A mel-cepstral analysis technique restoring high frequency components from low-sampling-rate speech

In conventional block compressed sensing (BCS), the images are divided into small fixed-size blocks sampled at the same sub-rate. The sparsities and high-frequency components of the images are ignored, and the reconstruction qualities of the complex texture images are poor. An adaptive multiscale variant of the block compressed sensing was proposed to reconstruct the texture details of the images. The texture features of the images were obtained from the high-frequency components by the three-level wavelet transform and analyzed on the basis of the gray level co-occurrence matrix. A mathematical model was established to adjust the block sizes of the images automatically and allocate the limited sampling resource adaptively. The smoothed projected Landweber (SPL) was utilized to reconstruct the images. The accuracy of the proposed algorithm was verified by the simulation experiments. Results demonstrate that the texture details of the reconstructed images are abundant. The image edges are also clear, and the blocking artifacts are effectively eliminated. The reconstruction qualities of images, especially the partial images, are considerably improved at different sub-sampling rates. The proposed algorithm achieves a 2.42–3.3 dB gain in reconstruction PSNR for the Barbara image over the original BCS-SPL at a sub-sampling rate of 0.3. No remarkable differences are noted between the reconstructed and original texture blocks in visual sensation. The proposed algorithm provides evidence for the compression and reconstruction of the images with complex texture details.

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Synchronous Mixing Architecture for Digital Bandwidth Interleaving Sampling System

Electronics ◽

10.3390/electronics10161998 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1998

Author(s):

Xiaochang Jiang ◽

Jie Wu ◽

Yubo Ma

Keyword(s):

High Frequency ◽

Dynamic Range ◽

Sampling Rate ◽

Poor Performance ◽

Sampling System ◽

Analog To Digital ◽

Frequency Components ◽

Time Interleaved ◽

Evaluation Platform ◽

Hybrid Filter Bank

By using a mixer to down-convert the high frequency components of a signal, digital bandwidth interleaving (DBI) technology can simultaneously increase the sampling rate and bandwidth of the sampling system, compared to the time-interleaved and hybrid filter bank. However, the software and hardware of the classical architecture are too complicated, which also leads to poor performance. In particular, the pilot tone used to synchronize the analog and digital local oscillators (LO) of mixers intermodulates with the high frequency components of the signal, resulting in larger spurs. This paper proposes a synchronous mixing architecture for the DBI system, where the LO of the analog mixer is synchronized with the sampling clock of the analog-to-digital converter. Its hardware and software are simplified—the pilot tone used to synchronize the LOs can also be removed. An evaluation platform with a sampling rate of 250 MSPS is implemented to illustrate the performance of the new architecture. The result shows that the spurious free dynamic range (SFDR) of the new architecture is more than 20 dB higher than the classical one in a high frequency range. The rise time of a step signal of the new architecture is 0.578 ± 0.070 ns faster than the classical one with the same bandwidth (90 MHz).

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Simulations of high-resolution images of amorphous silicon films

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010014419x ◽

1986 ◽

Vol 44 ◽

pp. 544-545

Author(s):

G. Y. Fan ◽

J. M. Cowley

Keyword(s):

Electron Microscope ◽

High Frequency ◽

Fourier Transformation ◽

Amorphous Materials ◽

Low Frequency ◽

Chromatic Aberration ◽

Structure Information ◽

Frequency Components ◽

High Resolution Images ◽

Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

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A Novel Adaptive PET/CT Image Fusion Algorithm

Current Bioinformatics ◽

10.2174/1574893613666180704153946 ◽

2019 ◽

Vol 14 (7) ◽

pp. 658-666

Author(s):

Kai-jian Xia ◽

Jian-qiang Wang ◽

Jian Cai

Keyword(s):

Lung Cancer ◽

Image Fusion ◽

High Frequency ◽

Malignant Tumors ◽

Low Frequency ◽

Combination Method ◽

Ct Image ◽

Fusion Algorithm ◽

Pet Ct ◽

Frequency Components

Background: Lung cancer is one of the common malignant tumors. The successful diagnosis of lung cancer depends on the accuracy of the image obtained from medical imaging modalities. Objective: The fusion of CT and PET is combining the complimentary and redundant information both images and can increase the ease of perception. Since the existing fusion method sare not perfect enough, and the fusion effect remains to be improved, the paper proposes a novel method called adaptive PET/CT fusion for lung cancer in Piella framework. Methods: This algorithm firstly adopted the DTCWT to decompose the PET and CT images into different components, respectively. In accordance with the characteristics of low-frequency and high-frequency components and the features of PET and CT image, 5 membership functions are used as a combination method so as to determine the fusion weight for low-frequency components. In order to fuse different high-frequency components, we select the energy difference of decomposition coefficients as the match measure, and the local energy as the activity measure; in addition, the decision factor is also determined for the high-frequency components. Results: The proposed method is compared with some of the pixel-level spatial domain image fusion algorithms. The experimental results show that our proposed algorithm is feasible and effective. Conclusion: Our proposed algorithm can better retain and protrude the lesions edge information and the texture information of lesions in the image fusion.

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Despeckling of Sar Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

International Journal of Image and Graphics ◽

10.1142/s0219467821500236 ◽

2020 ◽

pp. 2150023

Author(s):

Priya R. Kamath ◽

Kedarnath Senapati ◽

P. Jidesh

Keyword(s):

High Frequency ◽

Low Frequency ◽

Relevant Information ◽

Detection Algorithm ◽

Two Dimensional ◽

Sar Images ◽

S Transform ◽

Processing Step ◽

Frequency Components ◽

Shock Filter

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

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RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs

Sensors ◽

10.3390/s21123955 ◽

2021 ◽

Vol 21 (12) ◽

pp. 3955

Author(s):

Jung-Cheng Yang ◽

Chun-Jung Lin ◽

Bing-Yuan You ◽

Yin-Long Yan ◽

Teng-Hu Cheng

Keyword(s):

Feedback Control ◽

Real Time ◽

High Frequency ◽

Indoor Environment ◽

Indoor Localization ◽

Sampling Rate ◽

Outdoor Environment ◽

Position Accuracy ◽

Graph Optimization ◽

Pose Graph Optimization

Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.

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