Yueting Chen

Antimicrobial resistance and molecular characterization of Staphylococcus haemolyticus in a Chinese hospital

The aim of this study was to perform the molecular characterization of methicillin-resistant Staphylococcus haemolyticus (MRSH) from clinical specimens of patients in a Chinese hospital. One hundred and thirty-three strains of S. haemolyticus collected from April 2002 to April 2003 were analyzed. Antimicrobial susceptibility to 15 antimicrobial agents was determined by the broth microdilution method. The resistant rates to penicillin G and oxacillin were higher than 90%. There were no isolates resistant to linezolid or vancomycin, and only 6.0% of the strains were resistant to teicoplanin. The positivity rate for mecA genes was 90.2% by polymerase chain reaction (PCR). Ninety MRSH (isolated from inpatients and mecA-gene-positive) were genotyped by pulsed-field gel electrophoresis (PFGE) after SmaI digestion. Twenty-five different PFGE patterns (A∼Y) were found and a major clone (type A; n = 36) with five subtypes was identified. Clone A was detected during a 1-year period. Identical PFGE types were found in different wards and patients. The results of this study suggest the clonal spread of MRSH within our hospital. This emphasizes the need for control and prevention measures.

Image Stabilization Using Motion Estimation and Micro-mechanical Compensation

Airborne and space-borne imaging systems are often limited in resolution by image degradation resulting from mechanical vibrations during image exposures. A new image stabilization method using digital motion estimation and micro-mechanical compensation is presented. Motion estimation is accomplished by an auxiliary high-speed camera using modified real-time digital image stabilization (DIS) algorithm. Then the image motions during exposure of the primary imaging system are eliminated by micro-mechanical compensation on the focal plane assembly with detected motion vectors to achieve high precision positioning. An imaging system model based on the proposed concept is built with commonly used facilities. Theoretical analysis and experimental result show that the performance of the proposed concept is convincing.

Image stabilization with support vector machine

We propose an image stabilization method based on support vector machine (SVM). Since SVM is very effective in solving nonlinear regression problems, an SVM model was constructed and trained to simulate the vibration characteristic. Then this model was used to predict and compensate for the vibration. A simulation system was built and four assessment metrics including the signal-to-noise ratio (SNR), gray mean gradient (GMG), Laplacian (LAP), and modulation transfer function (MTF) were used to verify our approach. Experimental results showed that this new method allows the image plane to locate stably on the CCD, and high quality images can be obtained.

Novel linear search for support vector machine parameter selection

Selecting the optimal parameters for support vector machine (SVM) has long been a hot research topic. Aiming for support vector classification/regression (SVC/SVR) with the radial basis function (RBF) kernel, we summarize the rough line rule of the penalty parameter and kernel width, and propose a novel linear search method to obtain these two optimal parameters. We use a direct-setting method with thresholds to set the epsilon parameter of SVR. The proposed method directly locates the right search field, which greatly saves computing time and achieves a stable, high accuracy. The method is more competitive for both SVC and SVR. It is easy to use and feasible for a new data set without any adjustments, since it requires no parameters to set.

Motion deblurring based on local temporal compressive sensing for remote sensing image

Abstract. This paper presents a motion deblurring method which can obtain both the motion information and the recovered image based on local temporal compressive photography. In this method, video blocks are reconstructed at the corners of the image sensor during a single exposure period. The displacement vector, which will be used to build the prior point spread function (PSF) for image deblurring, is then estimated from the reconstructed videos. With the use of the prior PSF, better recovered images can be obtained with much less iteration. An experiment system is also presented to validate the effectiveness of the proposed method. The experimental results show that the proposed method could provide recovered images of high quality.

Correcting temperature dependence in miniature spectrometers used in cold polar environments

Measurement biases arising from changes in temperature can be a major concern when using miniature spectrometers in extreme environments, particularly when temperature stabilization approaches are not feasible. Here, temperature-related biases of a low-power field spectrometry system comprised of a CMOS miniature monolithic spectrometer module and custom driver electronics were examined between -40°C and +25.6°C, well below the stated operating range of this particular spectrometer. Using these observations, a predictive model was developed to estimate the dark output of the spectrometry system within this extended operating range. This information was used to correct the signal at any measured integration time and temperature to that which would be measured at a reference integration time and temperature. This approach provides a general framework for assessing the temperature dependence of monolithic spectrometers whose field use will occur at temperatures outside of the range examined by the manufacturer.

Adaptive reconstruction for coded aperture temporal compressive imaging

This paper presents an adaptive reconstruction method for coded aperture temporal compressive imaging. A pixel-wise equal-exposure coding strategy is first implemented to induce speckle-like features in the moving area of the measured image. A moving area detection method is then proposed to reconstruct the adaptively segmented moving areas into a series of video frames, which are filled back into the stationary clear background. Both simulation and experimental results demonstrate that the proposed method significantly reduces the time consumption of video reconstruction without degradation of image quality.

Optical imaging system-based real-time image saliency extraction method

Abstract. Saliency extraction has become a popular topic in imaging science. One of the challenges in image saliency extraction is to detect the saliency content efficiently with a full-resolution saliency map. Traditional methods only involve computer calculation and thus result in limitations in computational speed. An optical imaging system-based visual saliency extraction method is developed to solve this problem. The optical system is built by effectively implementing an optical Fourier process with a Fourier lens to form two frequency planes for further operation. The proposed method combines optical components and computer calculations and mainly relies on frequency selection with precise pinholes on the frequency planes to efficiently produce a saliency map. Comparison shows that the method is suitable for extracting salient information and operates in real time to generate a full-resolution saliency map with good boundaries.

Image restoration of hybrid time delay and integration camera system with residual motion

In this paper, we present a hybrid system for time delay and integration (TDI) image restoration. Images are degraded by residual motion, which distorts and blurs the TDI images during exposures and excludes the along-track scanning motion. The motion trajectory is estimated from the image sequence captured by an auxiliary high-speed camera. In order to make the estimated results less sensitive to the imaging conditions and noise, a new method based on cross-correlation is introduced for motion estimation. Then geometric distortion of the TDI image is removed by choosing the correct blurred block according to the central of the corresponding motion trajectory and the final image is restored row by row with the Richardson-Lucy algorithm. Simulated and experimental results are given to prove the effectiveness of our system.

A Method of Autofocus for Remote Sensing Camera

Because of the flight of the remote sensing camera in the orbit, the successive images of the scenes captured by the remote sensing camera are different at any time. So it is difficult for the camera to implement autofocus. In this paper, an auto-focusing method in the remote sensing camera is proposed based on successive two images captured in a short time which have an overlapped region where scenes are same. Firstly, the space camera moving in the orbit, shoots one picture every time the camera adjusts its focus, and then we can obtain a sequence of images after several times, from which the displacement and the overlapped regions of two adjacent images can be calculated by image registration algorithm. We can take every two adjacent images as a group. Therefore, every image has a value of focusing accuracy by performing a sharpness evaluation function on the overlapped region of each image. Finally, according to the transfer characteristic of evaluation values of every two partly overlapped images, we can unify the evaluation values in a same merit evaluation system. And then find the maximum value of image evaluation values in a same evaluation system, so we can find the accurate focus. Simulation experiment shows that this method works pretty well in auto focusing when relative motion between the camera and the object is existed. This method can be used in aerial camera and remote sensing camera.