7527642
Spectrum Sensing for OFDM Systems Employing Pilot Tones and Application to DVB-T OFDM
Spectrum sensing is one of the core technologies of cognitive radio (CR) systems which provide a viable solution to the problem of sparsity of wireless spectrum. Nowadays, OFDM techniques are adopted by many existing or progressing wireless communication standards. Thus, a robust spectrum sensing algorithm for OFDM modulated signals is highly desired to implement CR when the primary signal uses OFDM modulation. Motivated by this demand, a time-domain symbol cross-correlation based spectrum sensing algorithm (TDSC method) is presented in this paper. The algorithm makes use of the property that the mean of the TDSC of two OFDM symbols is not zero if they have embedded the same frequency-domain pilot tones. The statistical behavior of the proposed spectrum sensor is explicitly analyzed and a theoretical lower bound on the misdetection probability is derived in this paper. An intuitive spectrum sensing method which utilizes the cyclic prefix nature of the OFDM modulated signals (CP method) is also described in this paper as a reference for comparison. Finally, we use the DVB-T Standard [8] as an application example to illustrate the proposed spectrum sensing algorithm. The simulated channel environments are the Ricean and Rayleigh channels defined in the DVB-T Standard and the additive white Gaussian noise (AWGN) channel. Four CP ratios defined in the DVB-T Standard are simulated for the probability of false alarm equaling 0.01 and the sensing time set equal to 50 ms. Simulation results show that the TDSC method can achieve a misdetection probability of 0.1 when the SNR equals -20.5 dB for all four CP ratios. The TDSC method outperforms the CP method in all cases. Furthermore, the proposed method has approximately the same detection performance for different CP ratios while the detection performance of the CP method degrades dramatically when the CP ratio becomes small. Results also reveal that the simulated performance is very close to the theoretical lower bound indicating that the lower bound can be used as a good prediction of performance.
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.
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.
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.
4996602
Optimization of a training set for more robust face detection
The performance of a learning-based method highly depends on the quality of a training set. However, it is very challenging to collect an efficient and effective training set for training a good classifier, because of the high dimensionality of the feature space and the complexity of decision boundaries. In this research, we study the methodology of automatically obtaining an optimal training set for robust face detection by resampling the collected training set. We propose a genetic algorithm (GA) and manifold-based method to resample a given training set for more robust face detection. The motivations behind lie in two folds: (1) dynamic optimization, diversity, and consistency of the training samples are cultivated by the evolutionary nature of GA and (2) the desirable non-linearity of the training set is preserved by using the manifold-based resampling. We demonstrate the effectiveness of the proposed method through experiments and comparisons to other existing face detectors. The system trained from the training set by the proposed method has achieved 90.73% accuracy with no false alarm on MIT+CMU frontal face test set-the best result reported so far to our knowledge. Moreover, as a fully automatic technology, the proposed method can significantly facilitate the preparation of training sets for obtaining well-performed object detection systems in different applications.
207059309
A framework for flexible summarization of racquet sports video using multiple modalities
While most existing sports video research focuses on detecting event from soccer and baseball etc., little work has been contributed to flexible content summarization on racquet sports video, e.g. tennis, table tennis etc. By taking advantages of the periodicity of video shot content and audio keywords in the racquet sports video, we propose a novel flexible video content summarization framework. Our approach combines the structure event detection method with the highlight ranking algorithm. Firstly, unsupervised shot clustering and supervised audio classification are performed to obtain the visual and audio mid-level patterns respectively. Then, a temporal voting scheme for structure event detection is proposed by utilizing the correspondence between audio and video content. Finally, by using the affective features extracted from the detected events, a linear highlight model is adopted to rank the detected events in terms of their exciting degrees. Experimental results show that the proposed approach is effective.
1648244
BLOSSOMS: A CAS/HKUST Joint Project to Build Lightweight Optimized Sensor Systems on a Massive Scale
A joint effort between the Chinese Academy of Sciences and the Hong Kong University of Science and Technology, the BLOSSOMS sensor network project aims to identify research issues at all levels from practical applications down to the design of sensor nodes. In this project, a heterogeneous sensor array including different types of application-dependent sensors as well as monitoring sensors and intruding sensors are being developed. Application-dependent power-aware communication protocols are also being studied for communications among sensor nodes. An ontology-based middleware is built to relieve the burden of application developers from collecting, classifying and processing messy sensing contexts. This project will also develop a set of tools allowing researchers to model, simulate/emulate, analyze, and monitor various functions of sensor networks.
18202569
Face reconstruction using fixation positions and foveated imaging
Face representation is important for face recognition system. Though most popular face representations are based on uniform grid sampling, some recent face recognition systems adopt weighted sampling on the different regions of a face. Psychological analysis of visual attention or human eye fixations on human face images may suggest some cues for face representation. human visual system (HVS) gives different weight to different region of human face via space-variant sampling on fovea and non-uniform distribution of fixations. This paper focuses on the problem of simulation of the foveated imaging phenomenon in HVS, and introduction of foveated imaging method into reconstruction of face in region of interest (ROI) using different fixation sources. We compare the effectiveness of actual fixation on reconstruction of face in ROI with uniform, random distribution fixation as well as fixation generated by artificial model. The experimental results on 100 face images from FRGC [7] data set show that actual fixation positions and model-generated fixation positions reconstruct the face in ROI with considerably better quality. A further analysis on the statistics of fixation positions also shows that the distribution of the fixation points is consistent with the weights of different regions on face images used in some other face recognition systems.
7843945
An adaptive skin color detection algorithm with confusing backgrounds elimination
In this paper a new skin detection method based on adaptive thresholds is proposed. Compared with the fixed threshold histogram method used widely, ours can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram (SPDH) to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A color deviation histogram (CDH) is also proposed to eliminate confusing backgrounds and refine optimal thresholds. The selection process of optimal thresholds is fast thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.