Step-edge vertical channel organic field-effect transistors (SVC-OFETs) with a very short channel have been fabricated by a novel selective electrospray deposition (SESD) method. We propose the SESD method for the fabrication of SVC-OFETs based on a 6,13-bis(triisopropyl-silylethynyl) pentacene (TIPS-pentacene) semiconductor layer formed by SESD. In the SESD method, an electric field is applied between the nozzle and selective patterned electrodes on a substrate. We demonstrated that the solution accumulates on the selected electrode pattern by controlling the voltage applied to the electrode.

For estimating user's location, Global Navigation Satellite System (GNSS) is very useful. Especially, Global Positioning System (GPS) by USA is very popular. A GPS receiver needs multiple satellites (usually 4 and more satellites). Propagation to the satellites needs line-of-sight. However, in urban area, there are many buildings. Received signals tend to become bad quality. Such signals are often called as non-line-of-sight (NLOS) or multipath signals. The problem is that the receiver cannot get line-of-sight signals from adequate number of the satellites coinstantaneously. This case leads to degradation of estimation quality or impossibility of estimation. In this paper, we will introduce a novel estimation algorithm, which can estimate own position with as low number of satellites as possible. The proposal achieves the estimation by only two satellites. The proposal also uses a traveling distance sensor which is often equipped on vehicles. By recorded satellite data, we will confirm our effectiveness.

Pedestrian dead reckoning (PDR) is an effective positioning means that can be used in urban-canyon environments where the accuracy of GPS is significantly degraded. Magnetic disturbances caused by artificial objects affect the accuracy of positioning if the PDR system uses a magnetometer to estimate the heading direction. Conventional PDR systems consider magnetic disturbances as unpredictable error sources, but the error becomes predictable and removable if the amount of the deviation in the magnetic field can be calculated at any position. In this study, we propose a method to correct the heading direction by referring to a map of magnetic deviation. The experimental results show that our method reduced the error in the heading direction caused by magnetic disturbances. Our approach removed the error components that differ depending on the position, and consequently, the resultant trajectory represented better the shape of the true trajectory.

Most recent work on cooperative spectrum sensing using cognitive radios has focused on issues involving the sensing channels and seemed to ignore those involving the reporting channels. Furthermore, no research has treated the effect of correlated composite Rayleigh-lognormal fading, also known as Suzuki fading, in cognitive radio. This paper proposes a technique for reuse of shadowed CRs, discarded during the sensing phase, as amplified-and-forward (AF) diversity relays for other surviving CRs to mitigate the effects of such fading in reporting channels. A thorough analysis of and a closed-form expression for the outage probability of the resulting cooperative AF diversity network in correlated composite Rayleigh-lognormal fading channels are presented in this paper. In particular, an efficient solution to the “PDF of sum-of-powers” of correlated Suzuki-distributed random variables using moment generating function (MGF) is proposed.

Rainbow is one of signature schemes based on the problem solving a set of multivariate quadratic equations. While its signature generation and verification are fast and the security is presently sufficient under suitable parameter selections, the key size is relatively large. Recently, Quaternion Rainbow — Rainbow over a quaternion ring — was proposed by Yasuda, Sakurai and Takagi (CT-RSA'12) to reduce the key size of Rainbow without impairing the security. However, a new vulnerability emerges from the structure of quaternion ring; in fact, Thomae (SCN'12) found that Quaternion Rainbow is less secure than the same-size original Rainbow. In the present paper, we further study the structure of Quaternion Rainbow and show that Quaternion Rainbow is one of sparse versions of the Rainbow. Its sparse structure causes a vulnerability of Quaternion Rainbow. Especially, we find that Quaternion Rainbow over even characteristic field, whose security level is estimated as about the original Rainbow of at most 3/4 by Thomae's analysis, is almost as secure as the original Rainbow of at most 1/4-size.

This letter presents a new blind audio watermarking scheme using eigenvalue decomposition (EVD). Initially, the original audio is divided into frames and the samples of each frame are arranged into a square matrix. EVD is applied to each of these matrices. Watermark data is embedded into the largest eigenvalue of each diagonal matrix by quantization. Data embedding rate of the proposed scheme is 172.39bps. Simulation results confirm the imperceptibility of the proposed scheme and its higher robustness against various attacks compared to the state-of-the-art watermarking methods available in the literature.

The stability theory of stream ciphers plays an important role in designing good stream cipher systems. Two algorithms are presented, to determine the optimal shift and the minimum linear complexity of the sequence, that differs from a given sequence over Fq with period qn-1 by one digit. We also describe how the linear complexity changes with respect to one digit differing from a given sequence.

In this paper, we consider distributed estimation where the measurement at each of the distributed sensor nodes is quantized before being transmitted to a fusion node which produces an estimate of the parameter of interest. Since each quantized measurement can be linked to a region where the parameter is found, aggregating the information obtained from multiple nodes corresponds to generating intersections between the regions. Thus, we develop estimation algorithms that seek to find the intersection region with the maximum likelihood rather than the parameter itself. Specifically, we propose two practical techniques that facilitate fast search with significantly reduced complexity and apply the proposed techniques to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our simulation results show that our proposed algorithms achieve good performance with reasonable complexity as compared with the minimum mean squared error (MMSE) and the maximum likelihood (ML) estimators.

Chronic liver disease is a major worldwide health problem. Diagnosis and staging of chronic liver diseases is an important issue. In this paper, we propose a quantitative method of analyzing local morphological changes for accurate and practical computer-aided diagnosis of cirrhosis. Our method is based on sparse and low-rank matrix decomposition, since the matrix of the liver shapes can be decomposed into two parts: a low-rank matrix, which can be considered similar to that of a normal liver, and a sparse error term that represents the local deformation. Compared with the previous global morphological analysis strategy based on the statistical shape model (SSM), our proposed method improves the accuracy of both normal and abnormal classifications. We also propose using the norm of the sparse error term as a simple measure for classification as normal or abnormal. The experimental results of the proposed method are better than those of the state-of-the-art SSM-based methods.

This paper shows a method to find a linear transformation that reduces the number of variables to represent a given incompletely specified index generation function. It first generates the difference matrix, and then finds a minimal set of variables using a covering table. Linear transformations are used to modify the covering table to produce a smaller solution. Reduction of the difference matrix is also considered.

Post-Silicon Tunable (PST) buffers are widely adopted in high-performance integrated circuits to fix timing violations introduced by process variations. In typical optimization procedures, the statistical timing analysis of the circuits with PST clock buffers will be executed more than 2000 times for large scale circuits. Therefore, the efficiency of the statistical timing analysis is crucial to the PST clock buffer optimization algorithms. In this paper, we propose a stochastic collocation based efficient statistical timing analysis method for circuits with PST buffers. In the proposed method, we employ the Howard algorithm to calculate the clock periods of the circuits on less than 100 deterministic sparse-grid collocation points. Afterwards, we use these obtained clock periods to derive the yield of the circuits according to the stochastic collocation theory. Compared with the state-of-the-art statistical timing analysis method for the circuits with PST clock buffers, the proposed method achieves up to 22X speedup with comparable accuracy.

This paper aims to achieve a high-quality exposure level quantification of whole-body average-specific absorption rates (WBA-SARs) for small animals in a medium-size reverberation chamber (RC). A two-step method, which incorporates the finite-difference time-domain (FDTD) numerical solutions with electric field measurements in an RC-type exposure system, has been used as an evaluation method to determine the whole-body exposure level in small animals. However, there is little data that quantitatively demonstrate the validity and accuracy of this method in an RC up to now. In order to clarify the validity of the two-step method, we compare the physical quantities in terms of electric field strength and WBA-SARs by using a direct numerical assessment method known as the method of moments (MoM) with ten homogenous gel phantoms placed in an RC with 2GHz exposure. The comparison results show that the relative errors between the two-step method and the MoM approach are approximately below 10%, which reveals the validity and usefulness of the two-step technique. Finally, we perform a dosimetric analysis of the WBA-SARs for anatomical mouse models with the two-step method and determine the input power related to our developed RC-exposure system to achieve a target exposure level in small animals.

Boneh et al. proposed the new idea of pairing-based cryptography by using the composite order group instead of prime order group. Recently, many cryptographic schemes using pairings of composite order group were proposed. Miller's algorithm is used to compute pairings, and the time of computing the pairings depends on the cost of calculating the Miller loop. As a method of speeding up calculations of the pairings of prime order, the number of iterations of the Miller loop can be reduced by choosing a prime order of low Hamming weight. However, it is difficult to choose a particular composite order that can speed up the pairings of composite order. Kobayashi et al. proposed an efficient algorithm for computing Miller's algorithm by using a window method, called Window Miller's algorithm. We can compute scalar multiplication of points on elliptic curves by using a window hybrid binary-ternary form (w-HBTF). In this paper, we propose a Miller's algorithm that uses w-HBTF to compute Tate pairing efficiently. This algorithm needs a precomputation both of the points on an elliptic curve and rational functions. The proposed algorithm was implemented in Java on a PC and compared with Window Miller's Algorithm in terms of the time and memory needed to make their precomputed tables. We used the supersingular elliptic curve y2=x3+x with embedding degree 2 and a composite order of size of 2048-bit. We denote w as window width. The proposed algorithm with w=6=2·3 was about 12.9% faster than Window Miller's Algorithm with w=2 although the memory size of these algorithms is the same. Moreover, the proposed algorithm with w=162=2·34 was about 12.2% faster than Window Miller's algorithm with w=7.

We proposed a through-hole based transformer between microstrip line and post-wall waveguide (PWW). The transformer is based on a through-hole which is electrically separated from top and bottom broad walls of a waveguide by ring-shaped spaces. The proposed transformer shows a considerable merit compared with a conventional one based on blind-via in terms of fabrication and cost because it can be realized by through-holes in general printed-circuit-board technology. We selected liquid crystal polymer (LCP) as the material of substrates because of its lower dielectric loss and easy fabrication. The transformer was fabricated and measured. The loss associated with the mode conversion is estimated to be around 0.32,dB, and the bandwidth for a reflection smaller than $-15$,dB is 8,GHz, i.e., from 59 to 67,GHz.

The Hough voting framework is a popular approach to parts based pedestrian detection. It works by allowing image features to vote for the positions and scales of pedestrians within a test image. Each vote is cast independently from other votes, which allows for strong occlusion robustness. However this approach can produce false pedestrian detections by accumulating votes inconsistent with each other, especially in cluttered scenes such as typical street scenes. This work aims to reduce the sensibility to clutter in the Hough voting framework. Our idea is to use object segmentation and object pose parameters to enforce votes' consistency both at training and testing time. Specifically, we use segmentation and pose parameters to guide the learning of a pedestrian model able to cast mutually consistent votes. At test time, each candidate detection's support votes are looked upon from a segmentation and pose viewpoints to measure their level of agreement. We show that this measure provides an efficient way to discriminate between true and false detections. We tested our method on four challenging pedestrian datasets. Our method shows clear improvements over the original Hough based detectors and performs on par with recent enhanced Hough based detectors. In addition, our method can perform segmentation and pose estimation as byproducts of the detection process.

Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.

An accurate time-of-arrival (TOA) estimation method for isolated pulses positioning system is proposed in this paper. The method is based on a multi-level crossing timing (MCT) digitizer and least square (LS) criterion, namely LS-MCT method, in which TOA of the received signal is directly described as a parameterized combination of a set of MCT samples of the leading and trailing edges of the signal. The LS-MCT method performs a receiver training process, in which a GPS synchronized training pulse generator (TPG) is used to obtain training data and determine the parameters of the TOA combination. The LS method is then used to optimize the combination parameters with a minimization criterion. The proposed method is compared to the conventional TOA estimation methods such as leading edge level crossing discriminator (LCD), adaptive thresholding (ATH), and signal peak detection (PD) methods. Simulation results show that the proposed algorithm leads to lower sensitivity to signal-to-noise ratio (SNR) and attains better TOA estimation accuracy than available TOA methods.

This study introduces an image sensor based visible light communication (VLC) and its application to pose, position, and range estimations. There are two types of visible-light receiver: a photodiode and an image sensor. A photodiode is usually used as a reception device of VLC, and an image sensor consisting of a large number of pixels can also be used as a VLC reception device. A photodiode detects the signal intensity of incoming light, while an image sensor not only detects the incoming signal intensity but also an accurate angle of arrival of light emitted from a visible light transmitter such as a white LED light. After angles of arrival of light are detected by an image sensor, positioning and data reception can be performed. The ability of an image sensor to detect an accurate angle of arrival will provide attractive applications of VLC such as pose, position calculation, and range estimation. Furthermore, because the image sensor has the ability to spatially separate sources, outdoor positioning even with strong sunlight is possible by discarding the associated pixels of noise sources.

Positive real approximation of sampled frequency data obtained from electromagnetic analysis or measurement is presented. The proposed two methods are based on the Fourier expansion method. The frequency data are approximated by the Laguerre series that becomes the Fourier series with an infinite interval at an imaginary axis of complex plane. The proposed methods do not require any passivity check algorithm. The first method approximates the real parts of sampled data by the piecewise linear matrix function. The second method uses discrete Fourier transform. It is here proven that the approximated matrix function is an interpolative function for the real parts of sampled data. The proposed methods are applied to the approximation of per unit length parameters of multi-conductor system. The capability of the proposed methods is demonstrated.

The next generation wireless broadcasting systems combining with MIMO technology has drawn much attention recently. Considering the coexistence of receivers equipped with different numbers of antennas in these systems, there exists the special requirement to maximize the transmission rate for receivers having more antennas, while guaranteeing the normal rate for receivers having less antennas. In this letter, superposition coding is proposed to fulfill this requirement and the concept of broadcast cluster is introduced, wherein the optimized power allocation parameters are derived. The BER simulations for multiple services are provided to verify the significant SNR performance gap between receivers with various numbers of receive antennas.