4,470 research outputs found
Learning Spatial-Aware Regressions for Visual Tracking
In this paper, we analyze the spatial information of deep features, and
propose two complementary regressions for robust visual tracking. First, we
propose a kernelized ridge regression model wherein the kernel value is defined
as the weighted sum of similarity scores of all pairs of patches between two
samples. We show that this model can be formulated as a neural network and thus
can be efficiently solved. Second, we propose a fully convolutional neural
network with spatially regularized kernels, through which the filter kernel
corresponding to each output channel is forced to focus on a specific region of
the target. Distance transform pooling is further exploited to determine the
effectiveness of each output channel of the convolution layer. The outputs from
the kernelized ridge regression model and the fully convolutional neural
network are combined to obtain the ultimate response. Experimental results on
two benchmark datasets validate the effectiveness of the proposed method.Comment: To appear in CVPR201
Bandwidth efficient multi-station wireless streaming based on complete complementary sequences
Data streaming from multiple base stations to a client is recognized as a robust technique for multimedia streaming. However the resulting transmission in parallel over wireless channels poses serious challenges, especially multiple access interference, multipath fading, noise effects and synchronization. Spread spectrum techniques seem the obvious choice to mitigate these effects, but at the cost of increased bandwidth requirements. This paper proposes a solution that exploits complete complementary spectrum spreading and data compression techniques jointly to resolve the communication challenges whilst ensuring efficient use of spectrum and acceptable bit error rate. Our proposed spreading scheme reduces the required transmission bandwidth by exploiting correlation among information present at multiple base stations. Results obtained show 1.75 Mchip/sec (or 25%) reduction in transmission rate, with only up to 6 dB loss in frequency-selective channel compared to a straightforward solution based solely on complete complementary spectrum spreading
A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems
In contrast to the classical cyclic prefix (CP)-OFDM, the time domain
synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard
interval (GI). Conventional channel estimation methods for TDS-OFDM are based
on the exploitation of the PN sequence and consequently suffer from intersymbol
interference (ISI). This paper proposes a novel dataaided channel estimation
method which combines the channel estimates obtained from the PN sequence and,
most importantly, additional channel estimates extracted from OFDM data
symbols. Data-aided channel estimation is carried out using the rebuilt OFDM
data symbols as virtual training sequences. In contrast to the classical turbo
channel estimation, interleaving and decoding functions are not included in the
feedback loop when rebuilding OFDM data symbols thereby reducing the
complexity. Several improved techniques are proposed to refine the data-aided
channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving
average and Wiener filtering. Finally, the MMSE criteria is used to obtain the
best combination results and an iterative process is proposed to progressively
refine the estimation. Both MSE and BER simulations using specifications of the
DTMB system are carried out to prove the effectiveness of the proposed
algorithm even in very harsh channel conditions such as in the single frequency
network (SFN) case
Decision-Feedback Detection Strategy for Nonlinear Frequency-Division Multiplexing
By exploiting a causality property of the nonlinear Fourier transform, a
novel decision-feedback detection strategy for nonlinear frequency-division
multiplexing (NFDM) systems is introduced. The performance of the proposed
strategy is investigated both by simulations and by theoretical bounds and
approximations, showing that it achieves a considerable performance improvement
compared to previously adopted techniques in terms of Q-factor. The obtained
improvement demonstrates that, by tailoring the detection strategy to the
peculiar properties of the nonlinear Fourier transform, it is possible to boost
the performance of NFDM systems and overcome current limitations imposed by the
use of more conventional detection techniques suitable for the linear regime
Deep Motion Features for Visual Tracking
Robust visual tracking is a challenging computer vision problem, with many
real-world applications. Most existing approaches employ hand-crafted
appearance features, such as HOG or Color Names. Recently, deep RGB features
extracted from convolutional neural networks have been successfully applied for
tracking. Despite their success, these features only capture appearance
information. On the other hand, motion cues provide discriminative and
complementary information that can improve tracking performance. Contrary to
visual tracking, deep motion features have been successfully applied for action
recognition and video classification tasks. Typically, the motion features are
learned by training a CNN on optical flow images extracted from large amounts
of labeled videos.
This paper presents an investigation of the impact of deep motion features in
a tracking-by-detection framework. We further show that hand-crafted, deep RGB,
and deep motion features contain complementary information. To the best of our
knowledge, we are the first to propose fusing appearance information with deep
motion features for visual tracking. Comprehensive experiments clearly suggest
that our fusion approach with deep motion features outperforms standard methods
relying on appearance information alone.Comment: ICPR 2016. Best paper award in the "Computer Vision and Robot Vision"
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Analysis and equalization of data-dependent jitter
Data-dependent jitter limits the bit-error rate (BER) performance of broadband communication systems and aggravates synchronization in phase- and delay-locked loops used for data recovery. A method for calculating the data-dependent jitter in broadband systems from the pulse response is discussed. The impact of jitter on conventional clock and data recovery circuits is studied in the time and frequency domain. The deterministic nature of data-dependent jitter suggests equalization techniques suitable for high-speed circuits. Two equalizer circuit implementations are presented. The first is a SiGe clock and data recovery circuit modified to incorporate a deterministic jitter equalizer. This circuit demonstrates the reduction of jitter in the recovered clock. The second circuit is a MOS implementation of a jitter equalizer with independent control of the rising and falling edge timing. This equalizer demonstrates improvement of the timing margins that achieve 10/sup -12/ BER from 30 to 52 ps at 10 Gb/s
Chaos-based wireless communication resisting multipath effects
This work is supported by NSFC (China) under Grants No. 61401354, No. 61172070, and No. 61502385; by the Innovative Research Team of Shaanxi Province under Grant No. 2013KCT-04; and by Key Basic Research Fund of Shaanxi Province under Grant No. 2016JQ6015.Peer reviewedPublisher PD
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