Global illumination is the core part of photo-realistic rendering. The photon mapping algorithm is an effective method for computing global illumination with its obvious advantage of caustic and color bleeding rendering. It is an active research field that has been developed over the past two decades. The deficiency of precise details and efficient rendering are still the main challenges of photon mapping. This report reviews recent work and classifies it into a set of categories including radiance estimation, photon relaxation, photon tracing, progressive photon mapping, and parallel methods. The goals of our report are giving readers an overall introduction to photon mapping and motivating further research to address the limitations of existing methods.

This paper describes a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a corner. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic corner-turning behavior by comparison with real data obtained from the experiments. Finally, we perform parameter analysis to show the believability of our model through a series of experiments.

The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding (LULDE). The proposed approach can be seen as an extension of a local discriminant embedding (LDE) framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.

Quantum-dot cellular automata (QCA) is an emerging area of research in reversible computing. It can be used to design nanoscale circuits. In nanocommunication, the detection and correction of errors in a received message is a major factor. Besides, device density and power dissipation are the key issues in the nanocommunication architecture. For the first time, QCA-based designs of the reversible low-power odd parity generator and odd parity checker using the Feynman gate have been achieved in this study. Using the proposed parity generator and parity checker circuit, a nanocommunication architecture is proposed. The detection of errors in the received message during transmission is also explored. The proposed QCA Feynman gate outshines the existing ones in terms of area, cell count, and delay. The quantum costs of the proposed conventional reversible circuits and their QCA layouts are calculated and compared, which establishes that the proposed QCA circuits have very low quantum cost compared to conventional designs. The energy dissipation by the layouts is estimated, which ensures the possibility of QCA nano-device serving as an alternative platform for the implementation of reversible circuits. The stability of the proposed circuits under thermal randomness is analyzed, showing the operational efficiency of the circuits. The simulation results of the proposed design are tested with theoretical values, showing the accuracy of the circuits. The proposed circuits can be used to design more complex low-power nanoscale lossless nanocommunication architecture such as nano-transmitters and nano-receivers.

We propose a new consensus model for group decision making (GDM) problems, using an interval type-2 fuzzy environment. In our model, experts are asked to express their preferences using linguistic terms characterized by interval type-2 fuzzy sets (IT2 FSs), because these can provide decision makers with greater freedom to express the vagueness in real-life situations. Consensus and proximity measures based on the arithmetic operations of IT2 FSs are used simultaneously to guide the decision-making process. The majority of previous studies have taken into account only the importance of the experts in the aggregation process, which may give unreasonable results. Thus, we propose a new feedback mechanism that generates different advice strategies for experts according to their levels of importance. In general, experts with a lower level of importance require a larger number of suggestions to change their initial preferences. Finally, we investigate a numerical example and execute comparable models and ours, to demonstrate the performance of our proposed model. The results indicate that the proposed model provides greater insight into the GDM process.

Endmember extraction is a key step in the hyperspectral image analysis process. The kernel new simplex growing algorithm (KNSGA), recently developed as a nonlinear alternative to the simplex growing algorithm (SGA), has proven a promising endmember extraction technique. However, KNSGA still suffers from two issues limiting its application. First, its random initialization leads to inconsistency in final results; second, excessive computation is caused by the iterations of a simplex volume calculation. To solve the first issue, the spatial pixel purity index (SPPI) method is used in this study to extract the first endmember, eliminating the initialization dependence. A novel approach tackles the second issue by initially using a modified Cholesky factorization to decompose the volume matrix into triangular matrices, in order to avoid directly computing the determinant tautologically in the simplex volume formula. Theoretical analysis and experiments on both simulated and real spectral data demonstrate that the proposed algorithm significantly reduces computational complexity, and runs faster than the original algorithm.

A standing wave oscillator (SWO) is a perfect clock source which can be used to produce a high frequency clock signal with a low skew and high reliability. However, it is difficult to tune the SWO in a wide range of frequencies. We introduce a frequency tunable SWO which uses an inversion mode metal-oxide-semiconductor (IMOS) field-effect transistor as a varactor, and give the simulation results of the frequency tuning range and power dissipation. Based on the frequency tunable SWO, a new phase locked loop (PLL) architecture is presented. This PLL can be used not only as a clock source, but also as a clock distribution network to provide high quality clock signals. The PLL achieves an approximately 50% frequency tuning range when designed in Global Foundry 65 nm 1P complementary metal-oxide-semiconductor (CMOS) technology, and can be used directly in a high performance multi-core microprocessor.

In wideband multi-pair two-way relay networks, the performance of beamforming at a relay station (RS) is intimately related to the accuracy of the channel state information (CSI) available. The accuracy of CSI is determined by Doppler spread, delay between beamforming and channel estimation, and density of pilot symbols, including transmit power of pilot symbols. The coefficient of the Gaussian-Markov CSI error model is modeled as a function of CSI delay, Doppler spread, and signal-to-noise ratio, and can be estimated in real time. In accordance with the real-time estimated coefficients of the error model, an adaptive robust maximum signal-to-interference-and-noise ratio (Max-SINR) plus maximum signal-to-leakage-and-noise ratio (Max-SLNR) beamformer at an RS is proposed to track the variation of the CSI error. From simulation results and analysis, it is shown that: compared to existing non-adaptive beamformers, the proposed adaptive beamformer is more robust and performs much better in the sense of bit error rate (BER); with increase in the density of transmit pilot symbols, its BER and sum-rate performances tend to those of the beamformer of Max-SINR plus Max-SLNR with ideal CSI.