7918052
Fast Identification of Error-Prone Patterns for LDPC Codes under Message Passing Decoding
Although density evolution accurately predicts the asymptotic performance of LDPC codes, it provides limited information for codes of short or moderate block length. For this reason, combinatorial methods have been employed to analyze the performance of finite-length LDPC codes under message passing decoding. The error floor phenomenon is a key issue of finite-length effects, which is caused largely by poorly connected subgraphs residing in the LPDC codes structure. Because of the unequal noise immunity of different bit levels, error floors become more pronounced for LDPC-coded high-order modulation. Nevertheless, the enumeration of error-prone patterns that lead to error floors has been proven to be NP-hard, which motivates us to find substitute metrics with manageable complexity. Considering trapping sets always comprise loops of low extrinsic message degree (EMD) and the connectivity of a loop with the residual graph can be characterized by its approximate EMD (ACE), in this paper we propose a fast method to identify loops with low ACE for a given code. In light of this, the vulnerable variable nodes residing on low-ACE loops are judiciously mapped to bit levels benefitting from stronger error protection. As a result, the error floor can be significantly improved at the cost of a minor increase of hardware complexity.
6724826
Spectrum Sensing Using Cyclostationary Properties and Application to IEEE 802.22 WRAN
Spectrum sensing in a very low SNR environment (less than -20 dB) is considered in this paper. We make use of the noise rejection property of the cyclostationary spectrum. The sensing algorithms are based on measurement of the cyclic spectrum of the received signals. The statistics of the cyclic spectrum of the stationary white Gaussian process are fully analyzed for three measurement methods of the cyclic spectrum. The application to IEEE 802.22 WRAN is presented and the probability of false alarm is analytically derived. The operating characteristic curves for the sensing algorithms are determined from computer simulations using ATSC A/74 DTV signal captures as a test database.
29804979
Radial basis function interpolation surface on space mesh
16262343
An Effective Mode Decision Scheme in Macroblock-Based PFGS
The progressive fine granularity scalable (PFGS) coding is a promising technique for streaming video applications. The macroblock-based PFGS coding proposes several INTER modes for the enhancement macroblock coding and provides a simple method to determine a coding mode for each macroblock. Because it does not consider the drifting error possibly occurs in decoder, the mode decision method is not effective. In this paper, we present a mode decision scheme to make a trade-off between the low drifting error and high coding efficiency, by adding a middle bit-rate loop in encoder to estimate the possible drifting error in decoder and using an R-D model to estimate the improvement of coding efficiency by selecting high quality reference. Then a nearly optimal coding mode for each enhancement macroblock can be easily determined. The experimental results show that the proposed scheme can effectively reduce the drifting error at low bit rates, while maintaining improved coding efficiency at high bit rates
9141277
Pedestrian detection via logistic multiple instance boosting
Pedestrian detection in still image should handle the large appearance and pose variations arising from the articulated structure and various clothing of human bodies as well as view points. So it is difficult to design effective classifier for this problem. In this paper, we address these variations in detection via multiple instance learning, specifically logistic multiple instance boosting (LMIB). In LMIB, a example is represented as a set of instances, which implicitly encode the variations. Giving different confidence to the instances in a bag, the LMIB will automatically reduce the influence of the variations at training stage. To obtain rapid detection speed, the LMIBs are grouped into the cascaded structure. The proposed detection algorithm is tested on MIT and NRIA human datasets where promising detection results are comparable with the baseline algorithms.
12725555
FGS Coding Using Cycle-Based Leaky Prediction Through Multiple Leaky Factors
This paper proposes a fine granularity scalable (FGS) coding using cycle-based leaky prediction, in which the multiple leaky factors are used to yield enhancement layer prediction to make a good compromise between coding efficiency and drift error. In this proposed method, first, the error propagation for leaky prediction with two leaky factors is theoretically analyzed in case only the base-layer bitstream and part of the enhancement-layer bitstream are available at the decoder. Based on this analysis, in this paper, we investigate how to effectively introduce enhancement-layer information into the prediction loop for enhancement-layer coding by the proper leaky factors to constrain drift error while keeping high coding efficiency. Furthermore, a coefficient scaling approach in the transform domain is proposed to address the decoding complexity issue for multiple reconstructions of partial enhancement layers at different quality levels. Finally, an encoder optimization approach is presented to further control drift error for multiple FGS layers coding. The experimental results show that compared to AR-FGS in JSVM, the proposed method can significantly improve the coding performance over a wide range of bitrates.
8467256
Illumination Invariant Shot Boundary Detection
Illumination variation poses a serious problem in video shot detection. It causes false cuts in many shot detection algorithms. A new illumination invariant measure metric is proposed in this paper. The metric is based on the assumption: the outputs of derivative filters to log-illumination are sparse. Thus the outputs of derivative filters to log-image are mainly caused by the scene itself. If the total output is larger than a threshold, it can be declared as a scene change or a shot boundary. Although this metric can detect gradual transitions as well as cuts, it is applied as a post-process procedure for a cut candidate because an illumination change is usually declared as a false cut.