Optimal ship imaging for shore-based ISAR using DCF estimation

The optimal imaging time selection of ship targets for shore-based inverse synthetic aperture radar （ISAR） in high sea conditions is investigated. The optimal imaging time includes opti- mal imaging instants and optimal imaging duration. A novel method for optimal imaging instants selection based on the estimation of the Doppler centroid frequencies （DCFs） of a series of images obtained over continuous short durations is proposed. Combined with the optimal imaging duration selection scheme using the image contrast maximization criteria, this method can provide the ship images with the highest focus. Simulated and real data pro- cessing results verify the effectiveness of the proposed imaging method.

B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.

Image enhancement of color fundus photographs for age-related macular degeneration:the Shanghai Changfeng Study

AIM:To develop and evaluate a new fundus image optimization software based on red,green,blue channels(RGB) for the evaluation of age-related macular degeneration(AMD) in the Chinese population.METHODS:Fundus images that were diagnosed as AMD from the Shanghai Changfeng Study database were analyzed to develop a standardized optimization procedure.Image brightness,contrast,and color balance were measured.Differences between central lesion area and normal retinal area under different image brightness,contrast,and color balance were observed.The optimal optimization parameters were determined based on the visual system to avoid image distortion.A paired-sample diagnostic test was used to evaluate the enhancement software.Fundus optical coherence tomography(OCT) was used as the gold standard.Diagnostic performances were compared between original images and optimized images using Mc Nemar’s test.RESULTS:A fundus image optimization procedure was developed using 86 fundus images of 74 subjects diagnosed with AMD.By observing gray-scale images,choroid can be best displayed in red channel and retina in green channel was found.There was limited information in blue channel.Totally 104 participants were included in the paired sample diagnostic test to assess the performance of the optimization software.After the image enhancement,sensitivity increased from 74% to 88%(P=0.008),specificity decreased slightly from 88% to 84%(P=0.500),and Youden index increased by 0.11.CONCLUSION:The standardized image optimization software increases diagnostic sensitivity and may help ophthalmologists in AMD diagnosis and screening.

Dwell Scheduling Algorithm for Digital Array Radar

Aiming at the problem of resource allocation for digital array radar（ DAR）,a dwell scheduling algorithm is proposed in this paper. Firstly,the integrated priority of different radar tasks is designed,which ensures that the imaging tasks are scheduled without affecting the search and tracking tasks; Then,the optimal scheduling model of radar resource is established according to the constraints of pulse interleaving; Finally,a heuristic algorithm is used to solve the problem and a sparse-aperture cognitive ISAR imaging method is used to achieve partial precision tracking target imaging. Simulation results demonstrate that the proposed algorithm can both improve the performance of the radar system,and generate satisfactory imaging results.

Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis （PCA） and multiple discriminant analysis （MDA） is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection （ORICS） plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.

Image feature optimization based on nonlinear dimensionality reduction

Image feature optimization is an important means to deal with high-dimensional image data in image semantic understanding and its applications. We formulate image feature optimization as the establishment of a mapping between highand low-dimensional space via a five-tuple model. Nonlinear dimensionality reduction based on manifold learning provides a feasible way for solving such a problem. We propose a novel globular neighborhood based locally linear embedding (GNLLE) algorithm using neighborhood update and an incremental neighbor search scheme, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE) algorithm based on path-based clustering. Due to its full consideration of correlations between image data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset on the manifold. Experimental results on two image sets show the effectiveness and efficiency of the proposed algorithms.

Novel Model Using Kernel Function and Local Intensity Information for Noise Image Segmentation

It remains a challenging task to segment images that are distorted by noise and intensity inhomogeneity.To overcome these problems, in this paper, we present a novel region-based active contour model based on local intensity information and a kernel metric. By introducing intensity information about the local region, the proposed model can accurately segment images with intensity inhomogeneity. To enhance the model＇s robustness to noise and outliers, we introduce a kernel metric as its objective functional. To more accurately detect boundaries, we apply convex optimization to this new model, which uses a weighted total-variation norm given by an edge indicator function. Lastly, we use the split Bregman iteration method to obtain the numerical solution. We conducted an extensive series of experiments on both synthetic and real images to evaluate our proposed method, and the results demonstrate significant improvements in terms of efficiency and accuracy, compared with the performance of currently popular methods.

An improved coverage-oriented retrieval algorithm for large-area remote sensing data

With the rapid development of satellite technology,the amount of remote sensing data and demand for remote sensing data analysis over large areas are greatly increasing.Hence,it is necessary to quickly filter out an optimal dataset from massive dataset to support various remote sensing applications.However,with the improvements in temporal and spatial resolution,remote sensing data have become fragmented,which brings challenges to data retrieval.At present,most data service platforms rely on the query engines to retrieve data.Retrieval results still have a large amount of data with a high degree of overlap,which must be manually selected for further processing.This process is very labour-intensive and time-consuming.This paper proposes an improved coverage-oriented retrieval algorithm that aims to retrieve an optimal image combination with the minimum number of images closest to the imaging time of interest while maximized covering the target area.The retrieval efficiency of this algorithm was analysed by applying different implementation practices:Arcpy,PyQGIS,and GeoPandas.The experimental results confirm the effectiveness of the algorithm and suggest that the GeoPandas-based approach is most advantageous when processing large-area data.

Adapting Nanotech Research as Nano-Micro Hybrids Approach Biological Complexity,A Review

Today＇s emergence of nano-micro hybrid structures with almost biological complexity is of fundamental interest. Our ability to adapt intelligently to the challenges has ramifications all the way from fundamentally changing research itself, over applications critical to future survival, to posing globally existential dangers. Touching on specific issues such as how complexity relates to the catalytic prowess of multi-metal compounds, we discuss the increasingly urgent issues in nanotechnology also very generally and guided by the motto ＇Bio Is Nature＇s Nanotech＇. Technology belongs to macro-evolution; for example integration with artificial intelligence （AI） is inevitable. Darwinian adaptation manifests as integration of complexity, and awareness of this helps in developing adaptable research methods that can find use across a wide range of research. The second half of this work reviews a diverse range of projects which all benefited from ＇playful＇ programming aimed at dealing with complexity. The main purpose of reviewing them is to show how such projects benefit from and fit in with the general, philosophical approach, proving the relevance of the ＇big picture＇ where it is usually disregarded.