A Dempster-Shafer Method for Multi-Sensor Fusion

The Dempster-Shafer Theory, a generalization of the Bayesian theory, is based on the idea of belief and as such can handle ignorance. When all of the required information is available, many data fusion methods provide a solid approach. Yet, most do not have a good way of dealing with ignorance. In the absence of information, these methods must then make assumptions about the sensor data. However, the real data may not fit well within the assumed model. Consequently, the results are often unsatisfactory and inconsistent. The Dempster-Shafer Theory is not hindered by incomplete models or by the lack of prior information. Evidence is assigned based solely on what is known, and nothing is assumed. Hence, it can provide a fast and accurate means for multi-sensor fusion with ignorance. In this research, we apply the Dempster-Shafer Theory in target tracking and in gait analysis. We also discuss the Dempster-Shafer framework for fusing data from a Global Positioning System (GPS) and an Inertial Measurement Unit (IMU) sensor unit for precise local navigation. Within this application, we present solutions where GPS outages occur

Feature-Fused SSD: Fast Detection for Small Objects

Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCALVOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some smallobjects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS. Code is available at https://github.com/wnzhyee/Feature-Fused-SSD. Keywords: small object detection, feature fusion, real-time, single shot multi-box detectorComment: Artificial Intelligence;8 pages,8 figure

Gaussian process tomography for soft x-ray spectroscopy at WEST without equilibrium information

International audienceGaussian process tomography (GPT) is a recently developed tomography method based on the Bayesian probability theory [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and Li et al., Rev. Sci. Instrum. 84, 083506 (2013)]. By modeling the soft X-ray (SXR) emissivity field in a poloidal cross section as a Gaussian process, the Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time reconstructions with a view to impurity transport and fast magnetohydrodynamic control. In addition, the Bayesian formalism allows quantifying uncertainty on the inferred parameters. In this paper, the GPT technique is validated using a synthetic data set expected from the WEST tokamak, and the results are shown of its application to the reconstruction of SXR emissivity profiles measured on Tore Supra. The method is compared with the standard algorithm based on minimization of the Fisher information

Time-Domain Data Fusion Using Weighted Evidence and Dempster–Shafer Combination Rule: Application in Object Classification

To apply data fusion in time-domain based on Dempster–Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes—smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts)

Feature fusion reveals slow and fast visual memories

Although the visual system can achieve a coarse classification of its inputs in a relatively short time, the synthesis of qualia-rich and detailed percepts can take substantially more time. If these prolonged computations were to take place in a retinotopic space, moving objects would generate extensive smear. However, under normal viewing conditions, moving objects appear relatively sharp and clear, suggesting that a substantial part of visual short-term memory takes place at a nonretinotopic locus. By using a retinotopic feature fusion and a nonretinotopic feature attribution paradigm, we provide evidence for a relatively fast retinotopic buffer and a substantially slower nonretinotopic memory. We present a simple model that can account for the dynamics of these complementary memory processes. Taken together, our results indicate that the visual system can accomplish temporal integration of information while avoiding smear by breaking off sensory memory into fast and slow components that are implemented in retinotopic and nonretinotopic loci, respectively

Minority and mode conversion heating in (3He)-H JET plasma

Radio frequency (RF) heating experiments have recently been conducted in JET (He-3)-H plasmas. This type of plasmas will be used in ITER's non-activated operation phase. Whereas a companion paper in this same PPCF issue will discuss the RF heating scenario's at half the nominal magnetic field, this paper documents the heating performance in (He-3)-H plasmas at full field, with fundamental cyclotron heating of He-3 as the only possible ion heating scheme in view of the foreseen ITER antenna frequency bandwidth. Dominant electron heating with global heating efficiencies between 30% and 70% depending on the He-3 concentration were observed and mode conversion (MC) heating proved to be as efficient as He-3 minority heating. The unwanted presence of both He-4 and D in the discharges gave rise to 2 MC layers rather than a single one. This together with the fact that the location of the high-field side fast wave (FW) cutoff is a sensitive function of the parallel wave number and that one of the locations of the wave confluences critically depends on the He-3 concentration made the interpretation of the results, although more complex, very interesting: three regimes could be distinguished as a function of X[He-3]: (i) a regime at low concentration (X[He-3] < 1.8%) at which ion cyclotron resonance frequency (ICRF) heating is efficient, (ii) a regime at intermediate concentrations (1.8 < X[He-3] < 5%) in which the RF performance is degrading and ultimately becoming very poor, and finally (iii) a good heating regime at He-3 concentrations beyond 6%. In this latter regime, the heating efficiency did not critically depend on the actual concentration while at lower concentrations (X[He-3] < 4%) a bigger excursion in heating efficiency is observed and the estimates differ somewhat from shot to shot, also depending on whether local or global signals are chosen for the analysis. The different dynamics at the various concentrations can be traced back to the presence of 2 MC layers and their associated FW cutoffs residing inside the plasma at low He-3 concentration. One of these layers is approaching and crossing the low-field side plasma edge when 1.8 < X[He-3] < 5%. Adopting a minimization procedure to correlate the MC positions with the plasma composition reveals that the different behaviors observed are due to contamination of the plasma. Wave modeling not only supports this interpretation but also shows that moderate concentrations of D-like species significantly alter the overall wave behavior in He-3-H plasmas. Whereas numerical modeling yields quantitative information on the heating efficiency, analytical work gives a good description of the dominant underlying wave interaction physics

Self-Organizing Information Fusion and Hierarchical Knowledge Discovery: A New Framework Using Artmap Neural Networks

Classifying novel terrain or objects from sparse, complex data may require the resolution of conflicting information from sensors woring at different times, locations, and scales, and from sources with different goals and situations. Information fusion methods can help resolve inconsistencies, as when eveidence variously suggests that and object's class is car, truck, or airplane. The methods described her address a complementary problem, supposing that information from sensors and experts is reliable though inconsistent, as when evidence suggests that an object's class is car, vehicle, and man-made. Underlying relationships among classes are assumed to be unknown to the autonomated system or the human user. The ARTMAP information fusion system uses distributed code representations that exploit the neural network's capacity for one-to-many learning in order to produce self-organizing expert systems that discover hierachical knowlege structures. The fusion system infers multi-level relationships among groups of output classes, without any supervised labeling of these relationships. The procedure is illustrated with two image examples, but is not limited to image domain.Air Force Office of Scientific Research (F49620-01-1-0423); National Geospatial-Intelligence Agency (NMA 201-01-1-2016, NMA 501-03-1-2030); National Science Foundation (SBE-0354378, DGE-0221680); Office of Naval Research (N00014-01-1-0624); Department of Homeland Securit