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With high real-time performance and a good filtering effect, the wavelet transform-based real-time filter is more commonly used in the fusion magnet power supply. Considering the signal characteristics of the fusion magnet power supply, the sliding time window method is widely used to satisfy the requirements of filtering efficiency. In this paper, an updated half-edge extension method is proposed to improve the boundary effect of sliding window data. Moreover, the hardware-based wavelet loopback structure is proposed to avoid the double computation of long sliding window data, thereby improving real-time performance and satisfying the demands of fusion magnet power supply filtering. The proposed half-edge extension method and the wavelet loopback structure are implemented by designed FPGA and experimented on the Experimental and Advanced Superconducting Tokamak (EAST) fusion magnet power supply system to verify the filtering efficiency. According to the experiment’s results, the proposed half-edge extension method has a better filtering effect, and the proposed wavelet loopback structure decreases the time by 67.4%, which can satisfy the requirements of the power supply system.

The ambisonics format is a powerful audio tool de-signed for spatial encoding of the pressure field. Anunder-exploited feature of this format is that it can bedirectly extracted from virtual acoustics simulations.Finite Difference Time Domain (FDTD) simulations areparticularly adapted as they simplify greatly the prob-lem of extracting spatially-encoded signals, and enablereal-time processing of the simulated pressure field. Inthis short contribution, we first write a time domainrepresentation of the ambisonics channels, in terms ofspatial derivatives of the acoustic field at the receiver lo-cation, and formulated as a set of ordinary differentialequations. We show that in general, the natural cor-responding discrete recursive integration yields a pro-hibitive polynomial drift in time. We then describe areal-time filtering strategy which stabilizes this numer-ical integration; in the discrete-time setting of FDTDsimulations, this real-time filtering process featuresvery low computational costs, avoiding the latency as-sociated with large convolutions and frequency-domainblock processing of previous approaches.

International audience; The ambisonics format is a powerful audio tool designed for spatial encoding of the pressure field. An under-exploited feature of this format is that it can be directly extracted from virtual acoustics simulations. Finite Difference Time Domain (FDTD) simulations are particularly adapted as they simplify greatly the problem of extracting spatially-encoded signals, and enable real-time processing of the simulated pressure field. In this short contribution, we first write a time domain representation of the ambisonics channels, in terms of spatial derivatives of the acoustic field at the receiver location, and formulated as a set of ordinary differential equations. We show that in general, the natural corresponding discrete recursive integration yields a prohibitive polynomial drift in time. We then describe a real-time filtering strategy which stabilizes this numerical integration; in the discrete-time setting of FDTD simulations, this real-time filtering process features very low computational costs, avoiding the latency associated with large convolutions and frequency-domain block processing of previous approaches.

National audience; The study of contextual features has been widely discussed in Information Retrieval (IR), but concrete applications on real data streams are not common. In this paper, we aim at doing retweet recommandation. Considering a user interest, we introduce a model to perform real-time online filtering of the Twitter stream using several contextual features. The model separates content and contextual aspects, achieving a very high velocity. Experiments were performed on the TREC Microblog 2015 and TREC 2016 Real-Time Summarization frameworks. Results show that our model is very efficient as well as effective on the 2015 collection. However, the results regarding effectiveness have not been confirmed on the 2016 framework. This led us to conduct a detailed analysis of the results with regard to our approach and the evaluation framework. This analysis showed an evaluation bias discussed at the end of the article.; L'étude des caractéristiques contextuelles a été largement traitée en Recherche d'Information (RI), mais les applications concrètes sur de vrais flux de données ne sont pas très répandues. Dans cet article, notre problématique concerne la décision automatique de retweeter un message. En considérant le centre d'intérêt d'un utilisateur, nous proposons un modèle pour effectuer un filtrage automatique en temps-réel du flux Twitter en utilisant de multiples caractéristiques contextuelles. Le modèle sépare l'aspect contextuel du contenu du message en lui-même, tout en conservant une très grande vitesse d'exécution. Notre modèle a été évalué dans le cadre des tâches TREC Microblog 2015 et TREC Real-Time Summarization 2016. Les résultats montrent la grande efficience (temps de retweet) de notre modèle, et son efficacité sur les mesures de 2015. Ces résultats en termes d'efficacité n'ont cependant pas été confirmés sur 2016. Ceci nous a conduit à une analyse plus en détail des résultats (approche et cadre d'évaluation). Cette analyse a notamment montré un biais dans l'évaluation, biais que nous discutons à la fin de l'article.

Twitter is widely perceived as a potential source of valuable information for responders to mass emergencies. Despite interest in the development of extraction systems for such information, little effort has been put towards systemic methods for obtaining all posts pertaining to a disaster from the live Twitter stream. Researchers rely on keyword-based filters to extract information in spite of evidence that such markers are absent in many informational tweets, and also neglect the topic and traffic dynamics of the relevant tweets as crises progress. Previous work has shown that such practices can often lead to the loss of critical information in the context of a disaster. We introduce an adaptive filter, tailored to the idiosyncrasies of the real-time Twitter feed, intended to extract disaster-related content. Furthermore, we introduce a novel data model based on a three-label classification scheme to describe the composition of the data-stream. We use this model to simulate Twitter streams, modelling various post-disaster scenarios, for the purpose of filter performance evaluation. The filter is able to remove over 85% of the non-crisis content, and achieves a three-fold reduction in the loss of relevant contents compared to the existing approaches. In combination, the method and the model are useful tools for extracting situational awareness and highlight important directions for future work in this area.

Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applica- tions of real-time filtering of great practical interest. We propose the use of Entity Linking (EL) in order to improve the retrieval effectiveness, by enriching the representation of microblog posts and filtering queries. EL is the process of recognizing in an unstructured text the mention of relevant entities described in a knowledge base. EL of short pieces of text is a difficult task, but it is also a scenario in which the information EL adds to the text can have a substantial impact on the retrieval process. We implement a start-of-the-art filtering method, based on the best systems from the TREC Microblog track realtime adhoc retrieval and filtering tasks , and extend it with a Wikipedia-based EL method. Results show that the use of EL significantly improves over non-EL based versions of the filtering methods., 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 - 17, 2015

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.

The Kalman filter (KF) is an extremely powerful and versatile tool for signal processing that has been applied extensively in various fields. We introduce a novel Kalman-based analysis procedure that encompasses robustness towards outliers, Kalman smoothing and real-time conversion from non-uniformly sampled inputs to a constant output rate. These features have been mostly treated independently, so that not all of their benefits could be exploited at the same time. Here, we present a coherent analysis procedure that combines the aforementioned features and their benefits. To facilitate utilization of the proposed methodology and to ensure optimal performance, we also introduce a procedure to calculate all necessary parameters. Thereby, we substantially expand the versatility of one of the most widely-used filtering approaches, taking full advantage of its most prevalent extensions. The applicability and superior performance of the proposed methods are demonstrated using simulated and real data. The possible areas of applications for the presented analysis procedure range from movement analysis over medical imaging, brain-computer interfaces to robot navigation or meteorological studies.

In this work we present a new algorithm for accelerating the colour bilateral filter based on a subsampling strategy working in the spatial domain. The base idea is to use a suitable subset of samples of the entire kernel in order to obtain a good estimation of the exact filter values. The main advantages of the proposed approach are that it has an excellent trade-off between visual quality and speed-up, a very low memory overhead is required and it is straightforward to implement on the GPU allowing real-time filtering. We show different applications of the proposed filter, in particular efficient cross-bilateral filtering, real-time edge-aware image editing and fast video denoising. We compare our method against the state of the art in terms of image quality, time performance and memory usage. © 2012 Wiley Periodicals, Inc.

erworben im Rahmen der Schweizer Nationallizenzen (www.nationallizenzen.ch), Typically, model misspecification is addressed by statistics relying on model-residuals, i.e., on one-step ahead forecasting errors. In practice, however, users are often also interested in problems involving multi-step ahead forecasting performances, which are not explicitly addressed by traditional diagnostics. In this article, we consider the topic of misspecification from the perspective of signal extraction. More precisely, we emphasize the connection between models and real-time (concurrent) filter performances by analyzing revision errors instead of one-step ahead forecasting errors. In applications, real-time filters are important for computing trends, for performing seasonal adjustment or for inferring turning-points towards the current boundary of time series. Since revision errors of real-time filters generally rely on particular linear combinations of one- and multi-step ahead forecasts, we here address a generalization of traditional diagnostics. Formally, a hypothesis testing paradigm for the empirical revision measure is developed through theoretical calculations of the asymptotic distribution under the null hypothesis, and the method is assessed through real data studies as well as simulations. In particular, we analyze the effect of model misspecification with respect to unit roots, which are likely to determine multi-step ahead forecasting performances. We also show that this framework can be extended to general forecasting problems by defining suitable artificial signals.

The purpose of this study was the development of a real-time filtering procedure of MRI artifacts in order to monitor the EEG activity during continuous EEG/fMRI acquisition. The development of a combined EEG and fMRI technique has increased in the past few years. Preliminary "spike-triggered" applications have been possible because in this method, EEG knowledge was only necessary to identify a trigger signal to start a delayed fMRI acquisition. In this way, the two methods were used together but in an interleaved manner. In real simultaneous applications, like event-related fMRI study, artifacts induced by MRI events on EEG traces represent a substantial obstacle for a right analysis. Up until now, the methods proposed to solve this problem are mainly based on procedures to remove post-processing artifacts without the possibility to control electrophysiological behavior of the patient during fMRI scan. Moreover, these methods are not characterized by a strong "prior knowledge" of the artifact, which is an imperative condition to avoid any loss of information on the physiological signals recovered after filtering. In this work, we present a new method to perform simultaneous EEG/fMRI study with real-time artifacts filtering characterized by a procedure based on a preliminary analytical study of EPI sequence parameters-related EEG-artifact shapes. Standard EEG equipment was modified in order to work properly during ultra-fast MRI acquisitions. Changes included: high-performance acquisition device; electrodes/cap/wires/cables materials and geometric design; shielding box for EEG signal receiver; optical fiber link; and software. The effects of the RF pulse and time-varying magnetic fields were minimized by using a correct head cap wires-locked environment montage and then removed during EEG/fMRI acquisition with a subtraction algorithm that takes in account the most significant EPI sequence parameters. The on-line method also allows a further post-processing utilization.