Gray Edge Research Articles

The Bi–O edge wavefront sensor

Context. Direct detection of exoplanets around nearby stars requires advanced adaptive optics (AO) systems. High-order systems are needed to reach a high Strehl ratio (SR) in near-infrared and optical wavelengths on future giant segmented-mirror telescopes (GSMTs). Direct detection of faint exoplanets with the European Southern Observatory (ESO) Extremely Large Telescope (ELT) will require some tens of thousands of correction modes. The resolution and sensitivity of the wavefront sensor (WFS) are key requirements for this science case. We present a new class of WFSs, the bi-orthogonal Foucault knife-edge sensors (or Bi–O edge), that is directly inspired by the Foucault knife-edge test. The idea consists of using a beam-splitter producing two foci, each of which is sensed by an edge with a direction orthogonal to the other focus. Aims. We describe two implementation concepts: The Bi–O edge sensor can be realised with a sharp edge and a tip-tilt modulation device (sharp Bi–O edge) or with a smooth gradual transmission over a grey edge (grey Bi–O edge). A comparison of the Bi–O edge concepts and the four-sided classical pyramid wavefront sensor (PWS) gives some important insights into the nature of the measurements. Methods. We analytically computed the photon noise error propagation, and we compared the results to end-to-end simulations of a closed-loop AO system. Results. Our analysis shows that the sensitivity gain of the Bi–O edge with respect to the PWS depends on the system configuration. The gain is a function of the number of control modes and the modulation angle. We found that for the sharp Bi–O edge, the gain in reduction of propagated photon noise variance approaches a theoretical factor of 2 for a large number of control modes and small modulation angle, meaning that the sharp Bi–O edge only needs half of the photons of the PWS to reach similar measurement accuracy. In contrast, the PWS is twice more sensitive than the Bi–O edge in the case of very low order correction and/or large modulation angles. Preliminary end-to-end simulations illustrate some of the results. The grey version of the Bi–O edge opens the door to advanced amplitude filtering, which replaces the need for a tip-tilt modulator while keeping the same dynamic range. We show that an additional factor of 2 in reduction of propagated photon noise variance can be obtained for high orders, such that the theoretical maximum gain of a factor of 4 in photon efficiency can be obtained. A diffractive Fourier model that accurately includes the effect of modulation and control modes shows that for the extreme AO (XAO) system configuration of the ELT, the overall gain will well exceed one magnitude in guide-star brightness when compared to the modulated PWS. Conclusions. We conclude that the Bi–O edge is an excellent candidate sensor for future very high order Adaptive Optics systems, in particular on GSMTs.

Research on High-Performance Fourier Transform Algorithms Based on the NPU

Backpack computers require powerful, intelligent computing capabilities for field wearables while taking energy consumption into careful consideration. A recommended solution for this demand is the CPU + NPU-based SoC. In many wearable intelligence applications, the Fourier Transform is an essential, computationally intensive preprocessing task. However, due to the unique structure of the NPU, the conventional Fourier Transform algorithms cannot be applied directly to it. This paper proposes two NPU-accelerated Fourier Transform algorithms that leverage the unique hardware structure of the NPU and provides three implementations of those algorithms, namely MM-2DFT, MV-2FFTm, and MV-2FFTv. Then, we benchmarked the speed and energy efficiency of our algorithms for the gray image edge filtering task on the Huawei Atlas200I-DK-A2 development kits against the Cooley-Tukey algorithm running on CPU and GPU platforms. The experiment results reveal MM-2DFT outperforms OpenCL-based FFT on NVIDIA Tegra X2 GPU for small input sizes, with a 4- to 8-time speedup. As the input image resolution exceeds 2048, MV-2FFTv approaches GPU computation speed. Additionally, two scenarios were tested and analyzed for energy efficiency, revealing that cube units of the NPU are more energy efficient. The vector and CPU units are better suited for sparse matrix multiplication and small-scale inputs, respectively.

Underwater Image Color Constancy Calculation with Optimized Deep Extreme Learning Machine Based on Improved Arithmetic Optimization Algorithm

To overcome the challenges posed by the underwater environment and restore the true colors of marine objects’ surfaces, a novel underwater image illumination estimation model, termed the iterative chaotic improved arithmetic optimization algorithm for deep extreme learning machines (IAOA-DELM), is proposed. In this study, the gray edge framework is utilized to extract color features from underwater images, which are employed as input vectors. To address the issue of unstable prediction results caused by the random selection of parameters in DELM, the arithmetic optimization algorithm (AOA) is integrated, and the search segment mapping method is optimized by using hidden layer biases and input layer weights. Furthermore, an iterative chaotic mapping initialization strategy is incorporated to provide AOA with a better initial search proxy. The IAOA-DELM model computes illumination information based on the input color vectors. Experimental evaluations conducted on actual underwater images demonstrate that the proposed IAOA-DELM illumination correction model achieves an accuracy of 96.07%. When compared to the ORELM, ELM, RVFL, and BP models, the IAOA-DELM model exhibits improvements of 6.96%, 7.54%, 8.00%, and 8.89%, respectively, making it the most effective among the compared illumination correction models.

A novel image quality evaluation method based on two-dimensional information entropy

In image transmission, with the consideration of system parameters, such as bandwidth, transmission delay and power consumption, the image compression coding is often used to reduce the amount of transmitted data. The formation mechanism of block effect in H.264 coding is analyzed. Taking H.264 coding as an example, the spatial domain image enhancement processing is adopted in the paper to obtain the two-dimensional information entropy covering the gray correlation and image edge feature information in adjacent spatial domain. By referring to the two-dimensional information entropy evaluation value of original and coded image, the image quality evaluation result is consistent with PSNR method used in common situations. Compared with the traditional PSNR evaluation method, the two-dimensional information entropy evaluation method only refers to the two-dimensional information entropy evaluation value of the original image, and the amount of data required is very low. Therefor, it is more suitable for the engineering implementation of real-time image quality evaluation in image transmission system. At the same time, with the edge feature information of the image used in the two-dimensional information entropy, the entropy method is closer to the actual feeling of human eyes than PSNR method.

Three-dimensional measurement method based on the singular operator and the shortest path search technique

Coded structured light plays a crucial role in the field of non-contact three-dimensional measurement. To improve the accuracy, we propose a novel method based on shortest path search and edge and central singular operator, which is able to detect the edge of Gray code and locate its line shift fringes more accurately. Starting from the patterns of the Gray code edge and the center of the line shift strip, we develop the corresponding singular operators and hence the cost function. The Euler distance is used as the distance condition for the adjacent key points, which are located by the fast-marching algorithm. We propose a method to compute the graph with minimum energy by finding the shortest path. The shortest path was automatically calculated and found both the edge of the Gray code and the center of the bar stripe accurately.

First report of Lasiodiplodia mahajangana causing canker of mango (Mangifera indica) in Puerto Rico.

Mango originated in the Indo-Burmese region (Alphonse de Candolle, 1885). In the Caribbean, Puerto Rico currently produces and exports mangoes to the United States and Europe. Globally, an important disease affecting mango production is dieback, caused by fungi belonging to Botryosphaeriaceae family. During a one-year survey from 2019 to 2020, conducted at the mango germplasm collection of the Agricultural Experiment Station of the University of Puerto Rico, located at Juana Díaz, PR, symptoms of dieback were observed in shoots, descending towards the woody part, and vascular necrosis. We sampled bimonthly, 35 Keitt trees for one year. At the end of the evaluation, we detected that a 74% disease incidence was caused by Botryosphaeriaceae. Lasiodiplodia mahajangana (syn. L. caatinguensis) was associated with 4% disease incidence. In addition, we identified other Botryosphaeriaceae species causing 70% of disease incidence. To identify the causal agent, sections of symptomatic tissue (4mm2) were surface disinfected by immersion in 70% ethanol, 10% sodium hypochlorite and rinsed with sterile-distilled water for 1 minute at each solution. Sections were transferred to petri dishes containing potato dextrose agar acidified with 85% lactic acid (aPDA). Ten fungal isolates were obtained with similar morphological characteristics such as colony color and texture, after 12 days. Of these, one representative (isolate 17) was selected and identified as L. mahajangana (Lm) using morphological parameters and sequences of four nuclear genes (Zhang, W. et al., 2021). In aPDA, Lm colonies showed sparse and slow-growing aerial mycelium with dark gray-greenish color at the center and light gray edges. Black pycnidia were observed after 15 days of incubation at 28°C and dark conditions. Hyaline, ovoid to ellipsoid immature conidia (n=40) with average size of 22 µm long and 12 µm wide were observed. Mature bicellular pigmented conidia (n=40) had longitudinal striate and its average size was 23 µm long and 12 µm wide. Internal transcribed spacer (ITS), β-tubulin (βtub), elongation factor 1-alpha (EF1-α) and large ribosomal subunit (LSU) genetic regions were amplified by PCR from the original and pathogenicity test recovered isolates. Sequences of PCR products were compared with NCBI database BLAST tool with other Lm sequences. Sequence accession numbers of the four genetic regions of Lm are as follows: OL375401 and OL375402 for the ITS region; OL405579 and OL405580 for β-tubulin; OL455922 and OL455923 for EF1-α; and OL375648 and OL375649 for LSU. All the sequences were grouped with the ex-type CMM1325 of Lm (BS=84). Pathogenicity tests were performed on 6-month-old mango trees of cv. Keitt. Three healthy trees were inoculated with 5 mm mycelial disks of Lm, on stems, with and without wounds. Controls were inoculated with aPDA disks only. Inoculated trees were covered for 3 days with plastic bags, keeping them in conditions of high relative humidity with constant irrigation, temperature of 28°C, and 12 hours of light and 12 hours of darkness for 12 days. Twelve days after inoculation, Lm isolates caused stem necrosis and canker, with differences in lesion severity from 2 to 17 mm2 with wound, and 0 to 6 mm2 without wound. Untreated controls showed no symptoms of canker. Lasiodiplodia mahajangana was re-isolated from diseased stems fulfilling Koch's postulates, and a sequence of the recovered isolate from the pathogenicity test was compared and included in the phylogenetic analysis. Lasiodiplodia mahajangana has been reported to cause stem-end rot of mango in Malaysia (Li, L. et. al., 2021). To our knowledge, this is the first report of Lm causing canker of mango in Puerto Rico. Knowing L. mahajangana as a new pathogen that causes canker of mango is important to establish an adequate and effective control management of this disease in mango producing countries worldwide.

Analysis of Deep Learning Techniques for Brain Tumour Classification from CT & MRI Images

Brain tumour detection in an initialpoint is a critical step to saving human life. Computed Tomography (CT) and Magnetic Resonance Image (MRI) provide very detailed information about brain tumour tissues. So the segmentation of tumour region is possible from pancreatic CT and brain MRI. CT and MRI is a non-invasive technique and it does not produce any harmful radiation to the patient. The patient suspected of tumour undergoes radiological evaluation such that the area, location and grade of the tumour can be predicted from the CT and MRI analysis. This critical information helps the doctors to decide about further treatment like chemotherapy, surgery, or radiation. The diagnosis requires an accurate and very fast segmentation and classification of CT and MRI images. But nowadays radiologists are doing this task manually and it is a tedious and time-consuming procedure. Also, there is a chance of variation in the result from one expert to another. Here comes the significance of automatic segmentation and classification of tumour types with the help of computers.The proposed work aims to develop an efficient system that can detect pancreatic and brain tumour and can classify the pancreatic CT and brain MRI into normal, benign or malignant. This work can be categorized into two approaches. Thus the dataset prepared for this research work contains CT and MRI images.The first approach proposes traditional machine learning technologies to achieve the goal. Image pre-processing, feature extraction, segmentation and classification are the various steps of the traditional machine learning method. A detailed investigation is performed through various feature extraction techniques and classification techniques for pancreatic (CT) and brain MRI. Discrete Wavelet Transform (DWT) feature, Grey Level Co-occurrence Matrix (GLCM) feature, Gabor feature, Tamura features and Edge Orientation Histogram (EOH) features and their combinations are used for the extraction of CT and MRI features. Benign tumours are non-cancerous, but malignant tumours are cancerous. In the first approach, the Support Vector Machine (SVM) is the main classifier used for pancreatic CT and brain MRI classification as normal, benign or malignant.In this technology, a huge amount of data and machines with high computational capabilities like Graphic Processing Unit (GPU) are available. Thus the second approach of this paper is to exploit all these available resources to produce accurate results. In this part, deep learning, the latest fast growing technology introduced in 2015 is used for the classification of brain MRI. A Deep Convolutional Neural Networks (DCNN) model is proposed to perform the classification task efficiently. The CNN results are compared with the results of a simple neural network classifier. This method provides accurate and it shows that deep learning based classification outperforms traditional machine learning techniques which produce an accurate result only. This research work again concentrates on the Transfer Learning (TL) methods to classify pancreatic CT and brain MRI.

A vegetation classification method based on improved dual-way branch feature fusion U-net.

Aiming at the problems of complex structure parameters and low feature extraction ability of U-Net used in vegetation classification, a deep network with improved U-Net and dual-way branch input is proposed. Firstly, The principal component analysis (PCA) is used to reduce the dimension of hyperspectral remote sensing images, and the effective bands are obtained. Secondly, the depthwise separable convolution and residual connections are combined to replace the common convolution layers of U-Net for depth feature extraction to ensure classification accuracy and reduce the complexity of network parameters. Finally, normalized difference vegetation index (NDVI), gray level co-occurrence matrix (GLCM) and edge features of hyperspectral remote sensing images are extracted respectively. The above three artificial features are fused as one input, and PCA dimension reduction features are used as another input. Based on the improved U-net, a dual-way vegetation classification model is generated. Taking the hyperspectral remote sensing image of Matiwan Village, Xiong'an, Beijing as the experimental object, the experimental results show that the precision and recall of the improved U-Net are significantly improved with the residual structure and depthwise separable convolution, reaching 97.13% and 92.36% respectively. In addition, in order to verify the effectiveness of artificial features and dual-way branch design, the accuracy of single channel and the dual-way branch are compared. The experimental results show that artificial features in single channel network interfere with the original hyperspectral data, resulting in reduction of the recognition accuracy. However, the accuracy of the dual-way branch network has been improved, reaching 98.67%. It shows that artificial features are effective complements of network features.

Terahertz image enhancing based on the physical model and multiscale retinex algorithm.

To settle the terahertz (THz) image degradation problem, we propose an effective enhancement method based on the physical model and multiscale retinex (MSR) algorithm. The overall enhancing process involves two parts: reconstruction and enhancement. First, the original THz images are reconstructed by a mathematical model, which is built and considered the THz absorption variate and Gaussian distribution of the beam. Then, the original images are processed by the proposed algorithm, which combines the atmospheric scattering model and optimized MSR algorithm. The proposed algorithm not only recovers the image scene radiance and removes haze, but also can make a compromise of the dynamic range of gray scale and edge enhancement of the image. Results on a variety of THz images demonstrate our method can effectively improve the quality of THz images and retain sufficient image details.

Imagery, Irony, and Transcendence in Carl Jonas Love Almqvist's The Queen's Tiara

Imagery, Irony, and Transcendence in Carl Jonas Love Almqvist's <i>The Queen's Tiara</i>

Illumination correction of dyed fabric based on extreme learning machine with improved ant lion optimizer

AbstractIn order to eliminate the influence of scene illumination on the evaluation of the color difference of dyed fabrics, this paper proposes a dyed fabric illumination correction algorithm based on the extreme learning machine (ELM) with grey wolf optimizer (GWO)‐optimized ant lion optimizer (ALO). Firstly, the Grey Edge framework is used to extract the features of the dyed fabric image as the input vector. Then, to improve the optimization ability of the ALO algorithm, the GWO algorithm is used to provide a set of optimized initial populations to the ALO algorithm, and then the improved ALO algorithm is used to optimize the parameters of the ELM. Finally, the proposed GWO‐ALO‐ELM algorithm is used to correct the illumination of the dyed fabric, and restore the graphics to the effect display under standard illumination through the diagonal reduction model. Compared with the experimental results of GWO‐ELM, ALO‐ELM, backpropagation (BP), ELM, random vector function link (RVFL), and other algorithms, it can be seen that the GWO‐ALO‐ELM algorithm proposed in this paper has good predictive value and quasi‐bias effect, and good stability.

First report of Corynespora cassicola causing black spot on Sarcandra glabra in China.

Sarcandra glabra is an important Chinese medicinal plant, which was widely cultivated under forest in south China. Guangxi province is the main producing areas of this herb. In June 2019, a serious leaf disease was found causing severe defoliation in the S. glabra plantation under bamboo forest in Rongan country, Guangxi province (109°13'N''E). About 70% of the plants in the plantation (300 ha) showed the similar symptoms. Initially, circular lesions appeared on young leaves as black spots (about 1 to 2 mm). Then, the spots gradually enlarged usually with an obvious yellowish margin (6 to 8 mm). Finally, the lesions coalesced and formed irregular, black, and large necrotic areas, resulting in the leaf abscission. For pathogen isolation, small pieces of tissue (5×5 mm) taken from 25 diseased leaves were sterilized with 75% ethanol for 30 s, subsequently, soaked in 0.1% HgCl2 for 2 min, rinsed three times in sterile distilled water, dried, and then placed aseptically onto the potato dextrose agar (PDA) plates, and incubated at 28 °C (12 h/12 h light/dark). Three days later, the isolates were placed on a new PDA plate for subsequent purification and sporulation. 20 pure fungal isolates were obtained from single spores. Of which, 15 isolates showed similar morphological characteristics.The colonies on PDA were round, dense, gray edge and dark gray in center area. Conidia in culture were appeared light brown, cylindrical in shape, with 0 to 8 septa, and 55 to 165 μm × 5.2 to 13.5 μm in size (mean = 106.2 μm × 8.6 μm, n = 30). These morphological characteristics resemble those of Corynespora sp. (Berk. & M.A. Curtis) C.T. Wei (Ellis et al. 1971). A single-spore isolate (ZD5) was selected from the 15 fungal isolates for a subsequent molecular identification. The genes of internal transcribed spacer (ITS) of ribosomal DNA, β-tublin, and actin were amplified with the primer pairs ITS-1/ITS-4 (White et al. 1990), β-tubulin 2-Bt2a/Bt2b (Glass and Donaldson 1995), ACT-512F/ACT-783R (Carbone and Kohn 1999), respectively. And the ITS, β-tublin, and actin sequences were deposited in the GenBank database with the accession numbers MW362446, MW367029, and MW533122. Blast analysis and neighbor-joining analysis based on ITS, β-tublin, and actin sequences using MEGA 6 revealed that the isolate was placed in the same clade as C. cassicola with 100% bootstrap support. Pathogenicity test was performed on the two-year-old potted S. glabra. Six-mm-diameter mycelial plugs were attached to the healthy leaves of S. glabra for co-culture, while the control group was attached with PDA. All plants were covered with plastic bags for 2 days in order to maintain high humidity and cultured in a greenhouse at 28 °C with a 12-h/12-h light/dark cycle. The symptoms appeared 2 days after co-culture were identical to those observed in the field. The same fungus was re-isolated from the lesions, and further morphological characterization and molecular assays, as described above.The control leaves remained symptomless during the pathogenicity tests. According to the previous literatures, C. cassicola is a plant pathogenic fungus with a broad host range, which can damage diverse tropical plants including Salvia miltiorrhiza (Lu et al. 2019), Solanum americanum (Wagner and Louise 2019), Vitex rotundifolia (Yeh and Kirschner 2017), Cucumis sativus, Lycopersicon esculentum (Hsu et al. 2002), Carica papaya (Tsai et al. 2015),and so on. To our knowledge, this is the first report of C. cassicola causing leaf spot on S. glabra in China.

Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network

Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple Feature Pyramid Network (MFPN), which contains richer context information. Finally, a Cluster Region Proposal Network (C-RPN) with spatial attention and an adaptive cluster anchor are proposed to generate potential tampered regions with greater retention of location information. Extensive experiments, which were evaluated on the NIST16 and CASIA standard datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, because it achieves accurate tampered locations at the pixel level, and has great robustness in post-processing operations, such as noise, blur and JPEG recompression.

Dark topological valley Hall edge solitons

Abstract Topological edge solitons propagating along the edge of a photonic topological insulator are localized self-sustained hybrid states that are immune to defects/disorders due to the protection of the edge states stemming from the nontrivial topology of the system. Here, we predict that exceptionally robust dark valley Hall edge solitons may form at the domain walls between two honeycomb lattices with broken inversion symmetry. The underlying structure can be created with femtosecond laser inscription, it possesses a large bandgap where well-localized dark edge solitons form, and in contrast to systems with broken time-reversal symmetry, it does not require external magnetic fields or complex longitudinal waveguide modulations for the realization of the topological phase. We present the envelope equation allowing constructing dark valley Hall edge solitons analytically. Such solitons propagate without radiation into the bulk of the lattice and can circumvent sharp corners, which allows observing their persistent circulation along the closed triangular domain wall boundary. They survive over huge distances even in the presence of disorder in the underlying lattice. We also investigate interactions of closely located dark topological valley Hall edge solitons and show that they are repulsive and lead to the formation of two gray edge solitons, moving with different group velocities departing from group velocity of the linear edge state on which initial dark solitons were constructed. Our results illustrate that nonlinear valley Hall systems can support a rich variety of new self-sustained topological states and may inspire their investigation in other nonlinear systems, such as atomic vapors and polariton condensates.

On the evaluation of temporal and spatial stability of color constancy algorithms.

Computational color constancy algorithms are commonly evaluated only through angular error analysis on annotated datasets of static images. The widespread use of videos in consumer devices motivated us to define a richer methodology for color constancy evaluation. To this extent, temporal and spatial stability are defined here to determine the degree of sensitivity of color constancy algorithms to variations in the scene that do not depend on the illuminant source, such as moving subjects or a moving camera. Our evaluation methodology is applied to compare several color constancy algorithms on stable sequences belonging to the Gray Ball and Burst Color Constancy video datasets. The stable sequences, identified using a general-purpose procedure, are made available for public download to encourage future research. Our investigation proves the importance of evaluating color constancy algorithms according to multiple metrics, instead of angular error alone. For example, the popular fully convolutional color constancy with confidence-weighted pooling algorithm is consistently the best performing solution for error evaluation, but it is often surpassed in terms of stability by the traditional gray edge algorithm, and by the more recent sensor-independent illumination estimation algorithm.

Interaction with the CCT chaperonin complex limits APOBEC3A cytidine deaminase cytotoxicity.

The APOBEC3 cytidine deaminases are implicated as the cause of a prevalent somatic mutation pattern found in cancer genomes. The APOBEC3 enzymes act as viral restriction factors by mutating viral genomes. Mutation of the cellular genome is presumed to be an off-target activity of the enzymes, although the regulatory measures for APOBEC3 expression and activity remain undefined. It is therefore difficult to predict circumstances that enable APOBEC3 interaction with cellular DNA that leads to mutagenesis. The APOBEC3A (A3A) enzyme is the most potent deaminase of the family. Using proteomics, we evaluate protein interactors of A3A to identify potential regulators. We find that A3A interacts with the chaperonin-containing TCP-1 (CCT) complex, a cellular machine that assists in protein folding and function. Importantly, depletion of CCT results in A3A-induced DNA damage and cytotoxicity. Evaluation of cancer genomes demonstrates an enrichment of A3A mutational signatures in cancers with silencing mutations in CCT subunit genes. Together, these data suggest that the CCT complex interacts with A3A, and that disruption of CCT function results in increased A3A mutational activity.

Significant of multi-level pre-processing steps and its proper sequence in SegCaps skin lesion segmentation of dermoscopic images

In computer-assisted diagnosis of dermoscopy images, skin lesion segmentation is a critical but difficult process. Many segmentation methods fail to extract correct lesion boundaries due to the lack of pre-processing steps or a very few number of pre-processing steps that aren't in the right order. This paper demonstrates the significant of Multi-level pre-processing steps and its proper sequence before SegCaps segmentation of Lesion images. In the proposed work author have planned four pre-processing steps before segmentation of skin lesion images in the sequence of first is a 2nd Order grey edge algorithm is used to achieve color consistency;. Second process consists of two sub-processes in the hair removal procedure. To get started, we use the Prewitt filter, which is adaptable to the hair pixel level. The following procedure is the Repairing Operation, which is carried out using the anisotropic Fast Marching Method. Third process is a noise filtering with the Improved Switching Bilateral Filter (ISBF), which uses the Sorted Quadrant Median Vector (SQMV) to determine best median value and the last process is The Directed Edge Enhancer algorithm is used to perform edge enhancement. The proposed model is tested Over MatlabR2017b, using the HAM10000 dataset. The proposed method's output is assessed by comparing segmentation effects with and without pre-processing, as well as visual appearances of pre-processed images from input images. From the results it has been concludes that when pre-processing steps are used in the correct order and proper sequence, segmentation accuracy is very high, as compared to segmentation without pre-processing steps.

Traffic Light Controller using Image Processing

Traffic congestion at junctions is a serious issue on a daily basis. The prevailing traffic light controllers are unable to manage the different traffic flows. Most of the current systems operate on a timing mechanism that changes the signal after a particular interval of time. This may cause frustration and result in motorist's time waste. Traffic congestion is a major problem in the currently existing systems. Delays, safety, parking, and environmental problems are the main issues of current traffic systems that emit smoke and contribute to increasing Global Warming. Sensor-based systems reduce the waiting time and maximize the total number of vehicles that can cross an intersection. Our proposed system can control the traffic lights based on image processing without the need for traffic police. This can reduce congestion, delay, road accidents, need for manpower. Under image processing, we use sub techniques like RGB to Gray conversion, Image resizing, Image Enhancement, Edge detection, Image matching, and Timing allocation. A real-time image is captured for every 1 second. After edge detection procedure for both reference and real-time images, these images are compared using SURF Algorithm. Then the amount of traffic is detected and the details are stored in the server. Arduino is used for a traffic signal in the hardware part. It consists of a Wi-Fi module. The micro-controller used in the system Arduino. Four cameras are placed on respective roads and these cameras are used to capture images to analyze traffic density. Then the traffic signals are decided according to the density of traffic. Our technique can be effective to combat traffic on Indian Roads. A lot of time can be saved by deploying this system and also it conserves a lot of resources as well as the economy

First Report of Anthracnose on Jerusalem Cherry Caused by Colletotrichum liaoningense in Shandong, China.

Jerusalem cherry (Solanum pseudocapsicum), which belongs to the genus Solanum and the family Solanaceae, possesses high ornamental value and is widely cultivated as an indoor ornament due to its bright red berries at maturity (Xu et al., 2018). In September 2019, leaf spot was detected on jerusalem cherry plants in Yuxiu Park, Shizhong district, Jinan, Shandong Province. Field surveys were done in a 1/15 ha park. Disease incidence was estimated at approximately 18% across the survey area. Foliar symptoms began as small white spots. As the disease progressed, lesions expanded and merged, and developed into large irregular white spots, with pale grey edge. At last, lesions were densely distributed throughout the leaves. To isolate the pathogen, twenty leaf tissues (5 × 5 mm) were cut from the border between diseased and healthy tissue, surface disinfected in 75% alcohol for 15 s, soaked in 0.1% mercuric chloride for 1 min, washed with sterile distilled water three times, and cultured on potato dextrose agar (PDA) at 25°C. Nineteen fungal isolates were obtained and were single-spored to obtain pure cultures. The colony of LCL7, a representative isolate, on PDA was initially white to orange, but turned black after 3 to 4 days incubation with black conidial masses. Conidia were single-celled, hyaline, straight, cylindrical, apex obtuse, and ranged from 13.4 to 18.3 × 3.2 to 4.9 μm (n = 50) （Diao et al., 2017）. To validate the species identification, rDNA internal transcribed spacer (ITS) region (White et al., 1990), and the partial sequences of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), actin (ACT), β-tubulin (TUB2), and chitin synthase (CHS-1) (Damm et al., 2019; He et al., 2019), were amplified and sequenced. The ITS, GAPDH, ACT, TUB2, and CHS-1 sequences of isolate LCL7 were submitted to GenBank (MW221320, MW227217, MW227218, MW227219, and MW266988, respectively). ITS, ACT, TUB2, and CHS-1 BLAST showed 99-100% homology with sequences of Colletotrichum liaoningense (ITS, 100% to MH636504; ACT, 100% to MH622582; TUB2, 99.56% to MH622714, CHS-1, 99.33% to MH622446, respectively), although GAPDH showed 93.98% homology with sequence MH681383 (234/249bp). Neighbor-joining tree based on concatenated sequences of the five genes was constructed using MEGA7.0. The results showed the isolate was closely related to C. liaoningense. Based on morphological and molecular characteristics, the isolate LCL7 was identified as C. liaoningense. Pathogenicity tests were performed by spraying a conidial suspension (1 × 105 conidia/mL) on ten two-year-old healthy jerusalem cherry plants. Ten other plants with sterile water served as controls. All samples were incubated in a growth chamber at 25±2°C and transparent plastic bags to keep relative humidity high for 2 days. All inoculated plants showed symptoms similar to those observed in the field after 21 days, but no disease occurred on control plants. The same fungus was successfully reisolated from inoculated leaves and reidentified based on morphology and molecular characteristics, and the fungus was not isolated from the control plants, thus confirming Koch's postulates. Pathogenicity tests were repeated twice. C. liaoningense can cause anthracnose in chili and mango in China (Diao et al., 2017; Li et al., 2019).To our knowledge, this is the first report of anthracnose on jerusalem cherry caused by C. liaoningense in China, which influences ornamental value and reduces market value. Identification of the causes of the disease will help develop effective strategies for managing this disease.

High-precision location for occluded reference hole based on robust extraction algorithm

The reference hole is significant for pre-positioning in high-precision robot drilling. The visual and optical measurement of such holes is of great benefit in terms of improving location accuracy during robot drilling and subsequent rivet-in-hole assembly. However, the detection and location of the reference hole via visual sensors still presents signifcant challenges, owing to the occlusion of perforator clips prior to practical drilling. This study proposes a robust extraction algorithm, based on gray clustering and edge detection, for locating the occluded reference holes (ORH). Firstly, the boundary box of the ORH is segmented using a template matching and density clustering algorithm. Secondly, the discrete arcs of the ORH are extracted via a combination of K-means clustering, pixel-intensity, and concave points extraction based on edge concavity. Finally, the target arcs are filtered and obtained based on their ellipse geometric properties, followed by the center extraction of the ellipse fitting. The experimental results show that the location accuracy achieved in this way is within 0.03 mm, which is sufficiently accurate for robot drilling. Moreover, the ORH data can be extracted and located successfully even in challenging industrial environments, with varying levels of illumination.