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. In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully‐connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems.
In this paper, anomaly detection (AD) is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of Medical Things (IoMT) systems. Deep learning based on convolutional neural network models is employed in the training and testing phases. Each input image passes through a series of convolution layers with different numbers of kernel filters. For the classification task, pooling and fully‐connected layers are utilized. Computer simulation experiments reveal the superiority of the proposed models for automated medical diagnosis in IoMT systems.
PurposeThe objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as ...the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.Design/methodology/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).
Common carotid artery (CCA) diagnosis is very important for carrying out an assessment of the severity of vascular disease and being able to suggest treatment solutions, whether with careful surgical ...planning or even an interventional radiological surgery. Early diagnosis of carotid atherosclerosis is an essential step in preventing stroke from occurring. This is the motivation for us to develop a novel Computer-Aided Diagnosis (CAD) system for CCA disease diagnosis. Our novel CAD system contains four phases named: segmentation, localization, intima-media thickness (IMT) measurement, and classification of the CCA as normal and abnormal. Each phase in our integrated system has its role and novelty contribution that distinguishes it from any previous studies and researches. These roles and contributions of all phases will be discussed later in this paper. These phases have been applied for the CCA in transverse and longitudinal sections to help in the early diagnosis of atherosclerosis providing a complete diagnosis approach. The CCA has been localized in the transverse section images based on a deep learning technique called faster regional proposal convolutional neural network (Faster R-CNN). The IMT measurement of the CCA has been accomplished in a longitudinal section based on edge detection techniques. The CCA-lumen segmentation has been made in a longitudinal section using active contour criteria. The CCA longitudinal section has been classified as normal and abnormal using the transfer learning of the pre-trained convolutional neural network (CNN) called AlexNet. Experiments have been performed on three different ultrasound image datasets that were manually collected. The comparison between our suggested localization phase circles and the clinician’s delineations shows an average Jaccard similarity of 90.86% with an accuracy of 97.5%. The mean ± standard deviation (SD) of our method and the experts for IMT measurements are 0.7573 ± 0.52 mm and 0.7604 ± 0.52, respectively. The obtained classification results show 100% for specificity, sensitivity, and accuracy. These results, show the superiority of the proposed system over other systems in the literature.
There is a paradigm shift from traditional power distribution systems to smart grids (SGs) due to advances in information and communication technology. An advanced metering infrastructure (AMI) is ...one of the main components in an SG. Its relevance comes from its ability to collect, process, and transfer data through the internet. Although the advances in AMI and SG techniques have brought new operational benefits, they introduce new security and privacy challenges. Security has emerged as an imperative requirement to protect an AMI from attack. Currently, ensuring security is a major challenge in the design and deployment of an AMI. This study provides a systematic survey of the security of AMI systems from diverse perspectives. It focuses on attacks, mitigation approaches, and future visions. The contributions of this article are fourfold: First, the vulnerabilities that may exist in all components of an AMI are described and analyzed. Second, it considers attacks that exploit these vulnerabilities and the impact they can have on the performance of individual components and the overall AMI system. Third, it discusses various countermeasures that can protect an AMI system. Fourth, it presents the open challenges relating to AMI security as well as future research directions. The uniqueness of this review is its comprehensive coverage of AMI components with respect to their security vulnerabilities, attacks, and countermeasures. The future vision is described at the end.
•In-depth exploration and analysis of the state-of-the-art have been conducted.•Advanced metering infrastructure (AMI) and its components have been covered.•Security vulnerabilities, attacks, and countermeasures for AMI have been analyzed.•Lessons learned, current challenges, and future vision for AMI security have been discussed.
Controlling a quadcopter is a challenging task because of the inherent high nonlinearity of a quadcopter system. In this paper, a new quaternion based nonlinear feedback controller for attitude and ...altitude regulation of a quadcopter is proposed. The dynamic model of the quadcopter is derived using Newton and Euler equations. The proposed controller is established based on a feedback linearization technique to control and regulate the quadcopter. Global asymptotic stability of the designed controller is verified using Lyapunov stability criterion. A comparison of the proposed controller performance and that of the state-of-the-art quadcopter controllers is performed to ensure the effectiveness of the proposed model. The efficiency of the proposed controller is clearly shown when the quadcopter is in or near a corner pose. Simulations are performed to assess the transient and steady state performance. Steady State Error (<inline-formula> <tex-math notation="LaTeX">E_{ss} </tex-math></inline-formula>) and Max Error (<inline-formula> <tex-math notation="LaTeX">E_{M} </tex-math></inline-formula>) are used as evaluation metrics of the proposed model performance.
Summary
In this paper, a postdispersion compensation unit is proposed leading to a better performance for the optical communication systems. This unit utilizes a chirped fiber Bragg grating (CFBG). ...For enhanced performance of the CFBG, a proper apodization function is chosen to improve the quality factor (Q‐factor) and the bit error rate (BER) of the system. A 110‐km wavelength division multiplexing (WDM) optical link is investigated. The system performance is evaluated through its Q‐factor, eye diagram, and BER showing best performance when using the Hamming apodization function.
A postdispersion compensation chirped fiber Bragg grating (CFBG) is proposed leading to a better performance of the wavelength division multiplexing (WDM) 110‐km optical link. The system performance is evaluated by Q‐factor, eye diagram, and BER showing best performance when using the Hamming apodization function. Also, the power budget is investigated at BER 10−9.
The object of this study is to enhance dispersion compensation to maximize transmission bit rate in single mode silica fibers. This is achieved through a proposed model that starts with finding the ...optimal operating parameters for single mode silica fibers. These optimal operating parameters lead to near zero dispersion due to the nature of chromatic dispersion which mainly consists of material dispersion and waveguide dispersion. The proposed model employs soliton transmission technique, where the propagated pulse suffers the effects of nonlinearity self-phase modulation that shrinks the pulse in opposite effect to the chromatic dispersion. The balancing between chromatic dispersion and nonlinear effects will generate a soliton wave which propagates over a long transmission distance without any change. The proposed model consists of four identical stages cascaded apodized uniform fiber Bragg gratings and a soliton modulator. Different apodization functions are investigated. The maximum transmission bit rate per channel is 1.9932 Gbps of the proposed model with raised cosine apodized fiber Bragg grating and soliton at input signal wavelength = 1.70 µm, differential refractive index = 0.001, ambient temperature = 283 K, while at the same conditions the maximum transmission bit rate per channel of soliton only is 0.0452 Gbps.
Unmanned aerial vehicle quadcopters have applications in different real-life areas. They are nonlinear systems that necessitate the utilization of nonlinear control techniques. In this paper, we ...propose a new quaternion-based tracking controller for an underactuated quadcopter based on the pseudo linear feedback linearization technique. The quadcopter dynamic model was derived using Newton and Euler equations, and the global asymptotic stability of the quadcopter was verified using the Lyapunov stability criterion. The proposed controller has been compared to three state-of-the-art quadcopter controllers. Through simulation results, it has been shown that the proposed model has an effective and better performance than others. The metrics used in this evaluation are the steady-state error, maximum error, overshoot, and settling time. The different metrics proved the good performance of the proposed model in most of the different states that are presented.
Transfer learning (TL) appears to be a potential method for transferring information from general to specialized activities. Unfortunately, experimenting using various TL models does not yield good ...results. In this paper, we propose a model built from scratch with the Hough transform (HT) of constellation diagrams to improve modulation format recognition. The HT is utilized to project points on the constellation diagrams on the Hough space. The HT translates constellation diagram points into lines. Features can then be extracted from the HT domain. Constellation diagrams for eight different modulation formats (2/4/8/16—PSK and 8/16/32/64—QAM) are obtained at optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB. The proposed system is based on classification and TL. The obtained results indicate that even at low OSNR values, the proposed system can blindly recognize the wireless optical modulation format with a classification accuracy of up to 99%.
The authors propose an efficient nanosystem based on molecular communication technology. Molecular communication via diffusion (MCvD) is a promising trend for exchanging biochemical signals between a ...nanotransmitter (NT) and a nanoreceiver (NR) in aqueous media over short distances. Nanosystem-based MCvD has recently received a lot of attention in advanced targeted nanomedicine applications such as targeted drug delivery and healthcare monitoring (disease/diagnosis/analysis). However, the random nature of molecular diffusion causes counting noise, which significantly degrades the performance of the nanosystem-based molecular communication. In this paper, a reliable and simple denoising technique, namely Savitzky–Golay (SG) filter, is developed in the nanosystem-based MCvD to provide high accuracy of molecular information reception. The performance of the proposed nanosystem is evaluated in terms of bit error rate (BER) and correlation efficiency. The results reveal that the nanosystem-based MCvD using the proposed SG filter outperforms the MCvD using current denoising techniques such as moving average filter, wavelet denoising and I-filter. Actually, it was found that the SG filter increases the gain efficiency in terms of the correlation coefficient by more than 60% in comparison to the I-filter at low and high signal-to-noise ratios (SNRs), whereas in comparison to wavelet denoising, the SG filter achieves more than 10% enhancement in gain efficiency at low SNRs.
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