17,232 research outputs found
Robust Object Tracking Based on Self-adaptive Search Area
Discriminative correlation filter (DCF) based trackers have recently achieved
excellent performance with great computational efficiency. However, DCF based
trackers suffer boundary effects, which result in the unstable performance in
challenging situations exhibiting fast motion. In this paper, we propose a
novel method to mitigate this side-effect in DCF based trackers. We change the
search area according to the prediction of target motion. When the object moves
fast, broad search area could alleviate boundary effects and reserve the
probability of locating object. When the object moves slowly, narrow search
area could prevent effect of useless background information and improve
computational efficiency to attain real-time performance. This strategy can
impressively soothe boundary effects in situations exhibiting fast motion and
motion blur, and it can be used in almost all DCF based trackers. The
experiments on OTB benchmark show that the proposed framework improves the
performance compared with the baseline trackers.Comment: 10 pages, 4 figures, 3 tables, SPIE 10th International Symposium on
Multispectral Image Processing and Pattern Recognitio
The (2+1) Dirac Equations with Potential
In this Letter the bound states of (2+1) Dirac equation with the
cylindrically symmetric -potential are discussed. It is
surprisingly found that the relation between the radial functions at two sides
of can be established by an SO(2) transformation. We obtain a
transcendental equation for calculating the energy of the bound state from the
matching condition in the configuration space. The condition for existence of
bound states is determined by the Sturm-Liouville theorem.Comment: Latex 11 pages accepted by Found. Phys. Let
Exact Solutions to the Schr\"{o}dinger Equation for the Inverse-Power Potential in Two Dimensions
Utilizing an for the eigenfunctions, we arrive at an exact
closed form solution to the Schr\"{o}dinger equation with the inverse-power
potential, in two dimensions, where the
parameters of the potential satisfy a constraint.Comment: Latex file 9 pages and submit to Euro. Phys. J.
Exact Solutions to the Schr\"{o}dinger Equation for the potential in 2D
Making use of an for the eigenfunctions, we obtain an exact
closed form solution to the non-relativistic Schr\"{o}dinger equation with the
anharmonic potential, in two dimensions, where
the parameters of the potential satisfy some constraints.Comment: Latex file, pages 9 and 2 eps figures, accepted by J. Phys.
Long-range Effects on the Pyroelectric Coefficient of Ferroelectric Superlattice
Long-range effects on the pyroelectric coefficient of a ferroelectric
superlattice consisting of two different ferroelectric materials are
investigated based on the Transverse Ising Model. The effects of the
interfacial coupling and the thickness of one period on the pyroelectric
coefficient of the ferroelectric superlattice are studied by taking into
account the long-range interaction. It is found that with the increase of the
strength of the long-range interaction, the pyroelectric coefficient decreases
when the temperature is lower than the phase transition temperature; the number
of the pyroelectric peaks decreases gradually and the phase transition
temperature increases. It is also found that with the decrease of the
interfacial coupling and the thickness of one period, the phase transition
temperature and the number of the pyroelectric peaks decrease.Comment: 19 pages, 7 figure
Banzhaf Random Forests
Random forests are a type of ensemble method which makes predictions by
combining the results of several independent trees. However, the theory of
random forests has long been outpaced by their application. In this paper, we
propose a novel random forests algorithm based on cooperative game theory.
Banzhaf power index is employed to evaluate the power of each feature by
traversing possible feature coalitions. Unlike the previously used information
gain rate of information theory, which simply chooses the most informative
feature, the Banzhaf power index can be considered as a metric of the
importance of each feature on the dependency among a group of features. More
importantly, we have proved the consistency of the proposed algorithm, named
Banzhaf random forests (BRF). This theoretical analysis takes a step towards
narrowing the gap between the theory and practice of random forests for
classification problems. Experiments on several UCI benchmark data sets show
that BRF is competitive with state-of-the-art classifiers and dramatically
outperforms previous consistent random forests. Particularly, it is much more
efficient than previous consistent random forests.Comment: arXiv admin note: text overlap with arXiv:1302.4853 by other author
A PCA-Based Convolutional Network
In this paper, we propose a novel unsupervised deep learning model, called
PCA-based Convolutional Network (PCN). The architecture of PCN is composed of
several feature extraction stages and a nonlinear output stage. Particularly,
each feature extraction stage includes two layers: a convolutional layer and a
feature pooling layer. In the convolutional layer, the filter banks are simply
learned by PCA. In the nonlinear output stage, binary hashing is applied. For
the higher convolutional layers, the filter banks are learned from the feature
maps that were obtained in the previous stage. To test PCN, we conducted
extensive experiments on some challenging tasks, including handwritten digits
recognition, face recognition and texture classification. The results show that
PCN performs competitive with or even better than state-of-the-art deep
learning models. More importantly, since there is no back propagation for
supervised finetuning, PCN is much more efficient than existing deep networks.Comment: 8 pages,5 figure
Research on fuzzy PID Shared control method of small brain-controlled uav
Brain-controlled unmanned aerial vehicle (uav) is a uav that can analyze
human brain electrical signals through BCI to obtain flight commands. The
research of brain-controlled uav can promote the integration of brain-computer
and has a broad application prospect. At present, BCI still has some problems,
such as limited recognition accuracy, limited recognition time and small number
of recognition commands in the acquisition of control commands by analyzing eeg
signals. Therefore, the control performance of the quadrotor which is
controlled only by brain is not ideal. Based on the concept of Shared control,
this paper designs an assistant controller using fuzzy PID control, and
realizes the cooperative control between automatic control and brain control.
By evaluating the current flight status and setting the switching rate, the
switching mechanism of automatic control and brain control can be decided to
improve the system control performance. Finally, a rectangular trajectory
tracking control experiment of the same height is designed for small quadrotor
to verify the algorithm
Mildly relativistic X-ray transient 080109 and SN2008D: Towards a continuum from energetic GRB/XRF to ordinary Ibc SN
We analyze the hitherto available space-based X-ray data as well as
ground-based optical data of the X-ray transient 080109/SN2008D. From the data
we suggest that (i) The initial transient (\lesssim 800 sec) is attributed to
the reverse shock emission of a mildly relativistic (\Gamma \sim a few) outflow
stalled by the dense stellar wind. (ii) The subsequent X-ray afterglow
(\lesssim 2\times 10^4 sec) can be ascribed to the forward shock emission of
the outflow, with a kinetic energy \sim 10^{46} erg, when sweeping up the
stellar wind medium. (iii) The late X-ray flattening (\gtrsim 2\times 10^4$
sec) is powered by the fastest non-decelerated component of SN2008D's ejecta.
(iv) The local event rate of X-ray transient has a lower limit of \sim
1.6\times 10^4 yr^{-1} Gpc^{-3}, indicating a vast majority of X-ray transients
have a wide opening angle of \gtrsim 100 degree. The off-axis viewing model is
less likely. (v) Transient 080109/SN2008D may lead to a continuum from GRB-SN
to under-luminous GRB-/XRF-SN to X-ray transient-SN and to ordinary Ibc SN (if
not every Ibc SN has a relativistic jet), as shown in Figure 2 of this Letter.Comment: 4 pages, 2 figure
Shared control schematic for brain controlled vehicle based on fuzzy logic
Brain controlled vehicle refers to the vehicle that obtains control commands
by analyzing the driver's EEG through Brain-Computer Interface (BCI). The
research of brain controlled vehicles can not only promote the integration of
brain machines, but also expand the range of activities and living ability of
the disabled or some people with limited physical activity, so the research of
brain controlled vehicles is of great significance and has broad application
prospects. At present, BCI has some problems such as limited recognition
accuracy, long recognition time and limited number of recognition commands in
the process of analyzing EEG signals to obtain control commands. If only use
the driver's EEG signals to control the vehicle, the control performance is not
ideal. Based on the concept of Shared control, this paper uses the fuzzy
control (FC) to design an auxiliary controller to realize the cooperative
control of automatic control and brain control. Designing a Shared controller
which evaluates the current vehicle status and decides the switching mechanism
between automatic control and brain control to improve the system control
performance. Finally, based on the joint simulation platform of Carsim and
MATLAB, with the simulated brain control signals, the designed experiment
verifies that the control performance of the brain control vehicle can be
improved by adding the auxiliary controller
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