Готові списки джерел за темами / RF mapping

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Добірка наукової літератури з теми "RF mapping"

Автор: Grafiati
Опубліковано: 9 листопада 2024

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Статті в журналах з теми "RF mapping"

1

Schmitz, B., J. Köszegi, K. Alomari, O. Kugeler, and J. Knobloch. "Magnetometric mapping of superconducting RF cavities." Review of Scientific Instruments 89, no. 5 (May 2018): 054706. http://dx.doi.org/10.1063/1.5030509.

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2

Bonagura, V. R., S. E. Artandi, A. Davidson, I. Randen, N. Agostino, K. Thompson, J. B. Natvig, and S. L. Morrison. "Mapping studies reveal unique epitopes on IgG recognized by rheumatoid arthritis-derived monoclonal rheumatoid factors." Journal of Immunology 151, no. 7 (October 1, 1993): 3840–52. http://dx.doi.org/10.4049/jimmunol.151.7.3840.

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Abstract We have used chimeric IgG antibodies and their genetically engineered variants prepared by a combination of site-directed mutagenesis and exon exchange to define the structure(s) on IgG recognized by monoclonal rheumatoid factor (RF) autoantibodies from rheumatoid arthritis (RA) patients. Nineteen RF produced by EBV-transformed cell lines from the synovium or blood of RA patients were analyzed. Their binding patterns differ significantly from those seen with RF obtained from patients with Waldenstrom's macroglobulinemia (WMac). Half of the RA-derived RF bound IgG1, 2, and 4, but not 3 (Ga specificity), the common pattern in WMac. However, heterogeneity in fine specificity within the Ga reactivity pattern was observed. Moreover, seven others bound all four IgG subclasses, a pattern observed for only one WMac-derived RF from a patient who also had RA. Three RF had subclass specificities unlike any observed with WMac-derived RF. Most RA-derived RF bound IgG at a discontinuous epitope comprised of residues from both the CH2 and CH3 H chain constant regions. However, unlike any WMac-derived RF, one RA-derived RF bound IgG in CH2, another in CH3, and a third at an undetermined site outside of the CH2-CH3 interface. Some RA-derived RF bound aglycosylated IgG4 less well than glycosylated IgG4, suggesting that the carbohydrate moiety was important in establishing their binding epitope in CH2. These studies demonstrate that the repertoire of RF expressed by RA patients contains some unique binding specificities for IgG epitopes not found among our panel of WMac-derived RF. Our results therefore call into question whether WMac-derived RF with their limited diversity are appropriate models for disease-related RF. In addition, RF with their multiple specificities can serve as probes of antibody structure.
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3

Ouma, Y., B. Nkwae, D. Moalafhi, P. Odirile, B. Parida, G. Anderson, and J. Qi. "COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 681–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-681-2022.

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Abstract. This study compares four machine-learning algorithms comprising of Classification And Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features. Using multitemporal and multisensor Landsat data from 1984-2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, the aim of the study is to determine the performance of the classifiers in the extraction of different urban LULC features as built-up, bare-soil, water, grass, shrubs and forest. The results show that for mapping built-up areas, RF and SVM presented the best results with overall accuracy of 85%. Bare soil is best mapped using RF and CART with accuracy of up to 98%, while SVM and GTB were most suitable for mapping water bodies. The suitable classifiers for mapping the vegetation classes were RF for grass (94.5%), SVM for shrubland (81.5%) and GTB for forest (84.3%). In terms of class specific accuracy, RF achieved the highest performance with average overall accuracy (OA) of 95.9%, SVM (95.8%), GTB (95.6%) and CART (95.1%). The same performance pattern was observed from the F1-score, True Positive Rate (TPR), False Positive Rate (FPR) and Area under ROC curve (AUC) metrices for the class classification accuracies. The overall accuracy for the eight-epoch years were RF (87.8%), SVM (87.5%), GTB (86.4%) and CART (85.3%). To improve on the urban LULC mapping, the study proposes the post-classification feature fusion of the best classifier results.
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4

Akbari, Elahe, Ali Darvishi Boloorani, Najmeh Neysani Samany, Saeid Hamzeh, Saeid Soufizadeh, and Stefano Pignatti. "Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2." Remote Sensing 12, no. 9 (May 3, 2020): 1449. http://dx.doi.org/10.3390/rs12091449.

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Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.
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5

Abida, Khouloud, Meriem Barbouchi, Khaoula Boudabbous, Wael Toukabri, Karem Saad, Habib Bousnina, and Thouraya Sahli Chahed. "Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas." Agriculture 12, no. 9 (September 9, 2022): 1429. http://dx.doi.org/10.3390/agriculture12091429.

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Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning methods. The current investigation was conducted in the Nord-Est area of Tunisia, and an optical satellite image covering the study area was acquired from Sentinel-2. For LU mapping, we tested three machine learning models algorithms: Random Forest (RF), K-Dimensional Trees K-Nearest Neighbors (KDTree-KNN) and Minimum Distance Classification (MDC). According to our research, the RF classification provided a better result than other classification models. RF classification exhibited the best values of overall accuracy, kappa, recall, precision and RMSE, with 99.54%, 0.98%, 0.98%, 0.98% and 0.23%, respectively. However, low precision was observed for the MDC method (RMSE = 1.15). The results were more intriguing since they highlighted the value of the bare soil index as a covariate for LU mapping. Our results suggest that Sentinel-2 combined with RF classification is efficient for creating a LU map.
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6

Merfeldas, Audrius, Pranas Kuzas, Darius Gailius, Zilvinas Nakutis, Mindaugas Knyva, Algimantas Valinevicius, Darius Andriukaitis, Mindaugas Zilys, and Dangirutis Navikas. "An Improved Near-field Magnetic Probe Radiation Profile Boundaries Assessment for Optimal Radiated Susceptibility Pre-Mapping." Symmetry 12, no. 7 (June 28, 2020): 1063. http://dx.doi.org/10.3390/sym12071063.

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In this paper, the near-field radiated susceptibility pre-mapping method is proposed using the improved near-field probe power radiation profile assessment. The modelling of the electromagnetic field strength in 80–3000 MHz range in the proximity of the near-field probe was performed. The −6 dB aperture boundaries of the near-field probe and their variation, due to the proximity of the radio frequency (RF) printed circuit board (PCB) components, were determined, while the aperture map distortion, arising from the proximity of the passive RF PCB components were evaluated. The scanning path requirements for the RF susceptibility mapping were determined. The simulation of improved near-field probe absolute magnetic field strength reference map in open-air conditions is carried out in this work. The comparative analysis using the absolute maximum difference metric of orthogonal absolute magnetic field map cross-sections between the reference map and magnetic field maps affected by the proximity of the components was carried out. The experimental study of the RF amplifier stage susceptibility map with susceptibility mapping measurement results are presented in this work.
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7

Pasquali, J. L., A. M. Knapp, A. Farradji, and A. Weryha. "Mapping of four light chain-associated idiotopes of a human monoclonal rheumatoid factor." Journal of Immunology 139, no. 3 (August 1, 1987): 818–23. http://dx.doi.org/10.4049/jimmunol.139.3.818.

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Abstract In an effort to analyze both IgM rheumatoid factor (RF) repertoire and regulation of RF production in humans, we developed a panel of four mouse monoclonal antibodies (mAb) defining distinct K light chain-associated idiotopes (id) of a human monoclonal IgM RF (Alt). These mAb (A75, AM1, AM2, AM3) had equivalent reactivities with the immunizing RF during classic inhibition of antigen-binding assays. These anti-id reagents were reacting to neither other tested monoclonal IgM RF nor normal polyclonal IgM. It was possible to distinguish the id defined by the mAb from the results of four sets of experiments: dissociation of Alt RF heavy (H) and light (L) chains showed that A75, AM1, and AM2 reacted to id located on the L chain, whereas AM3 defined a conformational RF id; recombination experiments of H and L chains showed that A75 and AM2 reacted well with both homologous (Alt H + Alt L) and heterologous (Alt L + unrelated H) recombinants, whereas AM1 reacted better with the homologous recombinant than with the heterologous one; the relative affinities of the mAb were drawn from their ability to shift already bound labeled Alt RF from solid phase IgG; and radiolabeling of two mAb (A75 and AM3) and experiments of inhibition of id binding with cold anti-id and cold anti-CK showed that A75 recognized a proximal id (close to the K constant region), whereas AM3 defined a more distal id, AM2 and AM1 being located between A75- and AM3-defined sites. This topographic mapping of K light chain-associated id of a human RF with anti-id of known relative affinities could help in studying idiotypic regulation in humans.
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8

Wicaksono, Pramaditya, Prama Ardha Aryaguna, and Wahyu Lazuardi. "Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms." Remote Sensing 11, no. 11 (May 29, 2019): 1279. http://dx.doi.org/10.3390/rs11111279.

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This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.
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9

Kwak, Geun-Ho, Chan-won Park, Kyung-do Lee, Sang-il Na, Ho-yong Ahn, and No-Wook Park. "Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data." Remote Sensing 13, no. 9 (April 21, 2021): 1629. http://dx.doi.org/10.3390/rs13091629.

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Анотація:
When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.
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10

Khalighi, Mohammad Mehdi, Brian K. Rutt, and Adam B. Kerr. "Adiabatic RF pulse design for Bloch-SiegertB+ mapping." Magnetic Resonance in Medicine 70, no. 3 (October 5, 2012): 829–35. http://dx.doi.org/10.1002/mrm.24507.

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Більше джерел

Дисертації з теми "RF mapping"

1

Schwartz, Benjamin M. (Benjamin Matthew). "Mapping bulk electrical properties with non-contact RF measurements." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36118.

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Анотація:
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2006.
Includes bibliographical references (leaf 43).
The human body is composed primarily of dielectric tissue with spatially varying permittivity and conductivity. Traditional MRI does not measure these properties. Instead, the conductivity of the patient is a nuisance, causing unpredictable detuning of coils and field inhomogeneities. This thesis presents a method for mapping the electrodynamic properties of the patient's body with both MR and non-MR techniques. Such mapping has direct applications for medical imaging and SAR calculation.
by Benjamin M. Schwartz.
S.B.
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2

Dalveren, Taylan. "A Study of Sensitivity Mapping Techniques for Multi-Channel MR Coils." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1373403690.

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3

Brandejsky, Vaclav. "MR scanner independent 3D B1 field mapping of magnetic resonance spectroscopy RF coils using an automated measurement system." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15025.

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We developed a method for measurement of the B1+/- fields as an alternative approach to B1+/- fields assessment in MR scanner. Our setup incorporates an industrial coordinate table controlled by a PC-based program, search coils, a two--port vector network analyzer and an analog-to-digital (A/D) card. It is possible to measure in various liquid media (for example in saline solution) to mimic different shapes and dielectric properties of the human body.
Radiofrekventa - spolars känslighetsmönster är viktigt för avbildning med magnetkamera (MRI) och magnetresonansspektroskopi (MRS). Vetskap om RF-spolars känslighet för och förmåga att skapa RF-magnetfält (B1 kan användas för att åstadkomma korrekta excitationsflipvinklar och för att korrigera uppmätta signalstyrkor. Det är också viktigt för att göra MRI och MRS undersökningar snabbare och för att förbättra kvalitén på rekonstruerade bilder. Vi har utvecklat en metod för mätning av B1+ och B1- fält i en testbänk som alternativ till metoder där B1 fältet bestäms inne i magnetkameran. Uppställningen omfattar ett industriellt koordinatbord kontrollerat av PC-baserade program, sökspolar för detektion av B1 fält, en tvåports nätverksanalysator och ett analog till digital omvandlingskort. Mätningen är möjlig att utföra i olika vätskor, exempelvis saltlösning, för att efterlikna olika former och dielektriska egenskaper hos människokroppen.
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4

Brandejsky, Vaclav. "MR scanner independent 3D B1 field mapping of magnetic resonance spectroscopy RF coils using an automated measurement system." Thesis, Linköping University, Department of Biomedical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15025.

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Анотація:

We developed a method for measurement of the B1+/- fields as an alternative approach to B1+/- fields assessment in MR scanner. Our setup incorporates an industrial coordinate table controlled by a PC-based program, search coils, a two--port vector network analyzer and an analog-to-digital (A/D) card. It is possible to measure in various liquid media (for example in saline solution) to mimic different shapes and dielectric properties of the human body.


Radiofrekventa - spolars känslighetsmönster är viktigt för avbildning med magnetkamera (MRI) och magnetresonansspektroskopi (MRS). Vetskap om RF-spolars känslighet för och förmåga att skapa RF-magnetfält (B1 kan användas för att åstadkomma korrekta excitationsflipvinklar och för att korrigera uppmätta signalstyrkor. Det är också viktigt för att göra MRI och MRS undersökningar snabbare och för att förbättra kvalitén på rekonstruerade bilder. Vi har utvecklat en metod för mätning av B1+ och B1- fält i en testbänk som alternativ till metoder där B1 fältet bestäms inne i magnetkameran. Uppställningen omfattar ett industriellt koordinatbord kontrollerat av PC-baserade program, sökspolar för detektion av B1 fält, en tvåports nätverksanalysator och ett analog till digital omvandlingskort. Mätningen är möjlig att utföra i olika vätskor, exempelvis saltlösning, för att efterlikna olika former och dielektriska egenskaper hos människokroppen.

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5

Voigt, Tobias [Verfasser], and O. [Akademischer Betreuer] Dössel. "Quantitative MR Imaging of the Electric Properties and Local SAR based on Improved RF Transmit Field Mapping / Tobias Voigt ; Betreuer: O. Dössel." Karlsruhe : KIT Scientific Publishing, 2011. http://d-nb.info/1184497532/34.

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6

Björk, Marcus. "Contributions to Signal Processing for MRI." Doctoral thesis, Uppsala universitet, Avdelningen för systemteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-246537.

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Анотація:
Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties. Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information. In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued data, and minimizing banding artifacts due to field inhomogeneity. The performance of the proposed algorithms is evaluated using both simulation and in-vivo data. The results show improvements over previous approaches, while maintaining a relatively low computational complexity. Using new and improved estimation methods enables better tissue characterization and diagnosis. Furthermore, a sequence design problem is treated, where the radio-frequency excitation is optimized to minimize image artifacts when using amplifiers of limited quality. In turn, obtaining higher fidelity images enables improved diagnosis, and can increase the estimation accuracy in quantitative MRI.
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7

Okujeni, Akpona. "Quantifying urban land cover by means of machine learning and imaging spectrometer data at multiple spatial scales." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2014. http://dx.doi.org/10.18452/17082.

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Анотація:
Das weltweite Ausmaß der Urbanisierung zählt zu den großen ökologischen Herausforderungen des 21. Jahrhunderts. Die Fernerkundung bietet die Möglichkeit das Verständnis dieses Prozesses und seiner Auswirkungen zu erweitern. Der Fokus dieser Arbeit lag in der Quantifizierung der städtischen Landbedeckung mittels Maschinellen Lernens und räumlich unterschiedlich aufgelöster Hyperspektraldaten. Untersuchungen berücksichtigten innovative methodische Entwicklungen und neue Möglichkeiten, die durch die bevorstehende Satellitenmission EnMAP geschaffen werden. Auf Basis von Bilder des flugzeugestützten HyMap Sensors mit Auflösungen von 3,6 m und 9 m sowie simulierten EnMAP-Daten mit einer Auflösung von 30 m wurde eine Kartierung entlang des Stadt-Umland-Gradienten Berlins durchgeführt. Im ersten Teil der Arbeit wurde die Kombination von Support Vektor Regression mit synthetischen Trainingsdaten für die Subpixelkartierung eingeführt. Ergebnisse zeigen, dass sich der Ansatz gut zur Quantifizierung thematisch relevanter und spektral komplexer Oberflächenarten eignet, dass er verbesserte Ergebnisse gegenüber weiteren Subpixelverfahren erzielt, und sich als universell einsetzbar hinsichtlich der räumlichen Auflösung erweist. Im zweiten Teil der Arbeit wurde der Wert zukünftiger EnMAP-Daten für die städtische Fernerkundung abgeschätzt. Detaillierte Untersuchungen unterstreichen deren Eignung für eine verbesserte und erweiterte Beschreibung der Stadt nach dem bewährten Vegetation-Impervious-Soil-Schema. Analysen der Möglichkeiten und Grenzen zeigen sowohl Nachteile durch die höhere Anzahl von Mischpixel im Vergleich zu hyperspektralen Flugzeugdaten als auch Vorteile aufgrund der verbesserten Differenzierung städtischer Materialien im Vergleich zu multispektralen Daten. Insgesamt veranschaulicht diese Arbeit, dass die Kombination von hyperspektraler Satellitenbildfernerkundung mit Methoden des Maschinellen Lernens eine neue Qualität in die städtische Fernerkundung bringen kann.
The global dimension of urbanization constitutes a great environmental challenge for the 21st century. Remote sensing is a valuable Earth observation tool, which helps to better understand this process and its ecological implications. The focus of this work was to quantify urban land cover by means of machine learning and imaging spectrometer data at multiple spatial scales. Experiments considered innovative methodological developments and novel opportunities in urban research that will be created by the upcoming hyperspectral satellite mission EnMAP. Airborne HyMap data at 3.6 m and 9 m resolution and simulated EnMAP data at 30 m resolution were used to map land cover along an urban-rural gradient of Berlin. In the first part of this work, the combination of support vector regression with synthetically mixed training data was introduced as sub-pixel mapping technique. Results demonstrate that the approach performs well in quantifying thematically meaningful yet spectrally challenging surface types. The method proves to be both superior to other sub-pixel mapping approaches and universally applicable with respect to changes in spatial scales. In the second part of this work, the value of future EnMAP data for urban remote sensing was evaluated. Detailed explorations on simulated data demonstrate their suitability for improving and extending the approved vegetation-impervious-soil mapping scheme. Comprehensive analyses of benefits and limitations of EnMAP data reveal both challenges caused by the high numbers of mixed pixels, when compared to hyperspectral airborne imagery, and improvements due to the greater material discrimination capability when compared to multispectral spaceborne imagery. In summary, findings demonstrate how combining spaceborne imaging spectrometry and machine learning techniques could introduce a new quality to the field of urban remote sensing.
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8

Sandouno, Bernard Tamba. "Amélioration de la précision des cartes de couverture réseau par des techniques de lancer de rayons." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4020.

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Le lancer de rayons est un modèle de propagation déterministe qui estime la puissance du signal reçu en des emplacements géographiques spécifiques, en prenant en compte de manière exhaustive des facteurs environnementaux en 3D qui influencent la propagation du signal. Cette grande précision du lancer de rayons se fait toutefois au prix d'une charge de calcul élevée due aux complexités de ses processus. Cette charge de calcul se caractérise par une consommation élevée de la mémoire vive et un temps d'exécution élevé, rendant difficile le calcul efficace de la puissance du signal reçu.Conscients de la complexité inhérente au lancer de rayons, diverses techniques d'accélération ont émergé au fil du temps pour améliorer son efficacité computationnelle. Ces techniques optimisent les processus au sein du lancer de rayons, permettant ainsi des réductions significatives du temps d'exécution pour l'estimation de la puissance du signal à des emplacements individuels. Cependant, lorsqu'elles sont appliquées à la génération de cartes de fréquences radio sur des régions étendues telles que des villes ou des pays, ces techniques d'accélération perdent leur efficacité. Cette limitation découle de la conception initiale du lancer de rayons, conçue pour l'évaluation de la réception du signal en point à point plutôt que pour la cartographie radio fréquence à grande échelle.En réponse à cette problématique, cette thèse propose des optimisations adaptées spécifiquement à la génération de cartes de fréquences radio précises à grande échelle sans entraîner de surcharge computationnelle excessive. En affinant tous les aspects du pipeline du lancer de rayons avec un accent particulier sur la cartographie radio fréquence, nos optimisations visent à résoudre les inefficacités prédominantes dans les solutions actuelles de cartographie radio fréquence à grande échelle. Tout au long de ce travail, nous démontrons les lacunes des approches existantes et validons l'efficacité de nos optimisations en réduisant considérablement les temps d'exécutions tout en maintenant la précision de l'estimation dans des limites acceptables.Nous proposons d'abord une approche innovante du processus traditionnellement complexe de génération de rayons, en minimisant efficacement le nombre de rayons lancés à partir d'une antenne tout en préservant la précision. Adaptée aux conditions spécifiques du site, cette méthode prend en compte l'environnement de propagation lors du lancement des rayons, contrairement à l'approche traditionnelle qui lance les rayons dans tous les sens possibles. En mettant en œuvre cette technique de génération de rayons spécifique au site et en réimaginant le processus de test de réception, nous réalisons une réduction remarquable de près de 1200 fois du temps d'exécution du lancer de rayons pour la génération de cartes radio fréquence dans des terrains avec de légères variations d'altitude. En étendant cette solution à des terrains divers, nous introduisons des optimisations complètes à travers tous les processus de lancer de rayons, méticuleusement adaptées aux objectifs de la cartographie radio fréquence. Ces améliorations aboutissent à une augmentation de 50 fois de la vitesse de génération de cartes radio fréquence à grande échelle sur des terrains variés, démontrant la polyvalence et l'efficacité de notre approche
Ray Tracing is a deterministic propagation modeling approach that estimates the signal power received in specific geographical locations based on a thorough consideration of the 3D environmental factors that influence signal propagation. This high accuracy of Ray Tracing is however at the cost of a high computational load due to the complexities of the processes within its workflow. This computational demand is characterized by a high memory consumption and a high execution time, making it challenging to efficiently compute the received signal power.Acknowledging the complexity inherent in Ray Tracing, various acceleration techniques have emerged over time to enhance its computational efficiency. These techniques optimize processes within Ray Tracing, facilitating significant reductions in execution time for signal power estimation at individual locations. However, when applied to the generation of Radio Frequency (RF) maps across expansive regions like cities or countries, these acceleration techniques lose their efficiency. This limitation arises from Ray Tracing's original design, tailored for point-to-point signal reception assessment rather than large-scale RF mapping.In response, this thesis proposes optimizations designed specifically for generating accurate and precise RF maps at large scale without incurring excessive computational overhead. By refining all aspects of the Ray Tracing pipeline with a focus on RF mapping, our optimizations aim to address the inefficiencies prevalent in current large-scale RF mapping solutions. Throughout this work, we demonstrate the shortcomings of existing approaches and validate the effectiveness of our optimizations in significantly reducing execution times while maintaining estimation accuracy within acceptable bounds.We first propose an innovative approach to the traditionally complex ray generation process, by effectively minimizing the number of rays launched from an antenna while preserving the accuracy. Tailored to specific site conditions, this method considers the propagation environment when launching rays, in contrast to the traditional approach that launches rays in a brute-force fashion. Further, by implementing this site-specific ray generation technique and re-imagining the reception test process, we achieve a remarkable reduction of nearly 1200 times in Ray Tracing execution time for RF map generation in terrains with slight altitude variations. Extending this solution to diverse terrains, we then introduce comprehensive optimizations across all Ray Tracing processes, meticulously tailored for RF mapping objectives. These enhancements culminate in a 50-fold increase in the speed of large-scale RF map generation across varied terrains, showcasing the versatility and efficiency of our approach
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Wells, D. D., I. G. Siebörger, and Barry Vivian William Irwin. "Location and mapping of 2.4 GHz RF transmitters." 2008. http://hdl.handle.net/10962/d1009324.

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This paper describes the use of a MetaGeek WiSpy dongle in conjunction with custom developed client-server software for the accurate identication of Wireless nodes within an organisation. The MetaGeek WiSpy dongle together with the custom developed software allow for the determination of the positions of Wi-Fi transceivers to within a few meters, which can be helpful in reducing the area for physical searches in the event of rogue units. This paper describes the tool and methodology for a site survey as a component that can be used in organisations wishing to audit their environments for wireless networks. The tool produced from this project, the WiSpy Signal Source Mapping Tool, is a three part application based on a client-server architecture. One part interfaces with a low cost 2.4 GHz spectrum analyser, another stores the data collected from all the spectrum analysers and the last part interprets the data to provide a graphical overview of the Wi-Fi network being analysed. The location of the spectrum analysers are entered as GPS points, and the tool can interface with a GPS device to automatically update its geographical location. The graphical representation of the 2.4 GHz spectrum populated with Wi-Fi devices (Wi-Fi network) provided a fairly accurate method in locating and tracking 2.4 GHz devices. Accuracy of the WiSpy Signal Source Mapping Tool is hindered by obstructions or interferences within the area or non line of sight.
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Sousa, Duarte Fleming Oliveira de. "Human Sensing and Indoor Location: From coarse to fine detection algorithms based on consumer electronics RF mapping." Master's thesis, 2017. https://hdl.handle.net/10216/103010.

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Depois de definida toda a arquitectura para captura dos dados de Wi-Fi, foi desenvolvido um software para a criação de uma base de dados contendo informações de posição e de força de sinal. Inicialmente, foi desenvolvido um algoritmo preliminar de localização. Posteriormente, implementou-se um algoritmo de classificação para mapear os dados recebidos dos sensores com os guardados anteriormente.Foram feitos testes usando estes algoritmos, e comparados os resultados.Em fase de escrita do documento.
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Частини книг з теми "RF mapping"

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Ayad, Mustafa, and Richard Voyles. "RF Mapping for Sensor Nodes Connectivity and Communication Signal Recovery." In Lecture Notes in Networks and Systems, 666–84. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80119-9_43.

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Yazgan, Erdem, and Volkan Akan. "Conformal Mapping Techniques." In Encyclopedia of RF and Microwave Engineering. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471654507.eme552.

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Rosenheck, Shimon, Jeffrey Banker, Alexey Weiss, and Zehava Sharo. "Electromagnetic Mapping During Complex RF Ablations." In Cardiac Arrhythmias - New Considerations. InTech, 2012. http://dx.doi.org/10.5772/30391.

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Sharma, Shriya, Rohan M. Goswami, Juan Leoni, and Jose Ruiz. "New Insights into Cardiac Ablation." In Atrial Fibrillation - Current Management and Practice [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1005656.

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Catheter ablation has emerged as a significant treatment approach to treat symptoms and regulate heart rate or rhythm. This review highlights the evolution of catheter ablation, focusing on its applications in atrial fibrillation (AF). Radiofrequency (RF) catheter ablation has become a primary treatment option due to its high success rate and minimal adverse effects. Using three-dimensional (3D) reconstruction and mapping systems enhances precision in targeting arrhythmogenic substrates.
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"Appendix H Synthesis of Resonant-Type Metamaterial Transmission Lines by Means of Aggressive Space Mapping." In Artificial Transmission Lines for RF and Microwave Applications, 491–502. Hoboken, NJ: John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119058403.app8.

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Kadir, Herdawatie Abdul, and Mohd Rizal Arshad. "A Framework for RF-Visual SLAM for Cooperative Multi-Agent System." In Advances in Computational Intelligence and Robotics, 697–727. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7387-8.ch022.

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This chapter provides a framework for radio frequency visual simultaneous localization and mapping problems for a team of agents consisting of three blimps and beacons. In a cooperative system, each agent must establish reliable data sharing during a mission. Under these conditions, a framework was proposed which allows each agent to share the local information using peer-to-peer networking schemes. The RF-vSLAM algorithm seeks to acquire a map during navigation, simultaneously localizing itself using the map and received signal strength indicator information to predict the distance between agents. In this chapter, the authors address the problem of detection features using SIFT algorithms. The authors have considered the sea surface as the working environment. In this research, the framework consisted of two types of agents, where beacon representing the static agent and blimp representing the homogeneous mobile agent. The communication exchange between these two types of agents is an environmentally friendly monitoring system that preserves natural value of the selected area.
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Mohammadpour, Pegah, Crismeire Isbaex, Emilio Chuvieco, Domingos Xavier Viegas, and Carlos Viegas. "Vegetation Mapping with Random Forest using Sentinel 2- A case study for Lousã region, Portugal." In Advances in Forest Fire Research 2022, 444–62. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_71.

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Vegetation mapping requires accurate information to allow its use in applications such as sustainable forest management against the effects of climate change and the threat of wildfires. Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lousa district in Portugal. We used 10 multispectral bands with a spatial resolution of 10m, four vegetation indices including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying Principal Component Analysis (PCA) on the 10 S2A bands, four texture features including Mean (ME), Homogeneity (HO), Correlation (COR), and Entropy (EN)were derived for the first three Principal Components. After defining the land use classes by object-based, the Random Forest (RF) classifier was applied. The map accuracy will be evaluated by the confusion matrix, using the metrics of Overall Accuracy (OA), Producer Accuracy (PA), User Accuracy (UA), and Kappa Coefficient (K). The described classification methodology is expected to show a high overall accuracy for vegetation mapping.
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Hubert, G., and S. Silvia Priscila. "Comprehensive Prediction of Retinopathy in Preterm Infants Using Deep Learning Approaches." In Advances in Computer and Electrical Engineering, 353–70. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3739-4.ch018.

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Preterm newborns' visual condition is greatly endangered by retinopathy, which makes prompt and precise identification essential for successful treatment. Using the capabilities of ML (machine learning) computations, the current investigation suggests a combined strategy for retinopathy forecasting in preterm infants. To improve prediction accuracy, specificity, and sensitivity, the investigation also includes a gray-level co-occurrence matrix (GLCM) for collecting features and a median filter to reduce noise. The work uses the RF, SVM, and MLP algorithms for modeling predictions, using their different learning capacities. Together, the non-linear mapping of MLP strengthens the retinopathy forecasting framework. Experiments on a dataset consisting of retinal pictures from preterm infants with different levels of retinopathy show how effective the suggested combined strategy is; the tool used is Jupyter Notebook, and the language used is Python. The findings confirm the usefulness of the suggested method in healthcare settings, guaranteeing prompt and precise identification to avert vision-threatening problems. From the results obtained, the proposed MLP produces an accuracy of 90%, a sensitivity of 0.91, and a specificity of 0.86.
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Homère Ngandam Mfondoum, Alfred, Igor Casimir Njombissie Petcheu, Frederic Chamberlain Lounang Tchatchouang, Luc Moutila Beni, Mesmin Tchindjang, and Jean Valery Mefire Mfondoum. "Dynamics, Anomalies and Boundaries of the Forest-Savanna Transition: A Novel Remote Sensing-Based Multi-Angles Methodology Using Google Earth Engine." In GIS and Spatial Analysis [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105074.

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This chapter proposes a remote sensing multi-angles methodology to assess the transition at the interface of the forest-savanna land cover. On Sentinel2-A median images of successive dry seasons, three referential and nine analytical spectral indices were computed. The change vector analysis (CVA) was performed, selecting further one magnitude per index. The averaged moving standard deviation index (aMSDI) was proposed to compare spatial intensity of anomalies among selected CVA, and then statistically assessed through spatial and no-spatial autoregression tests. The cross-correlation and simple linear combination (SCL) computations spotted the overall anomaly extent. Three machine learning algorithms, i.e., classification and regression trees (CART), random forest (RF), and support vector machine (SVM), helped mapping the distribution of each specie. As result, the CVA confirmed each index ability to add new information. The aMSDI gave the harmonized interval [0–0.083] among CVA, confirmed with all p−values=0, z−scores>2.5, clustering of anomaly pixel,and adjusted R2≤0.19. Three trends of vegetation distribution were distinguished with 88.7% overall accuracy and 0.86 kappa coefficient. Finally, extremely affected areas were spotted in upper latitudes towards Sahel and desert.
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Moskal, Anna. "Ablations, Cryotherapy, and Maze Procedure." In Advanced Anesthesia Review, edited by Alaa Abd-Elsayed, 327—C124.S9. Oxford University PressNew York, 2023. http://dx.doi.org/10.1093/med/9780197584521.003.0123.

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Abstract Cardiac arrhythmias are common and an important source of mortality and morbidity. Current management options include surgical and catheter ablative techniques using surgical incisions, cryotherapy, or radio-frequency (RF) energy. General indications include drug-resistant arrhythmias, drug intolerance, severe symptoms, or a desire to avoid lifelong symptoms. Ablative procedures can abolish the origins of arrhythmias by interposition of scar tissue along a reentry pathway or by isolating an ectopic area. Paroxysmal atrial fibrillation (PAF) often results from ectopic beats originating in the pulmonary veins, and ablative strategy involves pulmonary vein isolation (PVI). Catheter ablation for persistent atrial fibrillation (AF) is less effective and requires an additional ablation of sites identified during a stepwise mapping process. Surgical ablation for atrial fibrillation (i.e., Maze procedure) can be performed without an additional risk of operative mortality or major morbidity and is recommended at the time of concomitant open heart surgery to restore sinus rhythm. The Cox maze III procedure achieved high cure rates and became the surgical gold standard for the treatment of atrial fibrillation. Different ablation techniques have unique complications and requirements for anesthesia. Monitored anesthesia care (MAC) is most commonly utilized to prevent arrhythmia suppression. However, patients who are unable to lie still, flat, or tolerate the procedure under sedation may require general anesthesia. Protracted procedures involving left atrial ablation and transseptal puncture may be better performed under general anesthesia. Major complications of ablation procedures include cardiac tamponade and esophageal injury.
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Тези доповідей конференцій з теми "RF mapping"

1

Kim, Sean, Hailey H. Kim, and Brian Choi. "Exploiting RF Signature in 5G Simultaneous Localization and Mapping." In 2024 IEEE Integrated STEM Education Conference (ISEC), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isec61299.2024.10665037.

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Li, Yiming, Zeyu Li, Zhihui Gao, and Tingjun Chen. "Geo2SigMap: High-Fidelity RF Signal Mapping Using Geographic Databases." In 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 277–85. IEEE, 2024. http://dx.doi.org/10.1109/dyspan60163.2024.10632773.

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Tukimin, Roha, Syarfa Zahirah Sapuan, Wan Syazlin Wan Yunoh, and Nur Afrina Zainal. "Measurement and Mapping of Radiofrequency Electromagnetic Field (RF-EMF) Radiation Exposure in Malaysia's Environment." In 2024 IEEE 15th Control and System Graduate Research Colloquium (ICSGRC), 23–28. IEEE, 2024. http://dx.doi.org/10.1109/icsgrc62081.2024.10690979.

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Rosine, Germanicus, and El-Hassani Othman. "Machine Learning for Predicting DataCube Atomic Force Microscope (AFM)—MultiDAT-AFM." In ISTFA 2024, 351–57. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.istfa2024p0351.

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Abstract In nanoscience, techniques based on Atomic Force Microscope (AFM) stand as a cornerstone for exploring local electrical, electrochemical and magnetic properties of microelectronic devices at the nanoscale. As AFM's capabilities evolve, so do the challenges of data analysis. With the aim of developing a prediction model for AFM mappings, based on Machine Learning, this work presents a step towards the analysis and benefit of Big Data recorded in the hyperspectral modes: AFM DataCube. The MultiDAT-AFM solution is an advanced 2000-line Python-based tool designed to tackle the complexities of multi-dimensional measurements and analysis. MultiDAT-AFM offers visualization options, from acquired curves to scanned mappings, animated mappings as movies, and a real 3D-cube representation for the hyperspectral DataCube modes. In addition, MultiDAT-AFM incorporates a Machine Learning algorithm to predict mappings of local properties. After evaluating two supervised Machine Learning algorithms (out of the eight tested) for regression, the Random Forest Regressor model emerged as the best performer. With the refinement step, a root mean square error (RMSE) of 0.18, an R2 value of 0.90 and an execution time of a few minutes were determined. Developed for all AFM DataCube modes, the strategy and demonstration of MultiDAT-AFM are outlined in this article for a silicon integrated microelectronic device dedicated to RF applications and analyzed by DataCube Scanning Spreading Resistance (DCUBE-SSRM).
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Rütschlin, M., and V. Sokol. "Complex permittivity extraction using a space-mapping technique." In 2nd Annual Passive RF and Microwave Components Seminar. IET, 2011. http://dx.doi.org/10.1049/ic.2011.0198.

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Ayad, Mustafa, Jun Jason Zhang, Richard Voyles, and Mohammad H. Mahoor. "Mobile robot connectivity maintenance based on RF mapping." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696840.

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Bozkurt, Sinem, Ahmet Yazici, Serkan Gunal, and Ugur Yayan. "A survey on RF mapping for indoor positioning." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130275.

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Schuette, Jason, Barry Fell, John Chapin, Steven Jones, James Stutler, Mark Birchler, and Dennis Roberson. "Performance of RF mapping using opportunistic distributed devices." In MILCOM 2015 - 2015 IEEE Military Communications Conference. IEEE, 2015. http://dx.doi.org/10.1109/milcom.2015.7357677.

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Murakowski, Janusz A., Garrett Schneider, and Dennis W. Prather. "Optical processing for phased-array and beamspace mapping." In Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XV, edited by Laurence P. Sadwick and Tianxin Yang. SPIE, 2022. http://dx.doi.org/10.1117/12.2613480.

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Jaw, Siow Wei, and Mazlan Hashim. "Accuracy of data acquisition approaches with ground penetrating radar for subsurface utility mapping." In 2011 IEEE International RF and Microwave Conference (RFM). IEEE, 2011. http://dx.doi.org/10.1109/rfm.2011.6168691.

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Звіти організацій з теми "RF mapping"

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, December 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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