High Frequency Coefficients Research Articles

Blind Remote Sensing Image Deblurring Based on Local Maximum High-Frequency Coefficient Prior and Graph Regularization

Blind Remote Sensing Image Deblurring Based on Local Maximum High-Frequency Coefficient Prior and Graph Regularization

Partially Etched Piezoelectric Film Filled with SiO2 Structure Applied to A1 Mode Resonators for Transverse Modes Suppression.

With the arrival of the Fifth Generation (5G) communication era, there has been an urgent demand for acoustic filters with a high frequency and ultrawide bandwidth used in radio-frequency (RF) front-ends filtering and signal processing. First-order antisymmetric (A1) lamb mode resonators based on LiNbO3 film have attracted wide attention due to their scalable, high operating frequency and large electromechanical coupling coefficients (K2), making them promising candidates for sub-6 GHz wideband filters. However, A1 mode resonators suffer from the occurrence of transverse modes, which should be addressed to make these devices suitable for applications. In this work, theoretical analysis is performed by finite element method (FEM), and the admittance characteristics of an A1 mode resonator and displacement of transverse modes near the resonant frequency (fr) are investigated. We propose a novel Dielectric-Embedded Piston Mode (DEPM) structure, achieved by partially etching a piezoelectric film filled with SiO2, which can almost suppress the transverse modes between the resonant frequency (fr) and anti-resonant frequency (fa) when applied on ZY-cut LiNbO3-based A1 mode resonators. This indicates that compared with Broadband Piston Mode (BPM), Filled-broadband Piston Mode (FPM) and standard structures, the DEPM structure is superior. Furthermore, the design parameters of the resonator are optimized by adjusting the width, depth and filled materials in the etched window of the DEPM structure to obtain a better suppression of transverse modes. The optimized A1 mode resonator using a DEPM structure exhibits a transverse-free response with a high fr of 3.22 GHz and a large K2 of ~30%, which promotes the application of A1 mode devices for use in 5G RF front-ends.

Image Steganography Using Modified DWT Technique

The modern computing world revolves around the word “DATA”, but just what is so intriguing about it? In today’s world, data is the power and business start realizing it because data can predict customer trends potentially, get increased sales, and help the organization to achieve newer heights. The technology has become so advanced and our topmost priority is to secure data.Here in this project, the high frequency coefficients produced by the discrete wavelet transform contain hidden messages. To enhance the quality of the images, low frequency sub-band coefficients are kept intact. Before embedding, some elementary mathematical operations are performed on the secret messages. These operations prevent messages from being stolen or destroyed by unauthorized internet users, and they do so while also providing adequate security.

Rocking Analysis of Towers Subjected to Horizontal Forces

In seismic-prone areas, ecclesiastical masonry complexes have shown very high vulnerability, as detected after the last Italian earthquakes, such as those that occurred in L’Aquila (2009), Emilia-Romagna (2012), Central Italy (2016), and Ischia (2017). Partial collapses often affect these types of aggregate buildings due to the presence of highly vulnerable elements, such as bell towers. Preliminary analyses, including straightforward and quick methods, are necessary to assess their vulnerability. This paper proposes a simplified method to analyse bell tower dynamic behaviour and the results obtained are compared with several different approaches. The first is based on the dynamics of two rigid blocks (bell tower and lower building), and the second concerns a single block (bell tower only). The proposed method can be considered as a quick procedure involving few parameters to provide a preliminary analysis before use of more complex models such as finite element models. It aims to provide a valuable tool for the initial evaluation of the stability and risk index of the structure. The double-block model considers the associations between the rocking of the bell tower and the sliding motion of the underlying building. A parametric evaluation for different friction coefficients is proposed. The results are represented as rotation time histories and compared with analysis of the single vulnerable element, i.e., the bell tower subjected to the floor spectrum. The results show that high excitation frequency and friction coefficient values make the bell tower stable, and that the simplified model provides a clear safety advantage.

A novel fuzzy logic, gray wolf optimization and convolutional neural network based image fusion framework using discrete frame-let transform

In this paper, a novel fuzzy logic, gray wolf optimization and convolutional neural network (CNN) based infrared and visible image framework using discrete frame-let transform is proposed. A new enhancement method using fuzzy logic has been introduced to improve the contrast and visual appearance of input images. Discrete frame-let transform has been applied on both enhanced source images in order to generate more featured multi-scale coefficients. Gray wolf optimization (GWO) based fusion strategy has been introduced for merging low frequency coefficients of both enhanced source images in order to generate better target and as well as background details in the resultant fused image. Siamese convolutional neural network is applied on enhanced source images for integrating pixel activity information in order to obtain a weight map. High frequency coefficients are merged using through CNN generated weight map via discrete frame-let decomposition and local similarity based fusion strategy for generating more detailed information in the final fused image. Finally the reconstruction of resultant fused image is obtained through using inverse discrete frame-let transform. The obtained experimental results achieved through simulation clearly demonstrates that our proposed algorithm has shown superior performance in both aspects (i.e. subjective assessment and as well as objective assessment) compared to related state of art methods.

Robust Watermarking Scheme for Vector Geographic Data Based on the Ratio Invariance of DWT–CSVD Coefficients

Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks.

Zero-Watermarking Algorithm for Remote Sensing Image via BEMD and DFT

In order to solve the problem of modifying the host image when the traditional image watermarking algorithm protects the copyright of remote sensing image, and to improve the robustness of remote sensing image watermarking algorithm, a remote sensing image zero watermarking algorithm based on bi-dimensional empirical mode decomposition (BEMD) and discrete Fourier transform (DFT) is proposed. Firstly, BEMD is used to decompose the host image to get the intrinsic mode function and margin, and the residual information is divided into 8×8 blocks DFT, and the high frequency coefficients are constructed into coefficient images. Then Arnold transform is used to scramble the watermark image to improve the security of the watermark image. Then the size of the watermark image is adjusted to solve the problem of image size matching between the host image and the watermark image in the process of watermark embedding. Finally, the coefficient image decomposed by DFT and the watermark image are constructed into feature image by XOR operation, and the construction of zero watermark in remote sensing image is realized. The experimental results of a large number of remote sensing images and watermark images show that the proposed algorithm can effectively resist all kinds of attacks such as filtering, sharpening, histogram equalization, Gamma transform, noise, scaling and so on. In the case of 12 attacks and 60 parameters, 95.0% of the extracted watermark images have a normalized correlation value of more than 0.990 with the original watermark image, compared with the traditional remote sensing image watermarking algorithm. The algorithm ensures the harmlessness of the host image, so that the feature image saved in the notary office for copyright protection verification will not cause any damage to the host image, and has better robustness.

Trade-off between Image Quality and Computational Complexity: Image Resizing Perspective

This study proposed a new approach for resizing image deal with quality and computational complexity. Here, previous methods in image resizing do analytical works to approximate the original picture element (pixel) or to remove high frequency coefficients. For images with huge pixel, this will result in computational burden due to number of multiplication and addition in the synthesized formula. Instead of the works, this study proposed a new approach in removing the coefficients by exploiting the second-order block matrix without the need to synthesize the formula. It can be called a fully numeric image resizing method. The result shows that the resized version of original image has peak signal to noise ratio (PSNR) equal to 35.24 dB for resizing the famous Lena image which means compareable to the conventional which has PSNR value around 35 dB but here deriving analytical formula is not required. Reducing computational complexity is also achieved as expected with result only 16 addition involved with no multiplication required. This is lower than the conventional in term of computational complexity. Overall, the proposed method has a good balance for both performances than the conventional approaches.

Medium K and temperature-stable Co2+-substituted LiNb0.6Ti0.5O3 microwave dielectric ceramics for LTCC miniaturized applications

At present, 5 G communication is developing rapidly, devices are becoming miniaturized, which requires the continuous emergence of materials with high dielectric constant. In this work, choosing Co2+ to substitute Li1–2xCoxNb0.6Ti0.5O3 (LCNT, 0 ≤ x ≤ 0.1) + 0.4 wt% LBSCA ceramics successfully achieved high permittivity εr and temperature stable resonant temperature frequency coefficient τf sintered at 950 ℃, including εr∼62, Q×f∼6255 GHz, and τf∼0.97 ppm/℃ (x = 0.02), εr∼56.55, Q×f∼7090 GHz, and τf∼− 28.07 ppm/℃ (x = 0.06). Meanwhile, the influence of Co2+ on LNT ceramics for phase composition, micro-structure, microwave dielectric performance and Raman vibration had been studied in detail. All samples belonged to M phase. Rod-like or blocky grains were gradually refined, indicating that Co2+ had the effect of grain refinement. In microwave dielectric performance, ionic polarizability played a leading role in εr, Q×f value was positively correlated with the bulk density, τf directly depended on the average repeatable layer N. In Raman spectroscopy, three modes of Raman vibration had been identified as translation or rotation of Li+ ions, Nb/Ti bond vibration, [Nb/TiO6] octahedral vibration. To meet low temperature co-fired applications, the LCNT (x = 0.02) ceramic didn’t react with Ag, which suggested it was achieving candidates for the next generation of medium K LTCC materials.

WCNN3D: Wavelet Convolutional Neural Network-Based 3D Object Detection for Autonomous Driving.

Three-dimensional object detection is crucial for autonomous driving to understand the driving environment. Since the pooling operation causes information loss in the standard CNN, we designed a wavelet-multiresolution-analysis-based 3D object detection network without a pooling operation. Additionally, instead of using a single filter like the standard convolution, we used the lower-frequency and higher-frequency coefficients as a filter. These filters capture more relevant parts than a single filter, enlarging the receptive field. The model comprises a discrete wavelet transform (DWT) and an inverse wavelet transform (IWT) with skip connections to encourage feature reuse for contrasting and expanding layers. The IWT enriches the feature representation by fully recovering the lost details during the downsampling operation. Element-wise summation was used for the skip connections to decrease the computational burden. We trained the model for the Haar and Daubechies (Db4) wavelets. The two-level wavelet decomposition result shows that we can build a lightweight model without losing significant performance. The experimental results on KITTI’s BEV and 3D evaluation benchmark show that our model outperforms the PointPillars-based model by up to 14% while reducing the number of trainable parameters.

Hiding data inside images using orthogonal moments

In this contribution we propose a novel steganographic method based on several orthogonal polynomials and their combinations. The steganographic algorithm embeds a secrete message at the first eight coefficients of a high frequency image. Moreover, this embedding method uses the Beta chaotic map to determine the order of the blocks where the secret bits will be inserted. In addition, from a 128-bit private key and the steps of a cryptography algorithm according to the Advanced Encryption Standard (AES) to generate the key expansion, the proposed method generates a key expansion of 2560 bits, with the purpose to permute the first eight coefficients of high frequency before the insertion. The insertion takes eventually place at the first eight high frequency coefficients in the transformed orthogonal moments domain. Before the insertion of the message the image undergoes a series of transformations. After the insertion the inverse transformations are applied to the original transformations in reverse order. The experimental work on the validation of the algorithm consists of the calculation of the Peak Signal-to-Noise Ratio (PSNR), the Universal Image Quality Index (UIQI), the Image Fidelity (IF), and the Relative Entropy (RE), comparing the same characteristics for the cover and stego image. The statistical undetectability of the proposed method is evaluated by using a blind steganalysis. The proposed algorithm improves the level of imperceptibility and security analyzed through the PSNR and RE values, respectively, and thus demonstrated resistance to steganalysis.

Financial time series forecasting using optimized multistage wavelet regression approach.

This paper presents financial time series forecasting with multistage wavelet transform (WT). First, the time series data is processed through WT with different mother wavelet functions to extract high frequency and low frequency coefficients. Later, standard particle swarm optimization (PSO) algorithm is utilized to find optimal regression models in order to predict future samples. Mean square error (MSE) is opted as cost function for PSO to find optimal coefficients of the regression model. This study further extended to various mother wavelet functions and their decomposition levels to investigate their impacts on time series prediction. These investigations help to data scientists for selection of process parameters and variables. Further, the impact of control parameters of PSO is also discussed to show the importance in the search mechanism especially in regression problems.

An image encryption algorithm based on discrete fractional wavelet transform and quantum chaos

Aiming at low security of traditional encryption methods, a new image encryption algorithm based on discrete fractional wavelet transform (DFRWT) and quantum Logistic mapping is proposed in this paper. Firstly, p-order multiscale decomposition is performed for the original image, with DFRWT regarded as decomposition tool. Then the obtained high and low frequency coefficients are shuffled in the time-fractional-frequency domain, followed by an inverse DFRWT to obtain a shuffled image. Finally, XOR operation is carried out between pseudo-random sequence generated by quantum Logistic chaotic and shuffled image, which achieves image diffusion to complete image encryption. Simulation results demonstrate that the algorithm has positive characteristics of large key space, good scrambling performance and quite strong robustness.

Efficient Hybrid DCT-Wiener Algorithm Based Deep Learning Approach For Semantic Shape Segmentation

Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional layers giving modularity in the design using multi scale convolution block. The impact of selection of DCT coefficients in the proposed model is validated on the benchmark database as City Spaces. The same level of accuracy compared to the conventional algorithm is achieved using only 40 % of the DCT coefficients. Extensive experiments validate the advantages of adaptive DCT modeling of CNN in semantic segmentation and image classification.

Perceptual Fusion of Electronic Chart and Marine Radar Image

Electronic charts and marine radars are indispensable equipment in ship navigation systems, and the fusion display of these two parts ensures that the vessel can display dangerous moving targets and various obstacles on the sea. To reduce the noise interference caused by external factors and hardware, a novel radar image denoising algorithm using the concept of Generative Adversarial Network (GAN) using Wasserstein distance is proposed. GAN focuses on transferring the image noise distribution between strong and weak noise, while the perceptual loss approach is to suppress the noise by comparing the perceptual characteristics of the output after denoising. Afterwards, an image registration method based on image transformation is proposed to eliminate the imaging difference between the radar image and chart image, in which the visual attribute transfer approach is used to transform images. Finally, the sparse theory is used to process the high frequency and low frequency subband coefficients of the detection image obtained by the fast Fourier transform in parallel to realizing the image fusion. The results show that the fused contour has a high consistency, fast training speed and short registration time.

A multi-scale block compression sensing sampling algorithm

All sub-blocks in the same layer use the same sampling rate, which leads to a deviation between the sampling and observation information in the multi-scale block compression sensing algorithm and the original image information, and results in reducing the accuracy of image reconstruction. Therefore a multi-scale block compression sensing algorithm (HEI) is proposed to reduce the deviation of information. Image blocks are decomposed to obtain multi-scale blocks in this method. And the high frequency coefficients of wavelet transform are used to extract the edge information, which is used as the adaptive sampling rate of sub-blocks. Experiments are carried out on 6 standard images, and the effects of different hierarchies of wavelet transform and the scale of graph subblocks on HEI method are analyzed. The experimental results show that, compared with the adaptive sampling method using low frequency information, the proposed method has better quality and visual effect on the reconstructed images of the test images.

An effective fuzzy logic and particle swarm optimization based thermal and visible-light image fusion framework using curve-let transform

Thermal and visible-light image fusion is an active and one of the most significant research area in image processing. The result produced through this fusion process can be very much useful in surveillance and military applications especially for improving visibility and situation awareness. This paper proposes an effective fuzzy logic and particle swarm optimization based thermal and visible-light image fusion framework using curve-let transform. A novel fuzzy logic based contrast enhancement method is introduced in this framework for improving the visual appearance of both thermal and visible-light source images. In this framework curve-let transform is used for extracting more multi-scale features from source images. Upon applying curve-let transform on enhanced source images will produce low frequency (approximation) and high frequency (detailed) coefficients. Particle swarm optimization based fusion strategy has been used for producing more featured fused approximation coefficients. Correspondingly Max fusion strategy has been used for generating more textures based fused detailed coefficients. Finally upon applying inverse curve-let transform generates final fused image. The obtained simulation results clearly demonstrate that our framework has shown superior results compared to other fusion methods both in terms of subjective and objective assessments.

A Zero-Watermark Hybrid Algorithm for Remote Sensing Images Based on DCT and DFT

Remote sensing image has significant advantages such as large detection range and fast acquisition speed, so it has been used in military, environmental, meteorological and other fields. In order to improve the robustness of the digital watermarking technology on the premise that the remote sensing image information is not modified, a remote sensing image zero watermarking algorithm based on Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT) is proposed in this paper. First of all, the color remote sensing image is converted into grayscale image. The transformed coefficient matrix is obtained by DFT, to the grayed host image. Secondly, in order to compress the amount of information and improve the efficiency of the algorithm, the high frequency coefficient image is transformed by DCT transform. The low frequency coefficient image which is consistent with the size of the watermark image after DCT is selected as the feature image of the host image. After that, the watermark information is scrambled by Arnold transform to improve the security of the watermark information. Finally, the zero-watermark image is obtained by XOR operation between the scrambled watermark information and the feature image. The zero-watermark image is saved in a third-party database for copyright verification. In order to verify the robustness of digital watermarking, this paper carries out sharpening, histogram equalization, Karma transform, noise, filtering and geometric attacks on the host image, and compares it with other algorithms. The NC value between the extracted watermark image and the original watermark image after attacking the host image show that the algorithm has a strong ability to resist common attacks.

Triangular Vertex Transform Based Watermarking on Contourlet Coefficients for Improved Robustness

A hybrid watermarking scheme based on Triangular Vertex Transform (TVT) and Contourlet coefficients for high robustness is implemented. During watermark embedding, the cover image is first decomposed using Contourlet Transform to obtain high frequency and low frequency coefficients. The lower frequency coefficients are applied with TVT. Then, the W coefficients obtained from TVT are again subdivided. The watermark bit is then embedded on the subdivided coefficients to obtain the watermarked image. Reverse operation is followed in the extraction phase. The performance of this algorithm is evaluated using embedding capacity, Normalized cross correlation (Ncc) and Peak Signal to Noise Ratio (PSNR) using standard test images. These evaluation results disclose the domination of proposed scheme over traditional schemes

Research on the fault diagnosis of dual-redundancy BLDC motor

In aerospace drive system, the double redundancy brushless DC motor can greatly improve the reliability of drive system. In this paper a double-redundancy brushless DC motor is designed, and a mathematical model is established. Several common motor faults are analyzed and the wavelet change detection method which can achieve the requirement of on-line detection of fault signals is adopted. Through the motor fault tree and the characteristics of the obvious change of armature current during the motor fault, the signal source chosen for fault diagnosis is current. And the coif5 wavelet function can accurately detect the fault information, the high frequency coefficient of the second layer decomposition is used as the eigenvalue, and the eigenvectors of various faults are obtained. The experimental results show that this detection method can accurately distinguish the fault of motor winding.