Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhancing the system performance in terms of reliability, resilience, flexibility, and energy efficiency. With the penetration of MGs, the communication-based distributed control is playing an increasingly important role in NMGs for coordinating a multitude of heterogeneous and spatially distributed DERs, which feature enhanced efficiency, reliability, resilience, scalability, and privacy-preserving as compared with conventional centralized control. This article aims to provide a comprehensive survey of distributed control and communication strategies in NMGs. We provide thorough discussions and elaborate on: 1) Essential merits of MGs and NMGs, and their practical implementations; 2) Distributed communication network characteristics and specific operation objectives of NMGs; 3) Classifications of distributed control strategies in NMGs and their salient features; 4) Communication reliability issues concerning data timeliness, data availability, and data accuracy, and the development of countermeasures; 5) Advancements in several promising communication and computation technologies and their potential applications in NMGs.

Distributed Control and Communication Strategies in Networked Microgrids

Image Encryption Based on Chaotic Sub-Block Scrambling and Chaotic Digit Selection Diffusion

While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. First, the watermarked image is utilized as the input of generative networks, and its generating target (i.e. attacked watermarked image) is the original image. Inspired by the U-Net network, the generative networks consist of encoder-decoder architecture with skip connection, which can combine the low-level and high-level information to ensure the imperceptibility of the generated image. Next, to further improve the imperceptibility of the generated image, instead of the loss function based on MSE, a perceptual loss based on feature extraction is introduced. In addition, a discriminative network is also introduced to make the appearance and distribution of generated image similar to those of the original image. The addition of the discriminative network can remove watermark information effectively. Extensive experiments are conducted to verify the feasibility of the proposed concealed attack method. Experimental and analysis results demonstrate that the proposed concealed attack method has better imperceptibility and attack ability in comparison to the existing watermarking attack methods.

Concealed Attack for Robust Watermarking Based on Generative Model and Perceptual Loss

Plaintext-related image encryption algorithm based on perceptron-like network

Image content analysis plays a dynamic role in various computer vision applications. These contents can be either visual (i.e. color, shape, texture) or the textual (i.e. text appearing within images). Both the contents involve fundamental characteristics of an image and thus can be an enormous asset for any intelligent application. For content based image retrieval (CBIR) systems, most of the art methods are either annotated text based or the visual search based. Due to high demand of multitasking, there is a great need of a system that can combine visual as well as textual features. Consequently, this work proposes a decisive CBIR approach that combines visual and textual features to retrieve similar images. Firstly, the method classifies the query image as textual and non-textual. If any text appears within the image then the query image is classified as textual, and the text is detected and formed as Bag of Textual words. If the query image is classified as non-textual, the visual salient features are extracted and formed as Bag of Visual words. Next, the method fuses the visual and textual features, and top similar images are retrieved based on the fused feature vector. It supports three modes of retrieval: Image query, Keywords, and a combination of both. The experimental results on four datasets show the efficiency and accuracy of the proposed approach for visual and textual images.

A decisive content based image retrieval approach for feature fusion in visual and textual images

Efficient copyright protection for three CT images based on quaternion polar harmonic Fourier moments

The traditional embedded watermarking scheme achieves copyright protection of images by embedding watermarks into the original images. This process will deteriorate the original image, so it is not suitable for medical images. In order to overcome this limitation, this paper proposes a geometrically invariant color medical image null-watermarking scheme based on quaternion polar harmonic Fourier moments (QPHFM), which achieves the copyright protection without changing the original medical image. First, a high-precision QPHFM computation method based on wavelet numerical integration is designed, which can effectively improve the computation precision. Then based on the precise QPHFM (PQPHFM) and chaotic map, a robust null-watermarking scheme for color medical images is proposed to protect the copyright of color medical images. This scheme uses the PQPHFM to construct feature images and its good stability and geometric invariance make the proposed null-watermarking scheme has good robustness and resistance against geometric attacks. Extensive simulation experiments show that the proposed scheme is highly robust in resisting common attacks and geometric attacks such as scaling, rotation, change of length-width ratio, and flipping. The performance of the proposed scheme is superior to that of similar robust null-watermarking schemes and other color medical image watermarking schemes.

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Geometrically Invariant Color Medical Image Null-Watermarking Based on Precise Quaternion Polar Harmonic Fourier Moments

Visual and textual information fusion using Kernel method for content based image retrieval

This work addresses the problem of searching and retrieving similar textual images based on the detected text and opens the new directions for textual image retrieval. For image retrieval, several methods have been proposed to extract visual features and social tags; however, to extract embedded and scene text within images and use that text as automatic keywords/tags is still a young research field for text-based and content-based image retrieval applications. The automatic text detection retrieval is an emerging technology for robotics and artificial intelligence. In this study, the authors have proposed a novel approach to detect the text in an image and exploit it as keywords and tags for automatic text-based image retrieval. First, text regions are detected using maximally stable extremal region algorithm. Second, unwanted false positive text regions are eliminated based on geometric properties and stroke width transform. Next, the true text regions are proceeded into optical character recognition for recognition. Third, keywords are formed using a neural probabilistic language model. Finally, the textual images are indexed and retrieved based on the detected keywords. The experimental results on two benchmark datasets show the dominancy of text is efficient and valuable for image retrieval specifically for textual images.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.5277

Salahuddin Unar

Papers

Efficient copyright protection for three CT images based on quaternion polar harmonic Fourier moments

A decisive content based image retrieval approach for feature fusion in visual and textual images

Geometrically Invariant Color Medical Image Null-Watermarking Based on Precise Quaternion Polar Harmonic Fourier Moments

Visual and textual information fusion using Kernel method for content based image retrieval

Detected text-based image retrieval approach for textual images