The concept of dual pairing vector spaces (DPVS) was introduced by Okamoto and Takashima in 2009, and it has been employed in various applications, functional encryption (FE) including attribute-based encryption (ABE) and inner-product encryption (IPE) as well as attribute-based signatures (ABS), generic conversion from composite-order group based schemes to prime-order group based ones and public-key watermarking. In this paper, we show the concept of DPVS, the major applications to FE and the key techniques employed in these applications. This paper presents them with placing more emphasis on plain and intuitive descriptions than formal preciseness.

This paper reviews several trials of re-designing conventional communication medium, i.e., characters, for enriching their functions by using data-embedding techniques. For example, characters are re-designed to have better machine-readability even under various geometric distortions by embedding a geometric invariant into each character image to represent class label of the character. Another example is to embed various information into handwriting trajectory by using a new pen device, called a data-embedding pen. An experimental result showed that we can embed 32-bit information into a handwritten line of 5 cm length by using the pen device. In addition to those applications, we also discuss the relationship between data-embedding and pattern recognition in a theoretical point of view. Several theories tell that if we have appropriate supplementary information by data-embedding, we can enhance pattern recognition performance up to 100%.

A new technology for video frame rate up-conversion (FRUC) is presented by combining a median filter and motion estimation (ME) with an occlusion detection (OD) method. First, ME is performed to obtain a motion vector. Then, the OD method is used to refine the MV in the occlusion region. When occlusion occurs, median filtering is applied. Otherwise, bidirectional motion compensated interpolation (BDMC) is applied to create the interpolated frames. The experimental results show that the proposed algorithm provides better performance than the conventional approach. The average gain in the PSNR (Peak Signal to Noise Ratio) is always better than the other methods in the Full HD test sequences.

In this paper, we present a new cryptanalytic tool that can reduce the complexity of integral analysis against Addition-Rotation-XOR (ARX) based designs. Our technique is based on the partial-sum technique proposed by Ferguson et al. at FSE 2000, which guesses subkeys byte to byte in turn, and the data to be analyzed is compressed for each key guess. In this paper, the technique is extended to ARX based designs. Subkeys are guessed bit by bit, and the data is compressed with respect to the value of the guessed bit position and carry values to the next bit position. We call the technique bitwise partial-sum. We demonstrate this technique by applying it to reduced-round versions of HIGHT, which is one of the ISO standard 64-bit block ciphers. Another contribution of this paper is an independent improvement specific to HIGHT. By exploiting linear computations inside the round function, the number of guessed bits during the key recovery phase can be greatly reduced. Together with the bitwise partial-sum, the integral analysis on HIGHT is extended from previous 22 rounds to 26 rounds, while full HIGHT consists of 32 rounds.

This paper analyzes the correlation between various acoustic features and perceptual voice quality similarity, and proposes a perceptually similar speaker selection technique based on distance metric learning. To analyze the relationship between acoustic features and voice quality similarity, we first conduct a large-scale subjective experiment using the voices of 62 female speakers and perceptual voice quality similarity scores between all pairs of speakers are acquired. Next, multiple linear regression analysis is carried out; it shows that four acoustic features are highly correlated to voice quality similarity. The proposed speaker selection technique first trains a transform matrix based on distance metric learning using the perceptual voice quality similarity acquired in the subjective experiment. Given an input speech, acoustic features of the input speech are transformed using the trained transform matrix, after which speaker selection is performed based on the Euclidean distance on the transformed acoustic feature space. We perform speaker selection experiments and evaluate the performance of the proposed technique by comparing it to speaker selection without feature space transformation. The results indicate that transformation based on distance metric learning reduces the error rate by 53.9%.

An optimal design method of linear processors intended for a multi-input multi-output (MIMO) full-duplex (FD) amplify-and-forward (AF) relay network is presented under the condition of spatial-domain self-interference nulling. This method is designed to suit the availability of channel state information (CSI). If full CSI of source station (SS)-relay station (RS), RS-RS (self-interference channel), and RS-destination station (DS) links are available, the instantaneous end-to-end capacity is maximized. Otherwise, if CSI of the RS-DS link is either partially available (only covariance is known), or not available, while CSI of the other links is known, then the ergodic end-to-end capacity is maximized. Performance of the proposed FD-AF relay system is demonstrated through computer simulations, especially under various correlation conditions of the RS-DS link.

A monolithic frequency synthesizer with wide tuning range, low phase noise and spurs was realized in 0.13,$mu$m CMOS technology. It consists of an analog PLL, a harmonic-rejection mixer and injection-locked frequency doublers to cover the whole 6--18,GHz frequency range. To achieve a low phase noise performance, a sub-sampling PLL with non-dividers was employed. The synthesizer can achieve phase noise $-$113.7,dBc/Hz@100,kHz in the best case and the reference spur is below $-$60,dBc. The core of the synthesizer consumes about 110,mA*1.2,V.

This paper presents a novel signal analysis algorithm, named High-order Bi-orthogonal Fourier Transform (HBFT), which can be seen as an expansion of Fourier transform. The HBFT formula and discrete HBFT formula are derived, some of their main characteristics are briefly discusses. This paper also uses HBFT to analyze the multi-LFM signals, obtain the modulate rate parameters, analyze the high dynamic signals, and obtain the accelerated and varying accelerated motion parameters. The result proves that HBFT is suitable for analysis of the non-stability signals with high-order components.

A certificateless aggregate signature scheme saves cost from complicated certificate management in PKI and compresses many signatures on different messages signed by different users to one single signature. It is originally required to be secure against a conspiring group of malicious signers (type I adversary) and against malicious KGC (type II adversary). In this paper, we define a novel fundamental type of adversary for certificateless aggregate signature schemes, type III adversary, called malicious KGC & Signers Coalition, who can break Zhang-Zhang scheme. We also propose two new certificateless aggregate schemes which are provably secure against all three types of adversary.

Energy conservation is one of the hot topics within the domain of traffic problems. It is well known that shortening the distance between vehicles reduces the aerodynamic drag of the lagging (or following) vehicle and leads to energy savings, which benefits the drivers. Recently, systems have been developed in which trucks or vehicles travel in a platoon with reduced headway from the preceding vehicle by using automated driving or driver assistance systems. The objective of the present study is to investigate how human factors, such as driving style, a driver's condition, or a driver's personal characteristics, influence the decision of a driver to close the gap with a preceding vehicle and obtain the benefit of aerodynamic drag reduction. We developed a realistic experimental paradigm for investigating the relationship between distance and several factors including the driver's personal characteristics and the size of preceding vehicle. Our experimental setup made use of real vehicles on a test track, as opposed to a vehicle simulator. We examined behavior of subjects that drove the following vehicle as well as subjects that sat in the passenger seat in the following vehicle. The experimental results demonstrate that all subjects attempted to reduce the distance to the preceding vehicle in order to gain the benefit. Based on the experimental and questionnaire results, we conclude that there are relationships between the category of subjects and subject's following distances.

Resonant scattering of flexural waves in acoustic waveguide is analysed by using the recursive transfer method (RTM). Because flexural waves are governed by a fourth-order differential equation, a localized wave tends to be induced around the scattering region and dampening wave tails from the localized wave may reach the ends of a simulation domain. A notable feature of RTM is its ability to extract the localized wave even if the dampening tail reaches the end of the simulation domain. Using RTM, the enhanced reflection caused by a localized wave is predicted and the shape of the localized wave is explored at its resonance with the incident wave.

This letter studies the problem of cooperative spectrum sensing in wideband cognitive radio networks. Based on the basis expansion model (BEM), the problem of estimation of power spectral density (PSD) is transformed to estimation of BEM coefficients. The sparsity both in frequency domain and space domain is used to construct a sparse estimation structure. The theory of L1/2 regularization is used to solve the compressed sensing problem. Simulation results demonstrate the effectiveness of the proposed method.

Primitive linear recurring sequences over rings are important in modern communication technology, and character sums of such sequences are used to analyze their statistical properties. We obtain a new upper bound for the character sum of primitive sequences of order n over the residue ring modulo a square-free odd integer m, and thereby improve previously known bound mn/2.

Our society has been getting more privacy-sensitive. Diverse information is given by users to information and communications technology (ICT) systems such as IC cards benefiting them. The information is stored as so-called big data, and there is concern over privacy violation. Visual information such as images and videos is also considered privacy-sensitive. The growing deployment of surveillance cameras and social network services has caused a privacy problem of information given from various sensors. To protect privacy of subjects presented in visual information, their face or figure is processed by means of pixelization or blurring. As image analysis technologies have made considerable progress, many attempts to automatically process flexible privacy protection have been made since 2000, and utilization of privacy information under some restrictions has been taken into account in recent years. This paper addresses the recent progress of privacy protection for visual information, showing our research projects: PriSurv, Digital Diorama (DD), and Mobile Privacy Protection (MPP). Furthermore, we discuss Harmonized Information Field (HIFI) for appropriate utilization of protected privacy information in a specific area.

We present a dynamic key generation method, KeyQ, for establishing shared secret keys in EPCglobal Generation 2 (Gen2) compliant systems. Widespread adoption of Gen2 technologies has increased the need for protecting communications in these systems. The highly constrained resources on Gen2 tags limit the usability of traditional key distribution techniques. Dynamic key generation provides a secure method to protect communications with limited key distribution requirements. Our KeyQ method dynamically generates fresh secret keys based on the Gen2 adaptive Q algorithm. We show that the KeyQ method generates fresh and unique secret keys that cannot be predicted with probability greater than 10-250 when the number of tags exceeds 100.

Misbehaving nodes intrinsic to the physical vulnerabilities of ad-hoc sensor networks pose a challenging constraint on the designing of data fusion. To address this issue, a statistics-based reputation method for reliable data fusion is proposed in this study. Different from traditional reputation methods that only compute the general reputation of a node, the proposed method modeled by negative binomial reputation consists of two separated reputation metrics: fusion reputation and sensing reputation. Fusion reputation aims to select data fusion points and sensing reputation is used to weigh the data reported by sensor nodes to the fusion point. So, this method can prevent a compromised node from covering its misbehavior in the process of sensing or fusion by behaving well in the fusion or sensing. To tackle the unexpected facts such as packet loss, a discounting factor is introduced into the proposed method. Additionally, Local Outlier Factor (LOF) based outlier detection is applied to evaluate the behavior result of sensor nodes. Simulations show that the proposed method can enhance the reliability of data fusion and is more accurate than the general reputation method when applied in reputation evaluation.

The maximization of non-Gaussianity is an effective approach to achieve the complex independent component analysis (ICA) problem. However, the traditional complex maximization of non-Gaussianity (CMN) algorithm does not consider the influence of noise. In this letter, a modification of the fixed-point algorithm is proposed for more practical occasions of the complex noisy ICA model. Simulations show that the proposed method demonstrates significantly improved performance over the traditional CMN algorithm in the noisy ICA model when the sample size is sufficient.

We consider single and multiple attacker scenarios in guessing and obtain bounds on various success parameters in terms of Renyi entropies. We also obtain a new derivation of the union bound.

An asymmetric zero correlation zone (A-ZCZ) sequence set is a type of ZCZ sequence set and consists of multiple sequence subsets. It is the most important property that is the cross-correlation function between arbitrary sequences belonging to different sequence subsets has quite a large zero-cross-correlation zone (ZCCZ). Our proposed A-ZCZ sequence sets can be constructed based on interleaved technique and orthogonality-preserving transformation by any perfect sequence of length P=Nq(2k+1) and Hadamard matrices of order T≥2, where N≥1, q≥1 and k≥1. If q=1, the novel sequence set is optimal ZCZ sequence set, which has parameters (TP,TN,2k+1) for all positive integers P=N(2k+1). The proposed A-ZCZ sequence sets have much larger ZCCZ, which are expected to be useful for designing spreading sequences for QS-CDMA systems.

As VLSI process node continue to shrink, chemical mechanical planarization (CMP) process for copper interconnect has become an essential technique for enabling many-layer interconnection. Recently, Edge-over-Erosion error (EoE-error), which originates from overpolishing and could cause yield loss, is observed in various CMP processes, while its mechanism is still unclear. To predict these errors, we propose an EoE-error prediction method that exploits machine learning algorithms. The proposed method consists of (1) error analysis stage, (2) layout parameter extraction stage, (3) model construction stage and (4) prediction stage. In the error analysis and parameter extraction stages, we analyze test chips and identify layout parameters which have an impact on EoE phenomenon. In the model construction stage, we construct a prediction model using the proposed multi-level machine learning method, and do predictions for designed layouts in the prediction stage. Experimental results show that the proposed method attained 2.7∼19.2% accuracy improvement of EoE-error prediction and 0.8∼10.1% improvement of non-EoE-error prediction compared with general machine learning methods. The proposed method makes it possible to prevent unexpected yield loss by recognizing EoE-errors before manufacturing.