Superconducting tunnel junction (STJ) array detectors can exhibit excellent performance with respect to energy resolution, detection efficiency, and counting rate in the soft X-ray energy range, by which those excellent properties STJ array detectors are well suited for detecting X-rays at synchrotron radiation facilities. However, in order to achieve a high throughput analysis for trace impurity elements such as dopants in structural or functional materials, the sensitive area of STJ array detectors should be further enlarged up to more than 10 times larger by increasing the pixel number in array detectors. In this work, for a large STJ-pixel number of up to 1000 within a 10,mm- square compact chip, we have introduced three-dimensional (3D) structure by embedding a wiring layer in a SiO$_{2}$ isolation layer underneath a base electrode layer of STJs. The 3D structure is necessary for close-packed STJ arrangement, avoiding overlay of lead wiring, which is common in conventional two-dimensional layout. The fabricated STJ showed excellent current-voltage characteristics having low subgap currents less than 2,nA, which are the same as those of conventional STJs. An STJ pixel has an energy resolution of 31,eV (FWHM) for C-K$alpha $ (277,eV).

The combination of a halogen-free solvent 1,2,4-trimethylbenzene and unmodified fullerene potentially provides a way to develop environmentally-friendly and cost-effective solution-processed organic photocells. In this paper, the thermal annealing effect on the optical absorption spectra in poly(3-hexylthiophene):unmodified-C$_{60}$ composites with various compositions is reported. It is found that the onset temperature of the absorption spectrum change is higher in the composites with higher fullerene content. It is speculated that strong interaction between the polymer main chain and C$_{60}$ tends to suppress the reorientation of polymer main chains in a composite with high C$_{60}$ content.

The fast multipole method (FMM) for N-body simulations is attracting much attention since it requires minimal communication between computing nodes. We implemented hardware pipelines specialized for the FMM on an FPGA device, the GRAPE-9. An N-body simulation with 1.6×107 particles ran 16 times faster than that on a CPU. Moreover the particle-to-particle stage of the FMM on the GRAPE-9 executed 2.5 times faster than on a GPU in a limited case.

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.

We aim at constructing adaptive oblivious transfer protocols, enjoying fully simulatable security, from various well-known assumptions such as DDH, d-Linear, QR, and DCR. To this end, we present two generic constructions of adaptive OT, one of which utilizes verifiable shuffles together with threshold decryption schemes, while the other uses permutation networks together with what we call loosely-homomorphic key encapsulation schemes. The constructions follow a novel designing approach called “blind permutation”, which completely differs from existing ones. We then show that specific choices of the building blocks lead to concrete adaptive OT protocols with fully simulatable security in the standard model under the targeted assumptions. Our generic methods can be extended to build universally composable (UC) secure OT protocols, with a loss in efficiency.

We previously proposed an inaudible non-blind digital-audio watermarking approach based on cochlear delay (CD) characteristics. There are, however, three remaining issues with regard to blind-detectability, frame synchronization related to confidentiality, and reversibility. We attempted to solve these issues in developing the proposed approach by taking blind-detectability and reversibility of audio watermarking into consideration. Frame synchronization was also incorporated into the proposed approach to improve confidentiality. We evaluated inaudibility, robustness, and reversibility with the new approach by carrying out three objective tests (PEAQ, LSD, and bit-detection or SNR) and six robustness tests. The results revealed that inaudible, robust, blindly-detectable, and semi-reversible watermarking based on CD could be accomplished.

While online social networking is a popular way for people to share information, it carries the risk of unintentionally disclosing personal information. One way to reduce this risk is to anonymize personal information in messages before they are posted. Furthermore, if personal information is somehow disclosed, the person who disclosed it should be identifiable. Several methods developed for anonymizing personal information in natural language text simply remove sensitive phrases, making the anonymized text message unnatural. Other methods change the message by using synonymization or structural alteration to create fingerprints for detecting disclosure, but they do not support the creation of a sufficient number of fingerprints for friends of an online social network user. We have developed a system for anonymizing personal information in text messages that generalizes sensitive phrases. It also creates a sufficient number of fingerprints of a message by using synonyms so that, if personal information is revealed online, the person who revealed it can be identified. A distribution metric is used to ensure that the degree of anonymization is appropriate for each group of friends. A threshold is used to improve the naturalness of the fingerprinted messages so that they do not catch the attention of attackers. Evaluation using about 55,000 personal tweets in English demonstrated that our system creates sufficiently natural fingerprinted messages for friends and groups of friends. The practicality of the system was demonstrated by creating a web application for controlling messages posted on Facebook.

We propose an unsharp-masking technique which preserves the hue of colors in images. This method magnifies the contrast of colors and spatially sharpens textures in images. The contrast magnification ratio is adaptively controlled. We show by experiments that this method enhances the color tone of photographs while keeping their perceptual scene depth.

A generalized chirp-like (GCL) sequence of period N is constructed by modulating a Zadoff-Chu sequence of period N with an arbitrary unimodular sequence of period m, where m divides N. Under some specific conditions, the cross-correlations between two GCL sequences are shown to have exactly the same magnitudes as those of their corresponding Zadoff-Chu sequences regardless of the employed unimodular sequences. In this paper, we first investigate the sufficient conditions under which such a relation holds. We then use them to construct a new class of optimal zero-correlation zone (ZCZ) sequence sets which can be considered to be an extension of the so-called GCL-ZCZ sequence sets.

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.

Radar systems using ultra-wideband (UWB) signals have definitive advantages in high range resolution. These are suitable for accurate 3-dimensional (3-D) sensing by rescue robots operating in disaster zone settings, where optical sensing is not applicable because of thick smog or high-density gas. For such applications, where no a priori information of target shape and position is given, an accurate method for 3-D imaging and motion estimation is strongly required for effective target recognition. In addressing this issue, we have already proposed a non-parametric 2-dimensional (2-D) imaging method for a target with arbitrary target shape and motion including rotation and translation being tracked using a multi-static radar system. This is based on matching target boundary points obtained using the range points migration (RPM) method extended to the multi-static radar system. Whereas this method accomplishes accurate imaging and motion estimation for single targets, accuracy is degraded severely for multiple targets, due to interference effects. For a solution of this difficulty, this paper proposes a method based on a novel matching scheme using not only target points but also normal vectors on the target boundary estimated by the Envelope method; interference effects are effectively suppressed when incorporating the RPM approach. Results from numerical simulations for both 2-D and 3-D models show that the proposed method simultaneously achieves accurate target imaging and motion tracking, even for multiple moving targets.

In this paper, an image prior based on soft-morphological filters and its application to image recovery are presented. In morphological image processing, a gray-scale image is represented as a subset in a three-dimensional space, which is spanned by spatial and intensity axes. Morphological opening and closing, which are basic operations in morphological image processing, respectively approximate the image subset and its complementary images as the unions of structuring elements that are translated in the three-dimensional space. In this study, the opening and closing filters are applied to an image prior to resolve the regularization problem of image recovery. When the proposed image prior is applied, the image is recovered as an image that has no noise component, which is eliminated by the opening and closing. However, the closing and opening filters are less able to eliminate Gaussian noise. In order to improve the robustness against Gaussian noise, the closing and opening filters are respectively approximated as soft-closing and soft-opening with relaxed max and min functions. In image recovery experiments, image denoising and deblurring using the proposed prior are demonstrated. Comparisons of the proposed prior with the existing priors that impose a penalty on the gradient of the intensity are also shown.

The combined linear frequency modulation continuous wave (LFMCW) and inverse synthetic aperture radar (ISAR) can be used for imaging long-distance targets because of its long-distance and high resolution imaging abilities. In this paper, we find and study the dechirp distortion phenomenon (DDP) for imaging long-distance targets by a dechirp-on-receive LFMCW radar. If the targets are very far from the radar, the maximum delay-time is not much smaller than a single sweep duration, and the dechirp distortion is triggered since the distance of the target is unknown in a LFMCW-ISAR system. DDP cannot be ignored in long-distance imaging because double images of a target appear in the frequency domain, which reduces resolution and degrades image quality. A novel LFMCW-ISAR signal model is established to analyze DDP and its negative effects on long-distance target imaging. Using the proportionately distributed energy of double images, the authors propose a method to correct dechirp distortion. In addition, the applicable scope of the proposed method is also discussed. Simulation results validate the theoretical analysis and the effectiveness of the proposed method.

In this paper, we use neural networks (NNs) for cross-dialectal (Mandarin-Shanghainese) voice conversion using a bi-dialectal speakers' recordings. This system employs a nonlinear mapping function, which is trained by parallel mandarin features of source and target speakers, to convert source speaker's Shanghainese features to those of target speaker. This study investigates three training aspects: a) Frequency warping, which is supposed to be language independent; b) Pre-training, which drives weights to a better starting point than random initialization or be regarded as unsupervised feature learning; and c) Sequence training, which minimizes sequence-level errors and matches objectives used in training and converting. Experimental results show that the performance of cross-dialectal voice conversion is close to that of intra-dialectal. This benefit is likely from the strong learning capabilities of NNs, e.g., exploiting feature correlations between fundamental frequency (F0) and spectrum. The objective measures: log spectral distortion (LSD) and root mean squared error (RMSE) of F0, both show that pre-training and sequence training outperform the frame-level mean square error (MSE) training. The naturalness of the converted Shanghainese speech and the similarity between converted Shanghainese speech and target Mandarin speech are significantly improved.

The dynamic time warping (DTW) algorithm is widely used to determine time series similarity search. As DTW has quadratic time complexity, the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. In this paper, we present a parallel approach for DTW on a heterogeneous platform with a graphics processing unit (GPU). In order to exploit fine-grained data-level parallelism, we propose a specific parallel decomposition in DTW. Furthermore, we introduce an optimization technique called diamond tiling to improve the utilization of threads. Results show that our approach substantially reduces computational time.

As the electromagnetic (EM) environment is becoming increasingly diverse, it is essential to estimate specific absorption rates (SARs) and temperature elevations of pregnant females and their fetuses under various exposure situations. This study presents calculated SARs and temperature elevations in a fetus exposed to EM waves. The calculations involved numerical models for the anatomical structures of a pregnant Japanese woman at gestational stages of 13, 18, and 26 weeks; the EM source was a wireless portable terminal placed close to the abdomen of the pregnant female model. The results indicate that fetal SARs and temperature elevations are closely related to the position of the fetus relative to the EM source. We also found that, although the fetal SAR caused by a half-wavelength dipole antenna is sometimes comparable to or slightly more than the International Commission Non-Ionizing Radiation Protection guidelines, it is lower than the guideline level in more realistic situations, such as when a planar inverted-F antenna is used. Furthermore, temperature elevations were significantly below the threshold set to prevent the child from being born with developmental disabilities.

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.

A novel method for calibrating the probes used in standard measurement systems to evaluate SAR (specific absorption rate) of the radio equipment operating at frequencies over 3GHz is proposed. As for the proposed method, the electric-field distribution produced by a waveguide aperture installed in a liquid container is used to calibrate the SAR probe. The field distribution is shown to be the same as that given by a conventional calibration method by analytically deriving a closed-form expression for the field produced by the waveguide aperture with the help of the paraxial approximation. Comparing the approximated and measured distributions reveals that the closed-form expression is valid for the electric-field distribution near the central axis of the aperture. The calibration factor for a commercial SAR probe is evaluated by the proposed method and agrees well with that provided by the manufacturer of the probe.

The goal of dimension reduction is to represent high-dimensional data in a lower-dimensional subspace, while intrinsic properties of the original data are kept as much as possible. An important challenge in unsupervised dimension reduction is the choice of tuning parameters, because no supervised information is available and thus parameter selection tends to be subjective and heuristic. In this paper, we propose an information-theoretic approach to unsupervised dimension reduction that allows objective tuning parameter selection. We employ quadratic mutual information (QMI) as our information measure, which is known to be less sensitive to outliers than ordinary mutual information, and QMI is estimated analytically by a least-squares method in a computationally efficient way. Then, we provide an eigenvector-based efficient implementation for performing unsupervised dimension reduction based on the QMI estimator. The usefulness of the proposed method is demonstrated through experiments.

A major challenge for speech emotion recognition is that when the training and deployment conditions do not use the same speech corpus, the recognition rates will obviously drop. Transfer learning, which has successfully addressed the cross-domain classification or recognition problem, is presented for cross-corpus speech emotion recognition. First, by using the maximum mean discrepancy embedding (MMDE) optimization and dimension reduction algorithms, two close low-dimensional feature spaces are obtained for source and target speech corpora, respectively. Then, a classifier function is trained using the learned low-dimensional features in the labeled source corpus, and directly applied to the unlabeled target corpus for emotion label recognition. Experimental results demonstrate that the transfer learning method can significantly outperform the traditional automatic recognition technique for cross-corpus speech emotion recognition.