Below-diffraction-limited hybrid recording using silicon thin film super-resolution structure

We report on new experimental results for below-diffraction-limited hybrid recording. In our experiments, by means of focused laser assisted magnetic recording, the magnetic domains within TbFeCo thin films are obtained under an external perpendicular direct magnetic field. For a single magnetic medium, the domain size is mainly determined by the focused spot, which is about 620 nm for the laser wavelength λ=406 nm, and a numerical aperture of the lens of 0.80. However, when a silicon thin film structure is inserted between the substrate and the magnetic medium, the recording domains can be reduced obviously. By optimizing the experimental condition, even the size can be reduced to about 100 nm, which is below the diffraction limit, i.e. about 1/6 of the spot size. This is very useful for improving the hybrid recording density in practical applications.

Shear Let Transform Residual Learning Approach for Single-Image Super-Resolution

Super-resolution techniques are employed to enhance image resolution by reconstructing high-resolution images from one or more low-resolution inputs.Super-resolution is of paramount importance in the context of remote sensing,satellite,aerial,security and surveillance imaging.Super-resolution remote sensing imagery is essential for surveillance and security purposes,enabling authorities to monitor remote or sensitive areas with greater clarity.This study introduces a single-image super-resolution approach for remote sensing images,utilizing deep shearlet residual learning in the shearlet transform domain,and incorporating the Enhanced Deep Super-Resolution network(EDSR).Unlike conventional approaches that estimate residuals between high and low-resolution images,the proposed approach calculates the shearlet coefficients for the desired high-resolution image using the provided low-resolution image instead of estimating a residual image between the high-and low-resolution image.The shearlet transform is chosen for its excellent sparse approximation capabilities.Initially,remote sensing images are transformed into the shearlet domain,which divides the input image into low and high frequencies.The shearlet coefficients are fed into the EDSR network.The high-resolution image is subsequently reconstructed using the inverse shearlet transform.The incorporation of the EDSR network enhances training stability,leading to improved generated images.The experimental results from the Deep Shearlet Residual Learning approach demonstrate its superior performance in remote sensing image recovery,effectively restoring both global topology and local edge detail information,thereby enhancing image quality.Compared to other networks,our proposed approach outperforms the state-of-the-art in terms of image quality,achieving an average peak signal-to-noise ratio of 35 and a structural similarity index measure of approximately 0.9.

AFBNet: A Lightweight Adaptive Feature Fusion Module for Super-Resolution Algorithms

At present,super-resolution algorithms are employed to tackle the challenge of low image resolution,but it is difficult to extract differentiated feature details based on various inputs,resulting in poor generalization ability.Given this situation,this study first analyzes the features of some feature extraction modules of the current super-resolution algorithm and then proposes an adaptive feature fusion block(AFB)for feature extraction.This module mainly comprises dynamic convolution,attention mechanism,and pixel-based gating mechanism.Combined with dynamic convolution with scale information,the network can extract more differentiated feature information.The introduction of a channel spatial attention mechanism combined with multi-feature fusion further enables the network to retain more important feature information.Dynamic convolution and pixel-based gating mechanisms enhance the module’s adaptability.Finally,a comparative experiment of a super-resolution algorithm based on the AFB module is designed to substantiate the efficiency of the AFB module.The results revealed that the network combined with the AFB module has stronger generalization ability and expression ability.

PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)research.However,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction performance.These methods commonly use standard convolutions,which increase the convolutional operation cost of the model.In this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this problem.Specifically,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image reconstruction.Additionally,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature representations.Inspired by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an image.MFFB can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature information.As the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

Efficient 2-D MUSIC algorithm for super-resolution moving target tracking based on an FMCW radar

Frequency modulated continuous wave(FMCW)radar is an advantageous sensor scheme for target estimation and environmental perception.However,existing algorithms based on discrete Fourier transform(DFT),multiple signal classification(MUSIC)and compressed sensing,etc.,cannot achieve both low complexity and high resolution simultaneously.This paper proposes an efficient 2-D MUSIC algorithm for super-resolution target estimation/tracking based on FMCW radar.Firstly,we enhance the efficiency of 2-D MUSIC azimuth-range spectrum estimation by incorporating 2-D DFT and multi-level resolution searching strategy.Secondly,we apply the gradient descent method to tightly integrate the spatial continuity of object motion into spectrum estimation when processing multi-epoch radar data,which improves the efficiency of continuous target tracking.These two approaches have improved the algorithm efficiency by nearly 2-4 orders of magnitude without losing accuracy and resolution.Simulation experiments are conducted to validate the effectiveness of the algorithm in both single-epoch estimation and multi-epoch tracking scenarios.

Pyramid Separable Channel Attention Network for Single Image Super-Resolution

Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.

Multi-prior physics-enhanced neural network enables pixel super-resolution and twin-imagefree phase retrieval from single-shot hologram

Digital in-line holographic microscopy(DIHM)is a widely used interference technique for real-time reconstruction of living cells’morphological information with large space-bandwidth product and compact setup.However,the need for a larger pixel size of detector to improve imaging photosensitivity,field-of-view,and signal-to-noise ratio often leads to the loss of sub-pixel information and limited pixel resolution.Additionally,the twin-image appearing in the reconstruction severely degrades the quality of the reconstructed image.The deep learning(DL)approach has emerged as a powerful tool for phase retrieval in DIHM,effectively addressing these challenges.However,most DL-based strategies are datadriven or end-to-end net approaches,suffering from excessive data dependency and limited generalization ability.Herein,a novel multi-prior physics-enhanced neural network with pixel super-resolution(MPPN-PSR)for phase retrieval of DIHM is proposed.It encapsulates the physical model prior,sparsity prior and deep image prior in an untrained deep neural network.The effectiveness and feasibility of MPPN-PSR are demonstrated by comparing it with other traditional and learning-based phase retrieval methods.With the capabilities of pixel super-resolution,twin-image elimination and high-throughput jointly from a single-shot intensity measurement,the proposed DIHM approach is expected to be widely adopted in biomedical workflow and industrial measurement.

Face image super-resolution reconstruction algorithm based on residual attention mechanism

Aiming at the problems such as low reconstruction efficiency,fuzzy texture details,and difficult convergence of reconstruction network face image super-resolution reconstruction algorithms,a new super-resolution reconstruction algorithm with residual concern was proposed.Firstly,to solve the influence of redundant and invalid information about the face image super-resolution reconstruction network,an attention mechanism was introduced into the feature extraction module of the network,which improved the feature utilization rate of the overall network.Secondly,to alleviate the problem of gradient disappearance,the adaptive residual was introduced into the network to make the network model easier to converge during training,and features were supplemented according to the needs during training.The experimental results showed that the proposed algorithm had better reconstruction performance,more facial details,and clearer texture in the reconstructed face image than the comparison algorithm.In objective evaluation,the proposed algorithm's peak signalto-noise ratio and structural similarity were also better than other algorithms.

Faster split-based feedback network for image super-resolution

Although most of the existing image super-resolution(SR)methods have achieved superior performance,contrastive learning for high-level tasks has not been fully utilized in the existing image SR methods based on deep learning.This work focuses on two well-known strategies developed for lightweight and robust SR,i.e.,contrastive learning and feedback mechanism,and proposes an integrated solution called a split-based feedback network(SPFBN).The proposed SPFBN is based on a feedback mechanism to learn abstract representations and uses contrastive learning to explore high information in the representation space.Specifically,this work first uses hidden states and constraints in recurrent neural network(RNN)to implement a feedback mechanism.Then,use contrastive learning to perform representation learning to obtain high-level information by pushing the final image to the intermediate images and pulling the final SR image to the high-resolution image.Besides,a split-based feedback block(SPFB)is proposed to reduce model redundancy,which tolerates features with similar patterns but requires fewer parameters.Extensive experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art methods.Moreover,this work extends the experiment to prove the effectiveness of this method and shows better overall reconstruction quality.

A super-resolution reconstruction algorithm for mural images based on improved generative adversarial network

In order to solve the problem of the lack of ornamental value and research value of ancient mural paintings due to low resolution and fuzzy texture details,a super resolution(SR)method based on generative adduction network(GAN)was proposed.This method reconstructed the detail texture of mural image better.Firstly,in view of the insufficient utilization of shallow image features,information distillation blocks(IDB)were introduced to extract shallow image features and enhance the output results of the network behind.Secondly,residual dense blocks with residual scaling and feature fusion(RRDB-Fs)were used to extract deep image features,which removed the BN layer in the residual block that affected the quality of image generation,and improved the training speed of the network.Furthermore,local feature fusion and global feature fusion were applied in the generation network,and the features of different levels were merged together adaptively,so that the reconstructed image contained rich details.Finally,in calculating the perceptual loss,the brightness consistency between the reconstructed fresco and the original fresco was enhanced by using the features before activation,while avoiding artificial interference.The experimental results showed that the peak signal-to-noise ratio and structural similarity metrics were improved compared with other algorithms,with an improvement of 0.512 dB-3.016 dB in peak signal-to-noise ratio and 0.009-0.089 in structural similarity,and the proposed method had better visual effects.

Rapid hologram generation through backward ray tracing and adaptive-resolution wavefront recording plane

An advanced method for rapidly computing holograms of large three-dimensional(3D)objects combines backward ray tracing with adaptive resolution wavefront recording plane(WRP)and adaptive angular spectrum propagation.In the initial phase,a WRP with adjustable resolution and sampling interval based on the object’s size is defined to capture detailed information from large 3D objects.The second phase employs an adaptive angular spectrum method(ASM)to efficiently compute the propagation from the large-sized WRP to the small-sized computer-generated hologram(CGH).The computation process is accelerated using CUDA and OptiX.Optical experiments confirm that the algorithm can generate high-quality holograms with shadow and occlusion effects at a resolution of 1024×1024 in 29 ms.

Orthogonal matrix of polarization combinations:concept and application to multichannel holographic recording

Orthogonal matrices have become a vital means for coding and signal processing owing to their unique distributional properties.Although orthogonal matrices based on amplitude or phase combinations have been extensively explored,the orthogonal matrix of polarization combinations(OMPC)is a novel,relatively unexplored concept.Herein,we propose a method for constructing OMPCs of any dimension encompassing 4n(where n is 1,2,4,8,…)mutually orthogonal 2ncomponent polarization combinations.In the field of holography,the integration of polarization multiplexing techniques with polarization-sensitive materials is expected to emerge as a groundbreaking approach for multichannel hologram multiplexing,offering considerable enhancements in data storage capacity and security.A multidimensional OMPC enables the realization of multichannel multiplexing and dynamical modulation of information in polarization holographic recording.Despite consolidating all information into a single position within the material,we effectively avoided extraneous crosstalk during the reconstruction process.Our results show that achieving four distinct holographic images individually and simultaneously depends on the polarization combination represented by the incident wave.This discovery opens up a new avenue for achieving highly holographic information storage and dynamically displayed information,harnessing the potential of OMPC to expand the heretofore limited dimensionality of orthogonal polarization.

Isolation of Protoplast and Ion Channel Recording in Plasma Membrane of Suspension Cells of Populus euphratica

The authors used suspension cells of Populus euphratica to isolate protoplast in the present study. Protoplasts were successfully obtained after 4 hours incubation in enzyme solution containing 1 0% cellulase “onozuka” R\|10, 0\^01% pectolyase Y\|23,0\^15% macerozyme R\|10 and 0\^1% hemicellulase at 25℃. Outward and inward single channels in plasma membrane were observed using cell\|attached recording of patch\|clamp technique. In this study, single channel records showed that more than one species of channel were obtained. These attempts in protoplast isolation and ion channel recording offers the opportunity to characterize cellular mechanisms of salt tolerance in tree species.

Microelectrode array for bioelectrical signal stimulation and recording

A microelectrode array（MEA） is presented, which is composed of 60 independent electrodes with 59 working ones and one reference one, and they are divided into 30 pairs. Except for the reference electrode, each pair consists of one stimulating electrode and one recording electrode. Supported by the peripheral circuits, four electrode states to study the bioelectrical signal of biological tissue or slice cultured in-vitro on the surface of the electrodes can be realized through each pair of electrodes. The four electrode states are stimulation, recording, stimulation and recording simultaneously, and isolation. The state of each pair of working electrodes can be arbitrarily controlled according to actual needs. The MEAs are fabricated in printed circuit board （PCB） technology. The total area of the PCB-based MEA is 49 mm × 49 mm. The impedance measurement of MEA is carried out in 0.9% sodium chloride solution at room temperature by means of 2-point measurements with an Agilent LCR meter, and the test signal for the impedance measurement is sinusoidal （AC voltage 50 mV, sweeping frequency 20 Hz to 10 kHz）. The electrode impedance is between 200 and 3 kΩ while the frequency is between 500 and 1 000 Hz. The electrode impedance magnitude is inversely proportional to the frequency. Experiments of toad sciatic nerve in-vitro stimulation and recording and signal regeneration between isolated toad sciatic nerves are carried out on the PCB-based MEA. The results show that the MEA can be used for bioelectrical signal stimulation, recording, stimulation and recording simultaneously, and isolation of biological tissues or slices in-vitro.

FAULT LOCATION AND RECORDING SYSTEM OF TRANSMISSION LINES BASED ON COMPUTER NETWORK

In this paper a fault location and recording system based on a computer network is presented. A brief description of the system structure and main features are given. Emphasis is placed on the accurate fault location method for extra high voltage and long distance transmission lines.

An optimized recording method to characterize biophysical and pharmacological properties of acid-sensing ion channel

Objective To re-confirm and characterize the biophysical and pharmacological properties of endogenously expressed human acid-sensing ion channel 1a （hASIC1a） current in HEK293 cells with a modified perfusion methods. Methods With cell floating method, which is separating the cultured cell from coverslip and putting the cell in front of perfusion tubing, whole cell patch clamp technique was used to record hASICla currents evoked by low pH external solution. Results Using cell floating method, the amplitude of hASICla currents activated by pH 5.0 in HEK293 cells is twice as large as that by the conventional method where the cells remain attached to coverslip. The time to reach peak at two different recording conditions is （21±5） ms and （270±25） ms, respectively. Inactivation time constants are （496±23） ms and （2284±120） ms, respectively. The cell floating method significantly increases the amiloride potency of block on hASIC 1 a [IC50 is （3.4± 1.1 ） μmol/L and （2.4± 0.9） μmol/L, respectively]. Both recording methods have similar pH activation ECs0 （6.6±0.6, 6.6±0.7, respectively）. Conclusion ASICs channel activation requires fast exchange of extracellular solution with the different pH values. With cell floating method, the presence of hASIC la current was re-confirmed and the biophysical and pharmacological properties of hASIC la channel in HEK293 cells was precisely characterized. This method could be used to study all ASICs and other ligand-gated channels that require fast extracellular solution exchange.

Super-resolution reconstruction for license plate images of moving vehicles

A novel reconstruction method to improve the recognition of license plate texts of moving vehicles in real traffic videos is proposed, which fuses complimentary information among low resolution （LR） images to yield a high resolution （HR） image. Based on the regularization super-resolution （SR） reconstruction schemes, this paper first introduces a residual gradient （RG） term as a new regularization term to improve the quality of the reconstructed image. Moreover, L1 norm is used to measure the residual data （RD） term and the RG term in order to improve the robustness of the proposed method. Finally, the steepest descent method is exploited to solve the energy functional. Simulated and real acquired video sequence experiments show the effectiveness and practicability of the proposed method and demonstrate its superiority over the bi-cubic interpolation and discontinuity adaptive Markov random field （DAMRF） SR method in both signal to noise ratios （SNR） and visual effects.

Strong motion observations and recordings from the great Wenchuan Earthquake

The National Strong Motion Observation Network System （NSMONS） of China is briefly introduced in this paper. The NSMONS consists of permanent free-field stations, special observation arrays, mobile observatories and a network management system. During the Wenchuan Earthquake, over 1,400 components of acceleration records were obtained from 460 permanent free-field stations and three arrays for topographical effect and structural response observation in the network system from the main shock, and over 20,000 components of acceleration records from strong aftershocks occurred before August 1, 2008 were also obtained by permanent free-field stations of the NSMONS and 59 mobile instruments quickly deployed after the main shock. The strong motion recordings from the main shock and strong aftershocks are summarized in this paper. In the ground motion recordings, there are over 560 components with peak ground acceleration （PGA） over 10 Gal, the largest being 957.7 Gal. The largest PGA recorded during the aftershock exceeds 300 Gal.

Improved HVSR site classification method for free-field strong motion stations validated with Wenchuan aftershock recordings

Local site conditions play an important role in the effective application of strong motion recordings.In the China National Strong Motion Observation Network System（NSMONS）,some of the stations do not provide borehole information,and correspondingly,do not assign the site classes yet.In this paper,site classification methodologies for free-field strong motion stations are reviewed and the limitations and uncertainties of the horizontal-to-vertical spectral ratio（HVSR） methods are discussed.Then,a new method for site classification based on the entropy weight theory is proposed.The proposed method avoids the head or tail joggle phenomenon by providing the objective and subjective weights.The method was applied to aftershock recordings from the 2008 Wenchuan earthquake,and 54 free-field NSMONS stations were selected for site classification and the mean HVSRs were calculated.The results show that the improved HVSR method proposed in this paper has a higher success rate and could be adopted in NSMONS.

Magnetic property and recording performance of chemical deposition CoP thin films

The thickness of CoP thin films prepared by wet chemical deposition is of crucial importance on magnetic property and recording perform-ance. The coercivity of CoP films decreased with increasing film thickness. The coercivity was 45.37 kA m 1 at the thickness of 300 nm, and decreased to 21.65 kA m 1 at 5.7 μm. Recording performance tests indicate that, for drums with the same size, different recorded magnetic pole density have different thickness requirements. For 40 mm diameter magnetic drum, the optimal CoP thickness is 3～10 μm for 256 re-corded magnetic poles, 1～2 μm for 512 recorded magnetic poles, and 500～800 nm for 1024 recorded magnetic poles.