Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network

Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms

In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In the preprocessing stage, the median filter has been used in order to remove salt-and-pepper noise because MRI images are normally affected by this type of noise, the grayscale images are also converted to RGB images in this stage. In the preprocessing stage, the histogram equalization has also been used to enhance the quality of each RGB channel. In the feature extraction stage, the three channels, namely, red, green, and blue, are extracted from the RGB images and statistical measures, namely, mean, variance, skewness, kurtosis, entropy, energy, contrast, homogeneity, and correlation, are calculated for each channel; hence, a total of 27 features, 9 for each channel, are extracted from an RGB image. After the feature extraction stage, different machine learning algorithms, such as artificial neural network, k -nearest neighbors’ algorithm, decision tree, and Naïve Bayes classifiers, have been applied in the classification stage on the features extracted in the feature extraction stage. We recorded the results with all these algorithms and found that the decision tree results are better as compared to the other classification algorithms which are applied on these features. Hence, we have considered decision tree for further processing. We have also compared the results of the proposed method with some well-known algorithms in terms of simplicity and accuracy; it was noted that the proposed method outshines the existing methods.

Identification of Secondary Metabolite Compounds and Cytotoxic Activity Test of Papuan Grape (Sararanga Sinuosa Hemsley) Extract using Brine Shrimp Lethality Test (BSLT) Method

Papua has a diversity of flora species, one of which is the Papuan Grape (Sararanga sinuosa Hemsley). It is commonly used by the Depapre community, Jayapura, as a stamina booster. This research aims to identify the secondary metabolite compounds, to test the cytotoxic activity of Papuan Grape (Sararanga sinuosa Hemsley) extract, and to determine the best concentration that inhabits the growth of Artemia salina larvae using the BSLT method. This study was conducted with the extraction stage using the maceration method by making use of 96% ethanol solvent. Subsequently, the concentration series 0, 50, 100, 150, 200, 250, 300 ppm of Papuan Grape (Sararanga sinuosa Hemsley) extract were made to test the cytotoxic activity on the mortality of Artemia salina shrimp larvae. The results showed that Alkaloids, Flavonoids, Saponins, and Tannins were compounded as secondary metabolite. An antioxidant research that had been carried out previously had LC50 of green-white fruit (12,49 ± 0,35 mg/ml), orange-red fruit (17,62 ± 3,49 mg/ml) and red fruit (12,23 ± 0,46 mg/ml). The community process one stalk of it into juice and used or consumed it two times a day. An inappropriate dose of traditional medicine usage can affect the organ system and had adverse effects in the future The result of cytotoxic research obtained the value of LC50 in ethanol extract of Papuan Grape was 140,863 ppm, and concentration of 250 ppm was the best concentration to inhibit the growth of shrimp larvae (Artemia salina L). The conclusion of this study was the ethanol extract of Papuan Grape (Sararanga sinuosa Hemsley) showed the highest cytotoxic activity and potentially become an anti-cancer agent.

Two-Stream Deep Fusion Network Based on VAE and CNN for Synthetic Aperture Radar Target Recognition

Usually radar target recognition methods only use a single type of high-resolution radar signal, e.g., high-resolution range profile (HRRP) or synthetic aperture radar (SAR) images. In fact, in the SAR imaging procedure, we can simultaneously obtain both the HRRP data and the corresponding SAR image, as the information contained within them is not exactly the same. Although the information contained in the HRRP data and the SAR image are not exactly the same, both are important for radar target recognition. Therefore, in this paper, we propose a novel end-to-end two stream fusion network to make full use of the different characteristics obtained from modeling HRRP data and SAR images, respectively, for SAR target recognition. The proposed fusion network contains two separated streams in the feature extraction stage, one of which takes advantage of a variational auto-encoder (VAE) network to acquire the latent probabilistic distribution characteristic from the HRRP data, and the other uses a lightweight convolutional neural network, LightNet, to extract the 2D visual structure characteristics based on SAR images. Following the feature extraction stage, a fusion module is utilized to integrate the latent probabilistic distribution characteristic and the structure characteristic for the reflecting target information more comprehensively and sufficiently. The main contribution of the proposed method consists of two parts: (1) different characteristics from the HRRP data and the SAR image can be used effectively for SAR target recognition, and (2) an attention weight vector is used in the fusion module to adaptively integrate the different characteristics from the two sub-networks. The experimental results of our method on the HRRP data and SAR images of the MSTAR and civilian vehicle datasets obtained improvements of at least 0.96 and 2.16%, respectively, on recognition rates, compared with current SAR target recognition methods.

INFLUENCE OF MACROSTRUCTURE ON THE DENSITY OF OAK WOOD

An individual process of staining of each trunk occurs as a result of the long-term presence of wood in the river soil without oxygen access. It consists in changing the structure and chemical composition of the wood. There are industrial reserves of this wood on the territory of the Russian Federation, in the floodplains of a number of rivers. One of the most important tasks at the extraction stage is the primary individual quality assessment of the trunk. One of the most effective diagnostic indicators for assessing wood quality can be the number of annual layers in one centimeter. This indicator correlates well with wood density. The purpose of the research is to establish the influence of the macrostructure of natural wood and stained oak wood, changes in the microstructure on its density. It was found that the density of stained oak wood, depending on the number of annual layers in 1 cm, is about 10% higher than that of natural wood, all other things being equal. The magnitude and nature of the decrease in density along the radius of the trunk is the same as in natural wood. It is about 20%. The performed studies will allow making an express analysis of the quality of each stained wood trunk at the stage of making a decision on the behavior of its extraction. This will significantly reduce the cost of logging and primary processing of stained oak wood

An Efficient Steganalysis of Medical Images by Using Deep Learning Based Discrete Scalable Alex Net Convolutionary Neural Networks Classifier

Steganalysis is the technique that tries to beat steganography by detecting and removing secret information. Steganalysis involves the detection of a message embedded in a picture. Deep Learning (DL) advances have offered alternative approaches to many difficult issues, including the field of image steganalysis using deep-learning architecture based on convolutionary neural networks (CNN). In recent years, many CNN architectures have been established that have enhanced the exact identification of steganographic images. This work presents a novel architecture which involves a preprocessing stage using histogram equalization and adaptive recursive median filter banks to reduce image noise, a feature extraction stage using shearlet multilinear local embedding methods and then finally the classification can be done by using the discrete scalable Alex NET CNN classifier. Performance was evaluated on the RGB-BMP Steganalysis Dataset with different experimental setups. To prove the effectiveness of the suggested algorithm it can be compared with the other existing methodologies. This work improves classification accuracies on all other existing algorithms over test data.

Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT

This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications.

Recovery of Natural Scenery Image by Content Using Wiener-Granger Causality: A Self-Organizing Methodology

One of the most important applications of data science and data mining is is organizing, classifying, and retrieving digital images on Internet. The current focus of the researchers is to develop methods for the content based exploration of natural scenery images. In this research paper, a self-organizing method of natural scenes images using Wiener-Granger Causality theory is proposed. It is achieved by carrying out Wiener-Granger causality for organizing the features in the time series form and introducing a characteristics extraction stage at random points within the image. Once the causal relationships are obtained, the k-means algorithm is applied to achieve the self-organizing of these attributes. Regarding classification, the k−NN distance classification algorithm is used to find the most similar images that share the causal relationships between the elements of the scenes. The proposed methodology is validated on three public image databases, obtaining 100% recovery results.

Antiviral Activity of Carrageenans and Processing Implications

Carrageenan and carrageenan oligosaccharides are red seaweed sulfated carbohydrates with well-known antiviral properties, mainly through the blocking of the viral attachment stage. They also exhibit other interesting biological properties and can be used to prepare different drug delivery systems for controlled administration. The most active forms are λ-, ι-, and κ-carrageenans, the degree and sulfation position being determined in their properties. They can be obtained from sustainable worldwide available resources and the influence of manufacturing on composition, structure, and antiviral properties should be considered. This review presents a survey of the antiviral properties of carrageenan in relation to the processing conditions, particularly those assisted by intensification technologies during the extraction stage, and discusses the possibility of further chemical modifications.

Classifier Performance Evaluation for Lightweight IDS Using Fog Computing in IoT Security

In this article, a Host-Based Intrusion Detection System (HIDS) using a Modified Vector Space Representation (MVSR) N-gram and Multilayer Perceptron (MLP) model for securing the Internet of Things (IoT), based on lightweight techniques and using Fog Computing devices, is proposed. The Australian Defence Force Academy Linux Dataset (ADFA-LD), which contains exploits and attacks on various applications, is employed for the analysis. The proposed method is divided into the feature extraction stage, the feature selection stage, and classification modeling. To maintain the lightweight criteria, the feature extraction stage considers a combination of 1-gram and 2-gram for the system call encoding. In addition, a Sparse Matrix is used to reduce the space by keeping only the weight of the features that appear in the trace, thus ignoring the zero weights. Subsequently, Linear Correlation Coefficient (LCC) is utilized to compensate for any missing N-gram in the test data. In the feature selection stage, the Mutual Information (MI) method and Principle Component Analysis (PCA) are utilized and then compared to reduce the number of input features. Following the feature selection stage, the modeling and performance evaluation of various Machine Learning classifiers are conducted using a Raspberry Pi IoT device. Further analysis of the effect of MLP parameters, such as the number of nodes, number of features, activation, solver, and regularization parameters, is also conducted. From the simulation, it can be seen that different parameters affect the accuracy and lightweight evaluation. By using a single hidden layer and four nodes, the proposed method with MI can achieve 96% accuracy, 97% recall, 96% F1-Measure, 5% False Positive Rate (FPR), highest curve of Receiver Operating Characteristic (ROC), and 96% Area Under the Curve (AUC). It also achieved low CPU time usage of 4.404 (ms) milliseconds and low energy consumption of 8.809 (mj) millijoules.