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.

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