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Silicon photonics has emerged as one of the most prominent technological platforms for photonic integration. Compared with silicon, silicon nitride has a much broader wavelength transparency range, lower propagation loss, and less sensitivity to temperature changes, making it suitable for the manufacture of passive devices. However, silicon nitride films deposited directly on the wafer by low-pressure chemical vapor deposition exhibit large tensile stress, which may cause cracks. In the work presented herein, a process was developed to fabricate crack-free 400-nm-thick silicon nitride films based on the 200-mm complementary metal–oxide–semiconductor platform. The silicon nitride films were deposited in two steps, each followed by annealing. During the annealing, Si–H and N–H bonds in the film were broken and Si–N bonds were formed, thus improving the uniformity of the film and releasing the stress. Fourier-transform infrared measurements indicated that the films were in near-stoichiometric condition. Wafer-level testing showed that the propagation loss of an 800-nm-wide strip waveguide was 0.5 dB/cm to 0.7 dB/cm for the C-band. The insertion loss of waveguide crossing was approximately 0.15 dB, and the crosstalk was below –43 dB. The insertion loss of 1 × 2 multimode interference was less than 0.3 dB, and the nonuniformity was smaller than 10%.

A compact multifunctional switchable filter based on parallel-coupled lines with three different filtering functions is presented in this letter. By using p-i-n switches to connect or disconnect microstrip line and shorted coupled lines, three different modes, i.e., wideband bandpass filter (BPF), bandstop filter (BSF), and dual-band BPF, can be realized. For demonstration, the switchable filter prototype is designed and fabricated. For the wideband BPF mode, the measured 3-dB fractional bandwidth is about 53.1% (1.41–2.43 GHz) with the measured insertion loss of 0.9 dB. For the BSF mode, the measured 10-dB fractional bandwidth is about 30.2% (1.63–2.21 GHz) with in-band suppression level of over 15.5 dB. For the dual-band BPF mode, the measured 3-dB bandwidths of the two passbands are around 3.8% and 2.7%, respectively.

Our study evaluates the effects of sacubitril/valsartan (SAC/VAL) in the rabbit model of doxorubicin-induced heart failure. Twenty rabbits (5 per group) were administered with doxorubicin (DOX, 1.5 mg kg−1, i.v.) to induce heart failure. Specific biomarkers such as BNP, CnT, CRP and ROMs were determined. The cardiac enzymatic anti-oxidant systems were recorded with their electrographic profiles. HR, SBP, DBP and MAP were restored at 5 or 10 mg kg−1 (p.o.) of SAC/VAL compared to DOX, followed by reduced levels of creatinine and BNP (p < 0.001). Significant improvements (p < 0.05) compared to DOX were also noticed in CAT, SOD and LPO with the same doses of SAC/VAL. Specific biomarkers such as BNP, CnT, CRP and ROMs descended significantly (p < 0.001) with treatment when compared to their baseline values. Our findings implied that SAC/VAL treatment reduced the inflammation and oxidative stress to improve the cardiac function.

Precision agriculture has been increasingly practised in recent years. Under precision management, farmland is divided into several management zones to implement different strategies and improve irrigation water productivity and revenues. In this research, six soil properties (silt, sand, soil moisture content, available nitrogen, electrical conductivity and elevation) were selected, and fuzzy c-means clustering was used to delineate management zones. The field was divided into three zones. The differences in the mean of the soil properties among the zones were large and within a zone were small. The coefficient of variation of the properties and yield were also smaller than that before classification. The optimization was carried out by using a genetic algorithm based on the Jensen model. Three objective functions were set as maximum yield, maximum irrigation water productivity (IWP) and maximum revenue, and the weights were kept equal to 1/3. The WHCNS (soil water heat carbon nitrogen simulator) model was used to simulate the maize yield under optimized irrigation schedule for the three management zones and to calculate IWP and revenue. Compared with uniform management, IWP and revenue were increased by 0.6 kg m−3 and $61 ha−1, respectively. The optimized irrigation schedule can be used as a reference for the actual irrigation management. It can increase the IWP and revenue under the premise of achieving the target yield. The results show that the method can guide precision agricultural production and management in large-scale farmland.

Common tracking algorithms only use a single feature to describe the target appearance, which makes the appearance model easily disturbed by noise. Furthermore, the tracking performance and robustness of these trackers are obviously limited. In this paper, we propose a novel multiple feature fused model into a correlation filter framework for visual tracking to improve the tracking performance and robustness of the tracker. In different tracking scenarios, the response maps generated by the correlation filter framework are different for each feature. Based on these response maps, different features can use an adaptive weighting function to eliminate noise interference and maintain their respective advantages. It can enhance the tracking performance and robustness of the tracker efficiently. Meanwhile, the correlation filter framework can provide a fast training and accurate locating mechanism. In addition, we give a simple yet effective scale variation detection method, which can appropriately handle scale variation of the target in the tracking sequences. We evaluate our tracker on OTB2013/OTB50/OBT2015 benchmarks, which are including more than 100 video sequences. Extensive experiments on these benchmark datasets demonstrate that the proposed MFFT tracker performs favorably against the state-of-the-art trackers.

This paper briefly describes the research status of oil shale pyrolysis technology and the main factors affecting oil shale pyrolysis, with emphasis on four kinds of commonly used catalysts: The effects of natural minerals, metal compounds, molecular sixes, and supported catalysts on the pyrolysis of oil shale were discussed. The changes of the pyrolysis mechanism and product composition of oil shale with the addition of different catalysts were discussed. Finally, the development direction of preparation of new catalysts was discussed, in order to provide a prospect for the development and utilization of unconventional and strategic alternative energy resources around the world.

Slot waveguide has attracted a lot of attention due to its ability to confine light in the low refractive index region, while strip waveguide acts as the basic component of guiding light due to its relatively low optical loss. In the multifunctional photonic integrated chips, it is critical to achieve the low loss transition between the strip waveguide and the slot waveguide. In this work, a silicon nitride strip-slot mode converter with high efficiency, large bandwidth, and large fabrication tolerance are proposed and demonstrated through the numerical investigation and experiments. The coupling efficiency of the mode converter is up to - 0.1 dB (97.7%), which enables the extremely low transition loss between the strip waveguide and the slot waveguide. Moreover, the fabrication process of silicon nitride photonic devices with high performance is introduced, which is fully compatible with the CMOS technology. Photonic devices based on silicon nitride with the characteristics of the low optical loss and the temperature insensitivity represent a new paradigm in realizing silicon-based photonic multifunctional chips.

In recent years, people’s daily lives have become inseparable from a variety of electronic devices, especially mobile phones, which have undoubtedly become necessity in people’s daily lives. In this paper, we are looking for a reliable way to acquire visual quality of the display product so that we can improve the user’s experience with the display product. This paper proposes two major contributions: the first one is the establishment of a new subjective assessment database (DPQAD) of display products’ screen images. Specifically, we invited 57 inexperienced observers to rate 150 screen images showing the display product. At the same time, in order to improve the reliability of screen display quality score, we combined the single stimulation method with the stimulation comparison method to evaluate the newly created display products’ screen images database effectively. The second one is the development of a new no-reference image quality assessment (IQA) metric. For a given image of the display product, first our method extracts 27 features by analyzing the contrast, sharpness, brightness, etc., and then uses the regression module to obtain the visual quality score. Comprehensive experiments show that our method can evaluate natural scene images and screen content images at the same time. Moreover, compared with ten state-of-the-art IQA methods, our method shows obvious superiority on DPQAD.

In this paper, a high-efficiency Doherty power amplifier (DPA) using an InGaP/GaAs heterojunction bipolar transistor (HBT) process is presented. The DPA aims for the application of 2.4 GHz wireless local area network (WLAN) systems with a high peak-to-average power ratio (PAPR). The traditional bulky quarter-wavelength line is realized using a low-pass n-type network for miniaturization and low loss. A thermally compensated dynamic bias circuit is applied to increase thermal stability. Under the excitation of a 40-MHz 64-quadratic-amplitude modu-lation(QAM) signal, the proposed DPA achieves a gain of 29 dB gain, an average power of 26 dBm, and an average power added efficiency (PAE) up to 21%. Good error vector magnitude (EVM) and adjacent channel leakage ratio (ACLR) performance are also measured, which demonstrates EVM and ACLR after digital pre-distortion (DPD) are −42 dB and −50.7 dBc. respectively.

Gas-liquid microextraction technique (GLME) has been integrated with dispersive solid phase extraction to establish a one-step sample pretreatment approach for rapid analysis of multi-class pesticides in different plant-derived foods. A 50 μL of organic solvent plus 40 mg of PSA were required throughout the 5-minute pretreatment procedure. Good trueness (recoveries of 67.2 – 105.4%) and precision (RSD ≤ 18.9%) were demonstrated by the one-step GLME method, with MLOQs ranged from 0.001 to 0.011 mg kg−1. As high as 93.6% pesticides experienced low matrix effect through this method, and the overall matrix effects (ME%) were generally better or comparable to QuEChERS. This method successfully quantified 2-phenylphenol, quintozene, bifenthrin and permethrin in the range of 0.001 – 0.008 mg kg−1 in real food samples. The multiresidue analysis feature of GLME has been validated, which displays further potential for on-site determination of organic pollutants in order to safeguard food safety and human health.

In recent years, increasing attention has been paid to devising tone mapping operators, which convert specialized high dynamic range (HDR) images to standard low dynamic range (LDR) ones for visualization on daily monitors. However, there lacks a reliable evaluation criterion for comparing distinct tone mapping operators, which is of great significance to the design and optimization of tone mapping methods. In this paper we propose an effective tone mapping evaluation system (TMES) based on a two-stage framework. In the first stage, features are extracted in view of the observations that luminance information, color saturation, statistical naturalness, structural fidelity and visual saliency have different and determinate influences on the perceptual quality of tone-mapped LDR images. In the second stage, the extracted features are integrated with a data-driven optimization strategy, which iteratively learns the parameters by applying thousands of collected tone-mapped LDR and natural images. Our TMES evaluation system can be implemented with or without reference HDR images, serving for the optimization and monitoring of tone mapping methods. Experiments conducted on three databases prove the superiority of our quality evaluation system.

Recent years have witnessed the spread of electronic devices especially the mobile phones, which have become almost the necessities in people’s daily lives. An effective and efficient technique for blindly assessing the quality of display products is greatly helpful to improve the experiences of users, such as displaying the pictures or texts in a more comfortable manner. In this paper, we put forward a novel no-reference (NR) quality metric of display products, dubbed as NQMDP. First, we have established a new subjective photo quality database, in which 50 photos shown on three different types of display products were captured to constitute a total of 150 photos and then scored by more than 40 inexperienced observers. Second, 19 effective image features are extracted by using six different influencing factors (including complexity, contrast, sharpness, brightness, colorfulness and naturalness) on the quality of display products and then were learned with the support vector regressor (SVR) to estimate the objective quality score of each photo. Results of experiments show that our proposed method has obtained better performance than the state-of-the-art algorithms.

Recently, many fire disasters took place all over the world. Therefore, the research communities pay more attention to the algorithms that can accurately detect fire disasters in the monitoring scene. Although the current detector algorithms achieve excellent performances in the benchmark object detection task, they can not achieve high recognition accuracy in fire monitoring tasks due to the fire disaster has no distinct rules in shape, color, and size. Moreover, the traditional learning-based object detection algorithms have limitations in model size and computational cost, so it can not be well applied in resource-constrained devices and Tactile Internet. In terms of the dataset, current learning-based object detection algorithms do not consider the impact of some fair weather. The models trained by such training sets are easily affected by the weather. We propose a new Lightweight model called LFNet, which has a small size and low computational cost to solve these problems. We also published a large-scale benchmark dataset to train the fire disaster detection model. To make the learning-based model trained by the proposed dataset possess higher robustness in various weather conditions, we utilize the atmospheric scattering model to simulate the original images into pictures affected by different degrees of simulated haze and dust for training. Experimental results show that the proposed LFNet can achieve state-of-the-art performance with limited model size. The presented data augmentation method can significantly improve the robustness of the current object detection model.

Underwater image enhancement is an important low-level task for marine industry. This paper proposed an underwater image enhancement method based on colour constancy theory. Compared with many existing researches, time and space complexity of the method proposed in this paper is low and our method achieves excellent performance. Firstly, we analyzed the underwater imaging model and distortion of underwater images. Then, by compensating the specific channel and local white balance, the linear transformation is performed on the images to restore distortion in the images. Finally, the results are obtained by stretching the histogram on RGB channel and intensity channel of images. Experiments show that our method has achieved excellent performance.

Sonar imaging has broad and critical applications in marine technologies since it is able to solve the problem that the optical system cannot operate effectively in turbid water. During the process of underwater acoustic channel transmission, sonar images are easy to degrade and thus seriously influence the resultant feature extraction and target recognition. To that end, we devise a new SOnar Image Quality Evaluation (SOIQE) method in this paper for better monitoring and guaranteeing the visual quality of sonar images. Guided by highlighting the important task objects, our proposed SOIQE method makes a pixel-wise fusion of structures extracted at multiple scales. Experimental results on the newly established sonar image quality database show that our SOIQE approach is superior to the state-of-the-art image quality models in terms of implement efficiency and evaluation performance.

Computationally efficient optimal solutions for selecting a subset of antennas to maximize the mutual information of a MIMO channel have eluded the practitioners due to its combinatorial nature, and the performance gap is widened with massive MIMO. In this work, recent advances in deep learning are leveraged to develop a deep neural network (DNN) based receive antenna selection solution for a given problem dimension. We detail the neural network structure and evaluate several relevant figures of merit via numerical simulations. This data-driven solution is shown to achieve near optimal mutual information in simple settings, but does not scale naturally with the problem dimension. For the practical scenario where the number of selected antennas is unknown a priori, hybrid greedy solutions are proposed which build on the DNN-based solution for a given dimension and then greedily increase or decrease the number of antennas to approximate the optimal solution of the new problem dimension. Numerical simulations demonstrate the effectiveness of the hybrid solutions.

Sonar technology plays an important role in the development of marine resources and military strategy. Due to the bad underwater acoustic channel, the sonar image collected by sonar technology equipment is affected by various kinds of distortions easily. To obtain high-quality sonar image, we devise a novel dual-path deep neural network (DPDNN) to measure the quality of sonar image. In these two paths, we use the batch normalization layer to reduce the training time and take the skip operation to speed up the feature extraction. Based on the above two operations, we extract the micro-scopic and macro-scopic structure of sonar image, respectively. Finally, the global average pooling layer and the fully connection layer are used to connect the above two paths. Experiments show that our DPDNN has a significant improvement in prediction performance and efficiency, respectively.

Silicon nitride material is widely used in photonics and other disciplines such as biology, life sciences, data communication, and sensing. These applications benefit from that silicon nitride has a much broader wavelength transparency range, lower propagation loss and lower thermo-optical coefficient compared with silicon material. However, it is very difficult to deposit thick silicon nitride films on large size wafer directly, which introduces a larger tensile stress and may produce cracks. In this paper, a process to fabricate crack-free 400-nm-thick silicon nitride films was discussed, leveraging 200- mm silicon photonics platform, which is compatible with CMOS process. The silicon nitride layer was deposited on the buried oxide layer via low-pressure chemical vapor deposition in two steps, followed by annealing separately, which can effectively overcome the problem of excessive stress in the films. Chemical mechanical polish technique was applied to planarize the silicon nitride layer and the oxide cladding layer. Annealing improves the uniformity and reduces the stress of the silicon nitride films. Using this process, 1-μm-wide strip waveguide was obtained with low propagation losses for the C-band (0.65 dB/cm). Furthermore, the propagation loss of 0.9-μm-wide strip waveguide was measured to be 0.7dB/cm; the insertion loss of 1×2 multi-mode interference was less than 0.3dB and the non-uniformity was smaller than 10%; the insertion loss of crossing was approximately 0.17dB and the crosstalk was lower than -40dB.

In this work, an ultra-compact and high sensitivity ring resonator-based liquid sensor on a CMOS-compatible SOI platform, utilizing a horizontal slot hybrid plasmonic (HP) waveguide consists of the Cu plasmonic layer suspending above the Si channel waveguide as the sensing element, is proposed. Numerical results suggest that the waveguide sensitivity, 𝑆𝑤, of the proposed HP waveguide reaches as high as 0.84 with the propagation loss less than 0.06 dB/μm, showing a similar Sw and a lower propagation loss in comparison to the typical plasmonic waveguide. In addition, with the scenario of SOI photonic platform continuously standardizing with the established CMOS methodologies for taking the advantages of the CMOS technology, the proposed miniaturized liquid sensor employing the Cu plasmonic element indicates the potential for the monolithic electronic-photonic integration. The sensitivity of the proposed liquid sensor with a miniaturized radius of 2 μm is numerically demonstrated as 540.0 nm/RIU, which is approximately four times higher than that of the conventional Si ring resonator with the identical geometry. Meanwhile a larger FOM is also achieved in comparison to other reported HP ring sensors.

The processes of warping and rendering in Depth Image-Based Rendering (DIBR) are usually performed without a reference image. Thus, an effective and fast reference-free (RF) image quality assessment (IQA) model devised for DIBR-synthesized views is more favorable. To this aim, this research propose a novel RF IQA method of DIBR-synthesized views based on Energy Loss Estimation. Firstly, considering that the geometric distortions caused by the processes of warping and rendering might increase the high-frequency (HF) information of a DIBR-synthesized view, the influence of the geometric distortion on the visual quality of a synthesized view can be effectively evaluated by estimating the HF information energies of a given DIBR-synthesized view in the log-gabor domain. Secondly, we estimate the energy loss in the process of JPEG compression as the image complexity, which is used to overcome the problem of different amounts of HF information contained in different content images. Finally, the proposed RF DIBR-synthesized IQA metric is obtained by using the energy loss in JPEG compression to normalize the HF information energy in the log-gabor domain. Experiments conducted on two public datasets demonstrate that the proposed method is advantageous over the relevant state-of-the-art RF IQA metrics.

Silicon nitride photonics has enabled a broad class of low-loss integrated devices and chip-scale solutions that benefit from transparency over a wide wavelength range and compatibility with complementary metal-oxide-semiconductor process, allowing applications of tele/datacom, artificial intelligence, sensing and bio-photonics. As a complimentary platform to silicon-on-insulator photonics, low-loss silicon nitride waveguide technology was present to extend the capabilities of silicon photonics platform. Silicon nitride films prepared by low pressure chemical vapor deposition have low intrinsic loss and good repeatability, but silicon nitride film is prone to crack when the thickness is no less than 300 nm. In order to reduce high stress, the wafer-level silicon nitride waveguides with 400 nm thickness were fabricated by two-step deposition method on 200-mm complementary metal-oxide-semiconductor pilot-line. Due to the different linewidth change between single-stripe and multiple-stripe structure after the same layer etch process, the layout compensation is needed to correct this deviation. But the linewidth sensitivity for silicon nitride waveguides is relatively low, propagation losses in the C-band were slightly reduced from 0.66 dB/cm for 0.9 μm width down to 0.62 dB/cm for 1 μm width.

With the rapid development of multimedia interactive applications, the processing volume of the screen content (SC) images is increasing day by day. The research on image quality assessment is the basis of many other applications. The focus of general image quality assessment (QA) research is natural scene (NS) images, now for the quality assessment research of SC images becomes very urgent and has received more and more attention. Accurate quality assessment of SC images helps improve the user experience. Based on these, this paper proposes an improved method using very sparse reference information for accurate quality assessment of SC images. Specifically, the proposed method extracts macroscopic, microscopic structure and color information respectively, and measures the differences in terms of macroscopic, microscopic features and color information between the original SC image and its distorted version, and finally calculates the overall quality score of the distorted SC image. The quality assessment model we built uses a dimension reduction histogram and only needs to transmit very sparse reference information. Experiments show that the proposed method has obvious superiority over the state-of-the-art relevant quality metrics in the visual quality assessment of SC images.

The aging phenomenon in current society is getting more and more serious, and the health status of the elderly is facing challenges. A fracture or other disease caused by a fall, or a fall due to a sudden illness, may result in serious consequences if not treated in time. According to the difference between fall and normal activities, this paper proposes a method for detecting human fall by ELM based on inertial measurement unit. By placing the inertial measurement unit on the human body, the human acceleration and angular velocity data are sampled and the human body attitude is calculated. The ELM training is performed on the human characteristic data to obtain the ELM classification function, and the measurement data is classified by the ELM classification function to determine whether it is in a falling state. The human fall experiment is carried out, and the result shows that the ELM method can accurately detect the fall state of the human. The use and promotion of this method is of great significance for the treatment of falls and reduction of casualties.

A novel cleanup technique termed as gas purge-microsyringe extraction (GP-MSE) was evaluated and applied for polycyclic aromatic hydrocarbon (PAH) determination in road dust samples. A total of 68 road dust samples covering almost the entire Shanghai area were analyzed for 16 priority PAHs using gas chromatography-mass spectrometry. The results indicate that the total PAH concentrations over the investigated sites ranged from 1.04μg/g to 134.02μg/g dw with an average of 13.84μg/g. High-molecular-weight compounds (4-6 rings PAHs) were significantly dominant in the total mass of PAHs, and accounted for 77.85% to 93.62%. Diagnostic ratio analysis showed that the road dust PAHs were mainly from the mixture of petroleum and biomass/coal combustions. Principal component analysis in conjunction with multiple linear regression indicated that the two major origins of road dust PAHs were vehicular emissions and biomass/fossil fuel combustions, which contributed 66.7% and 18.8% to the total road dust PAH burden, respectively. The concentration of benzo[a]pyrene equivalent (BaPeq) varied from 0.16μg/g to 24.47μg/g. The six highly carcinogenic PAH species (benz(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k)fluoranthene, dibenz(a,h)anthracene, and indeno(1,2,3-cd)pyrene) accounted for 98.57% of the total BaPeq concentration. Thus, the toxicity of PAHs in road dust was highly associated with high-molecular-weight compounds.

Spatial and temporal variability of soil properties, irrigation schedules, and fertilization management play an important role in crop growth and yield in agricultural systems. However, how to improve and accurately predict crop yield in different management zones (MZs) within a large-scale field are important but rarely discussed. In this study, coefficient of variation, linear correlations, partial least squares discriminant analysis and quantile regression were used to quantify relationships between crop and soil properties, and determine key explanatory factors of yield, and to identify suitable ways to improve crop yield among MZs. At the temporal scale, coefficients of variation of crop growth parameters (LAI and biomass), and correlations of crop growth parameters with soil water content and available nitrogen varied during crop growth stages in MZs across years. Crop–soil correlations varied with time and were most pronounced in heading and filling stages when LAI and biomass approached maximum values, respectively. At the spatial scale, 22 explanatory factors belonging to four categories: soil physical properties, initial soil properties, soil water and nitrogen dynamics, and topography were compared. Results showed that among the four categories, initial soil properties had the greatest influence on yield across the whole field, whereas soil water and nitrogen dynamics were the most influential for individual MZs. Key explanatory factors of yield varied across MZs in 2017 and 2018. Although key explanatory factors varied across years within a MZ, some remained unchanged, especially soil physical properties. Based on the quantile regression results, the same key explanatory factors might appear in MZs, but the reference ranges were different. For more sustainable and efficient agricultural production, distributed integrated management, including soil amelioration before sowing, and irrigation and N fertilization scheduling during the crop growth period should be implemented in MZs based on the spatial-temporal variability of soil properties.

Pedestrian navigation is an essential element in people's daily life. In some places where GPS signals are missing or weak, it is difficult to perform normal high precision navigation. As a strong supplement, inertial navigation does not depend on any external signals, but it has a large cumulative error. In this paper, a pedestrian navigation method with multi-sensor correction is studied based on the zero-velocity correction principle. The error source of pedestrian inertial navigation is analyzed and a multi-sensor error correction system is constructed. Based on the inertial sensor gyroscope and accelerometer, the magnetometer and altimeter are introduced to calculate the orientation and height. In the stance phase of pedestrian walking process the velocity and angular rate are reset and the orientation and height from navigation solution are corrected. The Kalman filter method is used to track and estimate the correction error for compensating the inertial navigation result. A pedestrian walking experiment was carried out. The results show that the constructed multi-sensor pedestrian navigation correction system can effectively track the pedestrian trajectory with a horizontal average error of 1.82% and a height average error of 2.53%. Therefore, the method can accurately perform pedestrian navigation and positioning.

The general tracking algorithm is vulnerable to noise because of using a single feature, makes the performance and robustness of the those algorithms greatly limited. In this paper, in order to achieve the robust and pretty performance, we propose a novel multiple feature fused model in correlation filter framework for visual tracking. The adoption of complementarity between different features can effectively eliminate the effects of noise and maintain their advantages of different features. While the correlation filter framework can provide a fast training and locate mechanism. In addition, we give a simple but effective scale detection method, which can appropriately handle the scale variation in the tracking sequences. We evaluate our tracker on OTB2013 benchmark, which include 51 video sequences. On this dataset, our results show that the proposed approach achieves a promising performance.

Robust visual object tracking is one of the most challenging issues in the field of computer vision. Because of the circular shifts strategy, correlation filter-based trackers show a great efficiency in tracking task and thus receive lots of attentions. However, most of the correlation filter-based trackers fix the scale of the targets in each frame and use single template to update the filters, which makes the trackers unreliable in the tracking task. In this paper, we intend to promote the robustness of the kernelized correlation filters (KCF) in the tracking task, through a fast scale pyramid solution to solve the scale variations problems. Furthermore, we introduce a sparse model selection scheme on template sets to solve the problem of contaminated templates in single template methods. We test our method on OTB-2013 dataset and the experimental results show the robustness of our method. The proposed tracker achieves promising performance both in terms of accuracy and speed comparing with the state-of-the-art trackers.

The accurate localization of a object target is a challenging research issue in visual tracking. Most correlation filter based tracking algorithms has been degraded their performances because of the weaknesses of their search strategy. This paper investigates the problem of accurate location the object target in visual tracking sequences. We propose a novel particle filter re-detection tracking approach for target re-location, when the kernelized correlation filters tracking result becomes unreliable. Additionally, we give a new target scale evaluation. Different from other proposed scale search strategies, our method merely consider the difference between the maximum value of the response map of adjacent frames. Extensive experiments are performed on the OTB2013 dataset. On the result of this benchmark, the proposed approach achieves a pretty performance.

Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed. However, in some complicated tracking scenes, there is a fatal defect that causes the object to be located inaccurately. In order to address this problem, we propose a particle filter redetection based tracking approach for accurate object localization. During the tracking process, the kernelized correlation filter (KCF) based tracker locates the object by relying on the maximum response value of the response map; when the response map becomes ambiguous, the KCF tracking result becomes unreliable. Our method can provide more candidates by particle resampling to detect the object accordingly. Additionally, we give a new object scale evaluation mechanism, which merely considers the differences between the maximum response values in consecutive frames. Extensive experiments on OTB2013 and OTB2015 datasets demonstrate that the proposed tracker performs favorably in relation to the state-of-the-art methods., Comment: Multimedia Tools and Applications

A high-efficiency high step-up dc-dc converter is proposed for renewable energy harvesting, where the sustainable power sources, such as PV panels and fuel cells, are characterized by low-voltage high-current output. The proposed converter employs the current-fed half-bridge structure to generate a square-wave current source. Then, a multiresonant tank is used to provide high voltage gain, absorb the parasitic parameters of the transformer, and create zero-current switching (ZCS) condition for all primary switches. By applying a single capacitor instead of LC for the output filter, the maximum voltage of the resonant capacitor is clamped to the output voltage, which reduces the circulating energy within the circuit and offers ZCS to the rectifying diodes. The operation principle is introduced and the steady-state analysis is also carried out for the derivation of the dc voltage gain and the ZCS conditions of the primary switches. The performance of the proposed converter was verified by a 150-W prototype. A peak efficiency of 95.2% was achieved by running the prototype at 255 kHz with 23-V input and 350-V output.

Masan Bay is a land locked shallow water system with poor water circulation. The bay is surrounded by thickly populated cities with considerable industrialization. A wastewater tratment plant (WWTP) manages both domestic and industrial sewage from Masan and Changwon cites and empties the treated water into the bay. Since persistent chemicals of toxic nature escape this process an experiment was designed to investigate in sediments the distribution and dissipation of 5 major groups of agro- and industrial chemicals (51 chemicals in total) from the point of discharge in all directions of the bay. Target chemicals showed patterns of distribution that agreed with expected spatial ditribution model and minor deviations due to diffuse sources of input (e.g. runoff from streams and creeks; ship building and port activities). Thus it is understood that WWTP plays an important role in distributing pollutants to Masan Bay.

In order to assess environmental quality and identifypollution sources, many kinds of molecular makers havebeen developed. For example, linear alkylbenzenes havebeen used as molecular tracers of domestic wastes (Chalauxet al., 1995; Ishiwatari et al., 1983); stanol and stenol forfecal pollution (Gagosian et al., 1979; Leeming et al.,1996); and polycyclic aromatic hydrocarbons (PAHs) foroil finger printing (Kennicutt et al., 1994; Yunker et al.,1996).Li et al. (2004a) reported that industrial wastewater is amajor source of nonylphenol in Korea, and concentrationsof nonylphenol determined in industrial areas are severalhundred times higher than those obtained in municipalareas. Nonylphenol is a degradation product of nonylphe-nol polyethoxylates (NPEOs), which is a surfactant used inindustry and household (Giger et al., 1984; Isobe et al.,2001). Due to toxicity and endocrine disruption resultingfrom the degradation products of NPEOs (Hemmeret al., 2002), these substances are no longer used in domes-tic applications in many developed countries. For example,the production and usage of NPEOs has been banned orrestricted in some countries, including the USA and EU(Renner, 1997). However, NPEOs are still in use as indus-trial detergents in the metal, paper and textile industries insome countries, including Korea, because they are inexpen-sive and have excellent cleaning properties. Hence nonyl-phenol and nonylphenol monoethoxylate were used asmolecular tracers of industrial pollution in this study.Coprostanol and related sterols are generally used asmolecular markers for tracing domestic pollution, such ashuman fecal pollution. Coprostanol is produced from thehydrogenation of cholesterol by bacteria in the humandigestive system (Martin et al., 1973; Rosenfeld and Galla-gher, 1964). High levels of coprostanol have been deter-mined in human and mammalian fecal material anddomestic waste waters (Leeming et al., 1996; Jeng et al.,1996). To study pollution sources in the environment, acritical analysis of the sterol group is required becausethe sterols are produced by various organisms and theircompositions vary accordingly (Leeming et al., 1996).Based on sterol composition, the relative contributionsfrom multiple sources of various organic matters (marine,terrestrial, natural and anthropogenetic inputs) can bedetermined (Hudson et al., 2001). In this study, we onlyanalyzed coprostanol, because our main intention was toinvestigate human fecal pollution sources. The study area(Kyeonggi Bay) is in a coastal zone surrounded by indus-trial and housing complexes, both of which are substantialcontributors to local pollution. Eleven phenolic com-pounds and coprostanol were analyzed from sediment sam-ples; nonylphenol and coprostanol were used as industrialand fecal pollution tracers, respectively.Surface sediment samples were collected from 33 sites inMay 2003, as shown in Fig. 1. Stations K1 to K6 werelocated in the Han River estuary area, which is the biggestriver in South Korea; stations K8 and K9 were located inIncheon North Harbor (INH); stations K7 and K10 werelocated in the mouth of INH; and stations K11 to K20were located along the waterway of INH, except for K18,located in Incheon South Harbor (ISH). Drain-outletpumping out industrial waste water produced from the Shi-hwa industrial complex is located nearby to station K30,and stations K28 to K32 were thus exposed to this. Theremaining stations (K21 to K27 and K33) are located farfrom suspected pollution sources. Global positioning sys-tem (GPS) was used to locate the sample positions. Thesediment samples were sampled with a van-Veen grab,and surface sediments ( 2 cm) were collected in glass bot-tles with a Teflon lined cap. Immediately after collection,samples were frozen on dry ice to protect them from microbiodegradation, then transferred to the laboratory andstored at 20 C until analysis.For alkylphenols (APs), chlorophenols and bisphenol A(BPA), sediment samples were analyzed according to themethods of Li et al. (2001, 2003). Briefly approximately3 g wet sediment was placed in a 50 mL Teflon centrifugetubewithaTefloncap.After theaddition ofsurrogate stan-dards (heptylphenol and bisphenol A-d

Occlusion is one of the most challenging problems in visual object tracking. Recently, a lot of discriminative methods have been proposed to deal with this problem. For the discriminative methods, it is difficult to select the representative samples for the target template updating. In general, the holistic bounding boxes that contain tracked results are selected as the positive samples. However, when the objects are occluded, this simple strategy easily introduces the noises into the training data set and the target template and then leads the tracker to drift away from the target seriously. To address this problem, we propose a robust patch-based visual tracker with online representative sample selection. Different from previous works, we divide the object and the candidates into several patches uniformly and propose a score function to calculate the score of each patch independently. Then, the average score is adopted to determine the optimal candidate. Finally, we utilize the non-negative least square method to find the representative samples, which are used to update the target template. The experimental results on the object tracking benchmark 2013 and on the 13 challenging sequences show that the proposed method is robust to the occlusion and achieves promising results.

Objective: To investigate the effect of berberine (BBR) on intestinal barrier function in nonalcoholic fat liver disease (NAFLD) in rats. Materials and Methods: Rats were divided into three groups: normal diet group (control group [CON group]), high-fat diet feeding group (HFD group), and HFD with BBR group. After 8 weeks of HFD feeding, rats in the BBR group were given BBR intragastrically at a dose of 150 mg/kg daily for 4 weeks. The same volume of normal saline was given to the CON and HFD groups. Liver index was detected, and Sudan black B staining was used to study fatty degeneration, also the expression level of occluding and intestinal flora was analyzed. Results: BBR administration significantly reduced HFD-induced increase in body weight (CON group: 379.83 ± 61.51 g, HFD group: 485.24 ± 50.15 g, and BBR group: 428.60 ± 37.37 g). It obviously alleviated the HFD-induced liver fatty degeneration and histopathological changes of intestinal mucosa according to liver index low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, and total cholesterol (P < 0.05). The triglyceride, alanine transaminase, and aspartate aminotransferase levels were greatly elevated after BBR treatment (P < 0.05); while endotoxin, intestinal fatty acid-binding protein, and tumor necrosis factor-α were significantly reduced (P < 0.05). Moreover, we found that BBR could obviously elevate the level of occludin and decrease the level of Faecalibacterium prausnitzii and upregulate the level of bacteroides. Conclusion: BBR provides significant protection in NAFLD through ameliorating intestinal barrier function. SUMMARY Berberine (BBR), an alkaloid that can be isolated from many plants, has been medically used for its wide range of antimicrobial and anti-inflammatory effectsThis is a study of BBR on liver function and intestinal barrier function in nonalcoholic fat liver disease (NAFLD)BBR treatment for NAFLD could significantly restore the liver function and provide significant protection in NAFLD through ameliorating intestinal barrier function. Abbreviations used: BBR: Berberine, NAFLD: Nonalcoholic fat liver disease, ALT: Alanine transaminase, AST: Aspartate aminotransferase, TG: Triglyceride, I-FABP: Intestinal-fatty acid-binding protein, IBD: Inflammatory bowel disease.

Based on the research of gas flow liquid phase microextraction (GF-LPME) system, a combining interface between GF-LPME system and gas chromatography (GC) and an auto-sampling equipment have been researched and developed, which can be used for rapid extraction, separation, enrichment and sampling for volatile or semi-volatile analyte. The control system of the combining technique is designed through virtual instrument technology, and the working conditions of the control system detected by control panel. Experiments show that, this device with the advantages of simple extraction structure, convenient operation, high accuracy and extensible functions, can be used in gas chromatography analysis for complex samples of trace compounds in the area of medicine, agriculture, food, environment, etc.

The two input filter inductors of the previously proposed current fed multi-resonant converter (CFMRC) occupy too much volume and hence restrict the power density of the converter. In this paper, an integrated magnetic structure is adopted to the CFMRC's input inductors to cut down the size, cost and loss of the CFMRC. With proper design, the inverse coupling of input inductors apparently reduces the ripple of the current flowing through the inductors. Thereby, the inductance can be reduced to further decrease the core size and copper length, and hence farther improve the power density as well as the efficiency of the converter. The above mentioned merits are verified by a 150 W prototype with integrated input inductors operating at 255 kHz switching frequency, the volume of the input inductors is reduced from 25410 mm3 to 7056 mm3 after integrating, and the full load efficiency is increased from 95.4% to 95.9%.

A high step-up current fed multi-resonant converter (CFMRC) has been proposed recently for renewable energy harvesting where the sustainable power sources such as PV panels and fuel cells are characterized by low-voltage, high-current output. An output voltage doubler is applied to the previously proposed CFMRC for the purpose of reducing the turns-ratio of the transformer. Compared to the CFMRC with full bridge rectifier, the CFMRC with output voltage doubler simplifies the transformer structure and hence reduces the cost. By applying the interleaving technique, the ac resistances of the transformer windings are greatly reduced. When running a 150 W prototype at 255 kHz with 23 V input and 350 V output, a peak efficiency of 95.95% is achieved.

A high efficiency high step-up dc-dc converter is proposed for renewable energy harvesting where the sustainable power sources such as PV panels and fuel cells are characterized by low-voltage, high-current output. The proposed converter employs the current fed half-bridge structure to generate a square wave current source. Then, a multi-resonant tank is used to provide high voltage gain, absorb the parasitic parameters of the transformer and create zero-current switching (ZCS) condition for all primary switches. By applying single capacitor instead of LC for the output filter, the maximum voltage of the resonant capacitor is clamped to the output voltage which minimizes the circulating energy within the circuit and offers ZCS to the rectifying diodes. The performance of the proposed converter was verified by a 150 W prototype. A peak efficiency of 95.2% was achieved by running the prototype at 255 kHz with 23 V input and 350 V output.

A high efficiency high step-up dc-dc converter is proposed for renewable energy harvesting where the sustainable power sources such as PV panels and fuel cells are characterized by low-voltage, high-current output. The proposed converter employs the current fed half-bridge structure to generate a square wave current source. Then, a multi-resonant tank is used to provide high voltage gain, absorb the parasitic parameters of the transformer and create zero-current switching (ZCS) condition for all primary switches. By applying single capacitor instead of LC for the output filter, the maximum voltage of the resonant capacitor is clamped to the output voltage which minimizes the circulating energy within the circuit and offers ZCS to the rectifying diodes. The performance of the proposed converter was verified by a 150 W prototype. A peak efficiency of 95.2% was achieved by running the prototype at 255 kHz with 23 V input and 350 V output.

In the development of grid-connected PV systems, efficiency plays a key role for solar farmers to shorten their capital payoff. Moreover, it is found that the architecture of a grid-connected PV system affects its efficiency greatly. After an overview for kinds of power architectures in grid-connected PV system, multi-string configuration attracts us most attention for its best compromise between efficiency and cost. However, strong anticipation for efficiency improvement continuously drives the development of grid-connected PV system. A new architecture for supper high efficiency maximum power point tracking (MPPT) based on multi-string configuration is proposed in this paper. This new architecture with the compensating concept for MPPT is theoretically developed and a 2.8 kW experimental PV platform is built. Experiment results verified the 99% high efficiency merit of the proposed PV architecture under complex climate condition for whole year operation.

LLC resonant converter has become an excellent candidate for front-end dc-dc converter because of its high efficiency and capability of optimizing the converter for normal operation condition over a wide input voltage and load range. However, lack of suitable driving scheme for the synchronous rectifier (SR) limits the efficiency improvement of LLC in the wide input voltage condition. Although corporations like International Rectifier (IR) and STMicroelectronics (ST) have offered some so-called idea SR driver like IR1167, the 500 kHz switching frequency restriction and the cost pressure of IR1167 limit its application. In this paper, a new structure for LLC converter which enables self-driven for its synchronous rectification is proposed. A 400 kHz, 1 kW LLC resonant converter with wide input voltage range (300 V to 400 V) and 12 V output is designed and simulated to verify the function of the proposed circuit.