A privacy-preserving support vector machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has some serious issues for end users, such as the unauthorized use of cloud services, data leaks, and privacy being compromised. Accordingly, we consider privacy-preserving SVM computing. We focus on protecting visual information of images by using a random unitary transformation. Some properties of the protected images are discussed. The proposed scheme enables us not only to protect images, but also to have the same performance as that of unprotected images even when using typical kernel functions such as the linear kernel, radial basis function (RBF) kernel and polynomial kernel. Moreover, it can be directly carried out by using well-known SVM algorithms, without preparing any algorithms specialized for secure SVM computing. In an experiment, the proposed scheme is applied to a face-based authentication algorithm with SVM classifiers to confirm the effectiveness.

In this paper, we propose an accurate calibration method for glassless stereoscopic systems. The method uses a lenticular lens on a general display. Glassless stereoscopic displays are currently used in many fields; however, accurately adjusting their physical display position is difficult because an accuracy of several microns or one hundredth of a degree is required, particularly given their larger display area. The proposed method enables a dynamic adjustment of the positions of images on the display to match various physical conditions in three-dimensional (3D) displays. In particular, compared with existing approaches, this avoids degradation of the image quality due to the image location on the screen while improving the image quality by local mapping. Moreover, it is shown to decrease the calibration time by performing simultaneous processing for each local area. As a result of the calibration, the offset jitter representing the crosstalk reduces from 14.946 to 8.645 mm. It is shown that high-quality 3D videos can be generated. Finally, we construct a stereoscopic viewing system using a high-resolution display and lenticular lens and produce high-quality 3D images with automatic calibration.

Distributed Video Coding (DVC), based on the theorems proposed by Slepian-Wolf and Wyner-Ziv, is attracting attention as a new paradigm for video compression. Some of the DVC systems use intra-frame compression based on discrete cosine transform (DCT). Unfortunately, conventional DVC systems have low affinity with DCT. In this paper, we propose a wavelet-based DVC scheme that utilizs current JPEG 2000 standard. Accordingly, the scheme has scalability with regard to resolution and quality. In addition, we propose two methods to increase the coding gain of the new DVC scheme. One is the introduction of a Gray code, and the other method involves optimum quantization. An interesting point is that though our proposed method uses Gray code, it still achieves quality scalability. Tests confirmed that the PSNR is increased about 5 [dB] by the two methods, and the PSNR of the new scheme (with methods) is about 1.5-3 [dB] higher than that of conventional JPEG 2000.

This paper proposes a parallelized DVC framework that treats each bitplane independently to reduce the decoding time. Unfortunately, simple parallelization generates inaccurate bit probabilities because additional side information is not available for the decoding of subsequent bitplanes, which degrades encoding efficiency. Our solution is an effective estimation method that can calculate the bit probability as accurately as possible by index assignment without recourse to side information. Moreover, we improve the coding performance of Rate-Adaptive LDPC (RA-LDPC), which is used in the parallelized DVC framework. This proposal selects a fitting sparse matrix for each bitplane according to the syndrome rate estimation results at the encoder side. Simulations show that our parallelization method reduces the decoding time by up to 35[%] and achieves a bit rate reduction of about 10[%].

Lossless video coding is required in the fields of archiving and editing digital cinema or digital broadcasting contents. This paper proposes multiresolution lossless video coding using a discrete wavelet transform and adaptive inter/intra-frame prediction in the wavelet domain. The multiresolution structure based on the wavelet transform facilitates interchange among several video source formats such as Super High Definition (SHD) images, HDTV, SDTV, and mobile applications. In order to increase the compression ratio, and to keep the computational cost low, the adaptive inter/intra-frame prediction is performed in the lowest wavelet transform domain. The adaptive inter/intra-frame prediction can adapt to changes in the local inter/intra-frame statistics. Experiments on digital cinema test sequences confirm effectiveness of the proposed algorithm.

In this paper, we propose L0 norm optimization in a scrambled sparse representation domain and its application to an Encryption-then-Compression (EtC) system. We design a random unitary transform that conserves L0 norm isometry. The resulting encryption method provides a practical orthogonal matching pursuit (OMP) algorithm that allows computation in the encrypted domain. We prove that the proposed method theoretically has exactly the same estimation performance as the nonencrypted variant of the OMP algorithm. In addition, we demonstrate the security strength of the proposed secure sparse representation when applied to the EtC system. Even if the dictionary information is leaked, the proposed scheme protects the privacy information of observed signals.

Pronunciation is a fundamental factor in speaking and listening. However, instructions for important articulation have not been sufficiently provided in conventional computer-assisted language learning (CALL) systems. One typical case is the articulation of rounded vowels. Although lip protrusion is essential for their correct pronunciation, the perception of lip protrusion is often difficult for beginners. To tackle this issue, we propose an innovative method that will provide a comprehensive visual explanation for articulation. Lip movements are three-dimensionally measured, and face images or videos are pseudocoloured on the basis of the movements. The coloured regions represent the lip protrusion of rounded vowels. To verify the learning effect of the proposed method, we conducted experiments with Japanese undergraduates in Chinese classes. The results showed that our method has advantages over conventional video materials.

We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.

We present an extended quarter-plane lattice model for generating two-dimensional (2-D) autoregressive fields. This work is a generalization of the extended lattice filter of diagonal form (ELDF) developed by Ertuzun et al. The proposed model represents a wider class of 2-D AR fields than conventional lattice models. Several examples are presented to demonstrate the applicability of the proposed model. Furthermore, the proposed structure is compared with other conventional lattice filters based on the computation of their entropy values.

To measure temperature dependent complex permittivity of dielectric materials, a rectangular cavity resonator with a heating system has been developed. In the experiment, microwave power with the frequency of 2.45 GHz is applied to heat the dielectric material. In order to reduce the error of the complex permittivity of dielectric material obtained from the perturbation method, an electromagnetic (EM) field simulator is applied which uses the Transmission Line Modeling (TLM) method. The uniformity of the temperature is also discussed by the use of heat transfer equation which applies the results of TLM simulation. It is found from the results that the accurate temperature dependence of complex permittivity of the material can be obtained by the method presented here.

In this paper, we propose an adaptive predictive coding method based on image segmentation for lossless compression. MAR (Multiplicative Autoregressive) predictive coding is an efficient lossless compression scheme. Predictors of the MAR model can be adapted to changes in the local image statistics due to its local image processing. However, the performance of the MAR method is reduced when applied to images whose local statistics change within the block-by-block subdivided image. Furthermore, side-information such as prediction coefficients must be transmitted to the decoder with each block. In order to enhance the compression performance, we improve the MAR coding method by using image segmentation. The proposed MAR predictor can be adapted to the local statistics of the image efficiently at each pixel. Furthermore, less side-information need be transmitted compared with the conventional MAR method.

Communications in locally structured wireless networks, such as ad hoc networks, will play an important role for network services and applications in the ubiquitous networking environments of the future. Most of the time, however, mobile networks are used in much the same way as fixed networks: most of the time, mobile terminals merely access information which is stored in the backbone network. Applications based on the local exchange of information gathered or generated by mobile terminals will open up many novel possibilities. A new online storage system named CAOSS facilitates such exchange and constitutes a building block for various new applications. CAOSS is a server-less system that provides high availability of data in mobile and wireless network environments. We describe CAOSS and its application in a mobile video-information-sharing system named GT. Thanks to the good performance of CAOSS, the GT system gives users a convenient and easy way to share and accumulate video data in a wireless environment. We confirm that CAOSS has strong enough performance to deal with large volumes of video data.

Multiple-input multiple-output (MIMO) OFDM technique is an attractive solution to increase the spectrum efficiency for mobile transmission applications. However, high spatial correlation makes signal detection difficult in real outdoor environments, and thus various methods have been developed to improve the detection performance. An iterative low-density parity-check (LDPC) coded multiple-input multiple-output (MIMO) system is a promising method for solving this problem, and its performance has been analyzed theoretically. This paper proposes an iterative LDPC minimum mean square error with soft interference cancellation (LDPC-MMSE-SIC) receiver with a time de-interleaver in front of the MMSE detector and evaluates its performance by computer simulation using channel state information (CSI) acquired in real outdoor measurements. We show that the iterative detection and decoding system with time interleaving, which is long enough to cover a fading cycle, achieves excellent error rate performance in mobile LOS environments and outperforms an LDPC maximum likelihood detection (LDPC-MLD) receiver with the same error correction and interleaving.

The method for estimating propagation loss that classifies receiving points into multiple groups by focusing on the number of reflections and diffractions, and applies a separate statistical model to each group was extended from only 2.4 GHz band to both 2.4 GHz and 5 GHz band. The extended statistical model was created from received power measurements. First, an appropriate grouping method was investigated based on the fitting error of statistical model. Non-line-of-sight (NLOS) receiving points were grouped in order of points that a wave reflected one time reaches, points that a wave reflected two times reaches, and points that a wave diffracted one time reaches. Next, the effectiveness of the proposed method was verified by comparison with conventional statistical models (one-slope, dual-slope, multi-wall, partitioned) on three office floors that differ from the environment used to create the statistical model. The average NLOS estimation error for the three evaluation environments was 4.9 dB, demonstrating that the proposed method has accuracy equal to or better than that of conventional methods.

For future adaptive networking services, it will be effective if autonomous agents utilize widely spread information that continuously changes. A communication network should provide a framework for agents to utilize information in a large-scale network and should positively support communications among agents. This paper proposes EcAgent (Environment for Communities of Agents). EcAgent provides agents with access to communities that are loose collections of agents. It gathers global information about communications among all agents and makes the agents share it in communities. It makes possible adaptive networking services that use various types of information. We implemented the EcAgent execution environment and some experimental applications to make an adaptive networking service. We also implemented a simulation system to evaluate how accurately the library recognized the embedded relationships as communities. The results of experiments show that the proposed methods for community recognition work efficiently.

High frame rate (HFR) video is attracting strong interest since it is considered as a next step toward providing Ultra-High Definition video service. For instance, the Association of Radio Industries and Businesses (ARIB) standard, the latest broadcasting standard in Japan, defines a 120 fps broadcasting format. The standard stipulates temporally scalable coding and hierarchical transmission by MPEG Media Transport (MMT), in which the base layer and the enhancement layer are transmitted over different paths for flexible distribution. We have developed the first ever MMT transmitter/receiver module for 4K/120fps temporally scalable video. The module is equipped with a newly proposed encapsulation method of temporally scalable bitstreams with correct boundaries. It is also designed to be tolerant to severe network constraints, including packet loss, arrival timing offset, and delay jitter. We conducted a hierarchical transmission experiment for 4K/120fps temporally scalable video. The experiment demonstrated that the MMT module was successfully fabricated and capable of dealing with severe network constraints. Consequently, the module has excellent potential as a means to support HFR video distribution in various network situations.

A rectangular waveguide compatible NRD guide E-plane bandpass filter is proposed for 55 GHz band OFDM applications. The NRD guide E-plane bandpass filter is constructed by inserting a metal foil array in the E-plane of NRD guide. Simulation, fabrication, and handling of the filter are not difficult because each resonator is constructed by a couple of metal foils of a simple shape. A Chebyshev response 5-pole bandpass filter with a very narrow bandwidth of 550 MHz is designed and fabricated at 55 GHz band. Simulated and measured filter performances agree well with the design specifications. Insertion loss of the fabricated filter is found to be around 2.0 dB. Although temperature stability of the fabricated filter are found to be within manageable level, the adoption of cyclo olefin polymer can be one of solution for the temperature stability improvement.

We have developed the world's largest 16.7-inch hybrid in-cell touch panel. To realize the large sized in-cell touch panel, we applied a vertical Vcom system and low resistance sensor, which are JDI's original technologies. For glove touch function, we applied mutual bundled driving, which increases the signal intensity higher. The panel also has a low surface reflection, curved-shaped, and non-rectangular characteristics, which are particular requirements in the automotive market. The over 15-inch hybrid in-cell touch panel adheres to automotive quality requirements. We have also developed a force touch panel, which is a new human machine interface (HMI) based on a hybrid in-cell touch panel in automotive display. This study reports on the effect of the improvements on the in-plane variation of force touch and the value change of the force signal under different environment conditions. We also a introduce force touch implemented prototype.

We have successfully applied Generalized Multiprotocol Label Switching (GMPLS) architecture to the Wavelength Assignment Photonic Switching System (WAPS) to create an internet access system that can provide, between terminals, not only conventional best-effort type of IP packet forwarding, but also high-speed and Quality of Service (QoS)-guaranteed IP forwarding. In this paper the system architecture, system specifications, and system hardware/software implementations are described.

The present paper examines a two-dimensional (2-D) joint-process lattice estimator and its implementation for image restoration. The gradient adaptive lattice (GAL) algorithm is used to update the filter coefficients. The proposed adaptive lattice estimator can represent a wider class of 2-D FIR systems than the conventional 2-D lattice models. Furthermore, its structure possesses orthogonality between the backward prediction errors. These results in superior convergence and tracking properties versus the transversal and other 2-D adaptive lattice estimators. The validity of the proposed model for image restoration is evaluated through computer simulations. In the examples, the implementation of the proposed lattice estimator as 2-D adaptive noise cancellator (ANC) and 2-D adaptive line enhancer (ALE) is considered.