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Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background clutter and uncontrolled viewing conditions, and the number of labelled training data is small. In this work, we formulate a Joint Recurrent Learning (JRL) model for exploring attribute context and correlation in order to...
Signal strength difference (SSD) is widely utilized as the feature for Wi-Fi fingerprint localization to tackle the heterogeneity between training device and target device, but the correlation between SSDs is largely ignored. In this paper, a novel scheme named LC-KDE is proposed. It utilizes local Fisher discriminant analysis (LFDA) to transform the original SSDs into weakly correlated features,...
As traditional spectrum sensing approaches unable to deal with the contradiction between detection accuracy and complexity in cognitive radio network, a novel q-weighed sequential cooperative energy detection method for spectrum sensing in time varying channel is proposed in this paper to achieve better performance with lower complexity. By adding the q- weighted log likelihood ratio (LLR) of the...
In this paper, we address the problem of heavy occlusion where the negative samples contaminate the translation model. In this setting, we decompose the task of tracking into translation and scale estimations of objects. We use hierarchical convolutional features to estimate target position and update translation model, and we use HOG features for the scale filter. In addition, we evaluate the translation's...
This paper concerns on inefficiency or even failure in secret key generation caused by the imperfect channel state information. We propose a secret key generation scheme based on wavelet analysis. Firstly, the channel estimates are pre-processed by wavelet analysis to improve the correlation. Secondly, to ensure the randomness of the secret keys, an adaptive equal probability quantization approach...
Synthetic Aperture Radar (SAR) imaging can suffer distortion in the presence of phase errors in the acquired signal data caused by the non-ideal platform motion trajectory. Autofocus algorithms are used to remove the undesired phase error through signal processing techniques. Multi-channel convolution model of the SAR autofocus problem is established on the condition that phase error is range-independent...
This paper presents the latest development of NSSC's integrated GNSS remote sensing instrument. This new instrument is flexible designed for a variety of GNSS science needs based on the successful GNOS receiver, but with more functional modules. The goal of this instrument is to be put onboard of future Chinese space-borne platforms to provide precise position and timing information for high-demand...
Modern industrial cameras mainly use the Bayer pattern as color filter array (CFA). However, this filtering limits the resolution of the color space. As interpolation methods cannot reconstruct the original image perfectly, they have to be optimized for a specific application. Therefore, the interpolation should match the purpose of the image processing system. Many standard algorithms are optimized...
Pump-probe spectroscopic data on KxFe2−ySe2 superconductors exhibit signatures of orbital-selective Mott transition with a significant enhancement of a slow decay component alongside a decrease in oscillatory coherent-phonon signals upon raising temperature to 160 K.
To remedy such defects as low reliability and unsatisfied accuracy of fault location by traveling wave correlation algorithm (TWCA), a single terminal traveling wave fault location based on fault location algorithm integrating MMG with correlation function is proposed. The surges caused by traveling wave are separated by a new type of MMG transform. Traveling wave surges are held to possess accurate...
Over the past few decades, numerous linear discriminant analysis based extensions are proposed for dimensionality reduction. However, most of them are developed intuitively according to specific motivations by employing various techniques. Therefore, it will be instructive to provide a unified discriminant analysis model for exploring the commonalities and differences. In this paper, we propose a...
An objective of blind source separation (BSS) is to recover potential source signals from their mixtures without a prior knowledge of the mixing process. In this paper, a new underdetermined blind source separation (UDBSS) approach, based on the local mean decomposition (LMD) method and the AMUSE algorithm, is proposed. To make the UDBSS problem simpler, some extra observation signals are first constructed...
Adaptive noise cancellation technology has been widely applied in all fields. The techniques are ideally suited for reducing spatially varying noise. For some fields noise is generally uncorrelated, in contrast to the useful signal. Adaptive filtering algorithms exploit the correlation properties of signals to enhance the signal-to-noise ratio of the output signal. However, in the case with few prior...
Electronic devices are nowadays an integral part of our everyday lives. The number of discarded electronical items has grown significantly over the last years. As the amount of precious materials used in the manufacturing of these devices has increased over the last years recycling of these devices is becoming more and more important. Currently the processes to regain some of these precious materials...
Time series analysis is to explain correlation and the main features of the data in chronological order by using appropriate statistical models. Since the past electricity generated sequence in China shows a strong seasonal variations and several values for January are lost in recent years, estimating the missing values is an important task before building a model. This paper will estimate the missing...
It has been proven that the batch renewal process is the least biased choice of traffic process given the infinite sets of measures of the traffic correlation, (i.e. indices of dispersion, covariances or correlation functions) in the discrete time domain. The same conclusion is expected to hold in the continuous time domain. That motivates the study and comparison of similar queues, fed by batch renewal...
This paper proposes a underwater acoustic coherent chaotic communication scheme which is suitable for shallow water channel mainly at 80bps rate. The encoding uses a decoupling mode, and the modulated signal doesn't contain common binary information. Orthogonal basis signals are engendered directly by Gaussian white noise carriers. The tolerance of time synchronization and the ability of resistance...
Based on wind speed sequences, three-layer neural network model of wind speed prediction is analyzed to obtain the selecting method of neural network input, output and hidden layers' node parameters, and to predict wind speed through rolling wind speed data. In accord with the nonlinear of wind speed sequences, a BP neural network model is established to forecast wind speed. The feasibility and validity...
Nowadays, topic detection and tracking (TDT) has been widely used. As one research tasks for TDT, new event detection can provide prior knowledge to TDT, so it has great theoretical research significance in the field of TDT. Because LDA model cannot automatically identify new events, and the number of LDA topic had been determined by the artificial, or by repeated experiments, it has low efficiency...
The generic aim of this paper is to study correlated traffic flows, within the continuous time domain, by employing batch Markovian arrival processes (BMAP). The correlations for count and interval are expressed by indices of dispersion. The expressions of correlation can be used to investigate the impact of correlation upon traffic process and queueing models.
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