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Most of the recent image segmentation methods have tried to achieve the utmost segmentation results using large-scale pixel-level annotated data sets. However, obtaining these pixel-level annotated training data is usually tedious and expensive. In this work, we address the task of semisupervised semantic segmentation, which reduces the need for large numbers of pixel-level annotated images. We propose a method for semisupervised semantic segmentation by improving the confidence of the predicted class probability map via two parts. First, we build an adversarial framework that regards the segmentation network as the generator and uses a fully convolutional network as the discriminator. The adversarial learning makes the prediction class probability closer to 1. Second, the information entropy of the predicted class probability map is computed to represent the unpredictability of the segmentation prediction. Then, we infer the label-error map of the segmentation prediction and minimize the uncertainty on misclassified regions for unlabeled images. In contrast to existing semisupervised and weakly supervised semantic segmentation methods, the proposed method results in more confident predictions by focusing on the misclassified regions, especially the boundary regions. Our experimental results on the PASCAL VOC 2012 and PASCAL-CONTEXT data sets show that the proposed method achieves competitive segmentation performance.

Light plays an important role in determining plant architecture, which greatly influences crop yield. However, the precise mechanisms by which light signaling regulates bud outgrowth remain to be identified. Here, we show that light regulates bud outgrowth via both HY5 and brassinosteroid (BR)-dependent pathways in tomato. Inactivation of the red-light photoreceptor PHYB, or deficiencies in PHYB or the blue-light photoreceptor CRY1a, inhibits bud outgrowth and leads to decreased accumulation of HY5 protein and increased transcript level of BRANCHED1 ( BRC1 ), a central integrator of branching signals. HY5, functioning as a mobile systemic signal from leaves, promotes bud outgrowth by directly suppressing BRC1 transcript and activating the transcript of BR biosynthesis genes within the lateral buds in tomato. Furthermore, BRC1 prevents the accumulation of cytokinin (CK) and gibberellin (GA) by directly inhibiting the transcript of CK synthesis gene LOG4 , while increasing the transcript levels of CK and GA degradation genes ( CKX7 , GA2ox4 , and GA2ox5 ), leading to an arrest of bud outgrowth. Moreover, bud outgrowth occurs predominantly in the day due to the suppression of BRC1 transcript by HY5. These findings demonstrate that light-inducible HY5 acts as a systemic signaling factor in fine-tuning the bud outgrowth of tomato.

Phytosulfokine (PSK) is a danger-associated molecular pattern recognized by PHYTOSULFOKINE RECEPTOR 1 (PSKR1) and initiates intercellular signaling to coordinate different physiological processes, especially in the defense response to the necrotrophic fungus Botrytis cinerea. The activity of peptide receptors is largely influenced by different posttranslational modifications, which determine intercellular peptide signal outputs. To date, the posttranslational modification to PHYTOSULFOKINE RECEPTOR 1 (PSKR1) remains largely unknown. Here, we show that tomato (Solanum lycopersicum) PSKR1 is regulated by the ubiquitin/proteasome degradation pathway. Using multiple protein–protein interactions and ubiquitylation analyses, we identified that plant U-box E3 ligases PUB12 and PUB13 interacted with PSKR1, among which PUB13 caused PSKR1 ubiquitylation at Lys-748 and Lys-905 sites to control PSKR1 abundance. However, this posttranslational modification was attenuated upon addition of PSK. Moreover, the disease symptoms observed in PUB13 knock-down and overexpression lines demonstrated that PUB13 significantly suppressed the PSK-initiated defense response. This highlights an important regulatory function for the turnover of a peptide receptor by E3 ligase-mediated ubiquitylation in the plant defense response.

Most existing studies performed video denoising by using multiple adjacent noisy frames to recover one clean frame; however, despite achieving relatively good quality for each individual frame, these approaches may result in visual flickering when the denoised frames are considered in sequence. In this paper, instead of separately restoring each clean frame, we propose a multiframe-to-multiframe (MM) denoising scheme that simultaneously recovers multiple clean frames from consecutive noisy frames. The proposed MM denoising scheme uses a training strategy that optimizes the denoised video from both the spatial and temporal dimensions, enabling better temporal consistency in the denoised video. Furthermore, we present an MM network (MMNet), which adopts a spatiotemporal convolutional architecture that considers both the interframe similarity and single-frame characteristics. Benefiting from the underlying parallel mechanism of the MM denoising scheme, MMNet achieves a highly competitive denoising efficiency. Extensive analyses and experiments demonstrate that MMNet outperforms the state-of-the-art video denoising methods, yielding temporal consistency improvements of at least 13.3% and running more than 2 times faster than the other methods.

The latest research in the field of recognition of image characters has led to various developments in the modern technological works for the improvement of recognition rate and precision. This technology is significant in the field of character recognition, business card recognition, document recognition, vehicle license plate recognition etc. for smart city planning, thus its effectiveness should be improved. In order to improve the accuracy of image text recognition effectively, this article uses canny algorithm to process edge detection of text, and k-means algorithm for cluster pixel recognition. This unique combination combined with maximally stable extremal region and optimization of stroke width for image text yields better results in terms of recognition rate, recall, precision, F-score and accuracy. The results show that the correct recognition rate is 88.3% and 72.4% respectively with an accuracy value of 90.5% for the proposed method. This algorithm has high image text recognition rate, can recognize images taken in complex environment, and has good noise removal function. It is significantly an optimal algorithm for image text recognition.

Phase-shifting profilometry (PSP) via sequential fringe projection is applicable for high-accuracy 3-D measurements of static objects. However, moving objects cannot be static or quasi-static during one projection cycle, thereby causing surface misalignment and phase errors in 3-D measurements. This article proposes a motion-estimated PSP based on the fringe-oriented synthetic phase correlation algorithm (FO-SPCA), which achieves automatic 3-D measurements of objects in nonuniform 2-D motion. To robustly estimate the motion of objects among fringe images, the proposed fringe-oriented feature extraction technique obtains the surface features by eliminating the modulation influence of sinusoidal illumination. Furthermore, the FO-SPCA is developed to accurately detect the subpixel displacements of moving objects according to object surface features. With the subpixel displacements, the alignment procedure is completed through a reverse 2-D transformation and then the phase errors are compensated accordingly. The simulation and experimental results verify the feasibility and robustness of the motion-estimated PSP for the in situ 3-D measurements of nonuniform moving objects.

Codewords used to determine fringe orders are significant for absolute phase retrieval based on the phase-coding method. However, because of inevitable interference from image blurring, image noise, and system nonlinearity, it is challenging to significantly increase the number of codewords for high-accuracy phase retrieval. To address this challenge, this article proposes a joint coding strategy to perform the coding of high-density stripes. More specifically, codewords are simultaneously embedded into the phase domain and the intensity domain of composite fringe patterns, which greatly extends the encoding range of valid codewords. Moreover, to improve the anti-interference capability of the proposed coding strategy, a regional correction technique (RCT) is presented to remove jump phase errors induced by the inevitable interference. The results of experiments indicate that the proposed method is effective and accurate for both complex objects and a standard sphere.

The 3-D measurement methods based on fringe projection intensity adjustment (FPIA) have been widely adopted to measure the multireflectivity scenes. However, the signal-to-noise ratios (SNRs) of the captured fringe patterns in the FPIA-based methods are reduced because of a large range of reflectivity variations (LRRVs). In our research, a reflection model and a crosstalk model are built to analyze the disturbances induced by LRRVs on the SNRs in the captured fringe patterns, and then, a 3-D measurement method based on nonlinear FPIA (NLFPIA) is proposed to alleviate the LRRV-induced disturbances and improve the measurement accuracy. Moreover, to improve the efficiency of the proposed 3-D measurement method, we present a cyclic utilization of fringe patterns (CUFPs) technique to decrease the number of projection patterns. Experimental results show that the proposed NLFPIA-based 3-D measurement method reduces the root-mean-square error (RMSE) in the measurements of the two standard ceramic geometries by 52.9845% and 36.6393% compared with the FPIA-based method.

Herbivory severely affects plant growth, posing a threat to crop production. Calcium ion (Ca

Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state-of-the-art methods. With features extraction-based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.

In this paper, a more effective Gaussian denoiser is designed to enhance the resulting image quality. We propose a novel image denoising method using a multiscale dilated residual network, named MDRNet. The proposed method is based on two main strategies. First, we adopt dilated convolutions in our network to enlarge the receptive field while requiring fewer parameters. The hybrid dilation rate pattern (HDP) is implemented such that each pixel in the pattern contributes similarly to the receptive field, allowing our network to learn the image details equally. Second, we employ a contextualized structure to take advantage of the low-level features which are mainly concentrated in the first two layers. Our method achieves competitive denoising performance and requires fewer parameters compared to existing denoising methods that using convolutional network. Through comprehensive experiments, we show that the denoising performance of our method is competitive with the state-of-the-art methods in terms of both quantitative and qualitative evaluation.

The photovoltaic cell pattern and geometry affect the origination, level and distribution of stresses in the modules. The cut cell patterns can be potential alternative of conventional full cell patterns in terms of enhanced stability, reduced hotspot effects, etc. Here, we study the thermo-mechanical behavior of cut cell (half cut and one by three cut cell) and full cell modules during manufacturing and hotspot formation. The 3D geometrical models of modules with maximum details are simulated for these processes. Modules with cut cells are found more resistant to thermo-mechanical loads. The transitory and permanent hotspot cycles with different temperature ends are applied; and it is found that the stress level increases with the increase in temperature and dwell time of hotspots. Due to permanent hotspots with upper extreme end of 110 °C, the busbars experience compressive stresses of −137 MPa, −122 MPa, and −106 MPa in full, half cut, and one by three cut cell modules respectively; and cells experience compressive stresses of −76 MPa, −80 MPa, −72 MPa in full, half cut, and one by three cut cell modules respectively. With the use of cut cells, interconnectors thickness can be reduced and parametric analysis shows that the thinner interconnectors lead to lower stresses in cells. During thermal loading, strain energy is stored in the interconnectors due to deformation and it increases with the increase in thermal loading i.e. for each degree rise in temperature, strain energy increases. In addition, solutions to avoid or reduce hotspot formation are also outlined.

With the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the automation of photovoltaic monitoring methods becomes important, as manual/visual inspection has limited applications. This research work deals with automatic detection of photovoltaic module defects in Infrared images with isolated deep learning and develop-model transfer deep learning techniques. An Infrared images dataset containing infrared images of normal operating and defective modules is collected and used to train the networks. The dataset is obtained from Infrared imaging performed on normal operating and defective photovoltaic modules with lab induced defects. An isolated learned model is trained from scratch using a light convolutional neural network design that achieved an average accuracy of 98.67%. For transfer learning, a base model is first developed (pre-trained) from electroluminescence images dataset of photovoltaic cells and then fine-tuned on infrared images dataset, that achieved an average accuracy of 99.23%. Both frameworks require low computation power and less time; and can be implemented with ordinary hardware. They also maintained real time prediction speed. The comparison shows that the develop-model transfer learning technique can help to improve the performance. In addition, we reviewed different kind of defects detectable from infrared imaging of photovoltaic modules, that can help in manual labelling for identifying different defect categories upon access to new huge data in future studies. Last of all, the presented frameworks are applied for experimental testing and qualitative evaluation.

The uncertainty in the output power of the photovoltaic (PV) power generation station due to variation in meteorological parameters is of serious concern. An accurate output power predictio...

Fruits are excellent sources of essential vitamins and health-boosting minerals. Recently, regulation of fruit ripening by both internal and external cues for the improvement of fruit quality and shelf life has received considerable attention. Rosmarinic acid (RA) is a kind of natural plant-derived polyphenol, widely used in the drug therapy and food industry due to its distinct physiological functions. However, the role of RA in plant growth and development, especially at the postharvest period of fruits, remains largely unknown. Here, we demonstrated that postharvest RA treatment delayed the ripening in tomato fruits. Exogenous application of RA decreased ripening-associated ethylene production and inhibited the fruit color change from green to red based on the decline in lycopene accumulation. We also found that the degradation of sucrose and malic acid during ripening was significantly suppressed in RA-treated tomato fruits. The results of metabolite profiling showed that RA application promoted the accumulation of multiple amino acids in tomato fruits, such as aspartic acid, serine, tyrosine, and proline. Meanwhile, RA application also strengthened the antioxidant system by increasing both the activity of antioxidant enzymes and the contents of reduced forms of antioxidants. These findings not only unveiled a novel function of RA in fruit ripening, but also indicated an attractive strategy to manage and improve shelf life, flavor, and sensory evolution of tomato fruits.

This With the development of science and technology, the impact of the ionosphere on satellite navigation, radio communication, broadcasting, space exploration and other human activities is more and more significant, the application of radar to the ionosphere detection technology is also more and more important. In this paper, the main development course of radar is introduced briefly, the composition and working principle of radar are expounded, and the digital signal processing of radar is analyzed and studied emphatically.

Defect detection in photovoltaic (PV) modules and their impact assessment is important to enhance the PV system performance and reliability. To identify and analyze the defects, an improved outdoor infrared (IR) thermography scheme is presented in this study. The indoor (dark) and outdoor (illuminated) IR experiments are carried out on normal operating and defective PV modules. The indoor and outdoor measurements for normal operating modules are similar. However, the measurements for defective modules show difference i.e. the outdoor images show fewer or not at all defects in comparison to indoor images. Subsequent to this, outdoor imaging is carried out with our improved outdoor thermography scheme. This scheme is based on modulating the temperature of PV module through altering the electrical behavior of single cell. Therein, a PV cell is shaded in different fractions to attain different current conditions between open circuit and maximum power point, that causes temperature changes in series connected cells leading to different temperature conditions. The images obtained by this scheme provide clearer and detailed information about defects which is much similar to that given by indoor IR images. The severe and mild defective regions show temperature difference of more than 30° and 20° respectively in outdoor. The performance factor (PF) based on translated power output is also calculated for studied two modules that represents the quantitative impact of defects. The PF for PV module 1 and 2 is reduced from 97% to 31% and from 96% to 88% respectively with induction of defects. The PF values correlate with IR measurements of these modules. Furthermore, an image processing scheme comprising image filtering, color quantization and edge detection operations, is presented, that locate the edges of severe and mild defective regions in IR images.

Thermo-mechanical loads induce stresses in photovoltaic (PV) modules, leading to crack formation. In this context, the understanding of module's thermo-mechanical behavior is important. To investigate the thermo-mechanical behavior of smart wire connected technology (SWCT) and busbarPV modules throughout their entire life, the present study is conducted that probes the stress distribution and deformation during production, transportation, and subsequent mechanical and thermal loading stages in a consecutive step-by-step manner using finite element modellingapproach. Pre-stresses and non-linearitiesare considered in simulation models. Stresses and displacements experienced by different parts/layers are examined, and crack sensitive regions are identified. In addition, the SWCTand busbarmodules are compared, and it is found that SWCTinterconnection is relatively a less stress inducing process and less susceptible to thermal and dynamic affects. During production stage, stresses of 39.3 MPaand 40.4 MPaare generated in SWCTcells and copper wires respectively; while, stresses of 60 MPaand 87 MPaare generated in busbarcells and busbarrespectively. Similarly, lower stresses are induced in SWCTPV modules during subsequent stages. The comparison results show advantages of SWCTmodule in terms of mechanical stability which can lead to improve the performance and reliability of PV modules.

Feature extraction and feature selection are vital steps to construct an intelligent diagnosis system for classifying the weld flaws from an X-ray image. Deep learning has been successfully used in image analysis and automatic object recognition. It has good performance for learning more representative hierarchical features that are more sensitive to classification. However, there are still few applications of deep learning in feature learning for classifying different weld flaws, and few studies have been performed to compare the feature classification ability of different feature extraction methods. In this paper, we developed a model based on a deep convolutional neural network (DCNN) to extract the deep features directly from X-ray images. To validate the effectiveness of deep features, we cropped patches from the X-ray images as the learning dataset. Furthermore, considering the imbalance of the number of patches with different weld flaws, we used 3 types of resampling methods for 3 balanced datasets. Using the datasets, we compared the classification ability of 5 types of features extracted using traditional methods and deep learning. The best results were obtained for the deep features from the proposed DCNN model, achieving an accuracy of 97.2%, which is considerably higher than that obtained using the traditional feature extraction methods. We believe that the proposed model could be used to help workers evaluate X-ray images more intelligently.