Recently, flexible and lightweight optical devices are desired from the practical viewpoint. We demonstrated roll-to-roll type Anti Reflection (AR) film fabricated by layer-by-layer (LBL) adsorption process. When deposition time was 2.5 min and repeating cycle was 8 cycles, refractive index of LBL layer was 1.499 at 632 nm and thickness was 93.1 nm, which are almost the same as those of batch type LBL layer. The minimum reflectance was about 0.6% at 600 nm and transmittance was over 75% at visible region. However as compared with batch type, roll type AR film has lower reflectance and transmittance. This reason is that the flow of solution and rinse and quantity of rinse was smaller, a number of bathes of roll type was lower than that of batch type. Furthermore, comparing the deposition time and film speed, LBL layer was fabricated clearly long deposition time and slow film speed. The roll-to-roll film had a problem of peeling off during the deposition process. By increasing the contact area between film and guided roll, vertical pressure was decrease and friction force was decreased. Furthermore, as rotational speed of guided roll and film speed was decreased, LBL layer was not peeled by friction force between film and guided roll. Because rotational speed of guided rolls and films were almost same in the range of less than about 30 mm/min. There was the problem that polymer complexes were likely to appear on the substrate when the surface was dried during moving between solution and rinse bath. This phenomenon was observed during the roll-to-roll as well as batch process. The quality of roll-to-roll LBL process was depending on deposition time and film speed and drying at moving process critically compared with batch type. It is necessary to design the roll-to-roll machine with care: important points are deposition time and film speed, drying at moving process.

Self-paced e-learning provides much more freedom in time and locale than traditional education as well as diversity of learning contents and learning media and tools. However, its limitations must not be ignored. Lack of information on learners' states is a serious issue that can lead to severe problems, such as low learning efficiency, motivation loss, and even dropping out of e-learning. We have designed a novel e-learning support system that can visually observe learners' non-verbal behaviors and estimate their learning states and that can be easily integrated into practical e-learning environments. Three pairs of internal states closely related to learning performance, concentration-distraction, difficulty-ease, and interest-boredom, were selected as targets of recognition. In addition, we investigated the practical problem of estimating the learning states of a new learner whose characteristics are not known in advance. Experimental results show the potential of our system.

This paper proposes a unified coding algorithm for the lossless and near-lossless compression of still color images. The proposed unified color image coding scheme can control the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image while the level of distortion on the RGB plane is suppressed to within a preset magnitude. In order to control the PSNR, the distortion level is adaptively changed at each pixel. An adaptive quantizer to control the distortion is designed on the basis of psychovisual criteria. Finally, experiments on Super High Definition (SHD) images show the effectiveness of the proposed algorithm.

Directions and speeds of the motion of rain areas are estimated for each type of rain fronts, using time differences detected in the rain attenuation of the Ku-band satellite radio wave signals that have been measured at Osaka Electro-Communication University (OECU) in Neyagawa, Osaka, Research Institute of Sustainable Humanosphere (RISH) in Uji, Kyoto, and MU Observatory (MU) of Kyoto University in Shigaraki, Shiga, for the past five years since September 2002. These directions and speeds are shown to agree well with those directly obtained from the motion of rain fronts in the weather charts published by Japan Meteorological Agency. The rain area motion is found to have characteristic directions according to each rain type, such as cold and warm fronts or typhoon. A numerical estimate of the effects of site diversity techniques indicates that between two sites among the three locations (OECU, RISH, MU) separated by 20-50 km, the joint cumulative time percentages of rain attenuation become lower as the two sites are aligned along the directions of rain area motion. In such a case, compared with the ITU-R recommendations, the distance required between the two sites may be, on an average, reduced down to about 60-70% of the conventional predictions.

Lossless video coding is required in the fields of archiving and editing digital cinema or digital broadcasting contents. This paper combines a discrete wavelet transform and adaptive inter/intra-frame prediction in the wavelet transform domain to create multiresolution lossless video coding. Based on the image statistics of the wavelet transform domains in successive frames, inter/intra frame adaptive prediction is applied to the appropriate wavelet transform domain. This adaptation offers superior compression performance. A progressive transmission scheme is also proposed for effective resolution scalability. Experiments on test sequences confirm the effectiveness of the proposed algorithm.

This paper presents two types of two-dimensional (2-D) adaptive beamforming algorithm which have high rate of convergence. One is a linearly constrained minimum variance (LCMV) beamforming algorithm which minimizes the average output power of a beamformer, and the other is a generalized sidelobe canceler (GSC) algorithm which generalizes the notion of a linear constraint by using the multiple linear constraints. In both algorithms, we apply a 2-D lattice filter to an adaptive filtering since the 2-D lattice filter provides excellent properties compared to a transversal filter. In order to evaluate the validity of the algorithm, we perform computer simulations. The experimental results show that the algorithm can reject interference signals while maintaining the direction of desired signal, and can improve convergent performance.

In the adaptive lattice estimation process, it is well known that the convergence speed of the successive stage is affected by the estimation errors of reflection coefficients in its preceding stages. In this paper, we propose block estimation methods of two-dimensional (2-D) adaptive lattice filter. The convergence speed of the proposed algorithm is significantly enhanced by improving the adaptive performance of preceding stages. Furthermore, this process can be simply realized. The modeling of 2-D AR field and texture image are demonstrated through computer simulations.

This paper describes the imaging method for a human forearm in the microwave transmission CT at 3GHz. To improve the spatial resolution, the correction method of the diffraction effects is adopted and the high directivity antennas are used. A cross-sectional image of the human forearm is obtained in vivo.

JPEG2000, an international standard for still image compression, offers 1) high coding performance, 2) unified lossless/lossy compression, and 3) resolution and SNR scalability. Resolution scalability is an especially promising attribute given the popularity of Super High Definition (SHD) images like digital-cinema. Unfortunately, its current implementation of resolution scalability is restricted to powers of two. In this paper, we introduce non-octave scalable coding (NSC) based on the use of filter banks. Two types of non-octave scalable coding are implemented. One is based on a DCT filter bank and the other uses wavelet transform. The latter is compatible with JPEG2000 Part2. By using the proposed algorithm, images with rational scale resolutions can be decoded from a compressed bit stream. Experiments on digital cinema test material show the effectiveness of the proposed algorithm.

An adaptive multichannel IIR lattice predictor for k-step ahead prediction is constructed and the effectiveness of the proposed predictor is evaluated using digital simulations.

This paper proposes and evaluates machine learning (ML)-based compensation methods for the transmit (Tx) weight matrices of actual singular value decomposition (SVD)-multiple-input and multiple-output (MIMO) transmissions. These methods train ML models and compensate the Tx weight matrices by using a large amount of training data created from statistical distributions. Moreover, this paper proposes simplified channel metrics based on the channel quality of actual SVD-MIMO transmissions to evaluate compensation performance. The optimal parameters are determined from many ML parameters by using the metrics, and the metrics for this determination are evaluated. Finally, a comprehensive computer simulation shows that the optimal parameters improve performance by up to 7.0dB compared with the conventional method.

Seen from the Internet Service Provider (ISP) side, network traffic monitoring is an indispensable part during network service provisioning, which facilitates maintaining the security and reliability of the communication networks. Among the numerous traffic conditions, we should pay extra attention to traffic anomaly, which significantly affects the network performance. With the advancement of Machine Learning (ML), data-driven traffic anomaly detection algorithms have established high reputation due to the high accuracy and generality. However, they are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. In this paper, we proposed an online learning framework for traffic anomaly detection by embracing Gaussian Process (GP) and Sparse Representation (SR) in two steps: 1). To extract traffic features from past records, and better understand these features, we adopt GP with a special kernel, i.e., mixture of Gaussian in the spectral domain, which makes it possible to more accurately model the network traffic for improving the performance of traffic anomaly detection. 2). To combat noise and modeling error, observing the inherent self-similarity and periodicity properties of network traffic, we manually design a feature vector, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.

This article introduces our investigation on learning state estimation in e-learning on the condition that visual observation and recording of a learner's behaviors is possible. In this research, we examined methods of adaptation for a new learner for whom a small number of ground truth data can be obtained.

The number of networked devices of sensors and actuators continues to increase. We are developing a data-sharing mechanism called uTupleSpace as middleware for storing and retrieving ubiquitous data that are input or output by such devices. uTupleSpace enables flexible retrieval of sensor data and flexible control of actuator devices, and it simplifies the development of various applications. Though uTupleSpace requires scalability against increasing amounts of ubiquitous data, traditional load-distribution methods using a distributed hash table (DHT) are unsuitable for our case because of the ununiformity of the data. Data are nonuniformly generated at some particular times, in some particular positions, and by some particular devices, and their hash values focus on some particular values. This feature makes it difficult for the traditional methods to sufficiently distribute the load by using the hash values. Therefore, we propose a new load-distribution method using a DHT called the dynamic-help method. The proposed method enables one or more peers to handle loads related to the same hash value redundantly. This makes it possible to handle the large load related to one hash value by distributing the load among peers. Moreover, the proposed method reduces the load caused by dynamic load-redistribution. Evaluation experiments showed that the proposed method achieved sufficient load-distribution even when the load was concentrated on one hash value with low overhead. We also confirmed that the proposed method enabled uTupleSpace to accommodate the increasing load with simple operational rules stably and with economic efficiency.

In this paper, we propose secure dictionary learning based on a random unitary transform for sparse representation. Currently, edge cloud computing is spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. Edge cloud computing poses several serious issues for end users, such as unauthorized use and leak of data, and privacy failures. The proposed scheme provides practical MOD and K-SVD dictionary learning algorithms that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary learning estimation performance as the non-encrypted variant of MOD and K-SVD algorithms. We apply it to secure image modeling based on an image patch model. Finally, we demonstrate its performance on synthetic data and a secure image modeling application for natural images.

In this paper, we propose a secure computation of sparse coding and its application to Encryption-then-Compression (EtC) systems. The proposed scheme introduces secure sparse coding that allows computation of an Orthogonal Matching Pursuit (OMP) algorithm in an encrypted domain. We prove theoretically that the proposed method estimates exactly the same sparse representations that the OMP algorithm for non-encrypted computation does. This means that there is no degradation of the sparse representation performance. Furthermore, the proposed method can control the sparsity without decoding the encrypted signals. Next, we propose an EtC system based on the secure sparse coding. The proposed secure EtC system can protect the private information of the original image contents while performing image compression. It provides the same rate-distortion performance as that of sparse coding without encryption, as demonstrated on both synthetic data and natural images.

The present paper investigates a two-dimensional (2-D) adaptive lattice filter used for modeling 2-D AR fields. The 2-D least mean square (LMS) lattice algorithm is used to update the filter coefficients. The proposed adaptive lattice filter can represent a wider class of 2-D AR fields than previous ones. Furthremore, its structure is also shown to possess orthogonality in the backward prediction error fields. These result in superior convergence and tracking properties to the adaptive transversal filter and other adaptive 2-D lattice models. Then, the convergence property of the proposed adaptive LMS lattice algorithm is discussed. The effectiveness of the proposed model is evaluated for parameter identification through computer simulation.

In this paper, we propose a layered multicast encryption scheme that provides flexible access control to motion JPEG2000 code streams. JPEG2000 generates layered code streams and offers flexible scalability in characteristics such as resolution and SNR. The layered multicast encryption proposal allows a sender to multicast the encrypted JPEG2000 code streams such that only designated groups of users can decrypt the layered code streams. While keeping the layering functionality, the proposed method offers useful properties such as 1) video quality control using only one private key, 2) guaranteed security, and 3) low computational complexity comparable to conventional non-layered encryption. Simulation results show the usefulness of the proposed method.

In this paper, we introduce layered low-density generator matrix (Layered-LDGM) codes for super high definition (SHD) scalable video systems. The layered-LDGM codes maintain the correspondence relationship of each layer from the encoder side to the decoder side. This resulting structure supports partial decoding. Furthermore, the proposed layered-LDGM codes create highly efficient forward error correcting (FEC) data by considering the relationship between each scalable component. Therefore, the proposed layered-LDGM codes raise the probability of restoring the important components. Simulations show that the proposed layered-LDGM codes offer better error resiliency than the existing method which creates FEC data for each scalable component independently. The proposed layered-LDGM codes support partial decoding and raise the probability of restoring the base component. These characteristics are very suitable for scalable video coding systems.

This paper describes a unified coding algorithm for lossless and near-lossless color image compression that exploits the correlations between RGB signals. A reversible color transform that removes the correlations between RGB signals while avoiding any finite word length limitation is proposed for the lossless case. The resulting algorithm gives higher performance than the lossless JPEG without the color transform. Next, the lossless algorithm is extended to a unified coding algorithm of lossless and near-lossless compression schemes that can control the level of the reconstruction error on the RGB plane from 0 to p, where p is a certain small non-negative integer. The effectiveness of this algorithm was demonstrated experimentally.