8940979
Signature Based Spectrum Sensing Algorithms for IEEE 802.22 WRAN
Spectral sensing for the presence of ATSC DTV signals in VHF/UHF TV bands under a very low SNR environment is one of the core technologies in IEEE 802.22 WRAN. There are multiple signatures embedded in the ATSC DTV signals and these signatures can be utilized to perform spectrum sensing. Signature-based spectrum sensing algorithms have an advantage in that when the signals other than noise are detected, we are almost sure that they are signals we want to detect. Thus, in this paper, several signature based spectrum sensing algorithms are presented. The false alarm rate for detectors employing different algorithms is analytically derived. The operating characteristic curves for different detectors are determined from computer simulations using ATSC A/74 DTV signal captures as a test database.
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.
13943079
Visual Sign Language Recognition Based on HMMs and Auto-regressive HMMs
A sign language recognition system based on Hidden Markov Models(HMMs) and Auto-regressive Hidden Markov Models(ARHMMs) has been proposed in this paper. ARHMMs fully consider the observation relationship and are helpful to discriminate signs which don't have obvious state transitions while similar in motion trajectory. ARHMM which models the observation by mixture conditional linear Gaussian is proposed for sign language recognition. The corresponding training and recognition algorithms for ARHMM are also developed. A hybrid structure to combine ARHMMs with HMMs based on the trick of using an ambiguous word set is presented and the advantages of both models are revealed in such a frame work.
15646488
Randomized Parallel Scheduling Algorithm for Input Queued Crossbar Switches
A significant research effort has been devoted in recent years to the design of simple and efficient scheduling algorithms for input-queued packet switches. Among these algorithms, scheduling policies based on maximum weight matching (MWM) were proved to achieve 100% throughput under any admissible traffic load. However, MWM is impractical for its high computational complexity O(N3). In this paper, we propose a new approximate algorithm to MWM using local search technique. Notably we observe that: (a) Local search is well suitable for parallel computation, (b) Currently each line card of high performance router has at least one processor. Based on the two important observations, a randomized parallel scheduling algorithm is proposed. It is proved to be rate stable and simulation results show that the proposed algorithm outperforms other randomized approximations to MWM
2847976
Improved error concealment algorithms based on H.264/AVC non-normative decoder
We propose several improved error concealment (EC) algorithms based on the H.264/AVC non-normative decoder. The major differences are that motion compensated EC is introduced for intra frames, whereas spatial EC is introduced for inter frames. As for the EC of intra frames, scene change detection, motion activity detection and MV retrieval are hierarchically performed to decide whether spatial or temporal information is to be used. As for the EC of inter frames, scene change is also detected to avoid merging the scenes from different video shots. Therefore, the main idea of the proposed algorithms is that both spatial and temporal correlations are utilized for the EC of intra and inter frames. Both subjective and objective simulations under Internet conditions show that the proposed algorithm greatly outperforms that in the H.264/AVC non-normative decoder.
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.
10048083
Are Gabor phases really useless for face recognition?
Gabor features have been recognized as one of the best representations for face recognition. Usually, only the magnitudes of the Gabor coefficients are thought of as being useful for face recognition, while the phases of the Gabor features are deemed to be useless and thus usually ignored by face recognition researchers. However, in this paper, our findings show that the latter should be reconsidered. By encoding Gabor phases through local binary patterns and local histograms, we have achieved very impressive recognition results, which are comparable to those of Gabor magnitudes-based methods. The results of our experiments also indicate that, by combining the phases with the magnitudes, higher accuracy can be achieved. Such observations suggest that more attention should be paid to the Gabor phases for face recognition.