Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture.

As the demand for high-bandwidth Internet connections continues to surge, industries are exploring innovative ways to harness this connectivity, and smart agriculture stands at the forefront of this evolution. In this paper, we delve into the challenges faced by Internet Service Providers (ISPs) in efficiently managing bandwidth and traffic within their networks. We propose a synergy between two pivotal technologies, Multi-Protocol Label Switching-Traffic Engineering (MPLS-TE) and Diffserv Quality of Service (Diffserv-QoS), which have implications beyond traditional networks and resonate strongly with the realm of smart agriculture.

Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain.

Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achieved significant breakthroughs in the field of image fusion because of their great efficiency. The DL methods in image fusion have become an active topic due to their high feature extraction and data representation ability.

Efficient utilization of Hough transform and orthogonal-triangular decomposition for optical wireless modulation format recognition.

Two schemes for optical wireless modulation format recognition (MFR), based on the orthogonal-triangular decomposition (OTD) and Hough transform (HT) of the constellation diagrams, are proposed in this paper. Constellation diagrams are obtained at optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB for seven different modulation formats (2/4/8/16-PSK and 8/16/32-QAM) as images. The first scheme depends on applying the HT of the obtained images; the second scheme is based on utilization of the decomposition of each of the obtained image matrices into an orthogonal matrix (Q) and an upper triangular matrix (R) followed by the HT.

Modulation format recognition based on constellation diagrams and the Hough transform.

High-speed wireless communication is necessary in our personal lives, in both working and living spaces. This paper presents a scheme for wireless optical modulation format recognition (MFR) based on the Hough transform (HT). The HT is used to project constellation diagrams onto another space for efficient feature extraction.

Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems.

Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems.

Deep convolutional neural networks for COVID-19 automatic diagnosis.

This article is mainly concerned with COVID-19 diagnosis from X-ray images. The number of cases infected with COVID-19 is increasing daily, and there is a limitation in the number of test kits needed in hospitals. Therefore, there is an imperative need to implement an efficient automatic diagnosis system to alleviate COVID-19 spreading among people.

COVID-19 Classification Based on Deep Convolution Neural Network Over a Wireless Network.

Corona Virus Disease 19 (COVID-19) firstly spread in China since December 2019. Then, it spread at a high rate around the world. Therefore, rapid diagnosis of COVID-19 has become a very hot research topic.

COVID-19 classification using deep feature concatenation technique.

Detecting COVID-19 from medical images is a challenging task that has excited scientists around the world. COVID-19 started in China in 2019, and it is still spreading even now. Chest X-ray and Computed Tomography (CT) scan are the most important imaging techniques for diagnosing COVID-19.

Blind multichannel reconstruction of high-resolution images using wavelet fusion.

We developed an approach to the blind multichannel reconstruction of high-resolution images. This approach is based on breaking the image reconstruction problem into three consecutive steps: a blind multichannel restoration, a wavelet-based image fusion, and a maximum entropy image interpolation. The blind restoration step depends on estimating the two-dimensional (2-D) greatest common divisor (GCD) between each observation and a combinational image generated by a weighted averaging process of the available observations.