The concept of threshold public key encryption (TPKE) with the special property called key re-splittability (re-splittable TPKE, for short) was introduced by Hanaoka et al. (CT-RSA 2012), and used as one of the building blocks for constructing their proxy re-encryption scheme. In a re-splittable TPKE scheme, a secret key can be split into a set of secret key shares not only once, but also multiple times, and the security of the TPKE scheme is guaranteed as long as the number of corrupted secret key shares under the same splitting is smaller than the threshold. In this paper, we show several new constructions of a re-splittable TPKE scheme by extending the previous (ordinary) TPKE schemes. All of our proposed schemes are based on discrete logarithm (DL)-type assumptions. Therefore, our results suggest that key re-splittability is a very natural property for DL-type TPKE schemes.

We present an explicit construction of a MAJn-2 °MAJn-2 circuit computing MAJn for every odd n≥7. This gives a partial solution to an open problem by Kulikov and Podolskii (Proc. of STACS 2017, Article No.49).

In this paper, dependency of transmission loss of shielded-flexible printed circuits (FPC) for differential-signaling on thickness of conductive shield is studied by numerical modeling based on an equivalent circuit model compared with the experimental results. Especially, the transmission loss due to the thin conductive shield is focused. The insufficient shielding performance for near magnetic field decreases the resistance due to the thin conductive shield. It is shown that the resistance due to the thin conductive shield at lower frequencies is smaller than that in the “thick conductive shield” case.

We are developing an Ultra-Low Field (ULF) Magnetic Resonance Imaging (MRI) system with a tuned high-Tc (HTS)-rf-SQUID for food inspection. We previously reported that a small hole in a piece of cucumber can be detected. The acquired image was based on filtered back-projection reconstruction using a polarizing permanent magnet. However the resolution of the image was insufficient for food inspection and took a long time to process. The purpose of this study is to improve image quality and shorten processing time. We constructed a specially designed cryostat, which consists of a liquid nitrogen tank for cooling an electromagnetic polarizing coil (135mT) at 77K and a room temperature bore. A Cu pickup coil was installed at the room temperature bore and detected an NMR signal from a sample. The signal was then transferred to an HTS SQUID via an input coil. Following a proper MRI sequence, spatial frequency data at 64×32 points in k-space were obtained. Then, a 2D-FFT (Fast Fourier Transformation) method was applied to reconstruct the 2D-MR images. As a result, we successfully obtained a clear water image of the characters “TUT”, which contains a narrowest width of 0.5mm. The imaging time was also shortened by a factor of 10 when compared to the previous system.

In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.

We address a BEMS (Building Energy Management System) to guarantee reliability of electric-power supply in dynamic uncertain environments. The building microgrid as the target of BEMS has multiple distributed power sources including a photo-voltaic power system and Electric-Vehicle (EV). EV is regarded as an autonomously-moving battery due to the original means of transportation and is hence a cause of dynamic uncertainty of the building microgrid. The main objective of synthesis of BEMS in this paper is to guarantee the continuous supply of power to the most critical load in a building microgrid and to realize the power supply to the other loads according to a ranking of load importance. We synthesize the BEMS as a reactive control system that monitors changes of dynamic uncertain environment of the microgrid including departure and arrival of an EV, and determines a route of power supply to the most critical load. Also, we conduct numerical experiments of the reactive BEMS using models of power flows in the building and of charging states of the batteries. The experiments are incorporated with data measured in a practical office building and demonstration project of EMS at Osaka, Japan. We show that the BEMS works for extending the time duration of continuous power supply to the most critical load.

A frequently occurring subcircuit consists of a loop of a resistor (R), a field-effect transistor (FET), and a capacitor (C). The FET acts as a switch, controlled at its gate terminal by a clock voltage. This subcircuit may be acting as a sample-and-hold (S/H), as a passive mixer (P-M), or as a bandpass filter or bandpass impedance. In this work, we will present a useful analysis that leads to a simple signal flow graph (SFG), which captures the FET-R-C circuit's action completely across a wide range of design parameters. The SFG dissects the circuit into three filtering functions and ideal sampling. This greatly simplifies analysis of frequency response, noise, input impedance, and conversion gain, and leads to guidelines for optimum design. This paper focuses on the analysis of a single-path FET-R-C circuit's signal transfer characteristics including the reconstruction of the complete waveform from the discrete-time sampled voltage.

In this paper, the problem of lifetime extension of wireless sensor networks (WSNs) with redundant sensor nodes deployed in 3D vegetation-covered fields is modeled, which includes building communication models, network model and energy model. Generally, such a problem cannot be solved by a conventional method directly. Here we propose an Artificial Bee Colony (ABC) based optimal grouping algorithm (ABC-OG) to solve it. The main contribution of the algorithm is to find the optimal number of feasible subsets (FSs) of WSN and assign them to work in rotation. It is verified that reasonably grouping sensors into FSs can average the network energy consumption and prolong the lifetime of the network. In order to further verify the effectiveness of ABC-OG, two other algorithms are included for comparison. The experimental results show that the proposed ABC-OG algorithm provides better optimization performance.

In this paper, we consider Bayesian image denoising based on a Gaussian Markov random field (GMRF) model, for which we propose an new algorithm. Our method can solve Bayesian image denoising problems, including hyperparameter estimation, in O(n)-time, where n is the number of pixels in a given image. From the perspective of the order of the computational time, this is a state-of-the-art algorithm for the present problem setting. Moreover, the results of our numerical experiments we show our method is in fact effective in practice.

Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is difficult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect significant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artificial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the significant peaks of heart cycle in 17 NS ECG data. In addition, it was verified that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was confirmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose early stage of heart diseases.

Most people are concerned about their appearance, and the easiest way to change the appearance is to change the hairstyle. However, except for professional hairstylists, it is difficult to objectively judge which hairstyle suits them. Currently, oval faces are generally said to be the ideal facial shape in terms of suitability to various hairstyles. Meanwhile, field of visual perception (FVP), proposed recently in the field of cognitive science, has attracted attention as a model to represent the visual perception phenomenon. Moreover, a computation model for digital images has been proposed, and it is expected to be used in quantitative evaluation of sensibility and sensitivity called “kansei.” Quantitative evaluation of “goodness of patterns” and “strength of impressions” by evaluating distributions of the field has been reported. However, it is unknown whether the evaluation method can be generalized for use in various subjects, because it has been applied only to some research subjects, such as characters, text, and simple graphics. In this study, for the first time, we apply FVP to facial images with various hairstyles and verify whether it has the potential of evaluating impressions of female faces. Specifically, we verify whether the impressions of facial images that combine various facial shapes and female hairstyles can be represented using FVP. We prepare many combinational images of facial shapes and hairstyles and conduct a psychological experiment to evaluate their impressions. Moreover, we compute the FVP of each image and propose a novel evaluation method by analyzing the distributions. The conventional and proposed evaluation values correlated to the psychological evaluation values after normalization, and demonstrated the effectiveness of the FVP as an image feature quantity to evaluate faces.

New deblocking artifact, or blocking artifact reduction, algorithms based on nonlinear adaptive soft-threshold anisotropic filter in wavelet are proposed. Our deblocking algorithm uses soft-threshold, adaptive wavelet direction, adaptive anisotropic filter, and estimation. The novelties of this paper are an adaptive soft-threshold for deblocking artifact and an optimal intersection of confidence intervals (OICI) method in deblocking artifact estimation. The soft-threshold values are adaptable to different thresholds of flat area, texture area, and blocking artifact. The OICI is a reconstruction technique of estimated deblocking artifact which improves acceptable quality level of estimated deblocking artifact and reduces execution time of deblocking artifact estimation compared to the other methods. Our adaptive OICI method outperforms other adaptive deblocking artifact methods. Our estimated deblocking artifact algorithms have up to 98% of MSE improvement, up to 89% of RMSE improvement, and up to 99% of MAE improvement. We also got up to 77.98% reduction of computational time of deblocking artifact estimations, compared to other methods. We have estimated shift and add algorithms by using Euler++(E++) and Runge-Kutta of order 4++ (RK4++) algorithms which iterate one step an ordinary differential equation integration method. Experimental results showed that our E++ and RK4++ algorithms could reduce computational time in terms of shift and add, and RK4++ algorithm is superior to E++ algorithm.

Distributed Reflective Denial of Services (DRDoS) attacks have gained huge popularity and become a major factor in a number of massive cyber-attacks. Usually, the attackers launch this kind of attack with small volume of requests to generate a large volume of attack traffic aiming at the victim by using IP spoofing from legitimate hosts. There have been several approaches, such as static threshold based approach and confirmation-based approach, focusing on DRDoS attack detection at victim's side. However, these approaches have significant disadvantages: (1) they are only passive defences after the attack and (2) it is hard to trace back the attackers. To address this problem, considerable attention has been paid to the study of detecting DRDoS attack at source side. Because the existing proposals following this direction are supposed to be ineffective to deal with small volume of attack traffic, there is still a room for improvement. In this paper, we propose a novel method to detect DRDoS attack request traffic on SDN(Software Defined Network)-enabled gateways in the source side of attack traffic. Our method adjusts the sampling rate and provides a traffic-aware adaptive threshold along with the margin based on analysing observed traffic behind gateways. Experimental results show that the proposed method is a promising solution to detect DRDoS attack request in the source side.

Several new memories are being studied as candidates of future DRAM that seems difficult to be scaled. However, the read signal in these new memories needs to be amplified in a single-end manner with reference signal supplied if they are aimed for being applied to the high-density main memory. This scheme, which is fortunately not necessary in DRAM's 1/2Vdd pre-charge sense amp, can become a serious bottleneck in the new memory development, because the device electrical parameters in these new memory cells are prone to large cell-to-cell variations without exception. Furthermore, the extent to which the parameter fluctuates in data “1” is generally not the same as in data “0”. In these situations, a new sensing scheme is proposed that can minimize the sensing error rate for high-density single-end emerging memories like STT-MRAM, ReRAM and PCRAM. The scheme is based on averaging multiple dummy cell pairs that are written “1” and “0” in a weighted manner according to the fluctuation unbalance between “1” and “0”. A detailed analysis shows that this scheme is effective in designing 128Mb 1T1MTJ STT-MRAM with the results that the required TMR ratio of an MTJ can be relaxed from 130% to 90% for the fluctuation of 6% sigma-to-average ratio of MTJ resistance in a 16 pair-dummy cell averaging case by using this technology when compared with the arithmetic averaging method.

This paper proposes an image denoising method using singular value decomposition (SVD) with block-rotation-based operations in wavelet domain. First, we decompose a noisy image to some sub-blocks, and use the single-level discrete 2-D wavelet transform to decompose each sub-block into the low-frequency image part and the high-frequency parts. Then, we use SVD and rotation-based SVD with the rank-1 approximation to filter the noise of the different high-frequency parts, and get the denoised sub-blocks. Finally, we reconstruct the sub-block from the low-frequency part and the filtered the high-frequency parts by the inverse wavelet transform, and reorganize each denoised sub-blocks to obtain the final denoised image. Experiments show the effectiveness of this method, compared with relevant methods.

In recent years, the number of traffic accidents caused by elderly drivers has increased in Japan. However, cars are an important mode of transportation for the elderly. Therefore, to ensure safe driving, a system that can assist elderly drivers is required. We propose a driver-agent system that provides support to elderly drivers during and after driving and encourages them to improve their driving. This paper describes the prototype system and the analysis conducted of the teaching records of a human instructor, the impression caused by the instructions on a subject during driving, and subjective evaluation of the driver-agent system.

Direct-binary search method has been used for converting complex holograms into binary format. However, this algorithm is optimized to reconstruct monochromatic digital holograms and is accurate only in a narrow-depth range. In this paper, we proposed an advanced direct-binary search method to increase the depth of field of 3D scenes reconstructed in RGB by binary holograms.

We report the characterization of hysteresis in SOI-based super-steep subthreshold slope FETs, which are conventional floating body and body-tied, and newly proposed PN-body-tied structures. We found that the hysteresis widths of the PN-body-tied structures are smaller than that of the conventional floating body and body-tied structures; this means that they are feasible for switching devices. Detailed characterizations of the hysteresis widths of each device are also reported in the study, such as dependency on the gate length and the impurity concentration.

In this paper, we propose the novel low voltage CMOS current mode reference circuit. It reduces the minimum supply voltage by consisting the subthreshold two stage operational amplifier (OPAMP) which is regarded as the combination of the proportional to absolute temperature (PTAT) and the complementary to absolute temperature (CTAT) current generators. It makes possible to implement without extra OPAMP. This proposed circuit has been designed and evaluated by SPICE simulation using TSMC 65nm CMOS process with 3.3V (2.5V over-drive) transistor option. From simulation results, the line sensitivity is as good as 0.196%/V under the condition that the range of supply voltage (VDD) is wide as 0.6V to 3.0V. The temperature coefficient is 71ppm/ under the condition that the temperature range is from -40 to 125 and VDD=0.6V. The power supply rejection ratio (PSRR) is -47.7dB when VDD=0.6V and the noise frequency is 100Hz. According to comparing the proposed circuit with prior current mode circuits, we could confirm the performance of the proposed circuit is better than that of prior circuits.

We propose a method of line-based Simultaneous Localization and Mapping (SLAM) using non-overlapping multiple cameras for vehicles running in an urban environment. It uses corresponding line segments between images taken by different frames and different cameras. The contribution is a novel line segment matching algorithm by warping processing based on urban structures. This idea significantly improves the accuracy of line segment matching when viewing direction are very different, so that a number of correspondences between front-view and rear-view cameras can be found and the accuracy of SLAM can be improved. Additionally, to enhance the accuracy of SLAM we apply a geometrical constraint of urban area for initial estimation of 3D mapping of line segments and optimization by bundle adjustment. We can further improve the accuracy of SLAM by combining points and lines. The position error is stable within 1.5m for the entire image dataset evaluated in this paper. The estimation accuracy of our method is as high as that of ground truth captured by RTK-GPS. Our high accuracy SLAM algorithm can be apply for generating a road map represented by line segments. According to an evaluation of our generating map, true positive rate around the vehicle exceeding 70% is achieved.