Dr. Ayman Tawfik is the head of the Electrical and Computer Engineering Department, College of Engineering and Information Technology, Ajman University. He has PhD in Electrical Engineering from the University of Victoria, Canada, 1995 and MSc/BSc from Ain Shams University, Egypt, 1989/1983 respectively. He has over 30 years experience in teaching different academic courses and vast experience in ABET, accreditation and reaccreditation of different electrical engineering programs. He published several research papers in renowned journals and conferences. He worked as a researcher for DND in Canada and a consultant for Egetronic company in Egypt. His research interests are Internet-of Things (IoT), digital signal processing, digital image processing, VLSI signal processing, digital communication and education technology.
Acne Vulgaris is a common type of skin disease that affects more than 85% of teenagers and frequently continues even in adulthood. While it is not a dangerous skin disease, it can significantly impact the quality of life. Hyperspectral imaging (HSI), which captures a wide spectrum of light, has emerged as a tool for the detection and diagnosis of various skin conditions. However, due to the high cost of specialised HS cameras, it is limited in its use in clinical settings. In this research, a novel acne detection system that will utilise reconstructed hyperspectral (HS) images from RGB images is proposed. A dataset of reconstructed HS images is created using the best-performing HS reconstruction model from our previous research. A new acne detection algorithm that is based on reconstructed HS images and RetinaNet algorithm is introduced. The results indicate that the proposed algorithm surpasses other techniques based on RGB images. Additionally, reconstructed HS images offer a promising and cost-effective alternative to using expensive HSI equipment for detecting conditions like acne or other medical issues.
The advanced technology of vehicles makes them vulnerable to external exploitation. The current trend of research is to impose security measures to protect vehicles from different aspects. One of the main problems that counter Intrusion Detection Systems (IDSs) is the necessity to have a low false acceptance rate (FA) with high detection accuracy without major changes in the vehicle network infrastructure. Furthermore, the location of IDSs can be controversial due to the limitations and concerns of Electronic Control Units (ECUs). Thus, we propose a novel framework of multistage to detect abnormality in vehicle diagnostic data based on specifications of diagnostics and stacking ensemble for various machine learning models. The proposed framework is verified against the KIA SOUL and Seat Leon 2018 datasets. Our IDS is evaluated against point anomaly attacks and period anomaly attacks that have not been used in its training. The results show the superiority of the framework and its robustness with high accuracy of 99.21%, a low false acceptance rate of 0.003%,and a good detection rate (DR) of 99.63% for Seat Leon 2018, and an accuracy of 99.22%, a low false acceptance rate of 0.005%, and good detection rate of 98.59% for KIA SOUL.
In this paper, a theorem for the instability checking of linear shift-invariant two-dimensional discrete systems is proposed. This is based on the results for the location of the roots of polynomials presented by Mori and thecriterion for the stability of 2-D discrete systems presented by Murray and Delsarte et al. The theorem provides a new sufficient criterion which can be applied easily for the 2-D instability checking. This is demonstrated by detailed examples.
The discrete time Lyapunov equation is used in many applications and there is interest in its inverse and direct solutions. New methods are proposed to obtain solutions for cases where the system matrix is in controllable canonical form. The approach is based on the relationship between the discrete Lyapunov equation and the entries of one of the stability tables presented by Jury. It is shown that the inverse solution,which is based on this stability table, can be obtained using LDLt decomposition. Also the direct solution ofthe discrete Lyapunov equation can be obtained directly from the entries of this stability table. The proposedalgorithms are illustrated by numerical examples.
This paper presents new stability conditions for two-dimensional (2-D) systems in statespace description. Both discrete and continuous systems are studied. These results are based on the criteria first presented by Huang, De Carlo, Strintzis, Murray, Delsarte, et al. and on the discrete Lyapunov equation with complex elements for 2-D systems. The stability properties of the Mansour matrix are also used for stability testing in state-space. Criteria for the VSHP property of 2-D polynomials are further presented using the continuous Lyapunov equation with complex elements and the stability properties of the Schwarz matrix form. The stability properties of the Schwarz matrix are also used for testing the VSHP property of 2-D polynomials in state-space. The proposed new criteria are nonconservative for the stability analysis of 2-D discrete and continuous systems and achieve the aim of reducing the original 2-D problem as much as possible to a set of 1-D stability tests. Numerical examples are given to illustrate the utility of the proposed conditions.
Breast cancer constitutes a significant threat to women’s health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes between normal and abnormal cases is comprised of Gray Level Co-occurrence Matrix (GLCM) as a feature extraction technique and random forest as a classifier, followed by the second classification stage which classifies the abnormal cases into benign or malignant cases and is comprised of Local Binary Patterns (LBP) as a feature extraction technique and random forest as a classifier. The classification accuracy for the first stage is 97% and an F1-score of 0.98 and 0.97 for normal and abnormal classes. While for the second stage, the classification accuracy is 75% and an F1-score of 0.76 and 0.74 for benign and malignant classes. The overall hierarchical classification system achieves a classification accuracy of 85%, Matthews correlation coefficient (MCC) of 0.76, and F1-score of 0.98, 0.7, and 0.74 for normal, benign, and malignant test cases.
During the past few decades, two and higher dimensional systems have been extensively applied in many areas of research. The representation of the 2-D systems in the frequency domain is usually given by its transfer function. The bounded-input bounded-output (BIBO) stability of the two dimensional discrete systems depends on the zeros of the characteristic polynomial which is the denominator of this transfer function. In this paper, a new sufficient criterion for the stability of two-dimensional linear shift-invariant discrete systems is presented. The new criterion is based on the sufficient condition for stable polynomials with complex coefficients and the stability criterion for 2-D discrete systems proposed by Murray and Delsarte et al.. The new criterion is non-conservative for the stability testing of 2-D discrete systems. It is shown that the proposed sufficient criterion is simple enough to be applied for the stability checking of the 2-D discrete systems. The utility of the proposed criterion is demonstrated by examples.
The primary role of the oily secretion (meibum) is to ensure tear film stability and retard evaporation. In addition to these is providing ocular surface lubrication, which is necessary for smooth eyelid movements. When a gland is blocked, it is described as a Meibomian gland cyst (MGC), which can be a meibomian cyst, usually referred to as chalazion (eye bump), or in the case of inflammation, it is considered to be a hordeolum (sty or stye). Topical ophthalmic ointments and eyelid heat massages can treat early diagnosed MGC; otherwise, surgical operation is required. The current techniques of diagnosing MGC are usually uncomfortable or invasive, such as examining the tarsal plate after everting the eyelid or by biopsy procedures. The purpose of this work is to propose a non-invasive MGC evaluation and classification technique using hyperspectral imaging and image processing. The proposed technique was carried out on a single patient (i.e., case study) for a period of 4 months to monitor the MGC evolution until postsurgery-recovery and was compared with a normal eyelid patient. The collected hypercube data were processed using Multivariate Curve Resolution (MCR) and image analysis to classify the MGC severity levels. The proposed work built the threshold of the complete system, where early diagnosis of an MGC is possible before it becomes visible to the eye,