The conventional covariance matrix technique based subspace methods, such as the 2-D Capon algorithm and computationally efficient ESPRIT-type algorithms, are invalid with a single snapshot in a bistatic MIMO radar. A novel matrix pencil method is proposed for the direction of departures (DODs) and direction of arrivals estimation (DOAs) estimation. The proposed method constructs an enhanced matrix from the direct sampled data, and then utilizes the matrix pencil approach to estimate DOAs and DODs, which are paired automatically. The proposed method is able to provide favorable and unambiguous angle estimation performance with a single snapshot. Simulation results are presented to verify the effectiveness of the proposed method.

We analyze the effect of the propagation of route request packets in ad hoc network routing protocols such as DSR and AODV. So far it has not been clear how the number density of route request packets depends on propagation and hop counts. By stochastic analysis, it is found that the collisions of route request packets can be avoided efficiently by adjusting the number of the relevant nodes in the early stages of propagation.

A fast and accurate architecture exploration for high performance and low energy VLIW data-path is proposed. The main contribution is a method to find Pareto optimal FU structures, i.e., the optimal number of FUs and the best instruction assignment for each FU. The proposed architecture exploration method is based on GA and enables the effective exploration of vast solution space. Experimental results showed that proposed method was able to achieve fast and accurate architecture exploration. For most cases, the estimation error was less than 1%.

Packet loss and energy dissipation are two major challenges of designing large-scale wireless sensor networks. Since sensing data is spatially correlated, compressed sensing (CS) is a promising reconstruction scheme to provide low-cost packet error correction and load balancing. In this letter, assuming a multi-hop network topology, we present a CS-oriented data aggregation scheme with a new measurement matrix which balances energy consumption of the nodes and allows for recovery of lost packets at fusion center without additional transmissions. Comparisons with existing methods show that the proposed scheme offers higher recovery precision and less energy consumption on TinyOS.

This paper presents an experimental evaluation of an ocean wave remote sensing system that uses bistatic GPS signal reflection to estimate wave characteristics. In our previous paper, a bistatic ocean wave remote sensing system by GPS was proposed to estimate the characteristics of sea swell near a harbor, and was also evaluated by numerical simulations. In the next phase, a prototype system has been developed and some basic experiments have been carried out in a coastal area in order to evaluate the system experimentally. In this paper, we will outline the prototype system. The system mainly consists of an array antenna, a front-end, and an estimator for ocean wave characteristics. Next, we explain that the estimator for ocean wave characteristics can identify each signal reflected from the ocean waves. Finally, the experiments show that the prototype system can receive the reflected signals from the sea-surface near the coast, and estimate the wave period and wavelength in the direction of the array antenna.

Given specified parameters, the number of check nodes, the expected girth and the variable node degrees, the Progressive Weight-Growth (PWG) algorithm is proposed to generate high rate low-density parity-check (LDPC) codes. Based on the theoretic foundation that is to investigate the girth impact by adding/removing variable nodes and edges of the Tanner graph, the PWG progressively increases column weights of the parity check matrix without violating the constraints defined by the given parameters. The analysis of the computational complexity and the simulation of code performance show that the LDPC codes by the PWG provide better or comparable performance in comparison with LDPC codes by some well-known methods (e.g., Mackay's random constructions, the PEG algorithm, and the bit-filling algorithm).

Cooperative spectrum sensing is an effective approach that utilizes spatial diversity gain to improve detection performance. Most studies assume that the background noise is exactly known. However, this is not realistic because of noise uncertainty which will significantly degrade the performance. A novel weighted hard combination algorithm with two thresholds is proposed by dividing the whole range of the local test statistic into three regions called the presence, uncertainty and absence regions, instead of the conventional two regions. The final decision is made by weighted combination at the common receiver. The key innovation is the full utilization of the information contained in the uncertainty region. It is worth pointing out that the weight coefficient and the local target false alarm probability, which determines the two thresholds, are also optimized to minimize the total error rate. Numerical results show this algorithm can significantly improve the detection performance, and is more robust to noise uncertainty than the existing algorithms. Furthermore, the performance of this algorithm is not sensitive to the local target false alarm probability at low SNR. Under sufficiently high SNR condition, this algorithm reduces to the improved one-out-of-N rule. As noise uncertainty is unavoidable, this algorithm is highly practical.

As the use of information technology (IT) is explosively spreading, reducing the power consumption of IT devices such as servers has become an important social challenge. Nevertheless, while the efficiency of the power supply modules integrated into computers has recently seen significant improvements, their overall efficiency generally depends on load rates. This is especially true under low power load conditions, where it is known that efficiency decreases drastically. Recently, power-saving techniques that work by controlling the power module configuration under low power load conditions have been considered. Based on such techniques, further efficiency improvements can be expected by an adaptive efficiency controls which interlocks the real-time data processing load status with the power supply configuration control. In this study, the performance counters built into the processor of a computer are used to predict power load variations and an equation that predicts the power consumption levels is defined. In a server application experiment utilizing prototype computer hardware and regression analysis, it is validated that the equation could precisely predict processor power consumption. The evaluation shows that significant power supply efficiency improvements could be achieved especially for light load condition. The dependency of the efficiency improvement and operation period is investigated and preferable time scale of the adaptive control is proposed.

Single view distributed video coding (DVC) is a coding method that allows for the computational complexity of the system to be shifted from the encoder to the decoder. This property promotes the use of DVC in systems where processing power or energy use at the encoder is constrained. Examples include wireless devices and surveillance. This paper proposes a multi-hypothesis transform domain single-view DVC system that performs symbol level coding with a non-binary low-density parity-check code. The main contributions of the system relate to the methods used for combining multiple side information hypotheses at the decoder. The system also combines interpolation and extrapolation in the side information creation process to improve the performance of the system over larger group-of-picture sizes.

This letter deals with a sparse signal recovery problem and proposes a new algorithm based on the iterative reweighted least squares (IRLS) algorithm. We assume that the non-zero values of a sparse signal is always greater than a given constant and modify the IRLS algorithm to satisfy this assumption. Numerical results show that the proposed algorithm recovers a sparse vector efficiently.

Conventional adaptive notch filter based on an infinite impulse response (IIR) filter is well known. However, this kind of adaptive notch filter has a problem of stability due to its adaptive IIR filter. In addition, tap coefficients of this notch filter converge to solutions with bias error. In order to solve these problems, an adaptive notch filter using Fourier sine series (ANFF) is proposed. The ANFF is stable because an adaptive IIR filter is not used as an all-pass filter. Further, the proposed adaptive notch filter is robust enough to overcome effects of a disturbance signal, due to a structure of the notch filter based on an exponential filter and line symmetry of auto correlation.

Many discrete functions are often compactly represented by Decision Diagrams (DD). The main problem in the construction of decision diagrams is the space and time requirements. While constructing a decision diagram the memory requirement may grow exponentially with the function. Also, large numbers of temporary nodes are created while constructing the decision diagram for a function. Here the problem of reducing the number of temporary nodes is addressed with respect to the PLA specification format of a function, where the function is represented using a set of cubes. Usually a DD is constructed by recursively processing the input cubes in the PLA specification. The DD, representing a sub function, is specified by a single cube. This DD is merged with a master DD, which represents the entire previously processed cubes. Thus the master DD is constructed recursively, until all the cubes in the input cube set are processed. In this paper, an efficient method is proposed, which reorders and also partitions the cube set into unequal number of cubes per subset, in such a way that, the number of temporary nodes created and the number of logical operations done, during the merging of cubes with the master DD are reduced. This results in the reduction of space and time required for the construction of DDs to a remarkable extent.

Ultra-wideband pulse radar exhibits high range resolution, and excellent capability in penetrating dielectric media. With that, it has great potential as an innovative non-destructive inspection technique for objects such as human body or concrete walls. For suitability in such applications, we have already proposed an accurate permittivity estimation method for a 2-dimensional dielectric object of arbitrarily shape and clear boundary. In this method, the propagation path estimation inside the dielectric object is calculated, based on the geometrical optics (GO) approximation, where the dielectric boundary points and its normal vectors are directly reproduced by the range point migration (RPM) method. In addition, to compensate for the estimation error incurred using the GO approximation, a waveform compensation scheme employing the finite-difference time domain (FDTD) method was incorporated, where an initial guess of the relative permittivity and dielectric boundary are employed for data regeneration. This study introduces the 3-dimensional extension of the above permittivity estimation method, aimed at practical uses, where only the transmissive data are effectively extracted, based on quantitative criteria that considers the spatial relationship between antenna locations and the dielectric object position. Results from a numerical simulation verify that our proposed method accomplishes accurate permittivity estimations even for 3-dimensional dielectric medium of wavelength size.

Outsourcing to a cloud storage brings forth new challenges for the efficient utilization of computing resources as well as simultaneously maintaining privacy and security for the outsourced data. Data deduplication refers to a technique that eliminates redundant data on the storage and the network, and is considered to be one of the most-promising technologies that offers efficient resource utilization in the cloud computing. In terms of data security, however, deduplication obstructs applying encryption on the outsourced data and even causes a side channel through which information can be leaked. Achieving both efficient resource utilization and data security still remains open. This paper addresses this challenging issue and proposes a novel solution that enables data deduplication while also providing the required data security and privacy. We achieve this goal by constructing and utilizing equality predicate encryption schemes which allow to know only equivalence relations between encrypted data. We also utilize a hybrid approach for data deduplication to prevent information leakage due to the side channel. The performance and security analyses indicate that the proposed scheme is efficient to securely manage the outsourced data in the cloud computing.

A stochastic hybrid system can express complex dynamical systems such as biological systems and communication networks, but computation for analysis and control is frequently difficult. In this paper, for a class of stochastic hybrid systems, a discrete abstraction method in which a given system is transformed into a finite-state system is proposed based on the notion of bounded bisimulation. In the existing discrete abstraction method based on bisimulation, a computational procedure is not in general terminated. In the proposed method, only the behavior for the finite time interval is expressed as a finite-state system, and termination is guaranteed. Furthermore, analysis of genetic toggle switches is also discussed as an application.

In this paper, the measurement of capacitance variation, of an on-chip power distribution network (PDN) due to the change of internal states of a CMOS logic circuit, is studied. A state-dependent PDN-capacitance model that explains measurement results will be also proposed. The model is composed of capacitance elements related to MOS transistors, signal and power supply wires, and substrate. Reflecting the changes of electrode potentials, the capacitance elements become state-dependent. The capacitive elements are then all connected in parallel between power supply and ground to form the proposed model. By using the proposed model, state-dependence of PDN-capacitances for different logic circuits are studied in detail. The change of PDN-capacitance exceeds 12% of its total capacitance in some cases, which corresponds to 6% shift of anti-resonance frequency. Consideration of the state-dependence is important for modeling the PDN-capacitance.

The second generation digital terrestrial broadcasting system (DVB-T2) is the first broadcasting system employing MISO (Multiple-Input Single-Output) algorithms. The potential MISO gain of this system has been roughly predicted through simulations and field tests. Of course, the potential MISO SFN gain (MISO-SFNG) differs according to the simulation conditions, test methods, and measurement environments. In this paper, network gains of SISO-SFN and MISO-SFN are theoretically derived. Such network gains are also analyzed with respect to the receive power imbalance and coverage distances of SISO and MISO SFN. From the analysis, it is proven that MISO-SFNG is always larger than SISO SFN gain (SISO-SFNG) in terms of the achievable SNR. Further, both MISO-SFNG and SISO-SFNG depend on the power imbalance, but the network gains are constant regardless of the modulation order. Once the field strength of the complete SFN is obtained by coverage planning tools or field measurements, the SFN service coverage can be precisely calibrated by applying the closed-form SFNG formula.

Combining the strong anti-interference advantages of OFDM technology and the time-frequency analysis features of fractional Fourier transform (FFT), we apply OFDM as the coding modulation technology for digital watermarking. Based on the Arnold scrambling and OFDM coding, an innovative DFRFT digital watermarking algorithm is proposed. First, the watermark information is subjected to the Arnold scrambling encryption and OFDM coding transform. Then it is embedded into the FFT domain amplitude. The three parameters of scrambling iterations number, t, FFT order, p, and the watermark information embedded position, L, are used as keys, so that the algorithm has high safety. A simulation shows that the algorithm is highly robust against noise, filtering, compression, and other general attacks. The algorithm not only has strong security, but also makes a good balance between invisibility and robustness. But the possibility of using OFDM technique in robust image watermarking has drawn a very little attention.

In collaborative software developments, many change processes implementing change requests are executed concurrently by different workers. The fact that the workers do not have sufficient information about the others' work and complicated dependencies among artifacts can lead to unexpected inconsistencies among the artifacts impacted by the changes. Most previous studies concentrated only on concurrent changes and considered them separately. However, even when the changes are not concurrent, inconsistencies may still happen if a worker does not recognize the impact of the changes made by other workers on his changes or the impact of his changes on other workers' changes. In addition, the changes in a change process are related to each other through their common target of realizing the change request and the dependencies among the changed artifacts. Therefore, to handle inconsistencies more effectively, we concentrate on both concurrent and non-concurrent changes, and the context of a change, i.e. the change process containing the change, rather than the ongoing changes only. In this paper, we present an inconsistency awareness mechanism and a Change Support Workflow Management System (CSWMS) that realizes this mechanism. By monitoring the progress of the change processes and the ongoing changes in the client workspaces, CSWMS can notify the workers of a (potential) inconsistency in advance along with the context of the inconsistency, that is, the changes causing the inconsistency and the change processes containing these changes. Based on the information provided by CSWMS, the workers can detect and resolve inconsistencies more easily and quickly. Therefore, our research can contribute to building a safer and more efficient collaborative software development environment.

Regularized forward selection is viewed as a method for obtaining a sparse representation in a nonparametric regression problem. In regularized forward selection, regression output is represented by a weighted sum of several significant basis functions that are selected from among a large number of candidates by using a greedy training procedure in terms of a regularized cost function and applying an appropriate model selection method. In this paper, we propose a model selection method in regularized forward selection. For the purpose, we focus on the reduction of a cost function, which is brought by appending a new basis function in a greedy training procedure. We first clarify a bias and variance decomposition of the cost reduction and then derive a probabilistic upper bound for the variance of the cost reduction under some conditions. The derived upper bound reflects an essential feature of the greedy training procedure; i.e., it selects a basis function which maximally reduces the cost function. We then propose a thresholding method for determining significant basis functions by applying the derived upper bound as a threshold level and effectively combining it with the leave-one-out cross validation method. Several numerical experiments show that generalization performance of the proposed method is comparable to that of the other methods while the number of basis functions selected by the proposed method is greatly smaller than by the other methods. We can therefore say that the proposed method is able to yield a sparse representation while keeping a relatively good generalization performance. Moreover, our method has an advantage that it is free from a selection of a regularization parameter.