Distance-aware quality adaptation is a potential approach to reduce the resource requirement for the transmission and rendering of textured 3D meshes. In this paper, we carry out a subjective experiment to investigate the effects of the distance from the camera on the perceptual quality of textured 3D meshes. Besides, we evaluate the effectiveness of eight image-based objective quality metrics in representing the user's perceptual quality. Our study found that the perceptual quality in terms of mean opinion score increases as the distance from the camera increases. In addition, it is shown that normalized mutual information (NMI), a full-reference objective quality metric, is highly correlated with subjective scores.

The abundance of information published on the Internet makes filtering of hazardous Web pages a difficult yet important task. Supervised learning methods such as Support Vector Machines (SVMs) can be used to identify hazardous Web content. However, scalability is a big challenge, especially if we have to train multiple classifiers, since different policies exist on what kind of information is hazardous. We therefore propose two different strategies to train multiple SVMs for personalized Web content filters. The first strategy identifies common data clusters and then performs optimization on these clusters in order to obtain good initial solutions for individual problems. This initialization shortens the path to the optimal solutions and reduces the training time on individual training sets. The second approach is to train all SVMs simultaneously. We introduce an SMO-based kernel-biased heuristic that balances the reduction rate of individual objective functions and the computational cost of kernel matrix. The heuristic primarily relies on the optimality conditions of all optimization problems and secondly on the pre-calculated part of the whole kernel matrix. This strategy increases the amount of information sharing among learning tasks, thus reduces the number of kernel calculation and training time. In our experiments on inconsistently labeled training examples, both strategies were able to predict hazardous Web pages accurately (> 91%) with a training time of only 26% and 18% compared to that of the normal sequential training.

A RFID group scanning protocol enables a RFID reader to produce a proof of co-existence of multiple RFID tags. This type of protocol is also referred to as yoking-proof, grouping-proof and co-existence proof. In this letter, we show that all of the previous group scanning protocols are vulnerable to relay attack.

This paper proposes a new channel estimation method and a new interference cancellation scheme for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in the presence of intersymbol interference (ISI). The proposed channel estimation method uses special training sequences (TSs) to have a desirable crest-factor of the transmitted training signal, and to prevent the influence of ISI on the channel estimation performance. By using the recommended training sequences, the ill-conditioned problem of the least square (LS) filter integrated in the proposed channel estimator can be avoided. The proposed interference cancellation scheme uses the estimated channel coefficients and the channel state information (CSI) to reproduce the interference components, which are then iteratively cancelled from the received signals. To reduced the error-floor of the demodulated symbols using for the calculations of the interference components, the so-called remodulation technique is also included in the proposed interference cancellation scheme. Simulation results show that the proposed channel estimation method outperforms conventional channel estimation methods, especially in the presence of ISI and if the signal-to-noise ratio (SNR) is larger than 15 dB. The combination of the proposed method with a space-time block code (STBC) to combat the interference influences results in an excellent system performance in terms of symbol error ratio (SER). In comparison with a STBC MIMO-OFDM system with sufficient guard interval (GI), this combination gains 1.52 dB of SNR at the same SER of 1.110-6 even after performing only one iteration of interference cancellation.

In this paper, a novel decentralised dynamic sub-carrier assignment (DSA) algorithm for orthogonal frequency division multiple access (OFDMA)-based adhoc and cellular networks operating in time division duplexing (TDD) mode is proposed to solve the hidden and exposed node problem in media access control (MAC). This method reduces the co-channel interference (CCI), and thus increases the overall throughput of the network. Reduced CCI and increased throughput can be achieved, if time and frequency selectivity of the multi-path fading channel and the channel reciprocity offered by the TDD are fully exploited. The time and frequency selectivity of the channel are usually the main problem in mobile communication. However, in the context of channel assignment for OFDMA-based networks in TDD mode, the time and frequency selectivity of the channel are the key to reduce the interference. In the proposed channel assignment mechanism, several clusters of sub-carriers are assigned for data transmission between a transmitter and a receiver only if the corresponding channels of those sub-carriers linking this transmitter to potential victim receivers are deeply faded. In addition, the proposed algorithm works in a fully decentralised fashion and, therefore, it is able to effectively support ad hoc and multihop communication as well as network self-organisation. Numerical results show that the throughput obtained by the proposed approach for a given quality of service is higher than those of the conventional methods in any precondition of adhoc geographic scenario.

Recently, lattice reduction aided (LRA) detectors have been introduced into Vertical Bell-Labs Layered Space-Time (V-BLAST) systems to obtain nearly optimal bit error rate (BER) performance for only small additional complexity. In this paper, the layer error characteristics of LRA-V-BLAST detectors are investigated and compared with those of conventional V-BLAST ones. Two important conclusions are drawn for the LRA-V-BLAST detectors. First, the variation of their mean square error (MSE) within each detection iteration is not as large as in conventional V-BLAST detectors. Second, thanks to lattice reduction there exists an inherent sub-optimal detection order from the last to the first layer. These conclusions allow LRA-V-BLAST detectors to avoid optimal ordering to further reduce the complexity. LRA-V-BLAST detectors without optimal ordering are shown to obtain almost the same BER performance of LRA-V-BLAST detector with optimal ordering.