A sensing efficiency optimization scheme based on two-stage spectrum sensing that maximizes the achievable throughput of the secondary network and minimizes the average sensing time is proposed in this paper. A selection method for the threshold is proposed and proved to ensure optimal sensing performance. An effective iterative algorithm is presented to solve the constructed efficiency optimization problem.

Polycrystalline Cu(In,Ga)Se2 (CIGS) films were prepared by heat treatment of metallic precursors using dimethylselenium (Se(CH3)2), which is a less hazardous Se source than H2Se gas. CIGS films were fabricated at various Se(CH3)2 supply rates. We investigated the effect of the Se(CH3)2 supply rate on the crystal phase and surface morphology of the films.

Mining and explaining relationships between concepts are challenging tasks in the field of knowledge search. We propose a new approach for the tasks using disjoint paths formed by links in Wikipedia. Disjoint paths are easy to understand and do not contain redundant information. To achieve this approach, we propose a naive method, as well as a generalized flow based method, and a technique for mining more disjoint paths using the generalized flow based method. We also apply the approach to classification of relationships. Our experiments reveal that the generalized flow based method can mine many disjoint paths important for understanding a relationship, and the classification is effective for explaining relationships.

We have proposed a novel geometric model of the eye in order to avoid the problems faced while using the conventional spherical model of the cornea for three dimensional (3D) model-based gaze estimation. The proposed model models the eye, including the boundary region of the cornea, as a general surface of revolution about the optical axis of the eye. Furthermore, a method for calculating the point of gaze (POG) on the basis of our model has been proposed. A prototype system for estimating the POG was developed using this method. The average root mean square errors (RMSEs) of the proposed method were experimentally found to be smaller than those of the gaze estimation method that is based on a spherical model of the cornea.

This paper presents a system for automatic generation of dancing animation that is synchronized with a piece of music by re-using motion capture data. Basically, the dancing motion is synthesized according to the rhythm and intensity features of music. For this purpose, we propose a novel meta motion graph structure to embed the necessary features including both rhythm and intensity, which is constructed on the motion capture database beforehand. In this paper, we consider two scenarios for non-streaming music and streaming music, where global search and local search are required respectively. In the case of the former, once a piece of music is input, the efficient dynamic programming algorithm can be employed to globally search a best path in the meta motion graph, where an objective function is properly designed by measuring the quality of beat synchronization, intensity matching, and motion smoothness. In the case of the latter, the input music is stored in a buffer in a streaming mode, then an efficient search method is presented for a certain amount of music data (called a segment) in the buffer with the same objective function, resulting in a segment-based search approach. For streaming applications, we define an additional property in the above meta motion graph to deal with the unpredictable future music, which guarantees that there is some motion to match the unknown remaining music. A user study with totally 60 subjects demonstrates that our system outperforms the stat-of-the-art techniques in both scenarios. Furthermore, our system improves the synthesis speed greatly (maximal speedup is more than 500 times), which is essential for mobile applications. We have implemented our system on commercially available smart phones and confirmed that it works well on these mobile phones.

Virtualization technologies are widely deployed in data centers to improve system utilization. However, they increase the workload for operators, who have to manage the structure of virtual networks in data centers. A virtual-network management system which automates the integration of the configurations of the virtual networks is provided. The proposed system collects the configurations from server virtualization platforms and VLAN-supported switches, and integrates these configurations according to a newly developed XML-based management information model for virtual-network configurations. Preliminary evaluations show that the proposed system helps operators by reducing the time to acquire the configurations from devices and correct the inconsistency of operators' configuration management database by about 40 percent. Further, they also show that the proposed system has excellent scalability; the system takes less than 20 minutes to acquire the virtual-network configurations from a large scale network that includes 300 virtual machines. These results imply that the proposed system is effective for improving the configuration management process for virtual networks in data centers.

This paper investigates the non-data-aided (NDA) carrier frequency estimation of amplitude and phase shift keying (APSK) signals. The true Cramer-Rao bound (CRB) for NDA frequency estimation of APSK signals are derived and evaluated numerically. Characteristic and jitter variance of NDA Luise and Reggiannini (L&R) frequency estimator are analyzed. Verified by Monte Carlo simulations, the analytical results are shown to be accurate for medium-to-high signal-to-noise ratio (SNR) values. Using the proposed closed-form expression, parameters of the algorithm are optimized efficiently to minimize the jitter variance.

In this paper, we consider the problem of waveform optimization for multi-input multi-output (MIMO) radar in the presence of signal-dependent noise. A novel diagonal loading (DL) based method is proposed to optimize the waveform covariance matrix (WCM) for minimizing the Cramer-Rao bound (CRB) which improves the performance of parameter estimation. The resulting nonlinear optimization problem is solved by resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution to the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

The RFID tag system has received a lot of attention for ubiquitous computing. An RFID tag is attached to an object. With the unique ID of the RFID tag, a user identifies the object provided with the RFID tag and derives appropriate information about the object. One important application in the RFID technology is localizing RFID tags, which can be very useful in acquiring the position information concerning the RFID tags. It can be applied to navigation systems and positional detection systems for mobile robots. This paper proposes a new adaptive multi-range-sensing method for 3D localization of passive RFID tags by using a probabilistic approach. In this method, a mobile object (human, robot, etc.) with an RFID reader estimates the positions of RFID tags with multiple communication ranges dynamically. The effectiveness of the proposed method was demonstrated in experiments.

We propose and implement a cloud architecture of virtual Trusted Platform Modules (TPMs) to improve the usability of TPMs. In this architecture, virtual TPMs can be obtained from the TPM cloud on demand. Hence, the TPM functionality is available for applications that do not have physical TPMs in their local platforms. Moreover, the TPM cloud allows users to access their keys and data in the same virtual TPM even if they move to untrusted platforms. The TPM cloud is easy to access for applications in different languages since cloud computing delivers services in standard protocols. The functionality of the TPM cloud is demonstrated by applying it to implement the Needham-Schroeder public-key protocol for web authentications, such that the strong security provided by TPMs is integrated into high level applications. The chain of trust based on the TPM cloud is discussed and the security properties of the virtual TPMs in the cloud is analyzed.

In this paper, to reduce the memory size requirements of IFFT for OFDM-based applications, we propose a new IFFT design technique based on a combined integer mapping of three IFFT input signals: modulated data, pilot and null signals. The proposed method focuses on reducing the size of memory cells in the first two stages of the single-path delay feedback (SDF) IFFT architectures since the first two stages require 75% of the total memory cells. By simulations of 2048-point IFFT design for cognitive radio systems, it is shown that the proposed IFFT design method achieves more than 13% reduction in gate count and 11% reduction in power consumption compared with conventional IFFT design.

A direct inter-mode selection algorithm for P-frames in fast homogeneous H.264/AVC bit-rate reduction transcoding is proposed in this paper. To achieve the direct inter-mode selection, we firstly develop a low-complexity distortion estimation method for fast transcoding, in which the distortion is directly calculated from the decoded residual together with the reference frames. We also present a linear estimation method to approximate the coding rate. With the estimated distortion and rate, the rate-distortion cost can be easily computed in the transcoder. In our algorithm, a method based on the normalized rate difference of P-frames (RP) is used to detect the high motion scene. To achieve fast transcoding, only for the P-frames with RP larger than a threshold, the rate-distortion optimized (RDO) mode decision is performed; meanwhile, the average cost of each inter-mode (ACM) is calculated. Then for the subsequent frames transcoding, the optimal coding mode can be directly selected using the estimated cost and the ACM threshold. Experiments show that the proposed method can significantly simplify the complex RDO mode decision, and achieve transcoding time reductions of up to 62% with small loss of rate-distortion performance.

We propose an optimal access-point (AP) selection algorithm for maximizing the aggregated throughput of each AP (system throughput) while preserving newly arrived-user throughput in multi rate WLAN system. In our algorithm, newly arrived users cooperate with a wireless local area network (WLAN) system they are trying to use, i.e., they are willing to move toward an appropriate AP before the newly arrived user connects to AP. To select the AP by using our AP selection algorithm, the newly arriving users request two novel parameter values, “the minimum acceptable throughput” with which newly arrived users can be satisfied and “the minimum movable distance” in which a user can move to an appropriate AP. While preserving these conditions, we maximize system throughput. When users cannot obtain a throughput greater than “the minimum acceptable throughput” with our proposed AP selection algorithm, they are rejected. Because, if users use streaming applications, which have strict bandwidth demands, with a very low bit-rate connection, they will not be satisfied. Thus, the newly arrived users having low bit-rate connection may be allowed to be rejected before the newly arrived user connects. In this paper, we show the optimal AP by using theoretical proof. We discuss the effectiveness of our proposed AP selection algorithm by using numerical analysis. We also clarify and analyze the characteristics of system throughput. Moreover, we show that a newly arrived user can select the movable distance and acceptable throughput by using examples from graphs depicting every position of newly arrived users. By using the graphs, we also show the relationship between the two parameters (the movable distance and the acceptable throughput) and the optimal AP, and the relationship between the two parameters and optimal system throughput when the movable distance and acceptable throughput are variable.

Most large-scale Peer-to-Peer (P2P) live streaming systems are constructed as a mesh structure, which can provide robustness in the dynamic P2P environment. The pull scheduling algorithm is widely used in this mesh structure, which degrades the performance of the entire system. Recently, network coding was introduced in mesh P2P streaming systems to improve the performance, which makes the push strategy feasible. One of the most famous scheduling algorithms based on network coding is R2, with a random push strategy. Although R2 has achieved some success, the push scheduling strategy still lacks a theoretical model and optimal solution. In this paper, we propose a novel optimal pull-push scheduling algorithm based on network coding, which consists of two stages: the initial pull stage and the push stage. The main contributions of this paper are: 1) we put forward a theoretical analysis model that considers the scarcity and timeliness of segments; 2) we formulate the push scheduling problem to be a global optimization problem and decompose it into local optimization problems on individual peers; 3) we introduce some rules to transform the local optimization problem into a classical min-cost optimization problem for solving it; 4) We combine the pull strategy with the push strategy and systematically realize our scheduling algorithm. Simulation results demonstrate that decode delay, decode ratio and redundant fraction of the P2P streaming system with our algorithm can be significantly improved, without losing throughput and increasing overhead.

In this paper, a low-complexity symbol-spaced turbo frequency domain equalization (FDE) algorithm based on Laurent decomposition is proposed for precoded binary continuous phase modulation (CPM) with modulation index h=1/2. At the transmitter, a precoder is utilized to eliminate the inherent memory of the CPM signal. At the receiver, a matched filter based on Laurent decomposition is utilized to make the detection symbol-spaced. As a result, the symbol-spaced iteration can be taken between the equalizer and the decoder directly without a CPM demodulator, and we derive a symbol-spaced soft interference cancellation frequency domain equalization (SSIC-FDE) algorithm for binary CPM with h=1/2. A new data block structure for FDE of partial response CPM is also presented. The computational complexity analysis and simulations show that this approach provides a complexity reduction and an impressive performance improvement over previously proposed turbo FDE algorithm for binary CPM with h=1/2 in multi-path fading channels.

We propose a novel modular network called the Self-Evolving Modular Network (SEEM). The SEEM has a modular network architecture with a graph structure and these following advantages: (1) new modules are added incrementally to allow the network to adapt in a self-organizing manner, and (2) graph's paths are formed based on the relationships between the models represented by modules. The SEEM is expected to be applicable to evolving functions of an autonomous robot in a self-organizing manner through interaction with the robot's environment and categorizing large-scale information. This paper presents the architecture and an algorithm for the SEEM. Moreover, performance characteristic and effectiveness of the network are shown by simulations using cubic functions and a set of 3D-objects.

This paper employs the nature-inspired approach to investigate the ideal architecture of communication networks as large-scale and complex systems. Conventional architectures are hierarchical with respect to the functions of network operations due entirely to implementation concerns and not to any fundamental conceptual benefit. In contrast, the large-scale systems found in nature are hierarchical and demonstrate orderly behavior due to their space/time scale dependencies. In this paper, by examining the fundamental requirements inherent in controlling network operations, we clarify the hierarchical structure of network operations with respect to time scale. We also describe an attempt to build a new network architecture based on the structure. In addition, as an example of the hierarchical structure, we apply the quasi-static approach to describe user-system interaction, and we describe a hierarchy model developed on the renormalization group approach.

This letter proposes an automatic IQ imbalance compensation technique for quadrature modulators by means of spectrum measurement of RF signal using a spectrum analyzer. The analyzer feeds back only magnitude information of the frequency spectrum of the signal. To realize IQ imbalance compensation, the conventional method of steepest descent is modified; the descent direction is empirically determined and a variable step-size is introduced for accelerating convergence. The experimental results for a four-channel transmitter operating at 11 GHz are presented for verification.

The quasi-ARX neurofuzzy (Q-ARX-NF) model has shown great approximation ability and usefulness in nonlinear system identification and control. It owns an ARX-like linear structure, and the coefficients are expressed by an incorporated neurofuzzy (InNF) network. However, the Q-ARX-NF model suffers from curse-of-dimensionality problem, because the number of fuzzy rules in the InNF network increases exponentially with input space dimension. It may result in high computational complexity and over-fitting. In this paper, the curse-of-dimensionality is solved in two ways. Firstly, a support vector regression (SVR) based approach is used to reduce computational complexity by a dual form of quadratic programming (QP) optimization, where the solution is independent of input dimensions. Secondly, genetic algorithm (GA) based input selection is applied with a novel fitness evaluation function, and a parsimonious model structure is generated with only important inputs for the InNF network. Mathematical and real system simulations are carried out to demonstrate the effectiveness of the proposed method.

Radiation integral areas are localized and reduced based upon the locality of scattering phenomena. In the high frequency, the scattering field is given by the currents, not the entire region, but on the local areas near the scattering centers, such as the stationary phase points and edge diffraction points, due to the cancelling effect of integrand in the radiation integral. The numerical calculation which this locality is implemented into has been proposed for 2-dimensional problems. The scattering field can be approximated by integrating the currents weighted by the adequate function in the local areas whose size and position are determined appropriately. Fresnel zone was previously introduced as the good criterion to determine the local areas, but the determination method was slightly different, depending on the type of scattering centers. The objective of this paper is to advance the Fresnel zone criteria in a 2-dimensional case to the next stage with enhanced generality and applicability. The Fresnel zone number is applied not directly to the actual surface but to the virtual one associated with the modified surface-normal vector satisfying the reflection law. At the same time, the argument in the weighting function is newly defined by the Fresnel zone number instead of the actual distance from the scattering centers. These two revisions bring about the following three advantages; the uniform treatment of various types scattering centers, the smallest area in the localization and applicability to 3-dimensional problems.