A global tree local X-net network (GTLX) is introduced to realize high-performance data transfer in a multiple-valued fine-grain reconfigurable VLSI (MVFG-RVLSI). A global pipelined tree network is utilized to realize high-performance long-distance bit-parallel data transfer. Moreover, a logic-in-memory architecture is employed for solving data transfer bottleneck between a block data memory and a cell. A local X-net network is utilized to realize simple interconnections and compact switch blocks for eight-near neighborhood data transfer. Moreover, multiple-valued signaling is utilized to improve the utilization of the X-net network, where two binary data can be transferred from two adjacent cells to one common adjacent cell simultaneously at each “X” intersection. To evaluate the MVFG-RVLSI, a fast Fourier transform (FFT) operation is mapped onto a previous MVFG-RVLSI using only the X-net network and the MVFG-RVLSI using the GTLX. As a result, the computation time, the power consumption and the transistor count of the MVFG-RVLSI using the GTLX are reduced by 25%, 36% and 56%, respectively, in comparison with those of the MVFG-RVLSI using only the X-net network.

In this paper, we consider multi-source multi-relay power allocation in cooperative wireless networks. A new intelligent optimization algorithm, multi-objective free search (MOFS), is proposed to efficiently allocate cooperative relay power to better support multiple sources transmission. The existence of Pareto optimal solutions is analyzed for the proposed multi-objective power allocation model when the objectives conflict with each other, and the MOFS algorithm is validated using several test functions and metrics taken from the standard literature on evolutionary multi-objective optimization. Simulation results show that the proposed scheme can effectively get the potential optimal solutions of multi-objective power allocation problem, and it can effectively optimize the tradeoff between network sum-rate and fairness in different applications by selection of the corresponding solution.

Despite the increasing use of mobile computing, exploiting its full potential is difficult due to its inherent characteristics such as error-prone transmission channels, diverse node capabilities, frequent disconnections and mobility. Mobile Cloud Computing (MCC) is a paradigm that is aimed at overcoming previous problems through integrating mobile devices with cloud computing. Mobile devices, in the traditional client-server architecture of MCC, offload their tasks to the cloud to utilize the computation and storage resources of data centers. However, along with the development of hardware and software technologies in mobile devices, researchers have begun to take into consideration local resource sharing among mobile devices themselves. This is defined as the cooperation based architecture of MCC. Analogous to the conventional terminology, the resource platforms that are comprised of surrounding surrogate mobile devices are called local resource clouds. Some researchers have recently verified the feasibility and benefits of this strategy. However, existing work has neglected an important issue with this approach, i.e., how to construct local resource clouds in dynamic mobile wireless networks. This paper presents the concept and design of a local resource cloud that is both energy and time efficient. Along with theoretical models and formal definitions of problems, an efficient heuristic algorithm with low computational complexity is also presented. The results from simulations demonstrate the effectiveness of the proposed models and method.

Millimeter-wave synthetic aperture imaging radiometer (SAIR) is a powerful sensor for near-field high-resolution observations. However, the large receiver number and system complexity affect the application of SAIR. To overcome this shortage (receiver number), an accurate imaging algorithm based on compressed sensing (CS) theory is proposed in this paper. For reconstructing the brightness temperature images accurately from the sparse SAIR with fewer receivers, the proposed CS-based imaging algorithm is used to accomplish the sparse reconstruction with fewer visibility samples. The reconstruction is performed by minimizing the $l_{1}$ norm of the transformed image. Compared to the FFT-based methods based on Fourier transform, the required receiver number can be further reduced by this method. The simulation results demonstrate that the proposed CS-based method has higher reconstruction accuracy for the sparse SAIR.

For break arcs occurring between Ag and Ag/SnO$_2$ 12,wt% electrical contact pairs, the electrical conductivity, viscosity and specific heat at constant pressure are calculated as thermodynamic and transport properties. Mixture rates of contact material vapor are 0%, 1%, 10% and 100%. Influence of the contact material on the properties is investigated. Temperature for the calculation ranges from 2000,K to 20000,K. Following results are shown. When the mixture rate is changed, the electrical conductivity varies at lower temperature (< 10000,K), and the viscosity and specific heat vary widely at all temperature range. The electrical conductivity is independent of the mixture rate when the temperature is exceeding 10000,K. The thermodynamic and transport properties are independent of the kind of the contact materials.

At present, vast numbers of information resources are available on the Internet. However, one emerging issue is how to search and exploit these information resources in an efficient and flexible manner with high scalability. In this study, we focused our attention on the design of a distributed hash table (DHT)-based search system that supports efficient scalable multi-attribute queries of information resources in a distributed manner. Our proposed system, named D-AVTree, is built on top of a ring-based DHT, which partitions a one-dimensional key space across nodes in the system. It utilizes a descriptive naming scheme, which names each resource using an attribute-value (AV) tree, and the resource names are mapped to d-bit keys in order to distribute the resource information to responsible nodes based on a DHT routing algorithm. Our mapping scheme maps each AV branch of a resource name to a d-bit key where AV branches that share a subsequence of AV pairs are mapped to a continuous portion of the key space. Therefore, our mapping scheme ensures that the number of resources distributed to a node is small and it facilitates efficient multi-attribute queries by querying only a small number of nodes. Further, the scheme has good compatibility with key-based load balancing algorithms for DHT-based networks. Our system can achieve both efficiency and a good degree of load balancing even when the distribution of AV pairs in the resource names is skewed. Our simulation results demonstrated the efficiency of our solution in terms of the distribution cost, query hit ratio, and the degree of load balancing compared with conventional approaches.

The large amounts of freely available open source software over the Internet are fundamentally changing the traditional paradigms of software development. Efficient categorization of the massive projects for retrieving relevant software is of vital importance for Internet-based software development such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, but were verified on only relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software development requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles. We design a SVM-based categorization framework and adopt a weighted combination strategy to aggregate different types of profile attributes from multiple repositories. Different basic classification algorithms and feature selection techniques are employed and compared. Extensive experiments are carried out on more than 21,000 projects across five repositories. The results show that our approach achieves significant improvements by using weighted combination. Compared to the previous work, our approach presents competitive results with more finer-grained and multi-layered category hierarchy with more than 120 categories. Unlike approaches that use source code or byte code, our approach is more effective for large-scale and language-independent software categorization. In addition, experiments suggest that hierarchical categorization combined with general keyword-based searching improves the retrieval efficiency and accuracy.

This paper presents a novel filter to keep from over-smoothing the edges and corners and rectify the outliers in the flow field after each incremental computation step, which plays a key role during the process of estimating flow field. This filter works according to the spatial-temporal derivatives distance of the input image and velocity field distance, whose principle is more reasonable in filtering mechanism for optical flow than other existing nonlinear filters. Moreover, we regard the spatial-temporal derivatives as new powerful descriptions of different motion layers or regions and give a detailed explanation. Experimental results show that our proposed method achieves better performance.

The last decade has witnessed an explosion of interest in research on human emotion modeling for generating intelligent virtual agents. This paper proposes a novel personality model based on the Revised NEO Personality Inventory (NEO PI-R). Compared to the popular Big-Five-Personality Factors (Big5) model, our proposed model is more capable than Big5 on describing a variety of personalities. Combining with emotion models it helps to produce more reasonable emotional reactions to external stimuli. A novel Resistant formulation is also proposed to effectively simulate the complicated negative emotions. Emotional reactions towards multiple stimuli are also effectively simulated with the proposed personality model.

This paper studies an underlay-based cognitive two-way relay network which consists of a primary network (PN) and a secondary network (SN). Two secondary users (SUs) exchange information with the aid of multiple single-antenna amplify-and-forward relays while a primary transmitter communicates with a primary receiver in the same spectrum. Unlike the existing contributions, the transmit powers of the SUs and the distributed beamforming weights of the relays are jointly optimized to minimize the sum interference power from the SN to the PN under the quality-of-service (QoS) constraints of the SUs determined by their output signal-to-interference-plus-noise ratio (SINR) and the transmit power constraints of the SUs and relays. This approach leads to a non-convex optimization problem which is computationally intractable in general. We first investigate two necessary conditions that optimal solutions should satisfy. Then, the non-convex minimization problem is solved analytically based on the obtained conditions for single-relay scenarios. For multi-relay scenarios, an iterative numerical algorithm is proposed to find suboptimal solutions with low computational complexity. It is shown that starting with an arbitrarily initial feasible point, the limit point of the solution sequence derived from the iterative algorithm satisfies the two necessary conditions. To apply this algorithm, two approaches are developed to find an initial feasible point. Finally, simulation results show that on average, the proposed low-complexity solution considerably outperforms the scheme without source power control and performs close to the optimal solution obtained by a grid search technique which has prohibitively high computational complexity.

Shadow mapping is an efficient method to generate shadows in real time computer graphics and has broad variations from hard to soft shadow synthesis. Soft shadowing based on shadow mapping is a blurring technique on a shadow map or on screen space. Blurring on screen space has an advantage for efficient sampling on a shadow map, since the blurred target array has exactly the same coordinates as the screen. However, a previous blurring method on screen space has a drawback: the generated shadow is not correct when a view direction has a large angle to the normal of the shadowed plane. In this paper, we introduce a new screen space based method for soft shadowing that is fast and generates soft shadows more accurately than the previous screen space soft shadow mapping method. The resultant images show shadows produced by our method just stand in the same place, while shadows by the previous method change in terms of penumbra while the view moves. Surprisingly, although our method is more complex than the previous method, the measurement results of the calculation time show our method is almost the same performance. This is because it controls the blurring area more accurately and thus successfully reduces multiplications for blurring.

A novel auto-tuning digital DC--DC converter is presented. In order to reduce the recovery time and undershoot, the auto-tuning control combines LnL, conventional PID and a predictive PID with a configurable predictive coefficient. A switch module is used to select an algorithm from the three control algorithms, according to the difference between the error signal and the two initially predefined thresholds. The detection and control logic is designed for both window delay line ADC and $Sigma Delta$ DPWM to correct the delay deviation. When the output of the converter exceeds the quantization range, the digital output of ADC is set at 0 or 1, and the delay line stops working to reduce power consumption. Theoretical analysis and simulations in the CSMC CMOS 0.5,$mu$m process are carried out to verify the proposed DC--DC converter. It is found that the converter achieves a power efficiency of more than 90% at heavy load, and reduces the recovery time and undershoot.

The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.

We propose a speaker adaptation method based on the probabilistic principal component analysis (PPCA) of acoustic models. We define a training matrix which is represented in a two-way array and decompose the training models by PPCA to construct bases. In the two-way array representation, each training model is represented as a matrix and the columns of each training matrix are treated as training vectors. We formulate the adaptation equation in the maximum a posteriori (MAP) framework using the bases and the prior.

With the development of personal electronic equipment, the use of a smartphone with a tri-axial accelerometer to detect human physical activity is becoming popular. In this paper, we propose a new feature based on FFT for activity recognition from tri-axial acceleration signals. To improve the classification performance, two fusion methods, minimal distance optimization (MDO) and variance contribution ranking (VCR), are proposed. The new proposed feature achieves a recognition rate of 92.41%, which outperforms six traditional time- or frequency-domain features. Furthermore, the proposed fusion methods effectively improve the recognition rates. In particular, the average accuracy based on class fusion VCR (CFVCR) is 97.01%, which results in an improvement in accuracy of 4.14% compared with the results without any fusion. Experiments confirm the effectiveness of the new proposed feature and fusion methods.

A challenge faced by the video game industry is to develop believable and more intelligent Non-Playable Characters (NPCs). To tackle this problem a low-cost and simple approach has been proposed in this research, which is the development of a gossip virtual social network for NPCs. The network allows simple individual NPCs to communicate their knowledge amongst themselves. The communication within this social network is governed by social-psychological rules. These rules are categorized into four types: Contact, whether the NPC are within a contactable range of each other; Observation, whether the NPCs actually want to talk to each other based on their personal traits; Status, the current representation of the NPCs; and Relationships which determines the long term ties of the NPCs. Evaluations of the proposed gossip virtual social network was conducted, both through statistical analysis and a survey of real users. Highly satisfactory results have been achieved.

This paper presents an experimental study that analyzes how conversational agents activate human communication in thought-evoking multi-party dialogues between multi-users and multi-agents. A thought-evoking dialogue is a kind of interaction in which agents act to provoke user thinking, and it has the potential to activate multi-party interactions. This paper focuses on quiz-style multi-party dialogues between two users and two agents as an example of thought-evoking multi-party dialogues. The experimental results revealed that the presence of a peer agent significantly improved user satisfaction and increased the number of user utterances in quiz-style multi-party dialogues. We also found that agents' empathic expressions significantly improved user satisfaction, improved user ratings of the peer agent, and increased the number of user utterances. Our findings should be useful for activating multi-party communications in various applications such as pedagogical agents and community facilitators.

The second edition of the international standard of IEC 61508, functional safety of electrical/electronic/programmable electronic safety-related system (SRS), was published in 2010. This international standard adopts a risk-based approach by which safety integrity requirements can be determined. It presents a formula to estimate the hazardous event rate taking account of non-perfect proof-tests. But it is not clear how to derive the formula. In the present paper, firstly, taking account of non-perfect proof-tests, the relationship between the dangerous undetected failure of SRS, the demand on the SRS and hazardous event is modeled by a fault tree and state-transition diagrams. Next, the hazardous event rate is formulated by use of the state-transition diagrams for the determination of the safety integrity requirements. Then, a comparison is made between the formulas obtained by this paper and given in the standard, and it is found that the latter does not always present rational formulation.

Due to the recent development of underlying hardware technology and improvement in installing environments, public display has been becoming more common and attracting more attention as a new type of signage. Any signage is required to make its content more attractive to its viewers by evaluating the current attractiveness on the fly, in order to deliver the message from the sender more effectively. However, most previous methods for public display require time to reflect the viewers' evaluations. In this paper, we present a novel system, called Mood-Learning Public Display, which automatically adapts its content design. This system utilizes viewers' involuntary behaviors as a sign of evaluation to make the content design more adapted to local viewers' tastes evolutionarily on site. The system removes the current gap between viewers' expectations and the content actually displayed on the display, and makes efficient mutual transmission of information between the cyberworld and the reality.

A new stock index trend analysis approach is proposed in this paper, which is based on a newly developed signal decomposition approach - adaptive Fourier decomposition (AFD). AFD can effectively extract the signal's primary trend, which specifically suits the Dow Theory based technique analysis. The proposed approach integrates two different kinds of forecasting approaches, including the Dow theory the RBF neural network. Effectiveness of the proposed approach is assessed through comparison with the direct RBF neural network approach. The result is proved to be promising.