Ultrasound Imaging Signal Analysis of Underwater Topography in River Model Experiment

In this paper, we analyze the feature of ultrasonic image and investigate the effect of topography material, flow velocity and sediment concentration on the imaging of underwater topography by imaging experiments of model sands. These imaging experiments are conducted in river engineering physical model.The results show that the vertical distribution of pixel values is changed hugely at the position of imaging bright band of underwater topography. The imaging of underwater topography is not affected when flow velocity is below 40 cm/s and sediment concentration is below 5.0 ‰. The main influence factors of imaging signals are flow velocity and sediment concentration near the topographical bed. The resolution of ultrasound imaging signals is high, and the topography consisted of model sands with particle size smaller than 0.1 mm can be monitored well in the river model experiment.

Susceptibility-weighted imaging is suitable for evaluating signal strength in different brain regions of a rabbit model of acute hemorrhagic anemia

Acute hemorrhagic anemia can decrease blood flow and oxygen supply to brain, and affect its physiological function. While detecting changes in brain function in patients with acute hemorrhagic anemia is helpful for preventing neurological complications and evaluating therapeutic effects, clinical changes in the nervous systems of these patients have not received much attention. In part, this is because current techniques can only indirectly detect changes in brain function following onset of anemia, which leads to lags between real changes in brain function and their detection.

ACCURATELY DETERMINING PROPAGATION VELOCITY OF CORTICAL SPREADING DEPRESSION IN RATS BY OPTICAL INTRINSIC SIGNAL IMAGING

Cortical spreading depression(CSD)is a wave of neuronal and glial depolarization that propagates across the cortex at a rate of 2–5mm/min accompanied by reversible electroencephalogram(EEG)suppression,a negative shift of direct current(DC)potential,and change of optical intrinsic signals(OIS).Propagation velocity of CSD is an important parameter used to study this phenomenon.It is commonly determined in an electrophysiological way that measures the time required for a CSD wave to pass along two electrodes.Since the electrophysiology technique fails to reveal the spreading pattern of CSD,velocity calculated in this manner might be inaccurate.In this study,we combined the electrophysiological recording and OIS imaging(OISI)for detecting changes in DC potential and OIS during CSD simultaneously.An optical method based on OISI to determine the CSD velocity,which is measured by generating a series of regions of interest(ROI)perpendicular to the advancing wavefront along propagation direction of CSD at different time points and then dividing by the distance between ROIs over time,is presented.Comparison of the accuracy of the two approaches in determining the CSD velocity is made as well.The average rate of 33 CSDs is 3.52±0.87mm/min by use of the optical method and 4.36±1.65mm/min by use of the electrophysiological method.Because of the information about spreading pattern of CSD provided optically,the velocity determined by OISI is of smaller deviation and higher accuracy.

Design of Image Signal Processor for Hardware Size

The Image sensor needs various image processing by Image Signal Processor （ISP） to improve image quality. Conventional cameras have their own software ISP functions to perform in PC instead of using commercial ISP chips. However these methods have problems such as large computation for image processing. In this paper, th authors proposed ISP that significantly reduced chip area by efficiently sharing of hardware and software. Large operation blocks are designed to hardware for high performances, and hardware is imployed simultaneously with software considering the size of the hardware. The implemented ISP can process Video Graphics Array （VGA） （640 ＊ 480） images and has 91 450 gates size in 0. 35 μm process.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Separation of cortical arteries and veins in optical neurovascular imaging

Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics,such as the changes of cerebral blood flow,perfusion or oxygen con-centration in arteries and veins under different pathological and physiological conditions.Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio.In this work,we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging(LSI)and optical intrinsic signals(OIS)imaging.The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis(eLASCA),algorithm.The vascular pattern was extracted and seg-mented using region growing algorithm from the eLASCA-based LSI.Meanwhile,OIS imageswere acquired altermatively with 630 and 870 nm to obtain an oxy hemoglobin concentration mapover cerebral cortex.Then the separation of arteries and veins was accomplished by Otsuthreshold segmentation algorithm based on the OIS information and segmentation of LSI.Finally,the segmentation and separation performances were assessed using area overlap measure(AOM).The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases,as well as in clinical intraoperative monitoring.

Recent Advances and Challenges of Visual Signal Quality Assessment

While quality assessment is essential for testing, optimizing, benchmarking, monitoring, and inspecting related systems and services, it also plays an essential role in the design of virtually all visual signal processing and communication algorithms, as well as various related decision-making processes. In this paper, we first provide an overview of recently derived quality assessment approaches for traditional visual signals (i.e., 2D images/videos), with highlights for new trends (such as machine learning approaches). On the other hand, with the ongoing development of devices and multimedia services, newly emerged visual signals (e.g., mobile/3D videos) are becoming more and more popular. This work focuses on recent progresses of quality metrics, which have been reviewed for the newly emerged forms of visual signals, which include scalable and mobile videos, High Dynamic Range (HDR) images, image segmentation results, 3D images/videos, and retargeted images.

EFFECTS OF NAAG AND MPEP ON RAT CORTICAL SPREADING DEPRESSION

Cortical spreading depression(CSD)is a pathophysiological phenomenon.There are sufficient evidences to prove that CSD plays an important role in some neurological disorders.However,exact mechanisms of its initiation and propagation are still unclear.Previous studies showed that glutamate receptors could be concerned with CSD,but those studies were mostly performed oriented to ionotropic glutamate receptors(iGluRs).There is relatively little report about effects of metabotropic glutamate receptors(mGluRs)on CSD.Here,we applied optical intrinsic signal imaging(OISI)combined with direct current(DC)potential recording to examine influences of some mGluRs antagonist(or agonist)on CSD propagation in rat’s brain,to indirectly validate actions of some mGluRs on CSD.We found that N-acetyl-l-aspartyl-l-glutamate(NAAG,an agonist at mGluR3)inhibited the propagation of CSD,and the inhibition was gradually developed with time.However,6-methyl-2-phenylethynyl-pyridine(MPEP,an antagonist of mGluR5)did not produce any significant alterations with the CSD propagation.Our findings suggest that mGluR3 could play an important role in the CSD propagation,but the activity of mGluR5 was comparatively weak.These findings can help to understand the propagation mechanism of CSD,and consider the therapy of some neurological diseases involved with CSD.

A general truncated regularization framework for contrast-preserving variational signal and image restoration: Motivation and implementation

Variational methods have become an important kind of methods in signal and image restoration—a typical inverse problem. One important minimization model consists of the squared ?_2 data fidelity(corresponding to Gaussian noise) and a regularization term constructed by a potential function composed of first order difference operators. It is well known that total variation(TV) regularization, although achieved great successes,suffers from a contrast reduction effect. Using a typical signal, we show that, actually all convex regularizers and most nonconvex regularizers have this effect. With this motivation, we present a general truncated regularization framework. The potential function is a truncation of existing nonsmooth potential functions and thus flat on(τ, +∞) for some positive τ. Some analysis in 1 D theoretically demonstrate the good contrast-preserving ability of the framework. We also give optimization algorithms with convergence verification in 2 D, where global minimizers of each subproblem(either convex or nonconvex) are calculated. Experiments numerically show the advantages of the framework.

4th National Conference on Speech,Image,Communication,and Signal Processing,held in Beijing,25—27 October 1989

The 4th National Conference on Speech,Image,Communication and Signal Pro-cessing,which was sponsored by the Institute of Speech,Hearing,and Music Acoustics,Acoustical Society of China and the Institute of Signal Processing,Electronic Society ofChina,was held,25—27 October,1989,at Beijing Institute of Post and Telecommun-ication.The conference drew a registration of 150 from different places in the country,which made it the largest conference in the last eight years.The president of Institute of Speech,Hearing,and Music Acoustics,ASC,professorZHANG Jialu made a openning speech at the openning session,and the honorary presi-dent of Acoustical Society of China,professor MAA Dah-You and the president of

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising,existing methods mostly rely on simple noise assumptions,such as additive white Gaussian noise(AWGN),JPEG compression noise and camera sensor noise,and a general-purpose blind denoising method for real images remains unsolved.In this paper,we attempt to solve this problem from the perspective of network architecture design and training data synthesis.Specifically,for the network architecture design,we propose a swin-conv block to incorporate the local modeling ability of residual convolutional layer and non-local modeling ability of swin transformer block,and then plug it as the main building block into the widely-used image-to-image translation UNet architecture.For the training data synthesis,we design a practical noise degradation model which takes into consideration different kinds of noise(including Gaussian,Poisson,speckle,JPEG compression,and processed camera sensor noises)and resizing,and also involves a random shuffle strategy and a double degradation strategy.Extensive experiments on AGWN removal and real image denoising demonstrate that the new network architecture design achieves state-of-the-art performance and the new degradation model can help to significantly improve the practicability.We believe our work can provide useful insights into current denoising research.The source code is available at https://github.com/cszn/SCUNet.

A small microring array that performs large complex-valued matrix-vector multiplication

As an important computing operation,photonic matrix-vector multiplication is widely used in photonic neutral networks and signal processing.However,conventional incoherent matrix-vector multiplication focuses on real-valued operations,which cannot work well in complex-valued neural networks and discrete Fourier transform.In this paper,we propose a systematic solution to extend the matrix computation of microring arrays from the real-valued field to the complex-valued field,and from small-scale(i.e.,4×4)to large-scale matrix computation(i.e.,16×16).Combining matrix decomposition and matrix partition,our photonic complex matrix-vector multiplier chip can support arbitrary large-scale and complex-valued matrix computation.We further demonstrate Walsh-Hardmard transform,discrete cosine transform,discrete Fourier transform,and image convolutional processing.Our scheme provides a path towards breaking the limits of complex-valued computing accelerator in conventional incoherent optical architecture.More importantly,our results reveal that an integrated photonic platform is of huge potential for large-scale,complex-valued,artificial intelligence computing and signal processing.