In this paper, we treat (k, L, n) ramp secret sharing schemes (SSSs) that can detect impersonation attacks and/or substitution attacks. First, we derive lower bounds on the sizes of the shares and random number used in encoding for given correlation levels, which are measured by the mutual information of shares. We also derive lower bounds on the success probabilities of attacks for given correlation levels and given sizes of shares. Next we propose a strong (k, L, n) ramp SSS against substitution attacks. As far as we know, the proposed scheme is the first strong (k, L, n) ramp SSSs that can detect substitution attacks of at most k-1 shares. Our scheme can be applied to a secret SL uniformly distributed over GF(pm)L, where p is a prime number with p≥L+2. We show that for a certain type of correlation levels, the proposed scheme can achieve the lower bounds on the sizes of the shares and random number, and can reduce the success probability of substitution attacks within nearly L times the lower bound when the number of forged shares is less than k. We also evaluate the success probability of impersonation attack for our schemes. In addition, we give some examples of insecure ramp SSSs to clarify why each component of our scheme is essential to realize the required security.

In this paper, we propose filter-and-forward beamforming (FF-BF) for cognitive two-way relay networks in which secondary users employ an orthogonal frequency-division multiplexing (OFDM) system. Secondary transceivers communicate with each other through multiple relays to obtain BF gain as well as to suppress the interference between the primary and secondary users who share the same spectrum. We consider two FF-BF design methods to optimize the relay filter. The first method enhances the quality of service of the secondary network by maximizing the worst subcarrier signal-to-interference-plus-noise ratio (SINR) subject to transmit power constraints. The second method suppresses the interference from the secondary network to the primary network through the minimization of the relay transmission power subject to subcarrier SINR constraints. Simulation results show that the proposed FF-BF improves system performance in comparison to amplify-and-forward relay BF.

Interconnection network is one of the inevitable components in parallel computers, since it is responsible to communication capabilities of the systems. It affects the system-level performance as well as the physical and logical structure of the systems. Although many studies are reported to enhance the interconnection network technology, we have to discuss many issues remaining. One of the most important issues is congestion management. In an interconnection network, many packets are transferred simultaneously and the packets interfere to each other in the network. Congestion arises as a result of the interferences. Its fast spreading speed seriously degrades communication performance and it continues for long time. Thus, we should appropriately control the network to suppress the congested situation for maintaining the maximum performance. Many studies address the problem and present effective methods, however, the maximal performance in an ideal situation is not sufficiently clarified. Solving the ideal performance is, in general, an NP-hard problem. This paper introduces particle swarm optimization (PSO) methodology to overcome the problem. In this paper, we first formalize the optimization problem suitable for the PSO method and present a simple PSO application as naive models. Then, we discuss reduction of the size of search space and introduce three practical variations of the PSO computation models as repetitive model, expansion model, and coding model. We furthermore introduce some non-PSO methods for comparison. Our evaluation results reveal high potentials of the PSO method. The repetitive and expansion models achieve significant acceleration of collective communication performance at most 1.72 times faster than that in the bursty communication condition.

In this paper, a multichannel speech enhancement system which adopts a denoising auto-encoder as part of the beamformer is proposed. The proposed structure of the generalized sidelobe canceller generates enhanced multi-channel signals, instead of merely one channel, to which the following denoising auto-encoder can be applied. Because the beamformer exploits spatial information and compensates for differences in the transfer functions of each channel, the proposed system is expected to resolve the difficulty of modelling relative transfer functions consisting of complex numbers which are hard to model with a denoising auto-encoder. As a result, the modelling capability of the denoising auto-encoder can concentrate on removing the artefacts caused by the beamformer. Unlike conventional beamformers, which combine these artefacts into one channel, they remain separated for each channel in the proposed method. As a result, the denoising auto-encoder can remove the artefacts by referring to other channels. Experimental results prove that the proposed structure is effective for the six-channel data in CHiME, as indicated by improvements in terms of speech enhancement and word error rate in automatic speech recognition.

This paper proposes a novel wavelet de-noising scheme regarding the existing burst noises that consist of background and impulsive noises in power-line communications. The proposed de-noising scheme employs multi-level threshold functions to efficiently and adaptively reduce the given burst noises. The experiment results show that the proposed de-noising scheme significantly outperformed the conventional schemes.

Preserving hue is an important issue for color image processing. In order to preserve hue, color image processing is often carried out in HSI or HSV color space which is translated from RGB color space. Transforming from RGB color space to another color space and processing in this space usually generate gamut problem. We propose image enhancement methods which conserve hue and preserve the range (gamut) of the R, G, B channels in this paper. First we show an intensity processing method while preserving hue and saturation. In this method, arbitrary gray-scale transformation functions can be applied to the intensity component. Next, a saturation processing method while preserving hue and intensity is proposed. Arbitrary gray-scale transform methods can be also applied to the saturation component. Two processing methods are completely independent. Therefore, two methods are easily combined by applying two processing methods in succession. The combination method realizes the hue-preserving color image processing with a high arbitrariness without gamut problem. Furthermore, the concrete enhancement algorithm based on the proposed processing methods is proposed. Numerical results confirm our theoretical results and show that our processing algorithm performs much better than the conventional hue-preserving methods.

In this paper, we propose a relaying strategy for single-carrier relay networks with frequency selective channels, where each relay node delays its received signal before amplify-and-forward processing it. We propose a computationally efficient delay design method which reduces the number of delay candidates. To further reduce computational complexity, we develop a simplified delay design method which reduces the number of weight computations. Also, we extend the design method to the case where only partial channel state information of relay-to-destination channels is available. Simulation results show that the proposed relaying strategy outperforms a conventional amplify-and-forward relaying strategy and achieves the performance close to that of a more complex filter-and-forward relaying strategy. It is also shown that the proposed delay design method achieves near-optimum performance.

Heart disease is one of the major causes of death in many advanced countries. For prevention or treatment of heart disease, getting an early diagnosis from a long time period of electrocardiogram (ECG) examination is necessary. However, it could be a large burden on medical experts to analyze this large amount of data. To reduce the burden and support the analysis, this paper proposes an arrhythmia detection method based on a deformable part model, which absorbs individual variation of ECG waveform and enables the detection of various arrhythmias. Moreover, to detect the arrhythmia in low processing delay, the proposed method only utilizes time domain features. In an experimental result, the proposed method achieved 0.91 F-measure for arrhythmia detection.

In this paper, we propose a method of embedding and detecting data in printed images with several formats, such as different resolutions and numbers of blocks, using the camera of a tablet device. To specify the resolution of an image and the number of blocks, invisible markers that are embedded in the amplitude domain of the discrete Fourier transform of the target image are used. The proposed method can increase the variety of images suitable for data embedding.

Depth estimation for a lense-array type light field camera is a challenging problem because of the sensor noise and the radiometric distortion which is a global brightness change among sub-aperture images caused by a vignetting effect of the micro-lenses. We propose a depth map estimation method which has robustness against sensor noise and radiometric distortion. Our method first binarizes sub-aperture images by applying the census transform. Next, the binarized images are matched by computing the majority operations between corresponding bits and summing up the Hamming distance. An initial depth obtained by matching has ambiguity caused by extremely short baselines among sub-aperture images. After an initial depth estimation process, we refine the result with following refinement steps. Our refinement steps first approximate the initial depth as a set of depth planes. Next, we optimize the result of plane fitting with an edge-preserving smoothness term. Experiments show that our method outperforms the conventional methods.

Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.

In ASIACRYPT2015, a new model for the analysis of block cipher against side-channel attack and a dedicated attack, differential bias attack, were proposed by Bogdanov et al. The model assumes an adversary who has leaked values whose positions are unknown and randomly chosen from internal states (random leakage model). This paper improves the security analysis on AES under the random leakage model. In the previous method, the adversary requires at least 234 chosen plaintexts; therefore, it is hard to recover a secret key with a small number of data. To consider the security against the adversary given a small number of data, we reestimate complexity. We propose another hypothesis-testing method which can minimize the number of required data. The proposed method requires time complexity more than t>260 because of time-data tradeoff, and some attacks are tractable under t≤280. Therefore, the attack is a threat for the long-term security though it is not for the short-term security. In addition, we apply key enumeration to the differential bias attack and propose two evaluation methods, information-theoretic evaluation and experimental one with rank estimation. From the evaluations on AES, we show that the attack is a practical threat for the long-term security.

Beamforming technology is an effective method to build a robust link. The commonly used digital beamforming is an expensive and power consuming approach to realizing millimeter-wave transmission. This makes radio frequency(RF) beamforming, which has low cost and low power consumption due to its use of phase shifters the more feasible approach to creating stable links in the millimeter-wave band. Unfortunately, the performance of RF processing is degraded by the limited precision of digital phase shifters. In this paper, we analyze the gain loss caused by the limited precision of phase shifter in millimeter wave single stream beam steering. We deduce a theoretical relationship between the array gain loss and variance of phase error. The theoretical results are validated by the Monte Carlo simulations, which indicate that gain loss could be reduced by the increased precision of phase shifter. In practical applications, 4-bit phase shifters provide sufficient accuracy for single stream beam steering.

A novel generalized side-information cancellation (GSIC) precoder is proposed for multiuser multi-input multi-output (MIMO) downlink systems with channel state information at the transmitter. The proposed transceiver involves the following stages. First, a minimum mean square error (MMSE) based channel inversion (MMSE-CI) technique is utilized to suppress multiuser broadcast interference. By using a GSIC technique, it can further reduce the residual multiuser interference and the noise induced by MMSE-CI preprocessing. Next, with a singular value decomposition method, the spatial stream interference of each user is suppressed by the pre-processing and post-processing eigenvector matrices. Finally, the proposed precoder can be extended to joint water filling and diagonal loading methods for performance enhancement. For the correlated MIMO channels, signal subspace and antenna selection methods, incorporating the proposed GSIC precoder, are further designed to maximize the sum rate performance. Simulation results show that the proposed GSIC precoder outperforms the conventional precoders. Besides, simulation results confirm that the proposed GSIC precoder with water filling, diagonal loading, and signal subspace techniques exhibits excellent performance.

In order to support seamless mobility in the Information-Centric Networking (ICN) Architecture we propose the Named-Node Network Architecture (3NA). 3NA introduces two independent namespaces to ICN, the 3N namespace used to uniquely identify nodes within a network and the Point of Attachment (PoA) namespace to identify a node's PoA to the network. The mappings between the two namespaces, along with all the necessary mechanisms to keep the mappings updated over time, are used when routing ICN packets to improve delay and the goodput when either the producer or the consumer are mobile. To support simultaneous producer and consumer mobility, we expand on the 3NA by adding a new Protocol Data Unit (PDU), the DU PDU. The DU PDU permits the encapsulation of ICN packets in a header that has source and destination name fields which belong to 3NA's 3N namespace. The new PDU permits seamless connectivity as long as 3NA's point of attachment signaling is strictly followed. We demonstrate the performance of the DU PDU against our previous defined communication methods and Named Data Networking's (NDN) Smart Flooding forwarding strategy using our open source nnnSIM module for the ns-3 framework. The new PDU outperforms all existing alternatives when the producer or both consumer and provider are mobile, obtaining overall lower mean network delay and higher median goodput.

This paper presents an approach to nonlinear impedance measurement exploiting an oscilloscope and Möbius transformation. Proposed system consists of a linear 4-port network and an oscilloscope. One of the port is excited by a high power source. The power is delivered to the second port, which is loaded with a DUT. Another set of two ports are used to observe a voltage set. This voltage set gives the impedance of the DUT through Möbius transformation. We formulated measurability M of the system, and derived the condition that M becomes constant for any DUT. To meet the condition, we propose a linear 4-port network consisting of a quarter-wavelength transmission line and resistors. We confirm the validity and utility of the proposed system by measuring the impedance of incandescent bulbs and an RF diode rectifier.

We proposed and theoretically discussed a polarization switch based on a multiple quantum well (MQW) microring resonator (MRR) with an L-shaped waveguide as a dynamic polarization control device. The L-shaped waveguide was useful to obtain the rotation angle of the principal axis of 45° even when the core layer has form birefringence such as an MQW. The MRR functions as both a polarization converter and a wavelength filter. As an MQW core layer, a multiple five-layer asymmetric coupled quantum well (FACQW) was assumed. Polarization switching of an inputted light with a selected wavelength was successfully demonstrated at an operation voltage as low as 0.2 V using the electrorefractive index change in the MRR waveguide. The maximum polarization extinction ratio was more than 35 dB. The high-speed and low-voltage polarization switching can be realized with the proposed MRR polarization switch.

In this paper, we propose a low-invasive framework to predict changes in cognitive performance using only heart rate variability (HRV). Although a lot of studies have tried to estimate cognitive performance using multiple vital data or electroencephalogram data, these methods are invasive for users because they force users to attach a lot of sensor units or electrodes to their bodies. To address this problem, we proposed a method to estimate cognitive performance using only HRV, which can be measured with as few as two electrodes. However, this can't prevent loss of worker productivity because the workers' productivity had already decreased even if their current cognitive performance had been estimated as being at a low level. In this paper, we propose a framework to predict changes in cognitive performance in the near future. We obtained three principal contributions in this paper: (1) An experiment with 45 healthy male participants clarified that changes in cognitive performance caused by mental workload can be predicted using only HRV. (2) The proposed framework, which includes a support vector machine and principal component analysis, predicts changes in cognitive performance caused by mental workload with 84.4 % accuracy. (3) Significant differences were found in some HRV features for test participants, depending on whether or not their cognitive performance changes had been predicted accurately. These results lead us to conclude that the framework has the potential to help both workers and managerial personnel predict what their performances will be in the near future. This will make it possible to proactively suggest rest periods or changes in work duties to prevent losses in productivity caused by decreases of cognitive work performance.

This paper presents a novel 60 GHz-band photonic-integrated array-antenna and module for radio-over-fiber (RoF)-based beam forming. An integrated photonic array-antennas (IPA), where eight photodiodes and 4×2 arrayed patch-antenna are integrated in a single board, is actually fabricated, and 3.5-Gbit/s QPSK digital signal transmission with beam forming of the IPA is experimentally demonstrated. In addition, a novel 60-GHz compact antenna module is proposed and fabricated for increasing the number of antenna elements and flexibility creating various beam patterns. The feasibility of beam forming operation for the proposed antenna module is confirmed by a 60-GHz RoF transmission experiment. The capability of detecting the mobile terminal direction, which is one of the indispensable functions for actual environment, is also studied. The obtained results in this paper will be useful for designing future radio access networks based on RoF transmission technology.

This paper considers the recovery problem of distributed compressed sensing (DCS), where J (J≥2) signals all have sparse common component and sparse innovation components. The decoder attempts to jointly recover each component based on {Mj} random noisy measurements (j=1,…,J) with the prior information on the support probabilities, i.e., the probabilities that the entries in each component are nonzero. We give both the sufficient and necessary conditions on the total number of measurements $sum olimits_{j = 1}^J M_j$ that is needed to recover the support set of each component perfectly. The results show that when the number of signal J increases, the required average number of measurements $sum olimits_{j = 1}^J M_j/J$ decreases. Furthermore, we propose an extension of one existing algorithm for DCS to exploit the prior information, and simulations verify its improved performance.