The range resolution of a time-of-flight (TOF) range image sensor with periodical charge-draining operation is analyzed under the influence of ambient light. The analysis explains the effectiveness of a small duty light pulse and a periodical charge-draining structure to reduce the influence of ambient light. A theoretical model of range resolution taking into consideration factors of the duty ratio, the ambient light component, and the diffusion component is presented. The theoretical model under the influence of ambient light well agrees with the experimental results. The offset-charge-canceling technique using two sub-frames effectively enables accurate range measurements under ambient light.

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As a fundamental biological problem, revealing the protein folding mechanism remains to be one of the most challenging problems in structural bioinformatics. Prediction of protein folding rate is an important step towards our further understanding of the protein folding mechanism and the complex sequence-structure-function relationship. In this article, we develop a novel approach to predict protein folding rates for two-state and multi-state protein folding kinetics, which combines a variety of structural topology and complex network properties that are calculated from protein three-dimensional structures. To take into account the specific correlations between network properties and protein folding rates, we define two different protein residue contact networks, based on two different scales Protein Contact Network (PCN) and Long-range Interaction Network (LIN) to characterize the corresponding network features. The leave-one-out cross-validation (LOOCV) tests indicate that this integrative strategy is more powerful in predicting the folding rates from 3D structures, with the Pearson's Correlation Coefficient (CC) of 0.88, 0.90 and 0.90 for two-state, multi-state and combined protein folding kinetics, which provides an improved performance compared with other prediction work. This study provides useful insights which shed light on the network organization of interacting residues underlying protein folding process for both two-state and multi-state folding kinetics. Moreover, our method also provides a complementary approach to the current folding rate prediction algorithms and can be used as a powerful tool for the characterization of the foldomics protein data. The implemented webserver (termed PRORATE) is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/folding/.

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In this paper, we propose a simple but flexible virtual machine consolidation method for power saving. This method is specifically designed for datacenters where heterogeneous high-density blade servers host dozens or even hundreds of virtual machines. This method utilizes an extended First-Fit Decreasing (FFD) algorithm. It selects a migration destination server on the basis of server rank. The rank represents server selection priority and is uniquely assigned to each physical server. Our simulation results show that this method reduces power consumption by 34.5% under a typical workload and 33.8% under a random workload.

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Facial expression recognition has many potential applications in areas such as human-computer interaction (HCI), emotion analysis, and synthetic face animation. This paper proposes a novel framework of facial appearance and shape information extraction for facial expression recognition. For appearance information extraction, a facial-component-based bag of words method is presented. We segment face images into four component regions: forehead, eye-eyebrow, nose, and mouth. We then partition them into 4 × 4 sub-regions. Dense SIFT (scale-invariant feature transform) features are calculated over the sub-regions and vector quantized into 4 × 4 sets of codeword distributions. For shape information extraction, PHOG (pyramid histogram of orientated gradient) descriptors are computed on the four facial component regions to obtain the spatial distribution of edges. Multi-class SVM classifiers are applied to classify the six basic facial expressions using the facial-component-based bag of words and PHOG descriptors respectively. Then the appearance and shape information is fused at decision level to further improve the recognition rate. Our framework provides holistic characteristics for the local texture and shape features by enhancing the structure-based spatial information, and makes it possible to use the bag of words method and the local descriptors in facial expression recognition for the first time. The recognition rate achieved by the fusion of appearance and shape features at decision level using the Cohn-Kanade database is 96.33%, which outperforms the state-of-the-art research works.

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In this paper we present a novel approach to modeling visual concepts effectively and automatically using web images. The selection of training data (positive and negative samples) is strongly related to the quality of learning algorithms and is an especially crucial step when using noisy web images. In this scheme, first, images are represented by regions from which training samples are selected. Second, region features effectively representing a semantic concept are determined, and on their basis, the representative regions corresponding to the concept are selected as reliable positive samples. Third, high quality negative samples are determined using the selected positive samples. Last, the visual model associated with a semantic concept is built through an unsupervised learning process. The presented scheme is completely automatic and performs well for generic images because of its robustness in learning from diverse web images. Experimental results demonstrate its effectiveness.

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We have proposed a semi-structured data mining method when there is a huge number of data items and a large amount of semi-structured data, in particular, a labeled ordered tree. Our approach is to compress data structure into a bit vector and then mine the compressed data structure using fast low-level bit operations. The experimental results showed that our approach is more effective than conventional methods when there is a large amount of semi-structured data.

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Agent-based middleware that has abilities of adaptation to dynamically changing environments is a significant direction for system developments in ubiquitous computing environments. In this paper, we focus on the communication infrastructure of agent-based middleware in ubiquitous computing environments. We propose an adaptive communication mechanism between agent platforms, which can select communication schemes flexibly, based on properties of inter-agent communication and resource status. We designed the proposed mechanism and implemented a prototype system. Furthermore, we performed an initial experiment by using the prototype system on a network environment with two types of access network. We confirmed that the dynamic selection of an inter-platform communication scheme works effectively according to the change of network resource status. From the experimental results, we confirmed that efficiency is improved 5% and stability is improved 22% when compared to that of the traditional mechanism.

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