Data exchange, in which two blocks of data are swapped between cores in distributed memory systems, necessitates additional memory buffer in a multiprocessor system-on-chip. In this paper, we propose a novel bidirectional inter-core communication mechanism called coherent direct memory access (CoDMA). The CoDMA ensures that the writing address is always less than the reading address in coherent read and write mode, so as to avoid read-after-write (RAW) errors. It features an efficient data exchanging scheme without using data buffer in the memory. A four-core single-instruction multiple-data processor is established for the experiments, based on a multi-bus network-on-chip. Experimental results show that the proposed method consumes no additional memory buffer and achieves 39% and 20% average performance improvement compared with traditional Methods 1 and 2, respectively. And a maximal of 43% reduction in memory usage is achieved, at the cost of only 0.22% more area overhead compared with the entire system.

Providing diverse Quality of Service (QoS) with ultra-low power consumption and support of mobility is the most important and challenging issue in wireless body area networks (WBANs). The IEEE 802.15.4 standard exhibits a desirable feature for WBAN, but its inability of mobility support makes it insufficient. In this paper, we show what is required for node mobility support and propose two strategies for the support. We observed that the amount of time required for the association process is the key reason IEEE 802.15.4 is unable to handle mobility. In this paper, we present a new fast association technique, which prevents nodes from scanning multiple channels. In the proposed scheme, by scanning just a single channel, a node can learn about all the coordinators working in different channels. The single channel scanning scheme is able to decrease the association time of IEEE 802.15.4 operating in 2.4GHz by 32 times. Furthermore, in this paper, a method to increase the node connectivity time with its coordinator in IEEE 802.15.4 beacon-enabled mode is introduced. The method tries to anticipate whether the node is moving towards or away from the coordinator by analyzing the signal strength of multiple beacons received from the same coordinator. Thus, the connectivity time is increased by choosing the coordinator with good signal strength, but located both furthest from the node and toward the direction which mobile node is moving. Our approach results in significant improvement by reducing the number of times the moving node switches coordinators. Experimental results have verified that our schemes work well in the mobile sensor network environment.

In this paper, we design an efficient P2P based mobile social network to facilitate contents search over mobile ad hoc networks. Social relation is established by considering both the locations and interests of mobile nodes. Mobile nodes with common interests and nearby locations are recommended as friends and are connected directly in a mobile social network. Contents search is handled by using social relationships of the mobile social network rather than those of the whole network. Since each mobile node manages only neighboring nodes that have common interests, network management overhead is reduced. Results of experiments have shown that our proposed method outperforms existing methods.

Soccer player tracking and labeling suffer from the similar appearance of the players in the same team, especially in long-shot scenes where the faces and the numbers of the players are too blurry to identify. In this paper, we propose an efficient multi-player tracking system. The tracking system takes the detection responses of a human detector as inputs. To realize real-time player detection, we generate a spatial proposal to minimize the scanning scope of the detector. The tracking system utilizes the discriminative appearance models trained using the online Boosting method to reduce data-association ambiguity caused by the appearance similarity of the players. We also propose to build an online learned player recognition model which can be embedded in the tracking system to approach online player recognition and labeling in tracking applications for long-shot scenes by two stages. At the first stage, to build the model, we utilize the fast k-means clustering method instead of classic k-means clustering to build and update a visual word vocabulary in an efficient online manner, using the informative descriptors extracted from the training samples drawn at each time step of multi-player tracking. The first stage finishes when the vocabulary is ready. At the second stage, given the obtained visual word vocabulary, an incremental vector quantization strategy is used to recognize and label each tracked player. We also perform importance recognition validation to avoid mistakenly recognizing an outlier, namely, people we do not need to recognize, as a player. Both quantitative and qualitative experimental results on the long-shot video clips of a real soccer game video demonstrate that, the proposed player recognition model performs much better than some state-of-the-art online learned models, and our tracking system also performs quite effectively even under very complicated situations.

This paper proposes a secure wireless network system required for an ambient information society; it forms a privacy zone wherein terminals can securely communicate secret information using an arbitrary general radio channel. For this system, we introduce a scheme using a side-information from a special node. The information is used as an encryption key so that the detectable region of the key defines a privacy zone. We implement the scheme on the basis of IEEE 802.15.4 and realize the world's first ambient network platform with the above functionality. An experiment and demonstration show the effectiveness of the proposed system.

In this paper, we propose a robust registration method, named Bounded-Variables Least Median of Squares (BVLMS). It overcomes both the misassociations and the ill-conditioning due to the interactions between Bounded-Variables Least Squares (BVLS) and Least Median of Squares (LMS). Simulation results demonstrate the feasibility of this new registration method.

Computing the level of trust between two indirectly connected users in an online social network (OSN) is a problem that has received considerable attention of researchers in recent years. Most algorithms focus on finding the most accurate prediction of trust; however, little work has been done to make them fast enough to be applied on today's very large OSNs. To address this need we propose a method for fast trust computation that is suitable for very large social networks. Our method uses min-max trust propagation strategies along with the landmark based method. Path strength of every node is pre-computed to and from a small set of reference users or landmarks. Using these pre-computed values, we estimate the strength of trust paths from the source user to in-neighbors of the target user. The final trust estimate is obtained by aggregating information from most reliable in-neighbors of the target user. We also describe how the landmark based method can be used for fast trust computation according to other trust propagation models. Experiments on a variety of real social network datasets verify the efficiency and accuracy of our method.

This paper studies the static mapping of multiple applications on embedded many-core SoCs. The mapping techniques proposed in this paper take into account both inter-application and intra-application parallelism in order to fully utilize the potential parallelism of the many-core architecture. Two approaches are proposed for static mapping: one approach is based on integer linear programming and the other is based on a greedy algorithm. Experiments show the effectiveness of the proposed techniques.

Reducing the time complexity of character matching is critical to the development of efficient Japanese Optical Character Recognition (OCR) systems. To shorten the processing time, recognition is usually split into separate pre-classification and precise recognition stages. For high overall recognition performance, the pre-classification stage must both have very high classification accuracy and return only a small number of putative character categories for further processing. Furthermore, for any practical system, the speed of the pre-classification stage is also critical. The associative matching (AM) method has often been used for fast pre-classification because of its use of a hash table and reliance on just logical bit operations to select categories, both of which make it highly efficient. However, a certain level of redundancy exists in the hash table because it is constructed using only the minimum and maximum values of the data on each axis and therefore does not take account of the distribution of the data. We propose a novel method based on the AM method that satisfies the performance criteria described above but in a fraction of the time by modifying the hash table to reduce the range of each category of training characters. Furthermore, we show that our approach outperforms pre-classification by VQ clustering, ANN, LSH and AM in terms of classification accuracy, reducing the number of candidate categories and total processing time across an evaluation test set comprising 116,528 Japanese character images.

One of the largest challenges for coarse-grained reconfigurable arrays (CGRAs) is how to efficiently map applications. The key issues for mapping are (1) how to reduce the memory bandwidth, (2) how to exploit parallelism in algorithms and (3) how to achieve load balancing and take full advantage of the hardware potential. In this paper, we propose a novel parallelism scheme, called ‘Hybrid partitioning’, for mapping a H.264 high definition (HD) decoder onto REMUS-II, a CGRA system-on-chip (SoC). Combining good features of data partitioning and task partitioning, our methodology mainly consists of three levels from top to bottom: (1) hybrid task pipeline based on slice and macroblock (MB) level; (2) MB row-level data parallelism; (3) sub-MB level parallelism method. Further, on the sub-MB level, we propose a few mapping strategies such as hybrid variable block size motion compensation (Hybrid VBSMC) for MC, 2D-wave for intra 44, parallel processing order for deblocking. With our mapping strategies, we improved the algorithm's performance on REMUS-II. For example, with a luma 1616 MB, the Hybrid VBSMC achieves 4 times greater performance than VBSMC and 2.2 times greater performance than fixed 44 partition approach. Finally, we achieve 1080p@33fps H.264 high-profile (HiP)@level 4.1 decoding when the working frequency of REMUS-II is 200 MHz. Compared with typical hardware platforms, we can achieve better performance, area, and flexibility. For example, our performance achieves approximately 175% improvement than that of a commercial CGRA processor XPP-III while only using 70% of its area.

This study investigated the test-retest reliability and the criterion-related validity of the Implicit Association Test (IAT [1]) that was developed for measuring shyness among Japanese people. The IAT has been used to measure implicit stereotypes, as well as self-concepts, such as implicit shyness and implicit self-esteem. We administered the shyness IAT and the self-esteem IAT to participants (N = 59) on two occasions over a one-week interval (Time 1 and Time 2) and examined the test-retest reliability by correlating shyness IATs between the two time points. We also assessed the criterion-related validity by calculating the correlation between implicit shyness and implicit self-esteem. The results indicated a sufficient positive correlation coefficient between the scores of implicit shyness over the one-week interval (r = .67, p < .01). Moreover, a strong negative correlation coefficient was indicated between implicit shyness and implicit self-esteem (r = -.72, p < .01). These results confirmed the test-retest reliability and the criterion-related validity of the Japanese version of the shyness IAT, which is indicative of the validity of the test for assessing implicit shyness.

Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focused on predicting links in social networks using information flow via active links. The information flow heavily depends on link activeness. The links become active if the interactions happen frequently and recently with respect to the current time. The time stamps of the interactions or links provide vital information for determining the activeness of the links. In the present paper, we introduced a new algorithm, referred to as T_Flow, that captures the important aspects of information flow via active links in social networks. We tested T_Flow with two social network data sets, namely, a data set extracted from Facebook friendship network and a coauthorship network data set extracted from ePrint archives. We compare the link prediction performances of T_Flow with the previous method PropFlow. The results of T_Flow method revealed a notable improvement in link prediction for facebook data and significant improvement in link prediction for coauthorship data.

This paper proposes a simple decentralized cell association method for heterogeneous networks, where low transmission-power pico or femto base stations (BSs) overlay onto a high transmission-power macro BS. The focus of this investigation is on the downlink and the purpose of cell association is to achieve better user fairness, in other words, to increase the minimum average user throughput (worst user throughput). In the proposed method, an appropriate cell association for all users within a cell is achieved in an iterative manner based on the feedback information of each individual user assisted by a small amount of broadcast information from the respective BSs. The proposed method does not require cooperation between BSs. Furthermore, the proposed method is applicable to cases of inter-cell interference coordination (ICIC) between macro and pico/femto BSs through the use of protected radio resources exclusively used by the pico/femto BSs. Based on numerical results, we show that the proposed method adaptively achieves better cell association for all users according to the user location distributions compared to the conventional cell range expansion (CRE) method. The advantage of the proposed method over CRE is further enhanced in an ICIC scenario.

In recent years, heterogeneous cellular network (HetNet) topology has been attracting much attention. HetNet, which is a network topology with low power base stations installed inside the cell range of conventional macrocells, can realize network capacity enhancement through the effects of macrocell offloading and cell shrinkage. Due to the heterogeneity nature of HetNet, network designers should carefully consider about the interference management, resource allocation, user association and cell range expansion. These issues have been well studied in recent literatures. However, one of the important problems which has not been well investigated in conventional works is the base station (BS) deployment problem in HetNet. This paper investigates the optimal pico base station deployment in heterogeneous cellular networks especially with the existence of hotspots. In this paper, pico BS locations are optimized together with other network parameters including spectrum splitting ratio and signal-to-interference-noise ratio (SINR) bias for cell range expansion to maximize the total system rate, by considering two spectrum allocation strategies, i.e. spectrum overlapping and spectrum splitting. Numerical results show that the optimized pico BS locations can improve the system rate, the average user rate and outage user rate in HetNet with hotspots.

This paper presents an adaptive cache architecture for wide-range reliable low-voltage operations. The proposed associativity-reconfigurable cache consists of pairs of cache ways so that it can exploit the recovery feature of the novel 7T/14T SRAM cell. Each pair has two operating modes that can be selected based upon the required voltage level of current operating conditions: normal mode for high performance and dependable mode for reliable low-voltage operations. We can obtain reliable low-voltage operations by application of the dependable mode to weaker pairs that cannot operate reliably at low voltages. Meanwhile leaving stronger pairs in the normal mode, we can minimize performance losses. Our chip measurement results show that the proposed cache can trade off its associativity with the minimum operating voltage. Moreover, it can decrease the minimum operating voltage by 140 mV achieving 67.48% and 26.70% reduction of the power dissipation and energy per instruction. Processor simulation results show that designing the on-chip caches using the proposed scheme results in 2.95% maximum IPC losses, but it can be chosen various performance levels. Area estimation results show that the proposed cache adds area overhead of 1.61% and 5.49% in 32-KB and 256-KB caches, respectively.

This study investigated the relationship between social skills and facial asymmetry in facial expressions. Three-dimensional facial landmark data of facial expressions (neutral, happy, and angry) were obtained from Japanese participants (n = 62). Following a facial expression task, each participant completed KiSS-18 (Kikuchi's Scale of Social Skills; Kikuchi, 2007). Using a generalized Procrustes analysis, faces and their mirror-reversed versions were represented as points on a hyperplane. The asymmetry of each individual face was defined as Euclidian distance between the face and its mirror reversed face on this plane. Subtraction of the asymmetry level of a neutral face of each individual from the asymmetry level of a target emotion face was defined as the index of “expression asymmetry” given by a particular emotion. Correlation coefficients of KiSS-18 scores and expression asymmetry scores were computed for both happy and angry expressions. Significant negative correlations between KiSS-18 scores and expression asymmetries were found for both expressions. Results indicate that the symmetry in facial expressions increases with higher level of social skills.

Information affects almost all aspects of life, and thus the Quality of Information (IQ) plays a critical role in businesses and societies; It can have significant positive and negative impacts on the quality of life of citizens, employees and organizations. Over many years aspects and challenges of IQ have been studied within various contexts. As a result, the general approach to the study of IQ has offered numerous management and measurement approaches, IQ frameworks and list of IQ criteria. As the volume of data and information increases, IQ problems become pervasive. Whereas earlier studies investigated specific aspects of IQ, the next phase of IQ research will need to examine IQ in a wider context, thus its impact on the quality of life and societies. In this paper we apply an IQ oriented framework to two cases, cloud computing and lifelogging, illustrating the impact of IQ on the quality of life. The paper demonstrates the value of the framework, the impact IQ can have on the quality of life and in summary provides a foundation for further research.

In this paper, we propose a new data association method termed the highest probability data association (HPDA) and apply it to real-time recursive nonlinear tracking in heavy clutter. The proposed method combines the probabilistic nearest neighbor (PNN) with a modified probabilistic strongest neighbor (PSN) approach. The modified PSN approach uses only the rank of the measurement amplitudes. This approach is robust as exact shape of amplitude probability density function is not used. In this paper, the HPDA is combined with particle filtering for nonlinear target tracking in clutter. The measurement with the highest measurement-to-track data association probability is selected for track update. The HPDA provides the track quality information which can be used in for the false track termination and the true track confirmation. It can be easily extended to multi-target tracking with nonlinear particle filtering. The simulation studies demonstrate the HPDA functionality in a hostile environment with high clutter density and low target detection probability.

In this paper, we have addressed the problem of ordering transactions in network-on-chips (NoCs) for post-silicon validation. The main idea is to extract the order of the transactions from the local partial orders in each NoC tile based on a set of “happened-before” rules, assuming transactions do not have a timestamp. The assumption is based on the fact that implementation and usage of a global time as timestamp in such systems may not be practical or efficient. When a new transaction is received in a tile, we send special messages to the neighboring tiles to inform them regarding the new transaction. The process of sending those special messages continues recursively in all the tiles that receive them until another such special message is detected. This way, we relate local orders of different tiles with each other. We show that our method can reconstruct the correct transaction orders when communication delays are deterministic. We have shown the effectiveness of our method by correctly ordering the transaction in NoCs with mesh and torus topologies with different sizes from 5*5 to 9*9. Also, we have implemented the proposed method in hardware to show its feasibility.

Link prediction is the task of inferring the existence or absence of certain relationships among data objects such as identity, interaction, and collaboration. Link prediction is found in various applications in the fields of information integration, recommender systems, bioinformatics, and social network analysis. The increasing interest in dynamically changing networks has led to growing interest in a more general link prediction problem called temporal link prediction in the data mining and machine learning communities. However, only links among nodes at the same time point are considered in temporal link prediction. We propose a new link prediction problem called cross-temporal link prediction in which the links among nodes at different time points are inferred. A typical example of cross-temporal link prediction is cross-temporal entity resolution to determine the identity of real entities represented by data objects observed in different time periods. In dynamic environments, the features of data change over time, making it difficult to identify cross-temporal links by directly comparing observed data. Other examples of cross-temporal links are asynchronous communications in social networks such as Facebook and Twitter, where a message is posted in reply to a previous message. We adopt a dimension reduction approach to cross-temporal link prediction; that is, data objects in different time frames are mapped into a common low-dimensional latent feature space, and the links are identified on the basis of the distance between the data objects. The proposed method uses different low-dimensional feature projections in different time frames, enabling it to adapt to changes in the latent features over time. Using multi-task learning, it jointly learns a set of feature projection matrices from the training data, given the assumption of temporal smoothness of the projections. The optimal solutions are obtained by solving a single generalized eigenvalue problem. Experiments using a real-world set of bibliographic data for cross-temporal entity resolution and a real-world set of emails for unobserved asynchronous communication inference showed that introducing time-dependent feature projections improved the accuracy of link prediction.