Symmetric quadratic tetration interpolation using forward and backward operation combination

The existed interpolation method, based on the second-order tetration polynomial, has the asymmetric property. The interpolation results, for each considering region, give individual characteristics. Although the interpolation performance has been better than the conventional methods, the symmetric property for signal interpolation is also necessary. In this paper, we propose the symmetric interpolation formulas derived from the second-order tetration polynomial. The combination of the forward and backward operations was employed to construct two types of the symmetric interpolation. Several resolutions of the fundamental signals were used to evaluate the signal reconstruction performance. The results show that the proposed interpolations can be used to reconstruct the fundamental signal and its peak signal to noise ratio (PSNR) is superior to the conventional interpolation methods, except the cubic spline interpolation for the sine wave signal. However, the visual results show that it has a small difference. Moreover, our proposed interpolations converge to the steady-state faster than the cubic spline interpolation. In addition, the option number increasing will reinforce their sensitivity.

Fast magnetic resonance elastography with multiphase radial encoding and harmonic motion sparsity based reconstruction

Objective: To achieve fast magnetic resonance elastography (MRE) at a low frequency for better shear modulus estimation of the brain. Approach: We proposed a multiphase radial DENSE MRE (MRD-MRE) sequence and an improved GRASP algorithm utilizing the sparsity of the harmonic motion (SH-GRASP) for fast MRE at 20 Hz. For the MRD-MRE sequence, the initial position encoded by one spatial modulation of magnetization (SPAMM) was decoded by an arbitrary number of readout blocks without increasing the number of phase offsets. Based on the harmonic motion, a modified total variation and temporal Fourier transform were introduced to utilize the sparsity in the temporal domain. Both phantom and brain experiments were carried out and compared with that from multiphase Cartesian DENSE-MRE (MCD-MRE), and conventional gradient echo sequence (GRE-MRE). Reconstruction performance was also compared with GRASP and compressed sensing. Main results: Results showed the scanning time of a fully sampled image with four phase offsets for MRD-MRE was only 1/5 of that from GRE-MRE. The wave patterns and estimated stiffness maps were similar to those from MCD-MRE and GRE-MRE. With SH-GRASP, the total scan time could be shortened by additional 4 folds, achieving a total acceleration factor of 20. Better metric values were also obtained using SH-GRASP for reconstruction compared with other algorithms. Significance: The MRD-MRE sequence and SH-GRASP algorithm can be used either in combination or independently to accelerate MRE, showing the potentials for imaging the brain as well as other organs.

Fast Terahertz Imaging Model Based on Group Sparsity and Nonlocal Self-Similarity

In order to solve the problems of long-term image acquisition time and massive data processing in a terahertz time domain spectroscopy imaging system, a novel fast terahertz imaging model, combined with group sparsity and nonlocal self-similarity (GSNS), is proposed in this paper. In GSNS, the structure similarity and sparsity of image patches in both two-dimensional and three-dimensional space are utilized to obtain high-quality terahertz images. It has the advantages of detail clarity and edge preservation. Furthermore, to overcome the high computational costs of matrix inversion in traditional split Bregman iteration, an acceleration scheme based on conjugate gradient method is proposed to solve the terahertz imaging model more efficiently. Experiments results demonstrate that the proposed approach can lead to better terahertz image reconstruction performance at low sampling rates.

A block bayesian compressive sensing method based on the equivalent source method for near-field acoustic holography

The conventional equivalent source method for near-field acoustic holography is an effective noise diagnosis method using microphone array. However, its performance is limited by microphone spacing, so the effect is unsatisfied when the wave number is high. In this paper, to broaden the frequency suitability and improve the performance of sound source reconstruction with low signal-to-noise ratios, a block Bayesian compressive sensing method based on the equivalent source method is proposed. Numerical results show that this proposed method has a good reconstruction performance and makes wideband reconstruction possible. By changing the frequency, location, and signal-to-noise ratio of the sound source, the reconstruction performance of the proposed method can remain stable. Finally, the validity and practicability of the proposed method are verified by experiments.

Three-dimensional tomographically reconstructed optical emission profiles of Hall thruster plasmas

A diagnostic system was developed for spectrally resolved, three-dimensional tomographic reconstruction of Hall thruster plasmas, and local intensity profiles of Xe I and Xe II emissions were reconstructed. In this diagnostic system, 28 virtual cameras were generated using a single, fixed charge-coupled device (CCD) camera by rotating the Hall thruster to form a sufficient number of lines of sight. The Phillips-Tikhonov regularization algorithm was used to reconstruct local emission profiles from the line-integrated emission signals. The reconstruction performance was evaluated using both azimuthally symmetric and asymmetric synthetic phantom images including 5% Gaussian white noise, which resulted in a root-mean-square error of the reconstruction within an order of 10-3 even for a 1% difference in the azimuthal intensity distribution. Using the developed system, three-dimensional local profiles of Xe II emission (541.9 nm) from radiative decay of the excited state 5p4(3P2)6p2[3]˚5/2 and Xe I emission (881.9 nm) from 5p5(2P˚3/2)6p2[5/2]3 were obtained, and two different shapes were found depending on the wavelength and the distance from the thruster exit plane. In particular, a stretched central jet structure was distinctively observed in the Xe II emission profile beyond 10 mm from the thruster exit, while gradual broadening was found in the Xe I emission. Approximately 10% azimuthal nonuniformities were observed in the local Xe I and Xe II intensity profiles in the near-plume region (< 10 mm), which could not be quantitatively distinguished by analysis of the frontal photographic image. Three-dimensional Xe I and Xe II intensity profiles were also obtained in the plume region, and the differences in the structures of both emissions were visually confirmed.

A special Higgs challenge: measuring the mass and production cross section with ultimate precision at FCC-ee

The FCC-ee offers powerful opportunities to determine the Higgs boson parameters, exploiting over $$10^6$$ 10 6 $${ \hbox {e}^+\hbox {e}^- \rightarrow \hbox {ZH}}$$ e + e - → ZH events and almost $$10^5$$ 10 5 $${ \hbox {WW} \rightarrow \hbox {H}}$$ WW → H events at centre-of-mass energies around 240 and 365 GeV. This essay spotlights the important measurements of the ZH production cross section and of the Higgs boson mass. The measurement of the total ZH cross section is an essential input to the absolute determination of the HZZ coupling—a “standard candle” that can be used by all other measurements, including those made at hadron colliders—at the per-mil level. A combination of the measured cross sections at the two different centre-of-mass energies further provides the first evidence for the trilinear Higgs self-coupling, and possibly its first observation if the cross section measurement can be made accurate enough. The determination of the Higgs boson mass with a precision significantly better than the Higgs boson width (4.1 MeV in the standard model) is a prerequisite to either constrain or measure the electron Yukawa coupling via direct $${ \hbox {e}^+\hbox {e}^- \rightarrow \hbox {H}}$$ e + e - → H production at $$\sqrt{s} = 125$$ s = 125 GeV. Approaching the statistical limit of 0.1% and $${\mathcal {O}}(1)$$ O ( 1 ) MeV on the ZH cross section and the Higgs boson mass, respectively, sets highly demanding requirements on accelerator operation (ZH threshold scan, centre-of-mass energy measurement), detector design (lepton momentum resolution, hadronic final state reconstruction performance), theoretical calculations, and analysis techniques (efficiency and purity optimization with modern tools, constrained kinematic fits, control of systematic uncertainties). These challenges are examined in turn in this essay

Infrared Image Super-Resolution via Progressive Compact Distillation Network

Deep convolutional neural networks are capable of achieving remarkable performance in single-image super-resolution (SISR). However, due to the weak availability of infrared images, heavy network architectures for insufficient infrared images are confronted by excessive parameters and computational complexity. To address these issues, we propose a lightweight progressive compact distillation network (PCDN) with a transfer learning strategy to achieve infrared image super-resolution reconstruction with a few samples. We design a progressive feature residual distillation (PFDB) block to efficiently refine hierarchical features, and parallel dilation convolutions are utilized to expand PFDB’s receptive field, thereby maximizing the characterization power of marginal features and minimizing the network parameters. Moreover, the bil-global connection mechanism and the difference calculation algorithm between two adjacent PFDBs are proposed to accelerate the network convergence and extract the high-frequency information, respectively. Furthermore, we introduce transfer learning to fine-tune network weights with few-shot infrared images to obtain infrared image mapping information. Experimental results suggest the effectiveness and superiority of the proposed framework with low computational load in infrared image super-resolution. Notably, our PCDN outperforms existing methods on two public datasets for both ×2 and ×4 with parameters less than 240 k, proving its efficient and excellent reconstruction performance.

Calorimetric classification of track-like signatures in liquid argon TPCs using MicroBooNE data

The MicroBooNE liquid argon time projection chamber located at Fermilab is a neutrino experiment dedicated to the study of short-baseline oscillations, the measurements of neutrino cross sections in liquid argon, and to the research and development of this novel detector technology. Accurate and precise measurements of calorimetry are essential to the event reconstruction and are achieved by leveraging the TPC to measure deposited energy per unit length along the particle trajectory, with mm resolution. We describe the non-uniform calorimetric reconstruction performance in the detector, showing dependence on the angle of the particle trajectory. Such non-uniform reconstruction directly affects the performance of the particle identification algorithms which infer particle type from calorimetric measurements. This work presents a new particle identification method which accounts for and effectively addresses such non-uniformity. The newly developed method shows improved performance compared to previous algorithms, illustrated by a 93.7% proton selection efficiency and a 10% muon mis-identification rate, with a fairly loose selection of tracks performed on beam data. The performance is further demonstrated by identifying exclusive final states in νμCC interactions. While developed using MicroBooNE data and simulation, this method is easily applicable to future LArTPC experiments, such as SBND, ICARUS, and DUNE.

Generalized deep iterative reconstruction for sparse-view CT imaging

Sparse-view CT is a promising approach in reducing the X-ray radiation dose in clinical CT imaging. However, the CT images reconstructed from the conventional filtered backprojection (FBP) algorithm suffer from severe streaking artifacts. Iterative reconstruction (IR) algorithms have been widely adopted to mitigate these streaking artifacts, but they may prolong the CT imaging time due to the intense data-specific computations. Recently, model-driven deep learning (DL) CT image reconstruction method, which unrolls the iterative optimization procedures into the deep neural network, has shown exciting prospect in improving the image quality and shortening the reconstruction time. In this work, we explore the generalized unrolling scheme for such iterative model to further enhance its performance on sparse-view CT imaging. By using it, the iteration parameters, regularizer term, data-fidelity term and even the mathematical operations are all assumed to be learned and optimized via the network training. Results from the numerical and experimental sparse-view CT imaging demonstrate that the newly proposed network with the maximum generalization provides the best reconstruction performance.

Complexity Assessment of Chronic Pain in Elderly Knee Osteoarthritis Based on Neuroimaging Recognition Techniques

The chronic pain of knee osteoarthritis in the elderly is investigated in detail in this paper, as well as the complexity of chronic pain utilising neuroimaging recognition techniques. Chronic pain in knee osteoarthritis (KOA) has a major effect on patients’ quality of life and functional activities; therefore, understanding the causes of KOA pain and the analgesic advantages of different therapies is important. In recent years, neuroimaging techniques have become increasingly important in basic and clinical pain research. Thanks to the application and development of neuroimaging techniques in the study of chronic pain in KOA, researchers have found that chronic pain in KOA contains both injury-receptive and neuropathic pain components. The neuropathic pain mechanism that causes KOA pain is complicated, and it may be produced by peripheral or central sensitization, but it has not gotten enough attention in clinical practice, and there is no agreement on how to treat combination neuropathic pain KOA. As a result, using neuroimaging techniques such as magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS), this review examines the changes in brain pathophysiology-related regions caused by KOA pain, compares the latest results in pain assessment and prediction, and clarifies the central brain analgesic mechanistic. The capsule network model is introduced in this paper from the perspective of deep learning network structure to construct an information-complete and reversible image low-level feature bridge using isotropic representation, predict the corresponding capsule features from MRI voxel responses, and then, complete the accurate reconstruction of simple images using inverse transformation. The proposed model improves the structural similarity index by about 10%, improves the reconstruction performance of low-level feature content in simple images by about 10%, and achieves feature interpretation and analysis of low-level visual cortical fMRI voxels by visualising capsule features, according to the experimental results.