Existing salient object detection methods either simply use a threshold to detect desired salient objects from saliency map or search the most promising rectangular window covering salient objects on the saliency map. There are two problems in the existing methods: 1) The performance of threshold-dependent methods depends on a threshold selection and it is difficult to select an appropriate threshold value. 2) The rectangular window not only covers the salient object but also contains background pixels, which leads to imprecise salient object detection. For solving these problems, a novel saliency threshold-free method for detecting the salient object with a well-defined boundary is proposed in this paper. We propose a novel window search algorithm to locate a rectangular window on our saliency map, which contains as many as possible pixels belonging the salient object and as few as possible background pixels. Once the window is determined, GrabCut is applied to extract salient object with a well-defined boundary. Compared with existing methods, our approach doesn't need any threshold to binarize the saliency map and additional operations. Experimental results show that our approach outperforms 4 state-of-the-art salient object detection methods, yielding higher precision and better F-Measure.

This paper proposes a Reduced Explicitly Parallel Instruction Computing Processor (REPICP) which is an independently designed, 64-bit, general-purpose microprocessor. The REPICP based on EPIC architecture overcomes the disadvantages of hardware-based superscalar and software-based Very Long Instruction Word (VLIW) and utilizes the cooperation of compiler and hardware to enhance Instruction-Level Parallelism (ILP). In REPICP, we propose the Optimized Lock-Step execution Model (OLSM) and instruction control pipeline method. We also propose reduced innovative methods to optimize the design. The REPICP is fabricated in Artisan 0.13 µm Nominal 1P8M process with 57 M transistors. The die size of the REPICP is 100 mm2 (1010), and consumes only 12 W power when running at 300 MHz.

This paper describes a simulation platform for use in the quantitative assessment of different respiratory motion correction techniques in Coronary MR angiography (CMRA). The simulator incorporates acquisition of motion parameters from heart motion tracking and applies it to a deformable heart model. To simulate respiratory motion, a high-resolution 3-D coronary heart reference image is deformed using the estimated linear transformation from a series of volunteer coronal scout scans. The deformed and motion-affected 3-D coronary images are used to generate segmented k-space data to represent MR data acquisition affected by respiratory motion. The acquired k-space data are then corrected using different respiratory motion correction methods and converted back to image data. The resulting images are quantitatively compared with each other using image-quality measures. Simulation experiment results are validated by acquiring CMRA scans using the correction methods used in the simulation.

The cooperative orthogonal frequency-division multiplexing (OFDM) relaying system is widely regarded as a key design for future broadband mobile cellular systems. This paper focuses on channel estimation in such a system that uses amplify-and-forward (AF) as the relaying strategy. In the cooperative AF relaying, the destination requires the individual (disintegrated) channel state information (CSI) of the source-relay (S-R) and relay-destination (R-D) links for optimum combination of the signals received from source and relay. Traditionally, the disintegrated CSIs are obtained with two channel estimators: one at the relay and the other at the destination. That is, the CSI of the S-R link is estimated at relay and passed to destination, and the CSI of the R-D link is estimated at destination with the help of pilot symbols transmitted by relay. In this paper, a new disintegrated channel estimator is proposed; based on an expectation-maximization (EM) algorithm, the disintegrated CSIs can be estimated solely by the estimator at destination. Therefore, the new method requires neither signaling overhead for passing the CSI of the S-R link to destination nor pilot symbols for the estimation of the R-D link. Computer simulations show that the proposed estimator works well under the signal-to-noise ratios of interest.

This paper identifies a ripple effect problem (REP) that spreads interference to neighbors and proposes a novel channel localization mechanism to decrease the REP in a Wi-Fi system. The proposed mechanism has less blocking probability when compared to a random channel allocation mechanism and also has increased channel reusability. The proposed mechanism in simulation yields less channels BEm as the number of users and Tused increase.

Dynamic virtual network allocation is a promising traffic control model for cloud resident data center which offers virtual data centers for customers from the provider's substrate cloud. Unfortunately, dynamic virtual network allocation designed in the past was aimed to the Internet so it needs distributed control methods to scale with such a large network. The price for the scalability of the completely distributed control method at both virtual layer and substrate layer is the slow convergence of algorithm and the less stability of traffic. In this paper, we argue that the distributed controls in both virtual and substrate networks are not necessary for the cloud resident data center environment, because cloud resident data center uses centralized controller as the way to give network control features to customers. In fact, we can use the centralized algorithm in each virtual data center which is not very large network and the distributed algorithm is only needed in substrate network. Based on the specific properties of this model, we have used optimization theory to re-design the substrate algorithm for periodically re-adjusting virtual link capacity. Results from theoretical analysis, simulations, and experiments show that our algorithm has faster convergence time, simpler calculation and can make better use of the feedback information from virtual networks than the previous algorithm.

Dynamic Programming (DP) based Track-Before-Detect (TBD) algorithm is effective in detecting low signal-to-noise ratio (SNR) targets. However, its complexity increases exponentially as the dimension of the target state space increases, so the exact implementation of DP-TBD will become computationally prohibitive if the state dimension is more than two or three, which greatly prevents its applications to many realistic problems. In order to improve the computational efficiency of DP-TBD, a thresholding process based DP-TBD (TP-DP-TBD) is proposed in this paper. In TP-DP-TBD, a low threshold is first used to eliminate the noise-like (with low-amplitude) measurements. Then the DP integration process is modified to only focuses on the thresholded higher-amplitude measurements, thus huge amounts of computation devoted to the less meaningful low-amplitude measurements are saved. Additionally, a merit function transfer process is integrated into DP recursion to guarantee the inheritance and utilization of the target merits. The performance of TP-DP-TBD is investigated under both optical style Cartesian model and surveillance radar model. The results show that substantial computation reduction is achieved with limited performance loss, consequently TP-DP-TBD provides a cost-efficient tradeoff between computational cost and performance. The effect of the merit function transfer on performance is also studied.

Precise localization of the Wi-Fi Access Point (AP) is becoming increasingly important with the rise of diverse location-based and smart phone-based services. In this article, we propose a new method for precise Wi-Fi AP localization using GPS information of a smart phone. The idea is that the possible area of Wi-Fi AP location, termed AP_Area, is first determined by measuring GPS information and the received signal strength of smart phones. As the number of measurements from users' smart phones increases, the AP_Area is successively narrowed down to the true AP location. Simulation shows the proposed algorithm can detect the Wi-Fi AP's localization within 5 m (probability exceeds 90%).

This letter proposes a multiple symbol differential detection (MSDD) with majority decision method for differentially coded quadrature phase-shift keying (DQPSK) in Rician fading channels. The proposed method shows better BER performance than the conventional MSDD. Simulation results show that the proposed MSDD with a majority decision method improves the system's BER performance for DQPSK signals under the AWGN channel and it approaches asymptotically the theoretical BER performance of coherent detection. Furthermore, the proposed method shows better BER performance under the Rician fading channel with large frequency offsets especially for the range of C/M > 12 dB in comparison with the conventional MSDD.

Cloud computing is an emerging computing paradigm that may have a significant impact on various aspects of the development of information infrastructure. In a Cloud environment, different types of network resources need to be virtualized as a series of service components by network virtualization, and these service components should be further composed into Cloud services provided to end users. Therefore Quality of Service (QoS) aware service composition plays a crucial role in Cloud service provisioning. This paper addresses the problem on how to compose a sequence of service components for QoS guaranteed service provisioning in a virtualization-based Cloud computing environment. The contributions of this paper include a system model for Cloud service provisioning and two approximation algorithms for QoS-aware service composition. Specifically, a system model is first developed to characterize service provisioning behavior in virtualization-based Cloud computing, then a novel approximation algorithm and a variant of a well-known QoS routing procedure are presented to resolve QoS-aware service composition. Theoretical analysis shows that these two algorithms have the same level of time complexity. Comparison study conducted based on simulation experiments indicates that the proposed novel algorithm achieves better performance in time efficiency and scalability without compromising quality of solution. The modeling technique and algorithms developed in this paper are general and effective; thus are applicable to practical Cloud computing systems.

This paper proposes a lattice-reduction-aided MIMO-OFDM receiver with virtual channels; the receiver enables an increase in the downlink transmission speed for a user where the number of transmit antennas is considerably higher than that of the receive antennas. However, the receiver has a higher computational complexity than conventional lattice-reduction-aided MIMO receivers. Accordingly, we also propose novel techniques to reduce the computational complexity for the lattice-reduction-aided MIMO receivers with virtual channels. The proposed MIMO receiver achieves superior performance in 102 MIMO-OFDM systems. Furthermore, the proposed techniques are shown to reduce the computational complexity to approximately 40% of the original configuration in the 102 MIMO-OFDM systems.

Streak cameras are now widely used for measurements of ultra short phenomena, such as those in semi conductor luminescence and plasma gaseous discharge. To further improve the temporal resolution and carry out higher-dimensional measurements, it is necessary to understand the electron beam behavior in detail. Thus, numerical simulations play an important role in the analysis of the streak camera. The authors have been working on the development of a numerical simulation code that uses the finite difference method (FDM) for electric field analysis, the Runge-Kutta (R-K) method for charged particle motion determination, and the particle-in-cell (PIC) method for charge density calculation. However, the use of the PIC method leads to inaccuracy in the charge density calculation in cases of high-density electron beams. To improve the accuracy of the conventional analysis of the streak camera, we perform the boundary element (BE) analysis of the streak camera.

For the modeling of multipath propagation in every wireless systems, the ray tracing method has been widely studied. However, large errors may result due to the approximation of geometrical optics in curved surfaces. This paper therefore focused on the curved surfaces and edges, which are difficult to handle in ray tracing. Examples of curved surfaces can be found in arched cross-section tunnels which are common in highway networks of mountainous areas. The traditional ray tracing method of dividing the curved surface into smaller flat plates is not so accurate as the size of smaller plates may not satisfy the geometrical optics assumption, and the reflection point which satisfies Fermat's principle may not exist. In this work, a new ray tracing method is proposed with 2 contributions. The first one is the implementation of the reflection coefficient for curved surfaces in ray tracing. The second is applying the physical optics method on the caustics region. To evaluate these methods, path gain simulation results for an arched cross-section model are compared with measurements made inside an arched tunnel. To further improve the simulation results, the effect of rough surface is introduced, and the results are again compared with measurement.

We deal with the scattering of a scalar plane wave by a half plane with a sinusoidally deformed edge from a straight edge by a physical optics approximation. The normal incidence of a plane wave to an edge is assumed. A contribution of an edge to the field integral is asymptotically evaluated and the basic properties of the scattering caused by the edge deformation is clarified. The scattering pattern has peaks at specific scattering angles, which agree with diffraction angles calculated by the well-known grating formula for normal incidence. Some numerical examples are shown and it is shown that the results are in good agreement with the results obtained by the GTD method for low angle incidence.

We introduce a new kind of P2P traffic localization technique, called Netpherd, benefiting from the network virtualization technique for its successful deployment. Netpherd exploits one feature of P2P applications, a peer selection adaptation (i.e., preferring peers who are likely to provide better performance) for the traffic localization. Netpherd tries to enable local peers (i.e., peers in target network domain) to communicate with each other by affecting the peer selection adaptation. To affect the peer selection adaptation, Netpherd adds artificial delay to inter-domain traffic going to local peers. Our experiment conducted over Internet testbed verifies that Netpherd achieves the traffic localization and also improves the content download performance with the network delay insertion. In addition, we show that how the network virtualization technique can be utilized for efficient and graceful implementation of Netpherd.

In this paper, we study four simple but fundamental cooperative protocols operating in the decode-and-forward (DF) fashion. Intuitively, finding an appropriate relay for such protocols may greatly improve the outage performance in practice. To this end, we investigate the issue of relay selection in this paper. Specifically, using the asymptotic outage probability, we define and derive the cooperative gain (CG) which quantitatively evaluates the superiority of cooperation over direct transmission. To simplify the process of relay selection, we derive the cooperative region (CR) where a relay is necessarily invoked to aid the communication from source to destination. With the aid of CG and CR, we propose our relay selection algorithm requiring the geographical information rather than the instantaneous channel state information (CSI), and predict the optimal relay locations. In addition, two diversity bounds are also prepared and compared. Finally, both simulations and numerical results are provided on the asymptotic outage probability, CG and CR.

Multiparty Simultaneous Quantum Identity Authentication (MSQIA) is a form of quantum authentication protocol in which a verifier can simultaneously authenticate all users. Yang et al. previously proposed the MSQIA protocol, but in that protocol the user's key is revealed by the fake signal attack. This paper proposes a new MSQIA protocol that is secure against fake signal attacks. It also demonstrates that this scheme is secure against several well-known attacks on MSQIA. This protocol can be efficiently used for MSQIA in a network and is feasible with current technology, like the protocol of Yang et al.

We present a novel point of interest (POI) construction approach based on street-level imagery (SLI) such as Google StreetView. Our method consists of: (1) the creation of a conflation map between an SLI trace and a vector map; (2) the detection of the corresponding buildings between the SLI scene and the conflation map; and (3) POI name extraction from a signboard in the SLI scene by user-interactive text recognition. Finally, a POI is generated through a combination of the POI name and attributes of the building object on a vector map. The proposed method showed recall of 92.99% and precision of 97.10% for real-world POIs.

Recent idea visualization programs still lack automatic idea summarization capabilities. This paper presents a knowledge-based method for automatically providing a short piece of English text about a topic to each idea group in idea charts. This automatic topic identification makes used Yet Another General Ontology (YAGO) and Wordnet as its knowledge bases. We propose a novel topic selection method and we compared its performance with three existing methods using two experimental datasets constructed using two idea visualization programs, i.e., the KJ Method (Kawakita Jiro Method) and mind-mapping programs. Our proposed topic identification method outperformed the baseline method in terms of both performance and consistency.

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.