Watermarking of digital images is a well-known technique that is widely used for
securing image contents. A successful watermarking method must be accurate,
reversible, resilient, and robust against various attacks. In this paper, we propose a
reversible and robust color image watermarking method. A new set of multi-channel
fractional-order polar harmonic transforms and their geometric invariants have been
derived. These highly accurate and geometrically invariant features are used in the
watermarking process. The binary watermark’s bits were scrambled using a 1D chaotic
sine map to increase the security level. A set of experiments were performed
to evaluate the proposed watermarking method, and its performance was compared
with recent color image watermarking methods having similar colors. The obtained
results showed high visual imperceptibility and superior robustness against geometric
and signal processing attacks.

In the age of Information Technology, the day-life required transmitting millions of images
between users. Securing these images is essential. Digital image encryption is a well-known
technique used in securing image content. In image encryption techniques, digital images are
converted into noise images using secret keys, where restoring them to their originals required
the same keys. Most image encryption techniques depend on two steps: confusion and diffusion.
In this work, a new algorithm presented for image encryption using a hyperchaotic system and
Fibonacci Q-matrix. The original image is confused in this algorithm, utilizing randomly generated
numbers by the six-dimension hyperchaotic system. Then, the permutated image diffused using
the Fibonacci Q-matrix. The proposed image encryption algorithm tested using noise and data cut
attacks, histograms, keyspace, and sensitivity. Moreover, the proposed algorithm’s performance
compared with several existing algorithms using entropy, correlation coefficients, and robustness
against attack. The proposed algorithm achieved an excellent security level and outperformed the
existing image encryption algorithms.

Segmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

This paper presents two hub polling medium access control protocols for wireless local area networks based on the robust super poll protocol. The proposed protocols decrease the overhead and increase the throughput through eliminating broadcasting the polling list every super frame and eliminating the use of the chaining mechanism that is utilized in the robust super poll protocol in which all the remaining polling list is appended to every data frame that is sent by every station. The performance analysis of the two proposed protocols is introduced to evaluate their performance compared with Robust Super Poll protocol. The mathematical analysis and the experimental results show that the proposed protocols give higher throughput and lower overhead than Robust Super Poll protocol.

This paper presents two hub polling medium access control protocols for wireless local area networks based on the robust super poll protocol. The proposed protocols decrease the overhead and increase the throughput through eliminating broadcasting the polling list every super frame and eliminating the use of the chaining mechanism that is utilized in the robust super poll protocol in which all the remaining polling list is appended to every data frame that is sent by every station. The performance analysis of the two proposed protocols is introduced to evaluate their performance compared with Robust Super Poll protocol. The mathematical analysis and the experimental results show that the proposed protocols give higher throughput and lower overhead than Robust Super Poll protocol.

Recently, most of the data can be represented by graph structures, such as social media, Protein-Protein Interaction, transportation system, systems biology,..., etc. Many researches have been achieved to cluster very large graphs but more efficient algorithms are required since such a process takes a long time and requires more memory. In this paper, we propose an Efficient Spectral Clustering Algorithm on Large Scale Graphs in Spark (ESCALG), using map reduce function and shuffling phases in Dijkstra's algorithm. In addition, ESCALG depends mainly on a sparse matrix as a data structure, which less time in execution. Then, GraphX is applied to deal with graph data processing and in GraphX used Pregel in computing shortest path. To test the performance of ESCALG, it is compared with Large-Scale Spectral Clustering on Graphs and Standard Spectral Clustering Algorithms using seven datasets, where ESCALG proved high efciency in terms of memory and time performance.

Recently, most of the data can be represented by graph structures, such as social media, Protein-Protein Interaction, transportation system, systems biology,..., etc. Many researches have been achieved to cluster very large graphs but more efficient algorithms are required since such a process takes a long time and requires more memory. In this paper, we propose an Efficient Spectral Clustering Algorithm on Large Scale Graphs in Spark (ESCALG), using map reduce function and shuffling phases in Dijkstra's algorithm. In addition, ESCALG depends mainly on a sparse matrix as a data structure, which less time in execution. Then, GraphX is applied to deal with graph data processing and in GraphX used Pregel in computing shortest path. To test the performance of ESCALG, it is compared with Large-Scale Spectral Clustering on Graphs and Standard Spectral Clustering Algorithms using seven datasets, where ESCALG proved high efciency in terms of memory and time performance.

Recently, most of the data can be represented by graph structures, such as social media, Protein-Protein Interaction, transportation system, systems biology,..., etc. Many researches have been achieved to cluster very large graphs but more efficient algorithms are required since such a process takes a long time and requires more memory. In this paper, we propose an Efficient Spectral Clustering Algorithm on Large Scale Graphs in Spark (ESCALG), using map reduce function and shuffling phases in Dijkstra's algorithm. In addition, ESCALG depends mainly on a sparse matrix as a data structure, which less time in execution. Then, GraphX is applied to deal with graph data processing and in GraphX used Pregel in computing shortest path. To test the performance of ESCALG, it is compared with Large-Scale Spectral Clustering on Graphs and Standard Spectral Clustering Algorithms using seven datasets, where ESCALG proved high efciency in terms of memory and time performance.