Advances in Communication, Signal and Image Processing

Accelerated advancements in communication and computer technology have facilitated the creation of e-learning systems to meet the increasing demand for education and challenges caused by crises and wars. Interactive e-learning systems provide students with access to courses at any time and from any location through the internet. Applied science programs faced challenges with incorporating e-learning and blended learning into existing educational environments due to their dependence on laboratories and workshops for practical training. Consequently, efforts have been initiated to utilize modern technologies in the creation of laboratories accessible through the internet. In recent years, there has been an increasing interest in utilizing artificial intelligence, Internet of things, and mixed reality technologies for the development of remote laboratories. These applications allow students to engage with laboratory experiments that include both virtual and physical units from any location and at any time, using their mobile phones, tablets, or personal computers. The purpose of this chapter is to investigate the role that artificial intelligence and internet of things technologies play in the process of developing remote laboratories that are based on mixed reality technology. The laboratory is comprised of a variety of experiments that are designed to improve the practical portion of the specialization. To motivate students to improve their theoretical understanding and develop skills, the experiment is designed to make use of both real components that are present in the laboratory as well as simulated components that are part of an interactive environment. Artificial intelligence algorithms are employed to manage the educational process in the laboratory, assess the student’s academic proficiency, and provide them with suitable educational resources for the lab. Additionally, the design of experiments for the mechatronics lab and the renewable energy lab will be discussed. These labs serve as an interactive educational platform that enables students to engage with experimental components, acquire knowledge regarding the operation of each element, carry out tests, and submit technical reports. By incorporating the Internet of things and mixed reality technologies into the design of remote labs, an interactive environment is created that improves learning quality and skill acquisition. These labs give educational institutions the chance to work together to create innovative, high-caliber, and successful programs that resolve the problems caused by the availability and high cost of laboratory equipment, as well as the lack of technical staff needed by traditional labs.

In the modern industrial landscape, the convergence of SCADA (Supervisory Control and Data Acquisition), advanced HMI (Human-Machine Interface) systems, and cutting-edge communication technologies have revolutionized how production facilities are managed and optimized. These technologies collectively serve as the digital backbone of intelligent industrial operations, providing real-time visibility into processes, assets, and resource flows. By enabling the instantaneous collection, analysis, and visualization of statistical data, SCADA and HMI platforms empower management teams to make informed decisions rooted in accurate, actionable knowledge rather than; random guessing and assumptions; or historical approximations. The immediacy with which data is now available, facilitated by Ethernet, industrial wireless protocols, cloud integration, and mobile HMI access, has dramatically shortened the feedback loop between the shop floor and the decision-making hierarchy. Managers can monitor key performance indicators (KPIs), detect deviations from operational baselines, and respond to anomalies in real time, thus, minimizing downtime and maximizing throughput. Decisions are now data-driven and timely, enhancing the agility and competitiveness of manufacturing operations. Moreover, this data-centric visibility has extended into the supply chain domain. Real-time inventory tracking, consumption trends, and predictive usage models, fueled by SCADA data analytics, allow for more accurate and just-in-time procurement. This results in optimized sourcing strategies, reduced inventory carrying costs, and minimized material shortages. At the same time, predictive maintenance capabilities have emerged as a critical advantage. By leveraging continuous equipment health monitoring and pattern recognition, SCADA-integrated systems can preemptively schedule maintenance activities, thereby avoiding unexpected breakdowns and ensuring asset longevity. Collectively, these advancements have ushered in a new era of industrial efficiency. The seamless integration of real-time data acquisition, intelligent human interfaces, and robust communication protocols has not only improved productivity and reduced unplanned downtime but also transformed the very nature of industrial decision-making; from reactive to predictive, from manual to autonomous. As industries continue to evolve, the strategic use of SCADA, HMI, and communication technologies will remain a cornerstone of sustainable, data-informed production systems.

Device-to-device (D2D) communication stands out as a promising technology for future wireless networks. It enables direct communication between devices, improving latency, reliability, throughput, and power consumption. These reduce also network load and increases cellular coverage. In addition, a device can act as a relay between the base station and another device located outside. Therefore, D2D communication can help meet the requirements of future 5G (5th Generation) wireless networks (Pirinen in 1st International conference on 5G for ubiquitous connectivity, pp. 17–22, 2014). In this chapter, we will focus on energy consumption in direct and relayed D2D communications. We will study the basic model of D2D communication. This will be used for calculating the energy consumed by different proposed topologies. The study will be completed by evaluating the total energy consumed by each topology.

With the growing demand for high-speed online services and data-driven applications, the optical communication research community has turned its attention to utilizing the orbital angular momentum (OAM) of light for high-capacity data transmission through optical fibers. Among the promising advancements in this area are specialized OAM fibers, which offer a viable pathway for supporting stable and high-throughput OAM channels. This chapter introduces a novel type of OAM-supporting fiber: the inverse raised cosine few-mode fiber (IRCFMF). We explore its design methodology, optimization techniques, and performance across key parameters such as mode separation, differential group delay, chromatic dispersion, nonlinear behavior, resistance to mode coupling under perturbations, and bending tolerance. This chapter consolidates all our IRC-FMF analysis findings, aiming to provide the research community with a solid foundation for future developments and innovations involving this new class of OAM fibers.

To meet the growing demand for transmission capacity and spectral efficiency in optical communication systems, significant attention has shifted toward utilizing the orbital angular momentum (OAM) of light for information encoding and transmission through optical fibers. This trend has spurred the development of advanced specialty fibers tailored to support OAM channels with improved performance characteristics. In this chapter, we focus on a class of optical fibers specifically engineered for OAM mode propagation. We examine various design approaches and review recent advancements in this area. Furthermore, we introduce our custom-designed OAM-supporting fibers, inverse Gaussian fibers (IGFs). A comprehensive numerical investigation is carried out to analyze the key structural and optical parameters of IGFs that enable stable OAM mode guidance. We propose and optimize four IGF configurations that demonstrate enhanced intermodal separation, achieving a minimum effective refractive index difference of \(\varDelta n_{eff} = 2.74 \times 10^{-4}\). This large separation helps suppress unwanted mode coupling and significantly reduces inter-channel crosstalk. Additionally, we evaluate the transmission characteristics of the designed IG-FMFs in terms of differential group delay and chromatic dispersion across the ITU-T C\(+\)L wavelength bands. Comparative results reveal that our IGF designs offer superior performance relative to state-of-the-art specialty fibers, positioning them as strong candidates for future high-capacity optical communication networks incorporating wavelength-division multiplexing (WDM). This chapter aims to provide valuable insights and serve as a stimulus for further research and development within the optical fiber design community.

The Pareto Type II distribution has proven to be a robust statistical model for capturing the characteristics of high-resolution sea clutter, particularly under low grazing angle conditions. The application of the Generalized Likelihood Ratio Test Linear Threshold Detector (GLRT-LTD) within such clutter environments necessitates precise estimation of clutter parameters to maintain optimal detection performance. In this study, we conduct a comprehensive evaluation of several parameter estimation techniques-namely Maximum Likelihood Estimation (MLE), integer-order moments (HOME), fractional-order moments (FOME), and the \(z \log z\) estimator-under conditions of correlated Pareto-distributed clutter. Through extensive Monte Carlo simulations, we demonstrate that while MLE and \(z \log z \) estimators yield comparably accurate results in general, the \(z \log z\) method provides superior performance in scenarios involving strong correlation among clutter samples. This highlights the estimator’s robustness and computational efficiency, particularly in challenging detection environments where correlation effects are non-negligible.

Recently, In terms of broadcasting and transferring the procedures related to this area of contemporary technology, as well as in terms of its fundamental processing and coding, digital television has taken a while to gain traction. It is well recognized that the challenges associated with fundamental digital processing methods and digital image storage are the primary cause of this observed delay. It has taken a while for digital television to gain traction, both in terms of basic processing and coding as well as in terms of broadcasting and transferring the procedures related to this area of contemporary technology. It is well recognized that the primary cause of this observed delay is the challenges associated with digital image storage and fundamental digital processing methods. To enable digital picture reception on handheld devices, a few options are suggested. Digital Multimedia Broadcasting DMB (Asia), Media Flo (USA), and Digital Video Broadcasting-Handheld DVB-H (Europe) are the three primary standards that have been authorized. They are using the same notional TV or DAB platform transport stream. In this work, we provide a novel way to integrate digital video into GSM interfaces without altering any of the standard’s specifications. The novel suggested technique therefore makes it possible to accomplish this goal by utilizing GSM channels directly. The primary goal is to use the H265 advanced standard to reduce the bit rate coming from the video encoder by inserting a novel design known as a “Brewer-buffer”. To create the RF channels, the outputs of this newly suggested element will be routed to the GSM norm’s time division multiple access TDMA frames. Our receiver has to have the symmetric component “Invert brewerbuffer” in order to restore the digital video. In fact, we will also suggest a new receiver architecture for this handheld video reception application. We illustrate the basic architecture and define the uniqueness of the suggested approach.

Since the past twenty years, a new type of interface has developed in a spectacular way: the Brain-Computer Interfaces (BCI). These interfaces enable users to control computers solely with their thoughts, generating various forms of brainwave activity. Indeed, BCIs are a new means of communication for people with total or partial paralysis. A key challenge in BCI system design involves automatically interpreting the user’s brain activity patterns. Most BCIs are noninvasive systems that rely on the electroencephalogram (EEG) and use one of three mental tasks, among them motor imagery (MI). This chapter focuses on motor imagery-based BCI systems utilizing EEG signal processing. In conventional BCI systems, the detection and interpretation of brain signals typically depend on machine learning classifiers, which require carefully extracted EEG features for accurate operation. In this study, we specifically investigate motor imagery (MI) classification for left- and right-hand movements using EEG signals. Our novel approach introduces two key feature extraction methods. (1) Wavelet Transform (WT) for time-frequency analysis. (2) Mel-Frequency Cepstral Coefficients (MFCC) for spectral representation. These features serve as input to a Support Vector Machine (SVM) classifier, with a Genetic Algorithm (GA) optimizing feature selection. We rigorously evaluate the system’s classification performance against state-of-the-art benchmarks, demonstrating significant improvements in MI recognition accuracy.

This work presents a hybrid algorithm based on DCT-PMF-DWT-SVD. First, the Discrete Cosine Transform (DCT) is applied, followed by the use of the Pixel Movement Function (PMF) to mix the frequencies. Then, three levels of Discrete Wavelet Transform (DWT) are performed before embedding the watermark using the Singular Value Decomposition (SVD). The proposed approach provides excellent robustness against several types of attacks. The use of the Pixel Movement Function enhances security, as the position of the bit selected for watermark embedding remains unpredictable. The system also achieves high imperceptibility. All steps of the algorithm are described in detail, and the experimental results demonstrate its effectiveness. To evaluate performance, the PSNR (Peak Signal-to-Noise Ratio) and NC (Normalized Correlation) metrics were used.

In order to extract significant information for homogeneous extended targets, it is crucial to use speckle filtering in synthetic aperture radar (SAR) images. In this study,a hybrid filtering technique was proposed, which uses both the Sigma filter and the non-local mean filter. The non-local filtering principle is used to select pixels for this method. Pixels with high patch distances that were not similar in the search area were discarded. To estimate the variability of the pixels, the coefficient variation CV is used witch select homogeneous pixels in the search area. According to the results, the proposed hybrid filter improved the filtering criteria in terms of reducing speckle and conserving spatial detail in comparison to Lee filter and the improved Sigma filter. Airborne (Les-Landes, France acquired by NASA JBL) and Space-borne (Sentinel1 C-band hv SAR image of Sousse, Tunisia) synthetic aperture radar SAR data were used for validation.

For image compression, the Discrete Tchebichef Transform (DTT) has recently been proposed as a competitive alternative to the Discrete Cosine Transform (DCT). This chapter presents a compression framework based on an integer approximation of the DTT. For a set of standard test images with varying statistical characteristics, an adopted quantisation matrix is derived from the energy distribution of the integer DTT coefficients. Due to the non-normality of most integer DTT matrices, their diagonal components are integrated into the quantisation process. Experimental results demonstrate that employing the JPEG standard with integer DTT and the proposed quantisation matrix yields improved compression efficiency relative to the conventional JPEG luminance quantisation matrix, while maintaining high visual quality in the reconstructed grayscale images.

Recently, Medical imaging is the process by which a physician can examine the inside of a patient’s body without operating it through the development of devices for the diagnosis and treatment of patients. One intriguing medical imaging method that is thought to be a highly helpful tool for identifying the tumor growth of multiple sclerosis is magnetic resonance imaging (MRI). An efficient method for separating brain tumors from MRI pictures is segmentation. For the automatic identification of MS outliers, a number of recent methods for the segmentation and classification of MRI sequences have been put forth. The “Ant Colonies Optimization ACO” meta-heuristic technique is presented in this paper for MRI image segmentation. In order to optimize their overall rendering and compare with consensual segmentation, we suggest using the ant colony technique to estimate the segmentations of brain MRI images from a public MR dataset of 30 MS patients that were imaged with a 3T MR scanner using conventional sequences. We used MATLAB GUI program to evaluate the performance of this algorithm optimized by maximizing the intra-class variance criterion MVar.

Nowadays people are suffering from different diseases caused by pollution, stress and bad nutrition. Doctors and biologists have to diagnose, detect and find corresponding treatment. Even most of the diseases are treated; some type of disease like cancer needs early detection and preventive treatment. In this paper, we provide a solution for early cancer disease detection. Our method consists of analyzing miRNAs expression for many cancer types leveraging a novel machine learning approach grounded in the Support Vector Machine framework: Twin-Support Vector Machine (twinSVM). The predicted area under the curve (AUC) yielded up to 92.44% over a cancer disease-miRNA association dataset with the polynomial kernel. The accuracy, precision and recall were 94.73%, 98.16%, and 96%, respectively. With our model, the achieved AUC outperformed those obtained by methods existing in the state of the art. Further, a comparison with SVM showed that TWSVM outperformed SVM in terms of accuracy, and AUC. Indeed, SVM with linear kernel reached 93.55% as accuracy and 91.84% as AUC value.

Generalized Gaussian Density (GGD) is mainly a wavelet decomposition based technique. As wavelet transform is recognized as a technique for multi-resolution analysis, where image textures are processed by applying both low-pass and high-pass filters to the rows and columns of the image, wavelet coefficients distribution can provide highly discriminative features for images segmentation. In this chapter, we introduce the GGD technique as a tool for medical images texture analysis. First of all, we introduce the GGD analysis technique with theoretical details, and then we describe related works before giving details about the GGD based approach proposed to detect lung nodules, bone sarcoma and breast abnormalities. The results of using GGD analysis on these several tumors detection are illustrated and compared to other existing techniques.

Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Early detection of breast lesions signifi-cantly improves the chances of successful treatment and survival. However, identifying small or subtle abnormalities can be particularly challenging for radiologists, especially in women with dense breast tissue. This highlights the urgent need for computer-aided diagnosis (CAD) systems to support radiologists in detecting breast lesions at an earlier, more treatable stage. Many challenges are encountered when using CAD systems on digital mammograms. The first obstacle is the existence of elements that have a density similar to that of breast lesions such as artifacts and pectoral muscle. A pre-processing step is therefore essential in order to remove these components and thus optimize the results of breast anomalies segmentation. This chapter provides an overview of various image processing methods developed since 2000 for the detection and removal of the pectoral muscle in digital mammograms, specifically using the mini-MIAS database.