors influencing each component were discussed. From the analysis, we observed that the porosity of unconsolidated sediment was very high, and the decreasing trend of low frequency component versus depth was affected by compaction. In addition, the initial porosity and slope of low frequency component variation were affected by the content of fine grain and geothermal gradient respectively. The middle component could reflect the variation of lithology, which was affected by the content variation of different sized grains and gas hydrate. The high frequency component was affected by the frequent change of grain size. The existence of volcanic ash-rich sand caused a high value to the high frequency component. The results are applicable to porosity evaluation in gas hydrate bearing sediments.

Block-based Adaptive Image Watermarking Scheme using Visual Perception Model in DCT Domain

ABSTRACT In this paper, an improved DCT-based watermarking technique has been presented, which exploits human visual model for adapting the watermark data to local properties of the host image. In order to achieve more robustness and transparency, watermark has been embedded into the middle frequency components of the image. These components are significant in high textural regions so that the visibility of the image would not be affected and the watermark will not be removed by compression. Experimental results are provided to demonstrate that embedded watermarks can be almost fully extracted from the JPEG-compressed images with very high compression ratios and other kind of attacked images.

Super-Resolution Iris Image Restoration Based on Multiple MLPs and CLS Filter

Iris recognition is a biometric technology which shows a very high level of recognition accuracy, but low resolution (LR) iris images cause the degradation of the recognition performance. Therefore, a zoom lens with a long focal length is used in an iris camera. However, a bulky and costly zoom lens whose focal length is longer than 150 mm is required for capturing the iris image at a distance, which can increase the size and cost of the system. In order to overcome this problem, we propose a new super-resolution method which restores a single LR iris image into a high resolution (HR) iris image. Our research is novel in the following three ways compared to previous works. First, in order to prevent the loss of the middle and high frequency components of the iris patterns in the original image, the LR iris image is up-sampled using multiple multi-layeredperceptrons (MLPs). Second, a point spread function (PSF) and a constrained least square (CLS) filter are used to remove sensor blurring in the up-sampled image. Third, the optimal parameters of the CLS filter and PSF in terms of the recognition accuracy are determined according to the zoom factor of the LR image. The experimental results show that the accuracy of iris recognition with the HR images restored by the proposed method is much enhanced compared to the three previous methods.

A Double-function Digital Watermarking Algorithm Based on Chaotic System and LWT

Abstract A double- function digital watermarking technology is studied and a double-function digital watermarking algorithm of colored image is presented based on chaotic system and the lifting wavelet transformation (LWT).The algorithm has realized the double aims of the copyright protection and the integrity authentication of image content. Making use of feature of human visual system (HVS), the watermark image is embedded into the color image's low frequency component and middle frequency components by different means. The algorithm has great security by using two kinds chaotic mappings and Arnold to scramble the watermark image at the same time. The algorithm has good efficiency by using LWT. The emulation experiment indicates the algorithm has great efficiency and security, and the effect of concealing is really good.

Robust focus measure for unsupervised auto-focusing based on optimum discrete cosine transform coefficients

In this paper we present a robust focus measure for unsupervised auto-focusing based on optimum discrete cosine transform (DCT) coefficients. DCT has a low computational complexity due to real-valued coefficients, and a small number of coefficients have to be calculated to achieve a good spectral representation of an image. The focusing region of an input image is divided into nonoverlapping 8 × 8 sub-images, and spectra of sub-images are obtained by 8 × 8 DCT. The focusing measure is computed from the middle frequency components. By appropriately chosen DCT coefficients, the proposed focusing measure is robust to Gaussian and impulsive noises. Experimental results show that the proposed method is more effective than the traditional methods based on the comparison with well-known autofocusing uncertainty measure (AUM).