Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm

Thermal noise is the most common type of contamination in digital image acquisition operations, and is caused by the temperature condition of the industrial sensor devices used in the process. When it comes to picture improvement, removing noise from the image is one of the most crucial steps. However, in image processing, it is more critical to retain the characteristics of the original picture while eliminating the noise. Thermal noise removal is a challenging problem in image denoising. This article provides a strategy based on a Hybrid Adaptive Median (HAM) filtering approach for removing thermal noise from the image output of an industrial sensor. The demonstration of this proposed approach's ability, is to successfully detect and reduce thermal noise. In addition, this study examines an adaptive hybrid adaptive median filtering approach that has significant computational advantages, making it highly practical. Finally, this research report on experiments shows the high-quality industrial sensor imaging systems that have been successfully implemented in the real world.

Study on 3D Clothing Color Application Based on Deep Learning-Enabled Macro-Micro Adversarial Network and Human Body Modeling

In real life, people’s life gradually tends to be simple, so the convenience of online shopping makes more and more research begin to explore the convenience optimization of shopping, in which the fitting system is the research product. However, due to the immaturity of the virtual fitting system, there are a lot of problems, such as the expression of clothing color is not clear or deviation. In view of this, this paper proposes a 3D clothing color display model based on deep learning to support human modeling-driven. Firstly, the macro-micro adversarial network (MMAN) based on deep learning is used to analyze the original image, and then, the results are preprocessed. Finally, the 3D model with the original image color is constructed by using UV mapping. The experimental results show that the accuracy of the MMAN algorithm reaches 0.972, the established three-dimensional model is emotional enough, the expression of the clothing color is clear, and the difference between the color difference and the original image is within 0.01, and the subjective evaluation of volunteers is more than 90 points. The above results show that it is effective to use deep learning to build a 3D model with the original picture clothing color, which has great guiding significance for the research of character model modeling and simulation.

Clinical progression of patients with COVID-19 in Lagos State, Nigeria

Background The majority of COVID-19 research has been devoted to characterizing the epidemiology and early clinical aspects of the virus. In Lagos, Nigeria, we looked at the temporal progression of COVID-19 patients. We included 1337 confirmed COVID-19 cases in our study from February 27th to March 27th 2020. Of the 1337 patients enrolled, the median age was 50 years old, and 800 (59.83%) were male while 537 (40.16%) were female. Method In symptomatic patients, the time from the beginning of signs to admission was 4 (2–7) days. Fever occurred in 217 (16.2%) while cough occurred in 211(15.78%) patients respectively. Patients were given 5–6 treatment, including nutrition support, supplementary oxygen, and antiviral medicines (e.g., Remdesivir, dexamethasone) in a limited percentage of cases. The assessed median period of infection in all patients was 10 days after the start of symptoms (95 confidential intervals [CIs]: 8–11 days). The duration of fever was slightly longer in patients admitted to intensive care units (ICU) than in those who were not (31 days versus 9 days, respectively, P < 0.003). Results On day 7 after the onset of symptoms, radiological deterioration of the original picture was found in 500 (37.39%) patients. On day 13, 154 of these patients (94.5%) showed signs of radiological improvement. The average time it took for upper respiratory tract samples to test negative for reverse transcriptase PCR was 10 days (90 percent confidence interval: 10–12 days). Virus clearance was more significant in ICU patients than in non-ICU patients (P < 0.003). Conclusions Community members should continue to adhere to the recommended methods of preventing the spread of COVID-19 infection and patients should seek care early to reduce the risk of mortality associated with the infection as rapidly as possible.

SECURITY TECHNIQUE BASED ON WATERMARKING AND CHAOTIC ENCRYPTION FOR DIGITAL IMAGE

With the widespread usage of the Internet, security takes precedence above anything else when transmitting data. This research proposed a hybrid encoding approach with watermark embedding that provides excellent security. Our novel strategy is based on cryptography, which allows it to recognize higher secrecy and efficiency. In this research, a hybrid method is used to support visual watermarking and cryptography to embed vital data. Our effort aims to improve the security of hidden facts embedded in the cover picture. The embedding and encryption of the watermark image into the cover image document were performed in two steps. To begin, two keys and an XOR bit operation were used to generate a large number of distinct keys for encryption. Second, a modified approach of the least significant bit (LSB) technique was adopted to hide a high-resolution watermark picture in the cover picture. The suggested second stage involved encrypting the cover image using the asymmetric key cryptography method (RSA), which provides additional secrecy during picture transmission. The original picture, the watermark photo, can be recovered and decoded using the permitted techniques. As a result, encoding and watermarking may be combined, giving the term "joint encoding and watermarking" legitimacy. Peak Signal to Noise Ratio (PSNR) and relationship aspects are greater with this method. For the implementation of hiding and encoding for the watermark and encoding the digital cover picture, MATLAB-GUI software was utilized. Experimental results demonstrated a good performance with a good correlation for all encrypted images and very high PSNR of the Stego images.

Alexander Samokhvalov's picture frames : tradition and innovation

In recent years, two contradictory trends related to the study and display of picture frames have emerged in Russian museums. On the one hand, these works have at last caught the attention of scholars leading to publications on the evolution of art framing in Russia. However, the framing of paintings in museums is still not carried out on the basis of conducted research. In the light of a rapidly developing frame market, art historians and art critics have been increasingly replacing historical frames with massproduced ones, while disregarding historical reliability and authenticity of the frames, or the need to follow the author’s conception. Meanwhile, it is known that artists of different eras, as a rule, paid great attention to the selection of picture frames, with some among them, like A. N. Samokhvalov, creating their own. Thus, the purpose of the article is to examine the frames made by Samokhvalov, as well as to draw attention to the question of the author’s original picture framing. Samokhvalov’s frames are characterized by their unique design, which greatly affects the perception of the painting. Despite the fact that there exists a considerable body of art historical studies dedicated to the artist’s work, none of the researchers took notice of the problem of the picture framing. The article is the first to provide a scholarly introduction of designs and drawings of Samokhvalov’s frames, as well as works of art created on their basis. The artist’s frames from the collections of various museums have been attributed according to a comparative analysis. Likewise, traditional and innovative features in the works of Samokhvalov have been revealed as a result of stylistic assessment and comparison of frames produced by the artist with picture frames of the 17th – 20th centuries. The article illuminates the unexplored facet of the artist’s work presenting it in a new light. The research will not only allow to further preserve the unique frames of Samokhvalov in Russian museums, but also exhibit his works in accordance with the author’s intention.

THE ORTHODOX CONCEPT «SOUL» IN THE STORY «TAIGA» BY V. SHISHKOV

Повесть В.Я. Шишкова «Тайга» рассматривается с точки зрения воплощенной в нем христианской идеи. Автор исследует религиозный концепт душа , его взаимодействие с такими библейскими понятиями, как грех, праведность, жертвенность и ряд других. Делается вывод, что мотив человеческой души в христианском понимании является одним из ведущих в произведении Шишкова, он активно участвует в создании оригинальной картины мира, в основе которой лежат православные ценности. Исходя из анализа текста делается вывод о том, что традиционное восприятие повести «Тайга» как произведения революционно-демократической направленности не учитывает многих аспектов, связанных с православной верой, что существенно обедняет и сужает понимание ее идейного содержания. V. Shishkov’s novel «Taiga» is considered from the point of view of the Christian idea embodied in it. The author explores the religious concept of the soul, its interaction with such biblical concepts as sin, righteousness, sacrifice, and a number of others. It is concluded that the motive of the human soul in the Christian sense is one of the leading ones in the work of Shishkov, he actively participates in the creation of an original picture of the world, which is based on Orthodox values. Based on the analysis of the text, it is concluded that the traditional perception of the story «Taiga» as a work of revolutionary-democratic orientation does not take into account many aspects related to the Orthodox faith, which significantly impoverishes and narrows the understanding of its ideological content.

Validation of the FPGA-Based Image Processing Techniques using the efficient tool like Xilinx Device Generators

Foremost Image Enhancement's intent is to analyze an image in a direction that the output becomes more appropriate for a particular application, rather than the original picture. Image enhancement methods include a multitude of options for enhancing the image accuracy of photographs. The appropriate choice of such strategies is strongly determined by the imaging modality. FPGA has several main features that can be used as a tool for the processing of authentic time algorithms. It gives significantly higher efficiency over the programmable processor. This paper presents information regarding FPGA implementation of Image Processing Algorithms using Xilinx System Generator (XSG). Xilinx Application Generator is a Xilinx existing application process that makes FPGA hardware design relatively easy. For synthesis and simulation, the Xilinx device generator is initiated with MATLAB. To reintroduce a wide range of image processing algorithms, a model-based analysis approach will be used. Various classification algorithms for RGB to grayscale, image negativity, image retrieval, contrast stretching, threshold, boundaries extraction, as well as various image fusion methods are explored, and therefore how they are implemented using available Device Generator components

T-RexNet—A Hardware-Aware Neural Network for Real-Time Detection of Small Moving Objects

This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off.

Marketing Improvement of Chinese Original Picture Books From Dissatisfaction Evaluation - Text Mining Based on LDA Model

Chinese original picture books play an important role in inheriting traditional culture and forming cultural identity, which is very important for children. We analyzes the dissatisfaction evaluation of Chinese original picture books by using the topic model of Latent Dirichlet Allocation (LDA). It is found that the dissatisfaction of consumers mainly focuses on smell, AR function, preaching, quality, picture, content, painting style and so on. In the future, we should take quality as the bottom line, focus on the content creation, and carry out integrated marketing through social media.