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People count is an important indicator in video surveillance. Due to the overlapping objects and cluttered background, counting people accurately in actual crowded scene remains a non-trivial problem. Existing regression-based methods either learn a single model mapping the global feature to people count, or estimate localized count by training a large number of regressors. In this paper, we present...
An important problem in channel estimation of time-varying channels is how to reduce the influence of the channels time variation on the channel estimation error. And previous works give us some hints in a time-varying Gauss-Markov Rayleigh fading channel by using both the training sequences at the each boundaries of a data sequence to train the channels in-between, while traditionally a training...
In this paper, the effect of finite-resolution analog-to-digital converter (ADC) quantization on ultra-wideband (UWB) time of arrival (TOA) estimation is investigated. The deflection criterion is introduced for optimizing the nonuniform quantization. The training based TOA estimation algorithm using maximum likelihood (ML) rule is proposed. Compared with the full-resolution and energy-detection (ED)...
The solution of multi-output LS-SVR machines follows from solving a set of linear equations. Compared with ε-intensive SVR, it loses the advantage of a sparse decomposition. In order to limit the number of support vectors and reduce the computation cost, this paper presents a decremental recursive algorithm for multi-output LS-SVR machines. This algorithm removes one sample one time and large-scale...
Time-of-arrival (TOA) estimator design for impulse radio ultra-wideband (IR-UWB) ranging demands high sampling rate high resolution ADC, which is difficult to implement. Some tradeoffs can be made such as limiting amplitude resolution. In this paper, we propose a two-step threshold selection TOA estimator using a two-bit ADC. The quantization threshold of ADC is set as the coarse threshold to distinguish...
On the basis of introducing data preprocessing and mining technology, research is developed on clustering and modeling of mining about clinical data (biochemical indicators), to find potential information related with health assessment and disease prediction, and to indicate further research direction. Based on characteristics of clinical data, Sigmoid function is used to preprocess the original data,...
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