Contribution à la modélisation comportementale des circuits radio-fréquence

Avec les nouvelles tendances que sont les System-on-Chip (SoC) et les ASIC mixtes, les outils de CAO analogiques doivent évoluer pour permettre une conception hiérarchique, basée sur la réutilisation de blocs, utilisant largement la modélisation comportementale des circuits. L'avènement des langages de description standards pour les circuits analogiques et mixtes (VHDL-AMS par exemple) ouvre la voie à l'amélioration attendue mais il reste à développer divers outils et méthodes. Les travaux présentés dans ce mémoire apportent une contribution à la modélisation comportementale des circuits analogiques et mixtes en termes de méthodologie et de développement de bibliothèques de modèles standards. Ainsi, nous proposons une méthode systématique de modélisation comportementale, une bibliothèque de modèles pour les circuits RF et une étude du bruit de phase aboutissant à une modélisation de ce phénomène dans les oscillateurs.The new tendencies in electronic system design are the development of System-on-Chip (SoC) and mixed ASIC. The CAD tools should evolve to allow a hierarchical design, based on the re-use of blocks and the behavioral modeling of circuits. The development of standard description languages for analogue and mixed systems (VHDL-AMS for example) opens the way to the expected improvement, but different methods and tools should be developed. This work presents a contribution to the behavioral modeling of analogue and mixed circuits in terms of methodology and development of standard model libraries. We propose a systematic method of behavioral modeling, a library of models for RF circuits and a study of the phase noise effect in oscillators ending in the behavioral modeling of this phenomenon

Robust Color Image Encryption Scheme Based on RSA via DCT by Using an Advanced Logic Design Approach

تتزايد أهمية أمن المعلومات في تخزين البيانات ونقلها. من جانب اخر يتم استخدام الصور في العديد من الإجراءات. لذلك ، يعد منع الوصول غير المصرح به إلى بيانات الصورة أمرًا بالغ الأهمية من خلال تشفير الصور لاجل حماية البيانات الحساسة او الخصوصية. تتنوع طرق وخوارزميات إخفاء الصور أو تشفيرها من طرق المجال المكاني البسيطة إلى طرق مجال التردد والذي يعتبر الأكثر تعقيدًا وموثوقية. في هذا البحث ، نقترح نظام تشفير جديد يعتمد على منهجية تهجين مولد المفتاح العشوائي من خلال الاستفادة من خصائص DCT لتوليد مجموعة غير محددة من المفاتيح العشوائية والاستفادة من معاملات المنطقة منخفضة التردد بعد مرحلة DCT لتمريرها إلى نظام فرعي يتكون من مجموعة RLG للحصول على المفاتيح السرية التي يتم تمريرها إلى RSA لتنتهي بتشفير الصورة. تشير النتائج إلى أن الطريقة المقترحة لها القدرة على تولد مجموعة كبيرة جدًا من المفاتيح السرية شديدة التعقيد والآمنة التي يمكن استخدامها لاحقًا في مرحلة التشفير. علاوة على ذلك ، سيتغير عدد وتعقيد تلك المفاتيح في كل مرة يتم فيها تغيير الصورة، وهذا يمثل مساهمة الطريقة المقترحة. ولم نلاحظ اي ضياع للوقت أثناء عمليات التشفير وفك التشفير لاستخدامنا RLG ، مما يدل على أن النظام المقترح قام بعمل جيد في صنع مفاتيح مختلفة من نفس الصورة. ويختلف في قوة المفتاح من صورة إلى أخرى حسب طبيعة الصورة الملونة.Information security in data storage and transmission is increasingly important. On the other hand, images are used in many procedures. Therefore, preventing unauthorized access to image data is crucial by encrypting images to protect sensitive data or privacy. The methods and algorithms for masking or encoding images vary from simple spatial-domain methods to frequency-domain methods, which are the most complex and reliable. In this paper, a new cryptographic system based on the random key generator hybridization methodology by taking advantage of the properties of Discrete Cosine Transform (DCT) to generate an indefinite set of random keys and taking advantage of the low-frequency region coefficients after the DCT stage to pass them to a subsystem consisting of an Reversible Logic Gate (RLG) group to obtain the secret keys that are passed to Rivest Shamir Adleman (RSA) to finish encrypting the image. The results indicate that the proposed method has the ability to generate a very large set of highly complex and secure secret keys that can be used later in the encryption stage. Moreover, the number and complexity of those keys will change each time the image is changed, and this represents the contribution of the proposed method. They experienced no time loss throughout the encryption and decryption processes when using RLG, which indicates that the proposed system did a good job in making different keys from the same image. And it differs in the strength of the key from one image to another, depending on the nature of the color imge

Energy Optimization Efficiency in Wireless Sensor Networks for Forest Fire Detection:: An Innovative Sleep Technique

Wireless Sensor Networks (WSNs) have the potential to play a significant role in forest fire detection and prevention. However, limited resources, such as short battery life pose challenges for the energy efficiency and longevity of WSN-based IoT networks. This paper focused on the energy efficiency aspect and proposed the ECP-LEACH protocol to optimize energy consumption in forest fire detection cases. The proposed protocol consists of two main components: a threshold monitoring module and a sleep scheduling module. The threshold monitoring module continuously monitors energy consumption and triggers sleep mode for nodes surpassing the predetermined threshold. The ECP-LEACH protocol offers a promising solution for improving energy efficiency in WSN-based IoT networks for forest fire detection. By optimizing sleep scheduling and duty cycles, the ECP-LEACH protocol enables significant energy savings and extended network lifetim

Robust and Reliable Security Approach for IoMT: Detection of DoS and Delay Attacks through a High-Accuracy Machine Learning Model

Internet of Medical Things (IoMT ) refers to the network of medical devices and healthcare systems that are connected to the internet. However, this connectivity also makes IoMT vulnerable to cyberattacks such as DoS and Delay attacks , posing risks to patient safety, data security, and public trust. Early detection of these attacks is crucial to prevent harm to patients and system malfunctions. In this paper, we address the detection and mitigation of DoS and Delay attacks in the IoMT using machine learning techniques. To achieve this objective, we constructed an IoMT network scenario using Omnet++ and recorded network traffic data. Subsequently, we utilized this data to train a set of common machine learning algorithms. Additionally, we proposed an Enhanced Random Forest Classifier for Achieving the Best Execution Time (ERF-ABE), which aims to achieve high accuracy and sensitivity as well as  low execution time for detecting these types of attacks in IoMT networks. This classifier combines the strengths of random forests with optimization techniques to enhance performance. Based on the results, the execution time has been reduced by implementing ERF-ABE, while maintaining high levels of accuracy and sensitivity

Variation of chemical composition of essential oils in wild populations of Thymus algeriensis Boiss. et Reut., a North African endemic Species

<p>Abstract</p> <p>Background</p> <p><it>Thymus algeriensis </it>is an endemic aromatic plant to Tunisia largely used in folk medicine and as a culinary herb. The bulks aromatic plants come from wild populations whose essential oils compositions as well as their biological properties are severely affected by the geographical location and the phase of the plant development. Therefore, the aim of the present work is to provide more information on the variation of essential oil composition of <it>T. algeriensis </it>collected during the vegetative and the flowering phases and from eight different geographical regions. Besides, influence of population location and phenological stage on yield and metal chelating activity of essential oils is also assessed.</p> <p>Methods</p> <p>The essential oil composition of <it>Thymus algeriensis </it>was determined mainly by GC/FID and GC/MS. The chemical differentiation among populations performed on all compounds was assessed by linear discriminate analysis and cluster analysis based on Euclidean distance.</p> <p>Results</p> <p>A total of 71 compounds, representing 88.99 to 99.76% of the total oil, were identified. A significant effect of the population location on the chemical composition variability of <it>T. algeriensis </it>oil was observed. Only 18 out of 71 compounds showed a statistically significant variation among population locations and phenological stages. Chemical differentiation among populations was high. Minor compounds play an important role to distinguish between chemical groups. Five chemotypes according to the major compounds have been distinguished. Chemotypes distribution is linked to the population location and not to bioclimate, indicating that local selective environmental factors acted on the chemotype diversity.</p> <p>Conclusions</p> <p>The major compounds at the species level were α-pinene (7.41-13.94%), 1,8-cineole (7.55-22.07%), <it>cis</it>-sabinene hydrate (0.10-12.95%), camphor (6.8-19.93%), 4-terpineol (1.55-11.86%), terpenyl acetate (0-14.92%) and viridiflorol (0-11.49%). Based on major compounds, the populations were represented by (α-pinene/1,8-cineole/<it>cis</it>-sabinene hydrate/camphor/viridiflorol), (1,8-cineole/camphor/terpenyl acetate), (α-pinene/1,8-cineole/camphor), (1,8-cineole/camphor/4-terpineol) and (α-pinene/1,8-cineole/<it>cis</it>-sabinene hydrate/camphor/4-terpineol) chemotypes. Variation of phenological stage did not have a statistically significant effect on the yield and metal chelating activity of the essential oil. These results can be used to investigate the geographical location and the harvesting time of this plant for relevant industries.</p

Estimation for Motion in Tracking and Detection Objects with Kalman Filter

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems

Pathotypic diversity of Rhynchosporium secalis (Oudem) in Tunisia

Scald, caused by Rhynchosporium secalis (Oudem), is an important disease of barley in Tunisia particularly in northern, northwestern and central parts of the country where the climate is usually cold and wet during most of the barley growing season. Pathogenic variability of the barley scald pathogen in Tunisia was determined by testing the pathogenicity of 100 isolates from 5 different regions on 19 host differentials. Pathotypic diversity was high, with 93 R. secalis pathotypes identified on two differential sets (one comprising 9 and the other 10 barley lines) containing known resistance genes. A few pathotypes comprised 2% of the isolates; however, the majorities were represented by a single isolate. None of the differential lines was resistant to all isolates. The differential cultivar “Astrix” was the least compatible with the scald pathotypes followed by the differential cultivars “Atlas” and “Abyssinia”. Compatibility of the pathotypes on “Rihane” (69%) was close to that on “Osiris” (73%) and “La Mesita” (61%). None of the pathotypes was found in all the five regions of Tunisia surveyed. Some pathotypes were specific to a single region while others were found in several regions. The incidence of pathotypes varied considerably among regions, with region 3 (northwestern Tunisia) comprising the largest number of pathotypes. Virulent pathotypes were recovered in all regions but more pathotypic variability (44%) was observed in the semi-arid region 3. Differential cultivars allowed classification of R. secalis in four virulence groups. Canonical discriminant analysis showed no apparent association between virulence and geographical origin of the populations. Pathogenic variability in R. secalis in Tunisia was found not to be associated with geographical region, hence, the necessity for deployment of different resistance sources in major barley growing areas.Key words: Rhynchosporium secalis, barley, virulence groups, pathotypic variation

Requirements for Energy-Harvesting-Driven Edge Devices Using Task-Offloading Approaches

Energy limitations remain a key concern in the development of Internet of Medical Things (IoMT) devices since most of them have limited energy sources, mainly from batteries. Therefore, providing a sustainable and autonomous power supply is essential as it allows continuous energy sensing, flexible positioning, less human intervention, and easy maintenance. In the last few years, extensive investigations have been conducted to develop energy-autonomous systems for the IoMT by implementing energy-harvesting (EH) technologies as a feasible and economically practical alternative to batteries. To this end, various EH-solutions have been developed for wearables to enhance power extraction efficiency, such as integrating resonant energy extraction circuits such as SSHI, S-SSHI, and P-SSHI connected to common energy-storage units to maintain a stable output for charge loads. These circuits enable an increase in the harvested power by 174% compared to the SEH circuit. Although IoMT devices are becoming increasingly powerful and more affordable, some tasks, such as machine-learning algorithms, still require intensive computational resources, leading to higher energy consumption. Offloading computing-intensive tasks from resource-limited user devices to resource-rich fog or cloud layers can effectively address these issues and manage energy consumption. Reinforcement learning, in particular, employs the Q-algorithm, which is an efficient technique for hardware implementation, as well as offloading tasks from wearables to edge devices. For example, the lowest reported power consumption using FPGA technology is 37 mW. Furthermore, the communication cost from wearables to fog devices should not offset the energy savings gained from task migration. This paper provides a comprehensive review of joint energy-harvesting technologies and computation-offloading strategies for the IoMT. Moreover, power supply strategies for wearables, energy-storage techniques, and hardware implementation of the task migration were provided

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century