With the rapid increase of various uses of wireless communications in modern life, the high microwave and millimeter wave frequency bands are attracting much attention. However, the existing databases on above 6GHz radio-frequency (RF) electromagnetic (EM) field exposure of biological bodies are obviously insufficient. An in-vivo research project on local and whole-body exposure of rats to RF-EM fields above 6GHz was started in Japan in 2013. This study aims to perform a dosimetric design for the whole-body-average specific absorption rates (WBA-SARs) of unconstrained rats exposed to 6GHz RF-EM fields in a reverberation chamber (RC). The required input power into the RC is clarified using a two-step evaluation method in order to achieve a target exposure level in rats. The two-step method, which incorporates the finite-difference time-domain (FDTD) numerical solutions with electric field measurements in an RC exposure system, is used as an evaluation method to determine the whole-body exposure level in the rats. In order to verify the validity of the two-step method, we use S-parameter measurements inside the RC to experimentally derive the WBA-SARs with rat-equivalent phantoms and then compare those with the FDTD-calculated ones. It was shown that the difference between the two-step method and the S-parameter measurements is within 1.63dB, which reveals the validity and usefulness of the two-step technique.

In this letter, we propose a new semantic parts learning approach to address the object detection problem with only the bounding boxes of object category labels. Our main observation is that even though the appearance and arrangement of object parts might have variations across the instances of different object categories, the constituent parts still maintain geometric consistency. Specifically, we propose a discriminative clustering method with sparse representation refinement to discover the mid-level semantic part set automatically. Then each semantic part detector is learned by the linear SVM in a one-vs-all manner. Finally, we utilize the learned part detectors to score the test image and integrate all the response maps of part detectors to obtain the detection result. The learned class-generic part detectors have the ability to capture the objects across different categories. Experimental results show that the performance of our approach can outperform some recent competing methods.

Suppose one of the edges is attacked in a graph G, where some number of guards are placed on some of its vertices. If a guard is placed on one of the end-vertices of the attacked edge, she can defend such an attack to protect G by passing over the edge. For each of such attacks, every guard is allowed either to move to a neighboring vertex, or to stay at where she is. The eternal vertex cover number τ∞(G) is the minimum number of guards sufficient to protect G from any length of any sequence of edge attacks. This paper derives the eternal vertex cover number τ∞(G) of such graphs constructed by replacing each edge of a tree by an arbitrary elementary bipartite graph (or by an arbitrary clique), in terms of easily computable graph invariants only, thereby showing that τ∞(G) can be computed in polynomial time for such graphs G.

Face recognition across pose is generally tackled by either 2D based or 3D based approaches. The 2D-based often require a training set from which the cross-pose multi-view relationship can be learned and applied for recognition. The 3D based are mostly composed of 3D surface reconstruction of each gallery face, synthesis of 2D images of novel views using the reconstructed model, and match of the synthesized images to the probes. The depth information provides crucial information for arbitrary poses but more methods are yet to be developed. Extended from a latest face reconstruction method using a single 3D reference model and a frontal registered face, this study focuses on using the reconstructed 3D face for recognition. The recognition performance varies with poses, the closer to the front, the better. Several ways to improve the performance are attempted, including different numbers of fiducial points for alignment, multiple reference models considered in the reconstruction phase, and both frontal and profile poses available in the gallery. These attempts make this approach competitive to the state-of-the-art methods.

A feedback vertex set F of an undirected graph G is a vertex subset of G whose removal results in a forest. Such a set F is said to be independent if F forms an independent set of G. In this paper, we study the problem of finding an independent feedback vertex set of a given graph with the minimum number of vertices, from the viewpoint of graph classes. This problem is NP-hard even for planar bipartite graphs of maximum degree four. However, we show that the problem is solvable in linear time for graphs having tree-like structures, more specifically, for bounded treewidth graphs, chordal graphs and cographs. We then give a fixed-parameter algorithm for planar graphs when parameterized by the solution size. Such a fixed-parameter algorithm is already known for general graphs, but our algorithm is exponentially faster than the known one.

In this paper, we propose an efficient tracking method that is formulated as a multi-task reverse sparse representation problem. The proposed method learns the representation of all tasks jointly using a customized APG method within several iterations. In order to reduce the computational complexity, the proposed tracking algorithm starts from a feature selection scheme that chooses suitable number of features from the object and background in the dynamic environment. Based on the selected feature, multiple templates are constructed with a few candidates. The candidate that corresponds to the highest similarity to the object templates is considered as the final tracking result. In addition, we present a template update scheme to capture the appearance changes of the object. At the same time, we keep several earlier templates in the positive template set unchanged to alleviate the drifting problem. Both qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods.

In this paper, we improve a width-3 joint sparse form proposed by Okeya, Katoh, and Nogami. After the improvement, the representation can attain an asymtotically optimal complexity found in our previous work. Although claimed as optimal by the authors, the average computation time of multi-scalar multiplication obtained by the representation is 563/1574n+o(n)≈0.3577n+o(n). That number is larger than the optimal complexity 281/786n+o(n)≈0.3575n+o(n) found in our previous work. To optimize the width-3 joint sparse form, we add more cases to the representation. After the addition, we can show that the complexity is updated to 281/786n+o(n)≈0.3575n+o(n), which implies that the modified representation is asymptotically optimal. Compared to our optimal algorithm in the previous work, the modified width-3 joint sparse form uses less dynamic memory, but it consumes more static memory.

What should be the ultimate form of the cloud computing environment? The solution should have two important features; “Fine-Granularity” and “Participation.” To realize an attractive and feasible solution with these features, we propose a “participating fine-grained cloud computing platform” that a large number of personal or small-company resource suppliers participate in, configure and provide cloud computing on. This enables users to be supplied with smaller units of resources such as computing, memory, content, and applications, in comparison with the traditional Infrastructure as a Service (IaaS). Furthermore, to search for nearby resources efficiently among the many available on the platform, we also propose Resource Breadcrumbs (RBC) as a key technology of our proposed platform to provide in-network guidance capability autonomously for users' queries. RBC allows supplier-nodes to distribute guidance information directed to themselves with dedicated control messages; in addition, the information can be logged along the trail of message from supplier to user. With this distributed information, users can to autonomously locate nearby resources. Distributed management also reduces computational load on the central database and enables a participating fine-grained cloud platform at lower cost.

This paper proposes an ultra-low power fully on-chip CMOS relaxation oscillator (ROSC) for a real-time clock application. The proposed ROSC employs a compensation circuit of a comparator's non-idealities caused by offset voltage and delay time. The ROSC can generate a stable, and 32-kHz oscillation clock frequency without increasing power dissipation by using a low reference voltage and employing a novel compensation architecture for comparators. Measurement results in a 0.18-$mu$m CMOS process demonstrated that the circuit can generate a stable clock frequency of 32.55,kHz with low power dissipation of 472,nW at 1.8-V power supply. Measured line regulation and temperature coefficient were 1.1%/V and 120,ppm/$^{circ}$C, respectively.

Developing a practical and accurate statistical parser for low-resourced languages is a hard problem, because it requires large-scale treebanks, which are expensive and labor-intensive to build from scratch. Unsupervised grammar induction theoretically offers a way to overcome this hurdle by learning hidden syntactic structures from raw text automatically. The accuracy of grammar induction is still impractically low because frequent collocations of non-linguistically associable units are commonly found, resulting in dependency attachment errors. We introduce a novel approach to building a statistical parser for low-resourced languages by using language parameters as a guide for grammar induction. The intuition of this paper is: most dependency attachment errors are frequently used word orders which can be captured by a small prescribed set of linguistic constraints, while the rest of the language can be learned statistically by grammar induction. We then show that covering the most frequent grammar rules via our language parameters has a strong impact on the parsing accuracy in 12 languages.

Objects tracking methods have been wildly used in the field of video surveillance, motion monitoring, robotics and so on. Particle filter is one of the promising methods, but it is difficult to apply to real-time objects tracking because of its high computation cost. In order to reduce the processing cost without sacrificing the tracking quality, this paper proposes a new method for real-time 3D objects tracking, using parallelized particle filter algorithms by MapReduce architecture which is running on GPGPU. Our methods are as follows. First, we use a Kinect to get the 3D information of objects. Unlike the conventional 2D-based objects tracking, 3D objects tracking adds depth information. It can track not only from the x and y axis but also from the z axis, and the depth information can correct some errors in 2D objects tracking. Second, to solve the high computation cost problem, we use the MapReduce architecture on GPGPU to parallelize the particle filter algorithm. We implement the particle filter algorithms on GPU and evaluate the performance by actually running a program on CUDA5.5.

Provable Data Possession (PDP) schemes enable users to efficiently check the integrity of their data in the cloud. Support for massive and dynamic sets of data and adaptability to third-party auditing are two key factors that affect the practicality of existing PDP schemes. We propose a secure and efficient PDP system called IDPA-MF-PDP, by exploiting the characteristics of real-world cloud storage environments. The cost of auditing massive and dynamic sets of data is dramatically reduced by utilizing a multiple-file PDP scheme (MF-PDP), based on the data update patterns of cloud storage. Deployment and operational costs of third-party auditing and information leakage risks are reduced by an auditing framework based on integrated data possession auditors (DPAs), instantiated by trusted hardware and tamper-evident audit logs. The interaction protocols between the user, the cloud server, and the DPA integrate MF-PDP with the auditing framework. Analytical and experimental results demonstrate that IDPA-MF-PDP provides the same level of security as the original PDP scheme while reducing computation and communication overhead on the DPA, from linear the size of data to near constant. The performance of the system is bounded by disk I/O capacity.

In a patch-based super-resolution algorithm, a low-resolution patch is influenced by surrounding patches due to blurring. We propose to remove this boundary effect by subtracting the blur from the surrounding high-resolution patches, which enables more accurate sparse representation. We demonstrate improved performance through experimentation. The proposed algorithm can be applied to most of patch-based super-resolution algorithms to achieve additional improvement.

For robust visual tracking, the main challenges of a subspace representation model can be attributed to the difficulty in handling various appearances of the target object. Traditional subspace learning tracking algorithms neglected the discriminative correlation between different multi-view target samples and the effectiveness of sparse subspace learning. For learning a better subspace representation model, we designed a discriminative graph to model both the labeled target samples with various appearances and the updated foreground and background samples, which are selected using an incremental updating scheme. The proposed discriminative graph structure not only can explicitly capture multi-modal intraclass correlations within labeled samples but also can obtain a balance between within-class local manifold and global discriminative information from foreground and background samples. Based on the discriminative graph, we achieved a sparse embedding by using L2,1-norm, which is incorporated to select relevant features and learn transformation in a unified framework. In a tracking procedure, the subspace learning is embedded into a Bayesian inference framework using compound motion estimation and a discriminative observation model, which significantly makes localization effective and accurate. Experiments on several videos have demonstrated that the proposed algorithm is robust for dealing with various appearances, especially in dynamically changing and clutter situations, and has better performance than alternatives reported in the recent literature.

This paper describes a method of identifying nonlocal dependencies in incremental parsing. Our incremental parser inserts empty elements at arbitrary positions to generate partial parse trees including empty elements. To identify the correspondence between empty elements and their fillers, our method adapts a hybrid approach: slash feature annotation and heuristic rules. This decreases local ambiguity in incremental parsing and improves the accuracy of our parser.

Compressive sensing (CS) exploits the sparsity or compressibility of signals to recover themselves from a small set of nonadaptive, linear measurements. The number of measurements is much smaller than Nyquist-rate, thus signal recovery is achieved at relatively expense. Thus, many signal processing problems which do not require exact signal recovery have attracted considerable attention recently. In this paper, we establish a framework for parameter estimation of a signal corrupted by additive colored Gaussian noise (ACGN) based on compressive measurements. We also derive the Cramer-Rao lower bound (CRB) for the frequency estimation problems in compressive domain and prove some useful properties of the CRB under different compressive measurements. Finally, we show that the theoretical conclusions are along with experimental results.

Secure sets and defensive alliances in graphs are studied. They are sets of vertices that are safe in some senses. In this paper, we first present a fixed-parameter algorithm for finding a small secure set, whose running time is much faster than the previously known one. We then present improved bound on the smallest sizes of defensive alliances and secure sets for hypercubes. These results settle some open problems paused recently.

The indoor fingerprinting localization technology has received more attention in recent years due to the increasing demand of the indoor location based services (LBSs). However, a high quality of the LBS requires a positioning solution with high accuracy and low computational complexity. The particle swarm optimization (PSO) technique, which emulates the social behavior of a flock of birds to search for the optimal solution of a special problem, can provide attractive performance in terms of accuracy, computational efficiency and convergence rate. In this paper, we adopt the PSO algorithm to estimate the location information. First, our system establishes a Bayesian-rule based objective function. It then applies PSO to identify the optimal solution. We also propose a hybrid access point (AP) selection method to improve the accuracy, and analyze the effects of the number and the initial positions of particles on the localization performance. In order to mitigate the estimation error, we use the Kalman Filter to update the initial estimated location via the PSO algorithm to track the trail of the mobile user. Our analysis indicates that our method can reduce the computational complexity and improve the real-time performance. Numerous experiments also demonstrate that our proposed localization and tracking system achieve higher localization accuracy than existing systems.

Network virtualization is one of the promising technologies that can increase flexibility, diversity, and manageability of networks. Building optimal virtual networks across multiple domains is getting much attention, but existing studies were based on an unrealistic assumption, that is, providers' private information can be disclosed; as is well known, providers never actually do that. In this paper, we propose a new method that solves this multi-domain problem without revealing providers' private information. Our method uses an advanced secure computation technique called multi-party computation (MPC). Although MPC enables existing unsecured methods to optimize virtual networks securely, it requires very large time to finish the optimization due to the MPC's complex distributed protocols. Our method, in contrast, is designed to involve only a small number of MPC operations to find the optimal solution, and it allows providers to execute a large part of the optimization process independently without heavy distributed protocols. Evaluation results show that our method is faster than an existing method enhanced with MPC by several orders of magnitude. We also unveil that our method has the same level of embedding cost.

Video Up-scaling is a hotspot in TV display area; as an important brunch of Video Up-scaling, Texture-Based Video Up-scaling (TBVU) method shows great potential of hardware implementation. Coarse-grained Reconfigurable Architecture (CGRA) is a very promising processor; it is a parallel computing platform which provides high performance of hardware, high flexibility of software, and dynamical reconfiguration ability. In this paper we propose an implementation of TBVU on CGRA. We fully exploit the characters of TBVU and utilize several techniques to reduce memory I/O operation and total execution time. Experimental results show that our work can greatly reduce the I/O operation and the execution time compared with the non-optimized ones. We also compare our work with other platforms and find great advantage in execution time and resource utilization rate.