Bibliografie tematiche / SCALP framework

Letteratura scientifica selezionata sul tema "SCALP framework"

Autore: Grafiati

Pubblicato: 22 giugno 2024

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Articoli di riviste sul tema "SCALP framework":

Pflieger, Mark E. "Inferring contextual field interactions from scalp EEG". Behavioral and Brain Sciences 26, n. 1 (febbraio 2003): 99–100. http://dx.doi.org/10.1017/s0140525x03390028.

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AbstractThis commentary highlights methods for using scalp EEG to make inferences about contextual field interactions, which, in view of the target article, may be specially relevant to the study of schizophrenia. Although scalp EEG has limited spatial resolution, prior knowledge combined with experimental manipulations may be used to strengthen inferences about underlying brain processes. Both spatial and temporal context are discussed within the framework of nonlinear interactions. Finally, results from a visual contour integration EEG pilot study are summarized in view of a hypothesis that relates receptive field and contextual field processing to evoked and induced activity, respectively.

Faes, Luca, Daniele Marinazzo, Giandomenico Nollo e Alberto Porta. "An Information-Theoretic Framework to Map the Spatiotemporal Dynamics of the Scalp Electroencephalogram". IEEE Transactions on Biomedical Engineering 63, n. 12 (dicembre 2016): 2488–96. http://dx.doi.org/10.1109/tbme.2016.2569823.

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Kotiuchyi, Ivan, Riccardo Pernice, Anton Popov, Luca Faes e Volodymyr Kharytonov. "A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks". Brain Sciences 10, n. 9 (22 settembre 2020): 657. http://dx.doi.org/10.3390/brainsci10090657.

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This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy.

Kilicarslan, Atilla, Robert G. Grossman e Jose Luis Contreras-Vidal. "A robust adaptive denoising framework for real-time artifact removal in scalp EEG measurements". Journal of Neural Engineering 13, n. 2 (10 febbraio 2016): 026013. http://dx.doi.org/10.1088/1741-2560/13/2/026013.

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Carvalhaes, Claudio G., e Patrick Suppes. "A Spline Framework for Estimating the EEG Surface Laplacian Using the Euclidean Metric". Neural Computation 23, n. 11 (novembre 2011): 2974–3000. http://dx.doi.org/10.1162/neco_a_00192.

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This letter develops a framework for EEG analysis and similar applications based on polyharmonic splines. This development overcomes a basic problem with the method of splines in the Euclidean setting: that it does not work on low-degree algebraic surfaces such as spherical and ellipsoidal scalp models. The method’s capability is illustrated through simulations on the three-sphere model and using empirical data.

Wei, Boxuan, Xiaohui Zhao, Lijuan Shi, Lu Xu, Tao Liu e Jicong Zhang. "A deep learning framework with multi-perspective fusion for interictal epileptiform discharges detection in scalp electroencephalogram". Journal of Neural Engineering 18, n. 4 (21 luglio 2021): 0460b3. http://dx.doi.org/10.1088/1741-2552/ac0d60.

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Tuckute, Greta, Sofie Therese Hansen, Nicolai Pedersen, Dea Steenstrup e Lars Kai Hansen. "Single-Trial Decoding of Scalp EEG under Natural Conditions". Computational Intelligence and Neuroscience 2019 (17 aprile 2019): 1–11. http://dx.doi.org/10.1155/2019/9210785.

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There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding.

Sharma, Shanu, Ashwani Kumar Dubey e Priya Ranjan. "Affective Video Tagging Framework using Human Attention Modelling through EEG Signals". International Journal of Intelligent Information Technologies 18, n. 1 (1 gennaio 2022): 1–18. http://dx.doi.org/10.4018/ijiit.306968.

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The explosion of multimedia content over the past years is not surprising, thus their efficient management and analysis methods are always in demand. The affectiveness of any multimedia content deals with analyzing human perception and cognition while watching it. Human attention is also one of the important parameters, as it describes the engagement and interestingness of the user while watching that content. Considering this aspect, a video tagging framework is proposed in which the EEG signals of participants are used to analyze human perception while watching videos. A rigorous analysis has been performed on different scalp locations and frequency rhythms of brain signals to formulate significant features corresponding to affective and interesting video content. The analysis presented in this paper shows that the extracted human attention-based features are generating promising results with the accuracy of 93.2% using SVM-based classification model which supports the applicability of the model for various BCI-based applications for automatic classification of multimedia content.

Gorina-Careta, Natàlia, Teresa Ribas-Prats, Sonia Arenillas-Alcón, Marta Puertollano, M. Dolores Gómez-Roig e Carles Escera. "Neonatal Frequency-Following Responses: A Methodological Framework for Clinical Applications". Seminars in Hearing 43, n. 03 (agosto 2022): 162–76. http://dx.doi.org/10.1055/s-0042-1756162.

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AbstractThe frequency-following response (FFR) to periodic complex sounds is a noninvasive scalp-recorded auditory evoked potential that reflects synchronous phase-locked neural activity to the spectrotemporal components of the acoustic signal along the ascending auditory hierarchy. The FFR has gained recent interest in the fields of audiology and auditory cognitive neuroscience, as it has great potential to answer both basic and applied questions about processes involved in sound encoding, language development, and communication. Specifically, it has become a promising tool in neonates, as its study may allow both early identification of future language disorders and the opportunity to leverage brain plasticity during the first 2 years of life, as well as enable early interventions to prevent and/or ameliorate sound and language encoding disorders. Throughout the present review, we summarize the state of the art of the neonatal FFR and, based on our own extensive experience, present methodological approaches to record it in a clinical environment. Overall, the present review is the first one that comprehensively focuses on the neonatal FFRs applications, thus supporting the feasibility to record the FFR during the first days of life and the predictive potential of the neonatal FFR on detecting short- and long-term language abilities and disruptions.

Bhagubai, Miguel, Kaat Vandecasteele, Lauren Swinnen, Jaiver Macea, Christos Chatzichristos, Maarten De Vos e Wim Van Paesschen. "The Power of ECG in Semi-Automated Seizure Detection in Addition to Two-Channel behind-the-Ear EEG". Bioengineering 10, n. 4 (20 aprile 2023): 491. http://dx.doi.org/10.3390/bioengineering10040491.

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Long-term home monitoring of people living with epilepsy cannot be achieved using the standard full-scalp electroencephalography (EEG) coupled with video. Wearable seizure detection devices, such as behind-the-ear EEG (bte-EEG), offer an unobtrusive method for ambulatory follow-up of this population. Combining bte-EEG with electrocardiography (ECG) can enhance automated seizure detection performance. However, such frameworks produce high false alarm rates, making visual review necessary. This study aimed to evaluate a semi-automated multimodal wearable seizure detection framework using bte-EEG and ECG. Using the SeizeIT1 dataset of 42 patients with focal epilepsy, an automated multimodal seizure detection algorithm was used to produce seizure alarms. Two reviewers evaluated the algorithm’s detections twice: (1) using only bte-EEG data and (2) using bte-EEG, ECG, and heart rate signals. The readers achieved a mean sensitivity of 59.1% in the bte-EEG visual experiment, with a false detection rate of 6.5 false detections per day. Adding ECG resulted in a higher mean sensitivity (62.2%) and a largely reduced false detection rate (mean of 2.4 false detections per day), as well as an increased inter-rater agreement. The multimodal framework allows for efficient review time, making it beneficial for both clinicians and patients.

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Tesi sul tema "SCALP framework":

Mao, Tingting. "Interoperable internet-scale security framework for RFID networks". Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47741.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2008.
Includes bibliographical references (leaves 124-129).
It is estimated that over 3 billion Radio Frequency Identification (RFID) tags have been deployed through 2007. Most tags are used in supply chains where the Electronic Product Code (EPC) and associated business event data are transmitted through RFID networks. Security and privacy issues are critically important in RFID networks because EPC data and their associated business events are valuable assets. Companies need to share these data with restricted business partners and, under some conditions, such as product recall, more widely with regulators and non business partners. At present, no security or privacy framework has been chosen as an EPCglobal standard(industry-driven standards for EPC) due to the difficulty of sharing information between parties who have no direct business relationships and hence no business rules for sharing these data. To date, no security schemes have been deployed that can support data exchange with multiple identity techniques and interchangeable complex business rules, as required by RFID networks. In this thesis, an Interoperable Internet-Scale Security (IISS) framework for RFID networks is proposed. The IISS framework performs authentication and authorization based on an aggregation of business rules, enterprise information, and RFID tag information. IISS provides a protocol for several authentication schemes and identity techniques. It also provides an engine for reasoning over business rules from different domains. Moreover, the IISS framework is able to resolve provenance information of RFID tags, which can identify the history of a particular piece of EPC data through the supply chain.
(cont.) The IISS framework and the IISS ontologies to model the information in RFID networks are also described, and how the IISS framework can be developed for access control in RFID enabled supply chains is discussed. Finally, the IISS framework's efficiency is tested using a supply chain EPC simulator as the testing platform, which allows optimization of the IISS protocol's performance.
by Tingting Mao.
Ph.D.

Kolmistr, Tomáš. "Frameworky pro jednotkové testování v jazyce Scala". Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-203973.

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This thesis deals with frameworks for unit testing in Scala programming language. In total, there are presented five frameworks in the thesis, two of which are designed for unit testing with mock objekt and three without mock objects. The first, theoretical part aims to introduce concepts regarding testing and Scala programming language. In another part of the thesis there are specified criteria for selecting frameworks, including the criteria for subsequent comparison. In the practical part there are written unit tests according to test scenarios and evaluated the comparison of frameworks.

Donepudi, Harinivesh. "An Apache Hadoop Framework for Large-Scale Peptide Identification". TopSCHOLAR®, 2015. http://digitalcommons.wku.edu/theses/1527.

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Peptide identification is an essential step in protein identification, and Peptide Spectrum Match (PSM) data set is huge, which is a time consuming process to work on a single machine. In a typical run of the peptide identification method, PSMs are positioned by a cross correlation, a statistical score, or a likelihood that the match between the trial and hypothetical is correct and unique. This process takes a long time to execute, and there is a demand for an increase in performance to handle large peptide data sets. Development of distributed frameworks are needed to reduce the processing time, but this comes at the price of complexity in developing and executing them. In distributed computing, the program may divide into multiple parts to be executed. The work in this thesis describes the implementation of Apache Hadoop framework for large-scale peptide identification using C-Ranker. The Apache Hadoop data processing software is immersed in a complex environment composed of massive machine clusters, large data sets, and several processing jobs. The framework uses Apache Hadoop Distributed File System (HDFS) and Apache Mapreduce to store and process the peptide data respectively.The proposed framework uses a peptide processing algorithm named CRanker which takes peptide data as an input and identifies the correct PSMs. The framework has two steps: Execute the C-Ranker algorithm on Hadoop cluster and compare the correct PSMs data generated via Hadoop approach with the normal execution approach of C-Ranker. The goal of this framework is to process large peptide datasets using Apache Hadoop distributed approach.

Park, Dong-Jun. "Video event detection framework on large-scale video data". Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2754.

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Detection of events and actions in video entails substantial processing of very large, even open-ended, video streams. Video data presents a unique challenge for the information retrieval community because properly representing video events is challenging. We propose a novel approach to analyze temporal aspects of video data. We consider video data as a sequence of images that form a 3-dimensional spatiotemporal structure, and perform multiview orthographic projection to transform the video data into 2-dimensional representations. The projected views allow a unique way to rep- resent video events and capture the temporal aspect of video data. We extract local salient points from 2D projection views and perform detection-via-similarity approach on a wide range of events against real-world surveillance data. We demonstrate our example-based detection framework is competitive and robust. We also investigate the synthetic example driven retrieval as a basis for query-by-example.

Kervazo, Christophe. "Optimization framework for large-scale sparse blind source separation". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS354/document.

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Lors des dernières décennies, la Séparation Aveugle de Sources (BSS) est devenue un outil de premier plan pour le traitement de données multi-valuées. L’objectif de ce doctorat est cependant d’étudier les cas grande échelle, pour lesquels la plupart des algorithmes classiques obtiennent des performances dégradées. Ce document s’articule en quatre parties, traitant chacune un aspect du problème: i) l’introduction d’algorithmes robustes de BSS parcimonieuse ne nécessitant qu’un seul lancement (malgré un choix d’hyper-paramètres délicat) et fortement étayés mathématiquement; ii) la proposition d’une méthode permettant de maintenir une haute qualité de séparation malgré un nombre de sources important: iii) la modification d’un algorithme classique de BSS parcimonieuse pour l’application sur des données de grandes tailles; et iv) une extension au problème de BSS parcimonieuse non-linéaire. Les méthodes proposées ont été amplement testées, tant sur données simulées que réalistes, pour démontrer leur qualité. Des interprétations détaillées des résultats sont proposées
During the last decades, Blind Source Separation (BSS) has become a key analysis tool to study multi-valued data. The objective of this thesis is however to focus on large-scale settings, for which most classical algorithms fail. More specifically, it is subdivided into four sub-problems taking their roots around the large-scale sparse BSS issue: i) introduce a mathematically sound robust sparse BSS algorithm which does not require any relaunch (despite a difficult hyper-parameter choice); ii) introduce a method being able to maintain high quality separations even when a large-number of sources needs to be estimated; iii) make a classical sparse BSS algorithm scalable to large-scale datasets; and iv) an extension to the non-linear sparse BSS problem. The methods we propose are extensively tested on both simulated and realistic experiments to demonstrate their quality. In-depth interpretations of the results are proposed

Schuchmann, Roberta. "A framework for unlocking large-scale urban regeneration projects". Thesis, Curtin University, 2018. http://hdl.handle.net/20.500.11937/75246.

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This thesis investigates three large-scale urban projects in Australia: Kogarah in NSW, Dandenong in Victoria and Canning in Western Australia. Each is a regeneration site based upon transit-oriented development (TOD) principles. The findings of this research culminated in the creation of a framework that can be used for other regeneration projects, such as the Canning City Centre, a TOD in the planning phase.

Chacin, Martínez Pablo Jesus. "A Middleware framework for self-adaptive large scale distributed services". Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/80538.

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Modern service-oriented applications demand the ability to adapt to changing conditions and unexpected situations while maintaining a required QoS. Existing self-adaptation approaches seem inadequate to address this challenge because many of their assumptions are not met on the large-scale, highly dynamic infrastructures where these applications are generally deployed on. The main motivation of our research is to devise principles that guide the construction of large scale self-adaptive distributed services. We aim to provide sound modeling abstractions based on a clear conceptual background, and their realization as a middleware framework that supports the development of such services. Taking the inspiration from the concepts of decentralized markets in economics, we propose a solution based on three principles: emergent self-organization, utility driven behavior and model-less adaptation. Based on these principles, we designed Collectives, a middleware framework which provides a comprehensive solution for the diverse adaptation concerns that rise in the development of distributed systems. We tested the soundness and comprehensiveness of the Collectives framework by implementing eUDON, a middleware for self-adaptive web services, which we then evaluated extensively by means of a simulation model to analyze its adaptation capabilities in diverse settings. We found that eUDON exhibits the intended properties: it adapts to diverse conditions like peaks in the workload and massive failures, maintaining its QoS and using efficiently the available resources; it is highly scalable and robust; can be implemented on existing services in a non-intrusive way; and do not require any performance model of the services, their workload or the resources they use. We can conclude that our work proposes a solution for the requirements of self-adaptation in demanding usage scenarios without introducing additional complexity. In that sense, we believe we make a significant contribution towards the development of future generation service-oriented applications.
Las Aplicaciones Orientadas a Servicios modernas demandan la capacidad de adaptarse a condiciones variables y situaciones inesperadas mientras mantienen un cierto nivel de servio esperado (QoS). Los enfoques de auto-adaptación existentes parecen no ser adacuados debido a sus supuestos no se cumplen en infrastructuras compartidas de gran escala. La principal motivación de nuestra investigación es inerir un conjunto de principios para guiar el desarrollo de servicios auto-adaptativos de gran escala. Nuesto objetivo es proveer abstraciones de modelaje apropiadas, basadas en un marco conceptual claro, y su implemetnacion en un middleware que soporte el desarrollo de estos servicios. Tomando como inspiración conceptos económicos de mercados decentralizados, hemos propuesto una solución basada en tres principios: auto-organización emergente, comportamiento guiado por la utilidad y adaptación sin modelos. Basados en estos principios diseñamos Collectives, un middleware que proveer una solución exhaustiva para los diversos aspectos de adaptación que surgen en el desarrollo de sistemas distribuidos. La adecuación y completitud de Collectives ha sido provada por medio de la implementación de eUDON, un middleware para servicios auto-adaptativos, el ha sido evaluado de manera exhaustiva por medio de un modelo de simulación, analizando sus propiedades de adaptación en diversos escenarios de uso. Hemos encontrado que eUDON exhibe las propiedades esperadas: se adapta a diversas condiciones como picos en la carga de trabajo o fallos masivos, mateniendo su calidad de servicio y haciendo un uso eficiente de los recusos disponibles. Es altamente escalable y robusto; puedeoo ser implementado en servicios existentes de manera no intrusiva; y no requiere la obtención de un modelo de desempeño para los servicios. Podemos concluir que nuestro trabajo nos ha permitido desarrollar una solucion que aborda los requerimientos de auto-adaptacion en escenarios de uso exigentes sin introducir complejidad adicional. En este sentido, consideramos que nuestra propuesta hace una contribución significativa hacia el desarrollo de la futura generación de aplicaciones orientadas a servicios.

Mohamed, Ibrahim Daoud Ahmed. "Automatic history matching in Bayesian framework for field-scale applications". Texas A&M University, 2004. http://hdl.handle.net/1969.1/3170.

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Conditioning geologic models to production data and assessment of uncertainty is generally done in a Bayesian framework. The current Bayesian approach suffers from three major limitations that make it impractical for field-scale applications. These are: first, the CPU time scaling behavior of the Bayesian inverse problem using the modified Gauss-Newton algorithm with full covariance as regularization behaves quadratically with increasing model size; second, the sensitivity calculation using finite difference as the forward model depends upon the number of model parameters or the number of data points; and third, the high CPU time and memory required for covariance matrix calculation. Different attempts were used to alleviate the third limitation by using analytically-derived stencil, but these are limited to the exponential models only. We propose a fast and robust adaptation of the Bayesian formulation for inverse modeling that overcomes many of the current limitations. First, we use a commercial finite difference simulator, ECLIPSE, as a forward model, which is general and can account for complex physical behavior that dominates most field applications. Second, the production data misfit is represented by a single generalized travel time misfit per well, thus effectively reducing the number of data points into one per well and ensuring the matching of the entire production history. Third, we use both the adjoint method and streamline-based sensitivity method for sensitivity calculations. The adjoint method depends on the number of wells integrated, and generally is of an order of magnitude less than the number of data points or the model parameters. The streamline method is more efficient and faster as it requires only one simulation run per iteration regardless of the number of model parameters or the data points. Fourth, for solving the inverse problem, we utilize an iterative sparse matrix solver, LSQR, along with an approximation of the square root of the inverse of the covariance calculated using a numerically-derived stencil, which is broadly applicable to a wide class of covariance models. Our proposed approach is computationally efficient and, more importantly, the CPU time scales linearly with respect to model size. This makes automatic history matching and uncertainty assessment using a Bayesian framework more feasible for large-scale applications. We demonstrate the power and utility of our approach using synthetic cases and a field example. The field example is from Goldsmith San Andres Unit in West Texas, where we matched 20 years of production history and generated multiple realizations using the Randomized Maximum Likelihood method for uncertainty assessment. Both the adjoint method and the streamline-based sensitivity method are used to illustrate the broad applicability of our approach.

Zafari, Afshin. "Adapting a Radial Basis Functions Framework for Large-Scale Computing". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-182859.

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This work is aimed at extending a parallel computing framework for radial basis functions methods for solving partial differential equations. Existing framework uses Task Based parallelization method in shared memory architectures to run tasks concurrently on multi-core machines using POSIX Threads. In this method, an algorithm is viewed as a set of tasks each of which performs a specific part of that algorithm while reading some data and producing others. All the dependencies between tasks are translated into data dependencies which makes the tasks decoupled. This work uses the same method but for distributed memory systems using message passing scheme of inter-process conversations. These frameworks cooperates with each other for distributing and running the tasks among nodes and/or cores in a hybrid way of multi-threading and message passing parallel programming paradigms. All the communication between processes (nodes) are performed asynchronously (non-blocking) to be overlapped with computations and the execution flow of the framework is implemented using state machine software construct.

Orgerie, Anne-Cécile. "An Energy-Efficient Reservation Framework for Large-Scale Distributed Systems". Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2011. http://tel.archives-ouvertes.fr/tel-00672130.

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Over the past few years, the energy consumption of Information and Communication Technologies (ICT) has become a major issue. Nowadays, ICT accounts for 2% of the global CO2 emissions, an amount similar to that produced by the aviation industry. Large-scale distributed systems (e.g. Grids, Clouds and high-performance networks) are often heavy electricity consumers because -- for high-availability requirements -- their resources are always powered on even when they are not in use. Reservation-based systems guarantee quality of service, allow for respect of user constraints and enable fine-grained resource management. For these reasons, we propose an energy-efficient reservation framework to reduce the electric consumption of distributed systems and dedicated networks. The framework, called ERIDIS, is adapted to three different systems: data centers and grids, cloud environments and dedicated wired networks. By validating each derived infrastructure, we show that significant amounts of energy can be saved using ERIDIS in current and future large-scale distributed systems.

Più fonti

Libri sul tema "SCALP framework":

Block, Sascha. Large-Scale Agile Frameworks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9.

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Block, Sascha. Large-Scale Agile Frameworks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-67782-7.

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Bunjongjit, Naruemol. Small-scale industries and institutional framework in Thailand. Paris: Organisation for Economic Co-operation and Development, 1992.

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Dewey, Allen M. Principles of VLSI system planning: A framework for conceptual design. Boston: Kluwer Academic Publishers, 1990.

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Cruickshank, John. Healthcare without walls: A framework for delivering telehealth at scale. London: 2020health.org, 2010.

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Chen-Becker, Derek. The definitive guide to Lift: A scala-based web framework. Berkeley, CA: Apress, 2009.

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Ikiara, Gerrishon K. Macro policy framework for small-scale industries (SSI) in Kenya. Addis Ababa: International Labour Organization, Eastern Africa Multidisciplinary Advisory Team, 1996.

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North, Brian. The development of a common framework scale of language proficiency. New York: P. Lang, 2000.

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Bhatty, I. Z. Policies for promoting small scale industries: A framework for analysis. Bangkok, Thailand: ILO, ARTEP, 1985.

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Pomeroy, R. S., e Neil Andrew. Small-scale fisheries management: Frameworks and approaches for the developing world. Wallingford, Oxfordshire, UK: CAB International, 2011.

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Capitoli di libri sul tema "SCALP framework":

Carrión, Salvador, Álvaro López-Chilet, Javier Martínez-Bernia, Joan Coll-Alonso, Daniel Chorro-Juan e Jon Ander Gómez. "Automatic Detection of Epileptic Seizures with Recurrent and Convolutional Neural Networks". In Lecture Notes in Computer Science, 522–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_46.

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AbstractComputer-aided diagnosis based on intelligent systems is an effective strategy to improve the efficiency of healthcare systems while reducing their costs. In this work, the epilepsy detection task is approached in two different ways, recurrent and convolutional neural networks, within a patient-specific scheme. Additionally, a detector function and its effects on seizure detection performance are presented. Our results suggest that it is possible to detect seizures from scalp EEGs with acceptable results for some patients, and that the DeepHealth framework is a proper deep learning software for medical research.

Karunakaran, Prem Kumar. "Play Views and Templating with Scala". In Introducing Play Framework, 65–74. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5645-9_4.

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Cho, Chung Yik, Rong Kun Jason Tan, John A. Leong e Amandeep S. Sidhu. "Query Framework". In Large Scale Data Analytics, 27–45. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-03892-2_4.

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Block, Sascha. "Large-Scale Agile Frameworks". In Large-Scale Agile Frameworks, 49–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_3.

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Block, Sascha. "Agiles Priorisierungsmodell für Software-Hersteller". In Large-Scale Agile Frameworks, 179–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_5.

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Block, Sascha. "Datenqualität – Lebenselixier der Digitalisierung". In Large-Scale Agile Frameworks, 311–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_8.

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Block, Sascha. "Digitale Transformation & Agile Priorisierung". In Large-Scale Agile Frameworks, 9–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_2.

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Block, Sascha. "Agile Tools: Werkzeugkasten für Produkt Owner & Agile Teams". In Large-Scale Agile Frameworks, 235–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_7.

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Block, Sascha. "Einleitung". In Large-Scale Agile Frameworks, 3–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_1.

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Block, Sascha. "Herausforderungen bei der Etablierung eines Large-Scale Agile Frameworks im Unternehmen". In Large-Scale Agile Frameworks, 209–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-62048-9_6.

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Atti di convegni sul tema "SCALP framework":

Anandakumar, Mithunjha, Jathurshan Pradeepkumar, Simon L. Kappel, Chamira U. S. Edussooriya e Anjula C. de Silva. "A Knowledge Distillation Framework for Enhancing Ear-EEG Based Sleep Staging with Scalp-EEG Data". In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2023. http://dx.doi.org/10.1109/smc53992.2023.10394011.

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Gambarotta, Luigi, Roberta Massabo e Renata Morbiducci. "Mechanical Characterization of Human Skin From In-Vivo Tests and Simulation of Reconstructive Surgery". In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-43335.

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A comprehensive experimental/numerical procedure is presented for the in vivo characterization of the mechanical properties of human skin and the simulation of reconstructive surgery. The procedure uses in vivo experimental tests, which can be performed during surgery, a numerical model formulated within the framework of nonlinear finite strain elasticity and a nonlinear parameter identification technique for the calibration of the model. The skin is treated as a time independent and hyperelastic membrane and the problem is solved through a finite element discretization. The procedure is applied to characterize the scalp skin tested by Raposio and Nordstro¨m, 1998, and to simulate a simple surgical procedure of excision and closure. The application highlights the validity of the proposed approach.

Georgiev, Dobromir, Emanouil Atanassov e Vassil Alexandrov. "A Framework for Parallel Genetic Algorithms for Distributed Memory Architectures". In 2014 5th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA). IEEE, 2014. http://dx.doi.org/10.1109/scala.2014.13.

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Knutz, Eva. "Scaling experiments in urban space – an exploratory framework". In Nordes 2021: Matters of Scale. Nordes, 2021. http://dx.doi.org/10.21606/nordes.2021.51.

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Rizzardini, Rocael Hernández, e Hector R. Amado-Salvatierra. "Full Engagement Educational Framework". In L@S 2017: Fourth (2017) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3051457.3053969.

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ENOS, RYAN, DIANYUN ZHANG e QINGXUAN WEI. "INTEGRATED OPTIMIZATION FRAMEWORK FOR COMPOSITE MANUFACTURING: MINIMIZING SPRING-IN EFFECTS AND IMPROVING CURE CYCLES". In Proceedings for the American Society for Composites-Thirty Eighth Technical Conference. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/asc38/36621.

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This study presents a structured methodology for the optimization of composite manufacturing processes, focusing specifically on autoclave techniques to mitigate issues such as the spring-in effect. The initial phase employs Sequential Quadratic Programming (SQP) in a weighted-sum approach to optimize the cure cycle, using Radford's equation for spring-in angle estimation and a multi-physics, multi-scale MATLAB model to investigate the cure and temperature-dependent laminate response. This phase underscores the efficacy of the selected optimization algorithm, demonstrating a significant reduction in spring-in while ensuring a high degree-of-cure. Subsequently, the study incorporates an integrated Finite Element Analysis (FEA) optimization framework linking ABAQUS and MATLAB. This framework utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization with an integrated composite manufacturing processing model. This second phase illustrates the framework's robust capabilities in composite cure cycle optimization, providing a well-distributed set of optimal solutions in an efficient timeframe. The study highlights the potential of the approaches and frameworks investigated to improve the efficiency, performance, and quality of composite parts.

Taylor, Ian, Brian Adamson, Ian Downard e Joe Macker. "AgentJ: Enabling Java NS-2 Simulations for Large Scale Distributed Multimedia Applications". In The 2nd International Conference on Distributed Frameworks for Multimedia Applications. IEEE, 2006. http://dx.doi.org/10.1109/dfma.2006.296911.

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Nystrom, Nathaniel. "A Scala framework for supercompilation". In SPLASH '17: Conference on Systems, Programming, Languages, and Applications: Software for Humanity. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3136000.3136011.

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Pestovs, Pavels, Ilze Saleniece e Dace Namsone. "SCIENCE LARGE-SCALE ASSESSMENT ALIGNMENT TO THE REVISED SCIENCE CURRICULUM". In 3rd International Baltic Symposium on Science and Technology Education (BalticSTE2019). Scientia Socialis Ltd., 2019. http://dx.doi.org/10.33225/balticste/2019.162.

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The research compared and assessed 6th grade student large-scale assessment item indicators according to the revised competency-based Science curriculum in Latvia, in order to understand what improvements are needed in the large-scale assessment for aligning the national assessments with the new national curriculum, and thus ensuring successful implementation of the educational reform. The theoretical framework of the research was developed by using the frameworks of the Programme for International Student Assessment (PISA), and cognitive level was measured according to the Structure of Observed Learning Outcomes (SOLO) taxonomy. Keywords: assessment alignment to curriculum, educational reform, scientific literacy.

Dodson, Samuel, Ido Roll, Matthew Fong, Dongwook Yoon, Negar M. Harandi e Sidney Fels. "An active viewing framework for video-based learning". In L@S '18: Fifth (2018) ACM Conference on Learning @ Scale. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3231644.3231682.

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Rapporti di organizzazioni sul tema "SCALP framework":

Smith, Jeremy. Multi-Scale Modeling Framework for Mercury Biogeochemistry. Office of Scientific and Technical Information (OSTI), ottobre 2020. http://dx.doi.org/10.2172/1671770.

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Palsson, Bernhard O., Ali Ebrahim e Steve Federowicz. The OME Framework for genome-scale systems biology. Office of Scientific and Technical Information (OSTI), dicembre 2014. http://dx.doi.org/10.2172/1169326.

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Rovinelli, A., A. Venkataraman e M. Messner. Initial framework for engineering-scale statistical creep-fatigue modeling. Office of Scientific and Technical Information (OSTI), settembre 2021. http://dx.doi.org/10.2172/1825886.

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Shen, Han-Wei. An Information-Theoretic Framework for Enabling Extreme-scale Science Discovery. Office of Scientific and Technical Information (OSTI), novembre 2014. http://dx.doi.org/10.2172/1164477.

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Peterman, Z. Hydrogeologic Framework Model for the Saturated-Zone Site-Scale Flow. Office of Scientific and Technical Information (OSTI), marzo 2003. http://dx.doi.org/10.2172/836529.

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Eardley, P., A. Morton, M. Bagnulo, T. Burbridge, P. Aitken e A. Akhter. A Framework for Large-Scale Measurement of Broadband Performance (LMAP). RFC Editor, settembre 2015. http://dx.doi.org/10.17487/rfc7594.

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Kwasinski, Alexis, Joseph Trainor, Brian Wolshon e Francis M. Lavelle. A Conceptual Framework for Assessing Resilience at the Community Scale. National Institute of Standards and Technology, gennaio 2016. http://dx.doi.org/10.6028/nist.gcr.16-001.

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Rose, Amy, Sam Koebrich, David Palchak, Ilya Chernyakhovskiy e Claire Wagner. A Framework for Readiness Assessments of Utility-Scale Energy Storage. Office of Scientific and Technical Information (OSTI), dicembre 2020. http://dx.doi.org/10.2172/1735627.

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Ackerman, Thomas P. Evaluating and Improving Cloud Processes in the Multi-Scale Modeling Framework. Office of Scientific and Technical Information (OSTI), marzo 2015. http://dx.doi.org/10.2172/1171707.

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Muliana, Anastasia H. A Multi-Scale Framework for Multi-Field Analyses of Smart Composites. Fort Belvoir, VA: Defense Technical Information Center, gennaio 2015. http://dx.doi.org/10.21236/ada612732.

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