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The control of the chemical reaction process duration is the key point under the deep liquid. The best way to get the reaction start duration is the reconstruction of four-dimensional temperature regression. However, there is no efficient ways to reconstruct the temperature field. Traditional methods have the weakness that long running time and low precise. In this paper, a new efficient method has been proposed to reconstruct the four dimensional field with the infrared camera. Some simulate has been tested to verify the advantage by comparing with ART Kaczmarz and SIRT Cimmino algorithm.

Unlike urine or blood samples with a single background, human fecal samples contain large amounts of food debris, amorphous particles, and undigested plant cells. It is difficult to segment such impurities when mixed with leukocytes. Cell degradation results in ambiguous nuclei, incompleteness of the cell membrane, and a changeable cell morphology, which are difficult to recognize. Aiming at the segmentation problem, a threshold segmentation method combining an inscribed circle and circumscribed circle is proposed to effectively remove the adhesion impurities with a segmentation accuracy reaching 97.6%. For the identification problem, five texture features (i.e., LBP-uniform, Gabor, HOG, GLCM, and Haar) were extracted and classified using four kinds of classifiers (support vector machine (SVM), artificial neural network, AdaBoost, and random forest). The experimental results show that using a histogram of oriented gradient features with an SVM classifier can achieve precision of 88.46% and recall of 88.72%.

With the development of the liquid crystal display (LCD) module industry, LCD modules become more and more precise with larger sizes, which demands harsh imaging requirements for automated optical inspection (AOI). Here, we report a high-resolution and clearly focused imaging optomechatronics for precise LCD module bonding AOI inspection. It first presents and achieves high-resolution imaging for LCD module bonding AOI inspection using a line scan camera (LSC) triggered by a linear optical encoder, self-adaptive focusing for the whole large imaging region using LSC, and a laser displacement sensor, which reduces the requirements of machining, assembly, and motion control of AOI devices. Results show that this system can directly achieve clearly focused imaging for AOI inspection of large LCD module bonding with 0.8 μm image resolution, 2.65-mm scan imaging width, and no limited imaging width theoretically. All of these are significant for AOI inspection in the LCD module industry and other fields that require imaging large regions with high resolution.

Traditional fecal erythrocyte detection is performed via a manual operation that is unsuitable because it depends significantly on the expertise of individual inspectors. To recognize human erythrocytes automatically and precisely, automatic segmentation is very important for extraction of characteristics. In addition, multiple recognition algorithms are also essential. This paper proposes an algorithm based on morphological segmentation and a fuzzy neural network. The morphological segmentation process comprises three operational steps: top-hat transformation, Otsu's method, and image binarization. Following initial screening by area and circularity, fuzzy c-means clustering and the neural network algorithms are used for secondary screening. Subsequently, the erythrocytes are screened by combining the results of five images obtained at different focal lengths. Experimental results show that even when the illumination, noise pollution, and position of the erythrocytes are different, they are all segmented and labeled accurately by the proposed method. Thus, the proposed method is robust even in images with significant amounts of noise.

Automatic identification of fungi in microscopic fecal images provides important information for evaluating digestive diseases. To date, disease diagnosis is primarily performed by manual techniques. However, the accuracy of this approach depends on the operator’s expertise and subjective factors. The proposed system automatically identifies fungi in microscopic fecal images that contain other cells and impurities under complex environments. We segment images twice to obtain the correct area of interest, and select ten features, including the circle number, concavity point, and other basic features, to filter fungi. An artificial neural network (ANN) system is used to identify the fungi. The first stage (ANN-1) processes features from five images in differing focal lengths; the second stage (ANN-2) identifies the fungi using the ANN-1 output values. Images in differing focal lengths can be used to improve the identification result. The system output accurately detects the image, whether or not it has fungi. If the image does have fungi, the system output counts the number of different fungi types.

For the attitude control of a novel stair-climbing wheelchair with inertial uncertainties and external disturbance torques, a new synchronous control method is proposed via combing high-order sliding mode control techniques with cross-coupling techniques. For this purpose, a proper controller is designed, which can improve the performance of the system under conditions of uncertainties and torque perturbations and also can guarantee the synchronization of the system. Firstly, a robust high-order sliding mode control law is designed to track the desired position trajectories effectively. Secondly, considering the coordination of the multiple joints, a high-order sliding mode synchronization controller is designed to reduce the synchronization errors and tracking errors based on the controller designed previously. Stability of the closed-loop system is proved by Lyapunov theory. The simulation is performed by MATLAB to verify the effectiveness of the proposed controller. By comparing the simulation results of two controllers, it is obvious that the proposed scheme has better performance and stronger robustness.

The traditional manual wheelchairs and even electric wheelchairs do not have the function of climbing stairs, which limits freedom of the users. A flat-staircase intelligent wheelchair is presented in this article to improve the quality of the users’ life, making them have more space and freedom. The article firstly introduces the mechanical structure of the intelligent wheelchair and elaborate the stair-climbing and flat-moving principle. Then according to the concept of modularization design, this article respectively puts forward the driving and controlling scheme of stair-climbing module and flat-moving module. Afterwards, the main controller hardware circuit based on TMS320F28335, operating lever hardware circuit and the driving hardware circuit of stair-climbing motor and flat-moving motor are given respectively. Next, the system software modules of the wheelchair is put forward and underlying driving program is written to realize motor control of brushless commutation and closed-loop speed control based on Partition PI algorithm. Then the joint debugging of the intelligent wheelchair is carried on and the test data figure is provided. Finally, the article summarizes the project and puts forward the prospects for future work.

Identifying fungi in microscopic leucorrhea images provides important information for evaluating gynecological diseases. Subjective judgment and fatigue can greatly affect recognition accuracy. This paper proposes an automatic identification system to detect fungi in leucorrhea images that incorporates a convolutional neural network, the histogram of oriented gradients algorithm, and a binary support vector machine. In experiments, the detection rate of the positive samples was as high as 99.8%. The experimental results demonstrate the effectiveness of the proposed method and its potential as a primary software component of a completely automated system.

Automatic detection of trichomonads in leukorrhea provides important information for evaluating gynecological diseases. Traditional manual microscopy, which depends on the operator's expertise and subjective factors, has high false-positive rates (i.e., low specificity) and low efficiency. To date, there are many detection methods for biological cells based on morphological characteristics. However, the morphology of trichomonads changes, and its size is not fixed; moreover, they are similar to human leukocytes. Therefore, it is difficult to classify trichomonads based on morphological characteristics. In this study, a moving object detection method based on an improved Kalman background reconstruction algorithm is proposed to detect trichomonads automatically, considering the dynamic characteristics of trichomonads at room temperature. The experimental results show that the trichomonads can be accurately identified, and the phenomena of tailing and ghosts are eliminated. Furthermore, this algorithm easily adapts to continuous or sudden changes in light, focal length variation, and the impact of lens shift, and it has good robustness and only a moderate amount of calculation burden.

To analyze the components of the fatty oil extracted from Antrodia camphorata fungus powder and to evaluate the antioxidant activity of A. camphorata crude oil. A derivatized gas chromatography mass spectrometry (GC–MS) was developed for the quantification of 19 fatty acids in extracts of A. camphorata obtained by supercritical carbon dioxide (SC-CO2). Good separation was obtained under the optimized chromatographic conditions and 19 target compounds after methyl-esterification were identified by GC–MS on a HP-VOC capillary column (60 m × 320 μm × 1.8 μm) with an initial temperature set at 80 °C. The validity of the established method was examined experimentally with good linearity, intra-assay precisions, repeatability, stability and recovery. The antioxidant activity of A. camphorata crude oil was evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical-scavenging assay. Nineteen fatty acids showed good linearity over the tested ranges (r > 0.9956) and the recovery ranged from 93.47 to 104.89 %. The crude oil extracted from A. camphorata fungus powder also revealed its antioxidant activity. It was the report about simultaneous determination of 19 fatty acids in A. camphorata and antioxidant activity of its crude oil for the first time. The established method might also be utilized for the investigation of edible plant materials and agricultural products containing fatty acids.

To explore rapidly the potential chemical markers for differentiating Pseudostellariae Radix from different cultivated fields and germplasms, a method is proposed based on ultra-performance liquid chromatography-triple time-of-flight mass/mass spectrometry (UPLC-Triple TOF-MS/MS) coupled with multivariate statistical analysis. Peak matching, peak alignment, and noise filtering were used in analyzing mass spectrometric data. Accurate m/z value analysis of MS data based on software of database search and MS/MS fragment analysis were applied to identify constituents. The obtained data were statistically analyzed with hierarchical cluster analysis (HCA), principal component analysis (PCA), and partial least squared-discriminant analysis (PLS-DA) to compare the differences among these samples. The PLS-DA loading plot obtained from all mass data showed that 21 compounds were identified as the potential chemical markers to characterize the samples. The results provide experimental data to reveal the influence of ecological environments and germplasms on metabolite biosynthesis in Pseudostellariae Radix.

Anisotropic conductive film (ACF) bonding is widely used in the liquid crystal display (LCD) industry. It implements circuit connection between screens and flexible printed circuits or integrated circuits. Conductive microspheres in ACF are key factors that influence LCD quality, because the conductive microspheres’ quantity and shape deformation rate affect the interconnection resistance. Although this issue has been studied extensively by prior work, quick and accurate methods to inspect the quality of ACF bonding are still missing in the actual production process. We propose a method to inspect ACF bonding effectively by using automated optical inspection. The method has three steps. The first step is that it acquires images of the detection zones using a differential interference contrast (DIC) imaging system. The second step is that it identifies the conductive microspheres and their shape deformation rate using quantitative analysis of the characteristics of the DIC images. The final step is that it inspects ACF bonding using a back propagation trained neural network. The result shows that the miss rate is lower than 0.1%, and the false inspection rate is lower than 0.05%.

The application of photoelectric encoder in visual optical robot system with multi-phalanges was introduced, a new method of incremental photoelectric encoder signal processing was put forward, a high-precision visual optical robot system with multi-phalanges was made. The basic principle of this new method is to use FPGA instead of traditional complex processing circuit, reconstruct the encoder signal waveform with self-adaptive voting method; then 4 times frequency subdivide the reconstructed signal, finally, realize the direct- identification and pulse count basing on A and B signal phase. The experiment results show that, the servo motors in the robot have a fastest speed of 4500 RPM, the photoelectric encoders output 10000 A, B signal pulse per turn, noise interference exists obviously when motors work. With this method, get the encoder signal processing circuit simplified, the power consumption reduced, the system anti-interference ability improved, and realized the direct-identification and accurate pulse count, meet the high-precision robot pose identification requirements.