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Добірка наукової літератури з теми "RF robustness"

Автор: Grafiati

Опубліковано: 1 березня 2025

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Статті в журналах з теми "RF robustness"

Elyousseph, Hilal, and Majid Altamimi. "Robustness of Deep-Learning-Based RF UAV Detectors." Sensors 24, no. 22 (November 17, 2024): 7339. http://dx.doi.org/10.3390/s24227339.

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The proliferation of low-cost, small radar cross-section UAVs (unmanned aerial vehicles) necessitates innovative solutions for countering them. Since these UAVs typically operate with a radio control link, a promising defense technique involves passive scanning of the radio frequency (RF) spectrum to detect UAV control signals. This approach is enhanced when integrated with machine-learning (ML) and deep-learning (DL) methods. Currently, this field is actively researched, with various studies proposing different ML/DL architectures competing for optimal accuracy. However, there is a notable gap regarding robustness, which refers to a UAV detector’s ability to maintain high accuracy across diverse scenarios, rather than excelling in just one specific test scenario and failing in others. This aspect is critical, as inaccuracies in UAV detection could lead to severe consequences. In this work, we introduce a new dataset specifically designed to test for robustness. Instead of the existing approach of extracting the test data from the same pool as the training data, we allowed for multiple categories of test data based on channel conditions. Utilizing existing UAV detectors, we found that although coefficient classifiers have outperformed CNNs in previous works, our findings indicate that image classifiers exhibit approximately 40% greater robustness than coefficient classifiers under low signal-to-noise ratio (SNR) conditions. Specifically, the CNN classifier demonstrated sustained accuracy in various RF channel conditions not included in the training set, whereas the coefficient classifier exhibited partial or complete failure depending on channel characteristics.

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Bollmeyer, Christian, Mathias Pelka, Hartmut Gehring, and Horst Hellbrück. "Wireless medical sensors – context, robustness and safety." Current Directions in Biomedical Engineering 1, no. 1 (September 1, 2015): 349–52. http://dx.doi.org/10.1515/cdbme-2015-0086.

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AbstractWireless medical sensors are an emerging technology. Wireless sensors form networks and are placed in an unknown environment. For indoor scenarios context detection of medical sensors, e.g. removal of sensors from a specific room, is important. Current algorithms for context detection of wireless sensors are based on RF signals, but RF signal propagation and room location show only a weak correlation. Recent approaches with RSSI-measurements are based on prior fingerprinting and therefore costly. In our approach, we equip wireless sensor nodes with a barometric sensor to measure pressure disturbances that occur, when doors of rooms are opened or closed. By signal processing of these disturbances our proposed algorithm detects rooms and estimates distances without prior knowledge in an unknown environment. Based on these measurement we automatically build a topology graph representing the room context and distances for indoor environment in a model for buildings. We evaluate our algorithm within a wireless sensor network and show the performance of our solution.

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Palego, C., Jie Deng, Zhen Peng, S. Halder, J. C. M. Hwang, D. I. Forehand, D. Scarbrough, et al. "Robustness of RF MEMS Capacitive Switches With Molybdenum Membranes." IEEE Transactions on Microwave Theory and Techniques 57, no. 12 (December 2009): 3262–69. http://dx.doi.org/10.1109/tmtt.2009.2033885.

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Nguyen, Ngoc-Kim-Khanh, Quang Nguyen, Hai-Ha Pham, Thi-Trang Le, Tuan-Minh Nguyen, Davide Cassi, Francesco Scotognella, Roberto Alfierif, and Michele Bellingeri. "Predicting the Robustness of Large Real-World Social Networks Using a Machine Learning Model." Complexity 2022 (November 9, 2022): 1–16. http://dx.doi.org/10.1155/2022/3616163.

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Анотація:

Computing the robustness of a network, i.e., the capacity of a network holding its main functionality when a proportion of its nodes/edges are damaged, is useful in many real applications. The Monte Carlo numerical simulation is the commonly used method to compute network robustness. However, it has a very high computational cost, especially for large networks. Here, we propose a methodology such that the robustness of large real-world social networks can be predicted using machine learning models, which are pretrained using existing datasets. We demonstrate this approach by simulating two effective node attack strategies, i.e., the recalculated degree (RD) and initial betweenness (IB) node attack strategies, and predicting network robustness by using two machine learning models, multiple linear regression (MLR) and the random forest (RF) algorithm. We use the classic network robustness metric R as a model response and 8 network structural indicators (NSI) as predictor variables and trained over a large dataset of 48 real-world social networks, whose maximum number of nodes is 265,000. We found that the RF model can predict network robustness with a mean squared error (RMSE) of 0.03 and is 30% better than the MLR model. Among the results, we found that the RD strategy has more efficacy than IB for attacking real-world social networks. Furthermore, MLR indicates that the most important factors to predict network robustness are the scale-free exponent α and the average node degree <k>. On the contrary, the RF indicates that degree assortativity a, the global closeness, and the average node degree <k> are the most important factors. This study shows that machine learning models can be a promising way to infer social network robustness.

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Ayaz Atalan, Yasemin, and Abdulkadir Atalan. "Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance." Applied Sciences 15, no. 1 (December 30, 2024): 241. https://doi.org/10.3390/app15010241.

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This study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R2 value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (p < 0.001) and rotor speed (p < 0.001) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R2 values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods.

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Kheir, Mohamed, Heinz Kreft, Iris Hölken, and Reinhard Knöchel. "On the physical robustness of RF on-chip nanostructured security." Journal of Information Security and Applications 19, no. 4-5 (November 2014): 301–7. http://dx.doi.org/10.1016/j.jisa.2014.09.007.

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Liu, Alan, Yu-Tai Lin, and Karthikeyan Sundaresan. "View-agnostic Human Exercise Cataloging with Single MmWave Radar." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 8, no. 3 (August 22, 2024): 1–23. http://dx.doi.org/10.1145/3678512.

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Advances in mmWave-based sensing have enabled a privacy-friendly approach to pose and gesture recognition. Yet, providing robustness with the sparsity of reflected signals has been a long-standing challenge towards its practical deployment, constraining subjects to often face the radar. We present RF-HAC- a first-of-its-kind system that brings robust, automated and real-time human activity cataloging to practice by not only classifying exercises performed by subjects in their natural environments and poses, but also tracking the corresponding number of exercise repetitions. RF-HAC's unique approach (i) brings the diversity of multiple radars to scalably train a novel, self-supervised, pose-agnostic transformer-based exercise classifier directly on 3D RF point clouds with minimal manual effort and be deployed on a single radar; and (ii) leverages the underlying doppler behavior of exercises to design a robust self-similarity based segmentation algorithm for counting the repetitions in unstructured RF point clouds. Evaluations on a comprehensive set of challenging exercises in both seen and unseen environments/subjects highlight RF-HAC's robustness with high accuracy (over 90%) and readiness for real-time, practical deployments over prior art.

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Sanogo, Lamoussa, Eric Alata, Alexandru Takacs, and Daniela Dragomirescu. "Intrusion Detection System for IoT: Analysis of PSD Robustness." Sensors 23, no. 4 (February 20, 2023): 2353. http://dx.doi.org/10.3390/s23042353.

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The security of internet of things (IoT) devices remains a major concern. These devices are very vulnerable because of some of their particularities (limited in both their memory and computing power, and available energy) that make it impossible to implement traditional security mechanisms. Consequently, researchers are looking for new security mechanisms adapted to these devices and the networks of which they are part. One of the most promising new approaches is fingerprinting, which aims to identify a given device by associating it with a unique signature built from its unique intrinsic characteristics, i.e., inherent imperfections, introduced by the manufacturing processes of its hardware. However, according to state-of-the-art studies, the main challenge that fingerprinting faces is the nonrelevance of the fingerprinting features extracted from hardware imperfections. Since these hardware imperfections can reflect on the RF signal for a wireless communicating device, in this study, we aim to investigate whether or not the power spectral density (PSD) of a device’s RF signal could be a relevant feature for its fingerprinting, knowing that a relevant fingerprinting feature should remain stable regardless of the environmental conditions, over time and under influence of any other parameters. Through experiments, we were able to identify limits and possibilities of power spectral density (PSD) as a fingerprinting feature.

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Saha, Sunil, Anik Saha, Tusar Kanti Hembram, Biswajeet Pradhan, and Abdullah M. Alamri. "Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya." Applied Sciences 10, no. 11 (May 29, 2020): 3772. http://dx.doi.org/10.3390/app10113772.

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Landslides are known as the world’s most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling.

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Ouyang, Hui, Weibo Li, Feng Gao, Kangzheng Huang, and Peng Xiao. "Research on Fault Diagnosis of Ship Diesel Generator System Based on IVY-RF." Energies 17, no. 22 (November 20, 2024): 5799. http://dx.doi.org/10.3390/en17225799.

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Ship diesel generator systems are critical to ship navigation. However, due to the harsh marine environment, the systems are prone to failures, and traditional fault diagnosis methods are difficult to meet requirements regarding accuracy, robustness, and reliability. For this reason, this paper proposes a fault diagnosis method for a ship diesel generator system based on the IVY algorithm-optimized random forest (IVY-RF). Firstly, a model of a ship diesel generator system was constructed using MATLAB/Simulink, and the operation data under fault and normal working conditions were collected. Then, the data were preprocessed and time-domain features were extracted. Finally, the IVY-optimized random forest model was used to identify, diagnose, and classify faults. The simulation results show that the IVY-RF method could identify faulty and normal states with 100% accuracy and distinguish 12 types with 100% accuracy. Compared to seven different algorithms, the IVY-RF improved accuracy by at least 0.17% and up to 67.45% on the original dataset and by at least 1.19% and up to 49.40% in a dataset with 5% noise added. The IVY-RF-based fault diagnosis method shows excellent accuracy and robustness in complex marine environments, providing a reliable fault identification solution for ship power systems.

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Більше джерел

Дисертації з теми "RF robustness"

Bu, Long. "Linearity and Interference Robustness Improvement Methods for Ultra-Wideband Cmos Rf Front-End Circuits." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1211476269.

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Al, Issa Huthaifa A. "Position-adaptive Direction Finding for Multi-platform RF Emitter Localization using Extremum Seeking Control." University of Dayton / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1343755148.

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Said, Nasri. "Evaluation de la robustesse des technologies HEMTs GaN à barrière AlN ultrafine pour l'amplification de puissance au-delà de la bande Ka." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0425.

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La filière GaN est stratégique pour l'Union Européenne car elle permet d'améliorer la puissance et le rendement des systèmes radar et de télécommunication, notamment dans les bandes S à Ka (jusqu'à 30 GHz). Pour répondre aux besoins des futures applications, telles que la 5G et les systèmes militaires, le développement des technologies GaN vise à augmenter les fréquences jusqu'aux ondes millimétriques. Cela nécessite d'optimiser l'épitaxie et la réduction de la longueur de grille à moins de 150 nm, ainsi que l'utilisation de barrières ultrafines (<10 nm) pour éviter les effets de canaux courts. La substitution de la barrière AlGaN par du AlN représente une solution pour maintenir de bonnes performances tout en miniaturisant les composants. Dans ces travaux de thèse, plusieurs variantes technologiques à barrière AlN ultrafine (3 nm) sur des canaux GaN non-dopés de différentes épaisseurs, développées par le laboratoire IEMN sont étudiés. L'évaluation des performances et de la robustesse de ces technologies, cruciale pour leur qualification et utilisation dans des missions à long-terme, sont ainsi menées en mode DC et RF afin de définir les zones de sécurité de fonctionnement (SOA) et d’identifier les mécanismes de dégradation.La campagne de caractérisation DC et pulsée a révélé une faible dispersion des composants après leur stabilisation électrique, reflétant une bonne maîtrise technologique : ceci permet par ailleurs des études statistiques et des analyses génériques plus pertinentes sur l’ensemble des lots de composants étudiés. L'analyse de la sensibilité des dispositifs à des températures allant jusqu'à 200°C a prouvé la forte stabilité thermique des performances en mode diode et transistor, en suivant les indicateurs paramétriques représentatifs des modèles électriques des composants (courants de saturation et courants de fuite, tension de seuil, taux de retard aux commandes entrée sortie, …). L’ajout d’une barrière arrière AlGaN sur une couche tampon moyennement dopée C a réglé le compromis entre confinement des électrons et densités de pièges. Les tests de vieillissement accéléré en mode DC à différents points de polarisation et en mode RF par paliers de puissance d’entrée ont montré que la barrière arrière AlGaN confère une meilleure stabilité des courants de fuite et des courbes I(V) statiques, une réduction des effets de piégeage et d'auto-échauffement, ainsi qu'une extension de la SOA-DC opérationnelle. Les tests de vieillissement accéléré en mode dynamique à 10 GHz sur des HEMTs avec différents espacements grille-drain ont montré que la SOA-RF ne dépend pas de cet espacement, mais plutôt de la capacité de la grille à supporter des signaux RF élevés, avant dégradation brutale de cette dernière. En utilisant une méthode de modélisation non linéaire originale, prenant en compte le phénomène d'auto-polarisation, les dispositifs avec barrière AlGaN se sont révélés plus robustes également en RF. Cela se traduit par leur compression plus tardive de gain, allant jusqu’à +10dB et sans dégradation électrique ainsi que structurelle apparente (observée par photoluminescence). Indépendamment de la variante AlN/GaN, le mécanisme de dégradation en stress RF correspond au claquage abrupt de la grille Schottky conduisant à sa défaillance. Ces résultats prouvent que les composants sont plus sensibles aux conditions de polarisation DC qu’au niveau de signal RF injecté [...]
The GaN industry is strategic for the European Union because it enhances the power and efficiency of radar and telecommunication systems, especially in the S to Ka bands (up to 30 GHz). To meet the needs of future applications such as 5G and military systems, GaN technology development aims to increase frequencies to the millimeter-wave range. This requires optimizing epitaxy and reducing the gate length to less than 150 nm, as well as using ultrathin barriers (<10 nm) to avoid short-channel effects. Replacing the AlGaN barrier with AlN is a solution to maintain good performance while miniaturizing devices. In this thesis, several technological variants with an ultrathin AlN barrier (3 nm) on undoped GaN channels of various thicknesses, developed by the IEMN laboratory, are studied. The evaluation of the performance and robustness of these technologies, crucial for their qualification and use in long-term profil missions, is conducted in both DC and RF modes to define the safe operating areas (SOA) and identify degradation mechanisms.The DC and pulsed characterization campaign revealed low component dispersion after electrical stabilization, reflecting good technological control. This also allows for more relevant statistical studies and generic analyses across all component batches studied. The sensitivity analysis of the devices at temperatures up to 200°C demonstrated strong thermal stability in diode and transistor modes, following parametric indicators representative of the electrical models of the components (saturation currents and leakage currents, threshold voltage, gate and drain lags rates, ...). The addition of a AlGaN back-barrier on a moderately C-doped buffer layer resolved the trade-off between electron confinement and trap densities. Accelerated aging tests in DC mode at various biasing conditions and in RF mode by input power steps showed that the AlGaN back-barrier provides better stability in leakage currents and static I(V) curves, reduces trapping and self-heating effects, and extends the operational DC-SOA.Dynamic accelerated aging tests at 10 GHz on HEMTs with different gate-drain spacings showed that the RF-SOA does not depend on this spacing but rather on the gate's ability to withstand high RF signals before abrupt degradation occurs. Using an original nonlinear modeling method that considers the self-biasing phenomenon, devices with the AlGaN back-barrier proved to be more robust in RF as well. This is reflected in their later gain compression, up to +10 dB, without apparent electrical or structural degradation (as observed by photoluminescence). Regardless of the AlN/GaN variant, the RF stress degradation mechanism corresponds to the abrupt breakdown of the Schottky gate, leading to its failure. These results indicate that the components are more sensitive to DC bias conditions than to the level of injected RF signals [...]

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Pinault, Bastien. "Évaluation de topologies d'amplificateurs faible bruit et robustes en filière GaN pour applications radar ou télécom en bande X." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSES070.

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Dans l'optique d'accroitre les débits des télécommunications ainsi que la précision et la portée de détection des systèmes radars modernes, l'amélioration du facteur de bruit d'une liaison est devenue essentielle. Comme le démontre la formule de FRIIS , le facteur de bruit d'une architecture de réception est majoritairement déterminé par le facteur de bruit du premier étage. Il apparait alors nécessaire de placer en amont de cette dernière un amplificateur faible bruit (LNA). Ce dernier peut être confronté à des agressions électromagnétiques (EM) pouvant détériorer, voir rendre inopérant le système, en fonction de l'application visée. Il est donc nécessaire de concevoir des LNA robustes vis-à-vis de ces agressions EM. Par leurs caractéristiques intrinsèques (large bande interdite, tension de claquage élevée et bonne conductivité thermique...) les technologies nitrure de gallium (GaN) sont d'excellentes candidates pour l'implémentation d'amplificateur faible bruit. En effet, la robustesse naturelle offerte par ce matériau permet de s'affranchir des dispositifs de protection tels que les limiteurs de puissance utilisés en technologie GaAs ou SiGe. Les contraintes des systèmes modernes poussent ces derniers à maintenir leurs caractéristiques même lorsqu'ils sont soumis à des agressions EM. Les topologies d'amplificateurs faible bruit doivent donc pouvoir répondre au double objectif de forte détectivité (faible facteur de bruit HF) et de grande linéarité (forte puissance) aux fréquences d'utilisation. Afin d'augmenter la linéarité d'un LNA, une stratégie consiste à surdimensionner le transistor (dimensions physiques ou électriques plus importantes que celles nécessaires pour un facteur de bruit optimal). Les performances non linéaires se trouvent alors améliorées au détriment des paramètres de bruits et/ou petits signaux. Cette thèse propose une nouvelle approche permettant d'exploiter simultanément les caractéristiques non linéaires d'une conception de LNA initialement optimisée sur les paramètres de bruit en améliorant le comportement non-linéaire de l'élément actif via un changement de son point de repos. Cette stratégie oblige à des études plus poussées sur la stabilité de tels systèmes, et des chemins critiques de repolarisation, toujours délicats à appréhender pour les technologies nitrures. Une étude comparative ainsi qu'un état de l'art plus global permet de situer notre solution dans le spectre des possibilités offertes dans l'optique d'obtenir un système faible bruit et robuste. Des tests dynamiques et en bruit HF avant et après contraintes sous signal RF par créneau vient appuyer l'approche proposée
In order to increase telecommunication data rates and enhance the precision and range of modern radar systems, improving the noise factor of a communication link has become crucial. As demonstrated by the FRIIS formula, the noise factor of a reception architecture is mainly determined by the noise factor of the first stage. It is, therefore, necessary to place a low-noise amplifier (LNA) upstream of the first stage. LNAs may face electromagnetic (EM) aggressions that can degrade or even render the system inoperative, depending on the intended application. Hence, it is essential to design LNAs that are robust against these EM aggressions. Due to their intrinsic characteristics (wide bandgap, high breakdown voltage, and good thermal conductivity), gallium nitride (GaN) technologies are excellent candidates for implementing low-noise amplifiers. The natural robustness offered by this material allows for the elimination of protection devices such as power limiters used in GaAs or SiGe technologies. Modern system constraints compel them to maintain their characteristics even when subjected to EM aggressions. Low-noise amplifier topologies must, therefore, meet the dual objective of high detectivity (low HF noise factor) and high linearity (high power) at usage frequencies. To increase the linearity of an LNA, one strategy involves oversizing the transistor (physical or electrical dimensions larger than those necessary for optimal noise factor). This approach improves nonlinear performance at the expense of noise parameters and/or small signals. This thesis proposes a novel approach that simultaneously exploits the nonlinear characteristics of an LNA design initially optimized for noise parameters by enhancing the nonlinear behavior of the active element through a change in its operating point. A comparative study and a broader state-of-the-art analysis position our solution within the spectrum of possibilities for achieving a low-noise and robust system

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Oudji, Salma. "Analyse de la robustesse et des améliorations potentielles du protocole RadioFréquences Sub-GHz KNX utilisé pour l’IoT domotique." Thesis, Limoges, 2016. http://www.theses.fr/2016LIMO0121/document.

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Анотація:

Cette thèse aborde la performance du protocole KNX-RF utilisé dans les applications domotiques en termes de robustesse Radio Fréquences dans un environnement multi-protocoles potentiellement sujet aux interférences. Dans ces travaux, le but est d’évaluer les problématiques d’interférences rencontrées par KNX-RF en utilisant des modèles de simulation qui permettraient d’augmenter à sa fiabilité radio. Ainsi, un premier modèle a été développé sur MATLAB/Simulink et a permis de connaître les performances et les limitations de ce protocole au niveau de la couche physique dans un scénario d’interférence se produisant à l’intérieur d’une box/gateway domotique multi-protocoles. Ces simulations ont été complétées par des essais expérimentaux sur le terrain qui ont permis de vérifier les résultats obtenus. Un deuxième modèle a été développé pour évaluer les mécanismes de la couche MAC, cette fois-ci, grâce au simulateur OMNet++/MiXiM. Ce modèle reprend tous les mécanismes d’accès au canal et d’agilité en fréquence spécifiés par la norme KNX. Un scénario de collisions de trames a été simulé et plusieurs propositions d’améliorations sont discutées dans ce manuscrit. Les modèles développés permettent d’analyser et de prédire en avance de phase le comportement de KNX-RF dans un environnement radio contraignant
This thesis addresses the performance of the KNX-RF protocol used for home automation applications in terms of radiofrequency robustness in a multi-protocol environment that is potentially subject to interferences. In this work, the aim is to assess the interference problems encountered by KNX-RF using simulation models that would increase its RF reliability. Thus, a first model was developed on MATLAB / Simulink and allowed to investigate the performance and limitations of this protocol at its physical layer in an interference scenario occurring inside a multiprotocol home and building automation box/gateway. These simulations were followed by field experimental tests in an indoor environment (house) to verify the results. A second model was developed to evaluate the MAC layer mechanisms of KNX-RF through the discrete event simulator OMNeT ++/Mixim. This model includes all the mechanisms of channel access and frequency agility specified by KNX-RF standard. A frame collision scenario was simulated and several improvement proposals are discussed in this manuscript. The developed models can be used to analyze and predict in advance phase the behavior of KNX-RF in a radio-constrained environment

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Частини книг з теми "RF robustness"

Sakian, Pooyan, Reza Mahmoudi, and Arthur van Roermund. "System-Level Design for Robustness." In RF-Frontend Design for Process-Variation-Tolerant Receivers, 7–38. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2122-1_2.

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Bartz-Beielstein, Thomas, and Martin Zaefferer. "Models." In Hyperparameter Tuning for Machine and Deep Learning with R, 27–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5170-1_3.

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AbstractThis chapter presents a unique overview and a comprehensive explanation of Machine Learning (ML) and Deep Learning (DL) methods. Frequently used ML and DL methods; their hyperparameter configurations; and their features such as types, their sensitivity, and robustness, as well as heuristics for their determination, constraints, and possible interactions are presented. In particular, we cover the following methods: $$k$$ k -Nearest Neighbor (KNN), Elastic Net (EN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and DL. This chapter in itself might serve as a stand-alone handbook already. It contains years of experience in transferring theoretical knowledge into a practical guide.

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Barbulescu, Ruxandra, Tiago Marques, and Arlindo L. Oliveira. "Contribution of V1 Receptive Field Properties to Corruption Robustness in CNNs." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240546.

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Recently, it has been shown that simulating computations in early primate visual areas, up to the primary visual cortex (V1), at the front of convolutional neural networks (CNNs) leads to improvements in robustness to image corruptions. However, it remains unclear whether this improvement requires precisely matching the receptive field (RF) properties of V1 neurons or if some aspects are sufficient. Here, we explore this question by building several variants of a CNN model with a front-end modeling the primate V1 using a classical neuroscientific model, a Gabor Filter Bank (GFB) followed by simple- and complex- cell nonlinearities. Each model variant had varying levels of biological detail according to how the RF properties were sampled. The model variant sampling these parameters according to empirical biological distributions was considerably more robust to image corruptions than the variant sampling the parameters uniformly and independently (relative difference of 8.72%). However, a uniform variant capturing correlations between some GFB parameters obtained the same performance as the biological sampling variant. Our results show that it is not sufficient to approximate V1 with only the right class of function and parameter range, as it is also required to include the observed correlations between RF properties. However, it is not necessary to fully replicate the empirical distributions of V1 RF properties to obtain the desired improvement in robustness.

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Curcio, Giuseppe. "Human Psychomotor Performance Under the Exposure to Mobile Phones-Like Electromagnetic Fields." In Advances in Computer and Electrical Engineering, 923–36. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7598-6.ch067.

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The first studies on humans addressing cognitive functioning changes as a consequence of radiofrequency (RF) EMFs exposure date back to almost 20 years ago. The effects on human behavior showed in those pioneering works indicated an improvement of performance under the exposure to the signal, compared with sham exposure. These first and striking results were not fully replicated by subsequent studies that were characterized by a more methodological robustness and attention to exposure aspects. In accordance with this view, latest reviews and metanalyses have confirmed the paucity of evidence and the lack of reliability of psychomotor and cognitive effects of acute RF EMF exposure on human volunteers, particularly when assessed in well controlled laboratory settings. Thus, despite the public opinion about potential biologic effects of acute RF EMFs irradiation, it can be concluded that to date there is substantial lack of evidence about a negative influence of non-ionizing radiations on cognitive functioning in humans.

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Curcio, Giuseppe. "Human Psychomotor Performance Under the Exposure to Mobile Phones-Like Electromagnetic Fields." In Encyclopedia of Information Science and Technology, Fourth Edition, 6124–35. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch532.

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The first studies on humans addressing cognitive functioning changes as a consequence of radiofrequency (RF) EMFs exposure, date back to almost 20 years ago. The effects on human behaviour showed in those pioneering works indicated a somewhat improvement of performance under the exposure to the signal, compared with sham exposure. These first and striking results were not fully replicated by subsequent studies that were characterized by a more methodological robustness and attention to exposure aspects. In accordance with this view, latest reviews and metanalyses have confirmed the paucity of evidence and the lack of reliability of psychomotor and cognitive effects of acute RF EMF exposure on human volunteers, particularly when assessed in well controlled laboratory settings. Thus, despite the public opinion about potential biologic effects of acute RF EMFs irradiation, it can be concluded that to date there is substantial lack of evidence about a negative influence of non-ionizing radiations on cognitive functioning in humans.

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Muthurajkumar, S., G. Kajeeth Kumar, and S. T. P. Mohana Priya. "Crayfish-Optimized CNN and Random Forest for Effective Plant Disease Detection." In Advances in Environmental Engineering and Green Technologies, 209–44. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8019-2.ch007.

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Plant disease detection is crucial for ensuring agricultural productivity and food security. This work presents a new hybrid model that combines a Convolutional Neural Network (CNN) with a Random Forest (RF) to increase the accuracy and efficiency of plant disease identification. Additionally, the Crayfish Optimization Algorithm (COA) is utilized to fine-tune the hyperparameters of the CNN, improving its ability to extract relevant features from crop images. Then, these features are fed into a RF leverages its robustness and interpretability to classify the presence of diseases. Extensive experiments are conducted on a Plant village dataset and results demonstrate that the proposed method outperforms other existing models. The proposed model not only enhances detection performance but also reduces computational complexity, making it suitable for deployment in real-world agricultural settings. This hybrid approach showcases the potential of integrating evolutionary optimization algorithms with deep learning and ensemble methods to address complex problems in precision agriculture.

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mihalache Ovidiu, Yamaouchi Toshihiko, and Ueda Masashi. "Validations of Multifrequency ECT Algorithms for Helical SG Tubes of FBR." In Studies in Applied Electromagnetics and Mechanics. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-407-7-109.

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The present paper focus on the multifrequency and a novel window multifrequency ECT algorithm applied specifically to the magnetic SG tubes of a Fast Breeder Reactor (FBR). The algorithms parameters are determined and validated using 3D numerical Finite Element Simulations of the RF-ECT signal based on codes previously developed in-house by authors. The algorithm robustness is checked and validated using first numerical simulations and then experimental measurements conducted with a small mock-up test tank with SG tubes and full circumferential grooves or partial defects located on the external SG tube surface near tube support plate.

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Urbanczyk, Guillaume. "Main Challenges of Heating Plasma with Waves at the Ion Cyclotron Resonance Frequency (ICRF)." In Advances in Fusion Energy Research - Theory, Models, Algorithms, and Applications [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105394.

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Of all the techniques used for heating plasmas in fusion devices, waves in the Ion Cyclotron Resonance Frequency (ICRF ∼ MHz) continue to be exceptionally advantageous and unique insofar as it enables to deposit of power directly on ions in the core, significantly enhancing fast ion population together with fusion reaction products. However, because of the multiple inherent challenges—such as matching robustness, antenna design, wave coupling efficiency, wave propagation, wave absorption, and plasma surface interactions due to radiofrequency (RF) sheath excitation—ICRF is often one of the most complex heating systems to implement successfully. This chapter provides a brief introduction of these challenges and their respective underlying physics, together with examples of both simulations and experimental results from various tokamaks around the world. Finally, ICRF advantages and applications on present and future devices and perspectives of technological solutions are discussed and summarized.

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Kundu, Krishanu. "Past, Present, and Future of Rate Splitting Multiple Access for Wireless Networks." In Radar and RF Front End System Designs for Wireless Systems, 275–95. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0916-2.ch011.

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Rate splitting multiple access (RSMA) is a promising approach that has gained momentous attention in the field of wireless communications. With the increasing demand for higher data rates and reliable connections, traditional multiple access approaches like SDMA, NOMA have limitations in terms of interference management as well as channel state information uncertainty. The effectiveness of the RSMA has been recognized in addressing these limitations and providing improved performance in multi-antenna systems. RSMA ruptures user messages into common as well as private parts – the common part is decoded by all users to mitigate interference while the private part is decoded only by the intended user. This bridges orthogonal and non-orthogonal multiple access schemes. Benefits of RSMA include optimal performance in many scenarios, robustness against imperfect CSIT, lower feedback overhead and complexity compared to alternatives. RSMA has many potential applications in 6G use cases like mMTC, URLLC, V2X, and integrated sensing-communications. Ongoing research is still needed, especially on implementation and standardization. The current study focuses on the notion of rate splitting, the benefits of RSMA, working principle of RSMA, implementation challenges, RSMA's implementation in 5G and beyond, a comparison with other multiple access techniques, as well as its problems and future trends in wireless networks.

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Liu, Guangyu, Xinying Qu, and Dongzhe Qu. "Application and Optimization of Artificial Intelligence in Distributed Energy Management Systems." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia241166.

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In order to solve the problem of inaccurate prediction performance of existing distributed energy management, the application and optimization of artificial intelligence in distributed energy management systems are proposed. This paper uses a lightweight gradient boosting machine model to conduct detailed analysis of energy data, and optimizes the LightGBM model through PSO to improve model performance. By predicting energy data, and conducting experimental comparisons with multiple linear regression (LR), random forest (RF), LightGBM and extreme gradient boosting (XGB). Experimental results show that the LightGBM model optimized by PSO performs better and its prediction effect is significantly better than other models. Compared with other models, PSO-LightGBM shows higher prediction accuracy, robustness and stability, and the three evaluation indicators used in the experiment all reach the optimal level (the mean square error is only 0.083, and the coefficient of determination reaches 91.56% , the explained variance is 91.60%). Conclusion: This method can more accurately predict the power consumption of enterprises and has important practical application value.

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Тези доповідей конференцій з теми "RF robustness"

Said, N., D. Saugnon, K. Harrouche, F. Medjdoub, N. Labat, N. Malbert, and J.-G. Tartarin. "RF-Robustness enhancement in AlN/GaN HEMT through AlGaN Back-Barrier: nonlinear model analysis." In 2024 19th European Microwave Integrated Circuits Conference (EuMIC), 2–5. IEEE, 2024. http://dx.doi.org/10.23919/eumic61603.2024.10732162.

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Kiasari, Mahmoud, and Hamed H. Aly. "Evaluating Solar Power Forecasting Robustness: A Comparative Analysis of XGBoost, RNN, KNN, RF, and LSTM with emphasis on Lagged Steps, Sensitivity, and Cross-Validation Techniques." In 2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 686–92. IEEE, 2024. http://dx.doi.org/10.1109/ccece59415.2024.10667188.

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Liu, Zhengdong, Yuanxin Liu, Dajie Zeng, Weichang Xue, and Yaohui Zhang. "RF LDMOS with excellent robustness for wideband application." In 2015 Asia-Pacific Microwave Conference (APMC). IEEE, 2015. http://dx.doi.org/10.1109/apmc.2015.7413377.

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Goldsmith, C. L., J. C. M. Hwang, C. Gudeman, O. Auciello, J. L. Ebel, and H. S. Newman. "Robustness of RF MEMS capacitive switches in Harsh Environments." In 2012 IEEE/MTT-S International Microwave Symposium - MTT 2012. IEEE, 2012. http://dx.doi.org/10.1109/mwsym.2012.6259627.

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Choudhuri, Chiranjib, Abhishek Ghosh, Urbashi Mitra, and Sudhakar Pamarti. "Robustness of xampling-based RF receivers against analog mismatches." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288538.

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Kaynak, M., F. Korndo, M. Wietstruck, D. Knoll, R. Scholz, C. Wipf, C. Krause, and B. Tillack. "Robustness and reliability of BiCMOS embedded RF-MEMS switch." In 2011 IEEE 11th Topical Meeting on Silicon Monolithic Integrated Circuits in RF Systems (SiRF 2011). IEEE, 2011. http://dx.doi.org/10.1109/sirf.2011.5719336.

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Sandupatla, Abhinay, Shih-Hung Chen, Nikhil Mane, Bertrand Parvais, Hao Yu, Nilam Pradhan, and Nadine Collaert. "Solutions To Improve HBM ESD Robustness of GaN RF HEMTs." In 2023 45th Annual EOS/ESD Symposium (EOS/ESD). IEEE, 2023. http://dx.doi.org/10.23919/eos/esd58195.2023.10287752.

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Kokalj-Filipovic, Silvija, Rob Miller, and Garrett Vanhoy. "Adversarial Examples in RF Deep Learning: Detection and Physical Robustness." In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969138.

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Kozlov, Mikhail, and Robert Turner. "RF transmit robustness of dual-row MRI array at 300 MHz." In 2013 Asia Pacific Microwave Conference - (APMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/apmc.2013.6694861.

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Scholz, Mirko, and Friedrich zur Nieden. "Early Evaluation of ESD Robustness of RF ICs on System-Level." In 2021 43rd Annual EOS/ESD Symposium (EOS/ESD). IEEE, 2021. http://dx.doi.org/10.23919/eos/esd52038.2021.9574774.

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Звіти організацій з теми "RF robustness"

Oh, Ju Hyun, Aimee Martinez, Huaixuan Cao, Garrett George, Jared Cobb, Poonam Sharma, Lauren Fassero, et al. Radio frequency heating of washable conductive textiles for bacteria and virus inactivation. Engineer Research and Development Center (U.S.), January 2024. http://dx.doi.org/10.21079/11681/48060.

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The ongoing COVID-19 pandemic has increased the use of single-use medical fabrics such as surgical masks, respirators, and other personal protective equipment (PPE), which have faced worldwide supply chain shortages. Reusable PPE is desirable in light of such shortages; however, the use of reusable PPE is largely restricted by the difficulty of rapid sterilization. In this work, we demonstrate successful bacterial and viral inactivation through remote and rapid radio frequency (RF) heating of conductive textiles. The RF heating behavior of conductive polymer-coated fabrics was measured for several different fabrics and coating compositions. Next, to determine the robustness and repeatability of this heating response, we investigated the textile’s RF heating response after multiple detergent washes. Finally, we show a rapid reduction of bacteria and virus by RF heating our conductive fabric. 99.9% of methicillin-resistant Staphylococcus aureus (MRSA) was removed from our conductive fabrics after only 10 min of RF heating; human cytomegalovirus (HCMV) was completely sterilized after 5 min of RF heating. These results demonstrate that RF heating conductive polymercoated fabrics offer new opportunities for applications of conductive textiles in the medical and/or electronic fields.

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