The development of new wireless technologies has increased the demand for innovative patch antenna designs capable of operating across multiple frequency bands. In this work, we propose a compact and reconfigurable ring-enhanced circular patch antenna optimized for sub-6 GHz 5G mobile communication and WiFi standards, including WiFi 2.4 GHz and WiFi 6E. The proposed antenna adopts a compact circular patch geometry, facilitating its integration into wireless systems and components. Frequency reconfigurability is ensured through the use of a PIN diode that acts as a switching element. The antenna structure is designed and analyzed using CST Studio Suite and Ansys HFSS, and is implemented on an FR4 epoxy substrate with a relative permittivity of 4.4 and a thickness of 1.6 mm.

This study presents an advanced encoder-decoder dual attention convolutional long short-term memory (ConvLSTM) model designed to predict sea surface temperatures (SSTs) along the Moroccan coastline, a region characterized by complex oceanographic dynamics. Our model leverages the power of convolutional operations to capture spatial dependencies and the LSTM architecture to model temporal sequences while incorporating a dual attention mechanism to enhance feature selection through contextual attention vectors, which allow the model to focus on the most significant spatial and temporal features in the data input and spatial attention to adaptively weigh the importance of different regions in the study area. In addition, the encoder-decoder architecture efficiently processes multidimensional oceanographic data, leveraging spatial and temporal correlations. Results demonstrate significant improvements in prediction accuracy and computational efficiency compared to traditional single-attention models.

This article explores the integration of Radio-Frequency Identification (RFID) technology within the Internet of Things (IoT) to enhance object monitoring systems, with a specific emphasis on addressing challenges such as RFID reader mobility and collisions. RFID, known for its capability to uniquely identify and track objects through radio-frequency signals, is synergistically combined with the IoT, creating a seamless framework for comprehensive object monitoring. This article proposes a hybrid anti-collision protocol leveraging both artificial neural networks (ANNs) and artificial immune networks (AINs) within the dynamic IoT environment. The performance of this hybrid protocol is evaluated against existing protocols, highlighting its superior read rate compared with other methods.

The study of coastal upwelling through the analysis of sea surface temperature (SST) satellite imagery has been a valuable approach because of its efficiency and practicality. Building on inception and residual structures, we introduce IncepResup-Net, a novel deep learning model for identifying upwelling regions along Morocco’s Atlantic coast. This model effectively addresses limitations in recent methods targeting the same upwelling system and outperforms them by more accurately detecting true upwelling areas, thereby minimizing false positives. Applied to SST data spanning from 2000 to 2022, IncepResup-Net demonstrates superior performance over traditional and contemporary deep learning models, marked by its precise segmentation capabilities and robustness in real-world detection scenarios. Our findings highlight the model’s effectiveness in leveraging SST imagery for upwelling detection, establishing a new benchmark in the application of deep learning within geoscience and remote sensing fields.

This study examines the variability of upwelling along eastern boundaries, specifically focusing on two upwelling systems in the northern hemisphere: the Moroccan Atlantic coast and the Californian coast. In this study, we use a specialized archive that is both highly efficient and robust. The archive is specifically designed to tackle the challenge of detecting upwelling phenomena in satellite data. We ensure accurate and reliable results by prioritizing cloud processing in upwelling zones and questionable pixels. The description and analysis of upwelling dynamics from 2000 to 2019 include the interannual and seasonal variability, which are examined using different upwelling indices. Furthermore, this study proposes a novel index to evaluate the agreement between Sea Surface Temperature (SST) and Chlorophyll-a concentration (Chl-a) as indicators of upwelling. This index successfully captures the relationship between biological and physical components at various spatio-temporal scales, indicating its potential for use in other upwelling systems.

Intelligent Transport Systems (ITS) usually rely on Vehicular Ad Hoc Networks (VANETs) to efficiently enable communication and information exchange between vehicles and the infrastructure. However, the openness and dynamic nature of these networks make them vulnerable to security issues. In fact, VANET networks can suffer from several types of attack due to the large number of participants and the homogeneity of the infrastructure. However, we only focus on the Sybil attack that involve malicious entities creating multiple fake identities to disrupt network operations, compromise data integrity, and deceive legitimate vehicles. On the other hand, Machine Learning caught the attention of several researchers in order to solve these security issues, due to it reliability to detect several types of attacks. In this respect, our paper proposes a LightGBM algorithm to detect sybil nodes in VANETs using the position verification approach. When Using the VeReMi dataset, simulations show that the suggested model has better performance when compared to existing methods.

Abstract The use of fingerprint continues to increase constantly with the propagation of biometric authentication technologies. Fingerprint is today the most widely used biometric modality for human verification and identification, due to its practical use and its discriminative structure that allows to different identities to be easily distinguished. Unfortunately, this emergence has been accompanied by a number of problems and challenges related to identity theft and to security issues in general. To address these concerns, many approaches have been proposed in which only few of them were able to reach an acceptable level of both security and performance. In this paper, we propose a new fingerprint template protection scheme that enhances the security of protected system while preserving performance. The proposed approach is a minutiae-based technique that performs fingerprint matching in a transformed space using irreversible minutiae tetrahedrons. Using the original Fingerprint Verification Competition (FVC) protocol, the provided experimental results on FVC2002 DB1, DB2 and DB3 fingerprint databases have shown satisfactory recognition rates. Our results are compared to some existing techniques that use the same protocol of test. We have proved as well that the proposed scheme meets the requirements of revocability, unlinkability and irreversibility.

Abstract Among the most major potential attacks against fingerprint authentication systems are those that target the stored reference templates. These threats are extremely damaging as they can lead to the invasion of user privacy. The countermeasures to secure fingerprint templates are therefore an indisputable necessity. In literature, although there are so many approaches that address this kind of vulnerability, it turns out to be very difficult to generalize their uses. Given that each system has its own particularities, going from the fingerprint trait acquisition to the matching process, the majority of protection schemes, that are proposed as generic solutions, are not sufficiently mature for large-scale deployment. Consequently, we believe that the methodology of fingerprint template protection schemes conception should be oriented to build specific protection schemes for every unprotected system, which will provide the best compromise between performance and security compared to any generic protection solution. By adopting this methodology, we propose in this paper a new protection scheme for fingerprint templates that is well adapted to a well-known existing unprotected fingerprint minutia system. Our experimental results, obtained using standard benchmarks such as FVC 2002 DB1 and DB2, have proven that the proposed technique meets the requirements of revocability, unlinkability, non-invertibility, and high recognition accuracy.

This research deals with the problem of identifying and extracting effectively the main Moroccan upwelling front. The proposed methodology, based on the image-fusion concept, comes to benefit from the information available in both sea-surface temperature (SST) and chlorophyll-a satellite images. Moreover, a new validation index is proposed by computing a simple gradient along the extracted upwelling limit. The developed procedure is applied over a database of 366 SST and 366 chlorophyll-a images from 2007 to 2014, covering the Moroccan Atlantic coast. The final results are validated qualitatively by an oceanographer and quantitatively by our innovative index. The findings of validation demonstrate the performance of our fusion approach.

Analysis and study of coastal upwelling using sea surface temperature (SST) satellite images is a common procedure because of its coast effectiveness (economic, time, frequency, and manpower). Developing on the Ekman theory, we propose a robust method to identify the upwelling regions along the north-west African margin. The proposed method comes to overcome the issues encountered in a recent method devoted for the same purpose and for the same upwelling system. Afterward, we show how our method can serve as a framework to study and monitor the spatio-temporal variability of the upwelling phenomenon in the studied region.

The region along the North-West African coast (20°N to 36°N and 4°W to 19°W) is characterized by a persistent and variable upwelling phenomenon almost all year round. In this article, the upwelling features are investigated using an algorithm dedicated to delimit the upwelling area from thermal and biological satellite observations. This method has been developed specifically for sea-surface temperature (SST) images, since they present a high latitudinal variation, which is not present in chlorophyll-a concentration images. Developing on the proposed approach, the spatial and temporal variations of the main physical and biological upwelling patterns are studied. Moreover, a study on the upwelling dynamics, which explores the interplay between the upwelling spatiotemporal extents and intensity, is presented, based on a 14-year time archive of weekly SST and chlorophyll-a concentration data.

In order to improve the decision-making on the Moroccan upwelling region detection, we present in this paper a simple and reliable fusion approach. In this context, we started by applying Fuzzy C-means algorithm on each 46 Sea Surface Chlorophyll images and on each 46 Sea Surface Temperature images during the year of 2014. After that, we implement post classification fusion by using logical AND operator set to combine FCM result of the both types and consequently having single image more informative and suitable for visual perception. The oceanographer validation indicate that the proposed methodology detect automatically and effectively the different Moroccan coastal upwelling scenarios of our database.

The aim of this work is to automatically identify and extract the upwelling area in the coastal ocean of Morocco using the satellite observation of chlorophyll concentration. The algorithm starts by the application of FCM algorithm for the purpose of finding regions of homogeneous concentration of the chlorophyll, resulting in c-partitioned labeled images. A region-growing algorithm is then used to filter out the noisy structures in the offshore waters not belonging to the upwelling regions. The proposed methodology has been validated by an oceanographer and tested over a database of 166 weekly Sea Surface chlorophyll data. The region of interst cover the southern part of Moroccan atlantic coast spanning from the years 2007 to 2012.

The phase codes is one of important pulse compression waveform methods. This paper shows and compares the PSLR (Peak Sidelobe Ratio), ISLR (Integrated Sidelobe Ratio) and doppler tolerance for different phase codes. At first, a presentation of the ambiguity function is given, as well as some measures to evaluate it. Then we define some existed phase code and plot their ambiguity function. We consider the best sequence, the sequence that has low PSLR, ISLR and low sidelobe in the presence of large doppler shift. The comparison results have shown that for zero doppler, the golay complementary pair is the best. However, the P3, P4 and Chu codes are the best in the case of large doppler shift.

An unsupervised classification method is developed for the coarse segmentation of Moroccan coastal upwelling using the sea surface temperature (SST) satellite images. The algorithm is started with the generation of a c-partitioned labeled image using Otsu's method for the purpose of finding regions of homogenous temperatures. Then, two well-known validity indices are used to select the c-partition that best reproduces the shape of upwelling area. A region-growing algorithm is developed that is used to remove the noisy structures in the offshore waters not belonging to the upwelling area. The algorithm is used to provide a seasonal variability of upwelling activity in the southern Moroccan Atlantic coast using 70 SST images of the years 2007 and 2008. The performance of the proposed methodology has been validated by an oceanographer, showing its effectiveness for automatic delimitation of the Moroccan upwelling region.