In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

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A review on genetic algorithm: past, present, and future

Background and objective During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic Method A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19 Rapid and critical analysis of the three crucial parameters, ie, abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic Result This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles It also addresses a few errors and challenges while using such algorithms in real-world problems The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead Conclusion The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic

Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

Partly as a side effect of the "Decade of the Brain," general readers with an interest in science have been afflicted with a surfeit of books about the brain from writers of curiously varied backgrounds. This one, with its garish title and disjointed assortment of 18 chapters, is by the distinguished codiscoverer of the structure of DNA. Some work is required to tease out of the disorderly text the two main components of the book. One of them, the better, consists of an account of current knowledge and trends of thinking about neural structure and function, with special emphasis on the cortical visual system of the higher mammals. The other component, appearing irrepressibly in bits and pieces throughout the book, as well as in longer passages, consists of a mixture of Crick's zealous and uncritical Newton-or-bust ways of thinking about the relation of consciousness to brain and an insouciant polemic,

The Astonishing Hypothesis: The Scientific Search for the Soul

Thank you very much for downloading spotlight synthetic aperture radar signal processing algorithms. Maybe you have knowledge that, people have search numerous times for their favorite books like this spotlight synthetic aperture radar signal processing algorithms, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their laptop.

Spotlight Synthetic Aperture Radar Signal Processing Algorithms

This is a book review of 'The Micro-Doppler Effect in Radar' by V. C. Chen. Artech House, 16 Sussex Street, London, SW1V 4RW, UK. 2011. 290pp + diskette. Illustrated. £90. ISBN 978-1-60807-057-2.

'The Micro-Doppler Effect in Radar' by V.C. Chen

Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset. Next, a semisupervised method is developed by adding a new regularization term into the loss function of CNN. As a result, the class probability information contained in the unlabelled samples can be maximally utilized. The experimental results on the MSTAR database demonstrate the effectiveness of the proposed algorithm despite the lack of the initial labelled samples.

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A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.

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A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images

In an attempt to exploit the automatic feature extraction ability of biologically-inspired deep learning models, and enhance the learning of target features, we propose a novel deep learning algorithm. This is based on a deep convolutional neural network (DCNN) trained with an improved cost function, and combined with a support vector machine (SVM). Specifically, class separation information, which explicitly facilitates intra-class compactness and inter-class separability in the process of learning features, is added to an improved cost function as a regularization term, to enhance the DCNN’s feature extraction ability. The enhanced DCNN is applied to learn the features of Synthetic Aperture Radar (SAR) images, and the SVM is utilized to map features into output labels. Simulation experiments are performed using benchmark SAR image data from the Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Comparative results demonstrate the effectiveness of our proposed method, with an average accuracy of 99% on ten types of targets, including variants and articulated targets. We conclude that our proposed DCNN method has significant potential to be exploited for SAR image target recognition, and can serve as a new benchmark for the research community.

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A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the CNN only consider the polarization information of the image, instead of incorporating the image’s spatial information. In this paper, a novel method based on a dual-branch deep convolution neural network (Dual-CNN) is proposed to realize the classification of PolSAR images. The proposed method is built on two deep CNNs: one is used to extract the polarization features from the 6-channel real matrix (6Ch) which is derived from the complex coherency matrix. The other is utilized to extract the spatial features of a Pauli RGB (Red Green Blue) image. These extracted features are first combined into a fully connected layer sharing the polarization and spatial property. Then, the Softmax classifier is employed to classify these features. The experiments are conducted on the Airborne Synthetic Aperture Radar (AIRSAR) data of Flevoland and the results show that the classification accuracy on 14 types of land cover is up to 98.56%. Such results are promising in comparison with other state-of-the-art methods.

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Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

Accurate detection of rivers plays a significant role in water conservancy construction and ecological protection, where airborne synthetic aperture radar (SAR) data have already become one of the main sources. However, extracting river information from radar data efficiently and accurately still remains an open problem. The existing methods for detecting rivers are typically based on rivers’ edges, which are easily mixed with those of artificial buildings or farmland. In addition, pixel-based image processing approaches cannot meet the requirement of real-time processing. Inspired by the feature integration and target recognition capabilities of biological vision systems, in this paper, we present a hierarchical method for automated detection of river networks in the high-resolution SAR data using biologically visual saliency modeling. For effective saliency detection, the original image is first over-segmented into a set of primitive superpixels. A visual feature set is designed to extract a regional feature histogram, which is then quantized based on the optimal parameters learned from the labeled SAR images. Afterward, three saliency measurements based on the specificity of the rivers in the SAR images are proposed to generate a single layer saliency map, i.e., local region contrast, boundary connectivity, and edge density. Finally, by exploiting belief propagation, we propose a multi-layer saliency fusion approach to derive a high-quality saliency map. Extensive experimental results on three airborne SAR image data sets with the ground truth demonstrate that the proposed saliency model consistently outperforms the existing saliency target detection models.

Jinping Sun

Papers

A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images

A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

Visual Saliency Modeling for River Detection in High-Resolution SAR Imagery