This paper proposes a new methodology to design optimal antennas for MIMO (Multi-Input Multi-Output) communication systems by using spherical mode expansion. Given spatial channel properties of a MIMO channel, such as the angular profile at both sides, the optimal MIMO antennas should provide the largest channel capacity with a constraint of the limited implementation space (volume). In designing a conventional MIMO antenna, first the antenna structure (current distribution) is determined, second antenna directivity is calculated based on the current distribution, and thirdly MIMO channel capacity is calculated by using given angular profiles and obtained antenna directivity. This process is repeated by adjusting the antenna structure until the performance satisfies a predefined threshold. To the contrary, this paper solves the optimization problem analytically and finally gives near optimal antenna structure (current distribution) without any greedy search. In the proposed process, first the optimal directivity of MIMO antennas is derived by applying spherical mode expansion to the angular profiles, and second a far-near field conversion is applied on the derived optimal directivity to achieve near optimal current distributions on a limited surface. The effectiveness of the proposed design methodology is validated via numerical calculation of MIMO channel capacity as in the conventional design method while giving near optimal current distribution with constraint of an antenna structure derived from proposed methodology.

We describe a field experiment of flexible modulation format adaptation on a real-time 400Gbit/s/ch DSP-LSI. This real-time DSP-LSI features OSNR estimation, practical simplified back propagation, and high gain soft-decision forward error correction. With these techniques, we have successfully demonstrated modulation format allocation and transmission of 56-channel 400Gbit/s-2SC-PDM-16QAM and 200Gbit/s-2SC-PDM-QPSK signals in 216km and 3246km standard single mode fiber, respectively.

This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.

A Wiener-based inpainting quality prediction method is presented in this paper. The proposed method is the first method that can predict inpainting quality both before and after the intensities have become missing even if their inpainting methods are unknown. Thus, when the target image does not include any missing areas, the proposed method estimates the importance of intensities for all pixels, and then we can know which areas should not be removed. Interestingly, since this measure can be also derived in the same manner for its corrupted image already including missing areas, the expected difficulty in reconstruction of these missing pixels is predicted, i.e., we can know which missing areas can be successfully reconstructed. The proposed method focuses on expected errors derived from the Wiener filter, which enables least-squares reconstruction, to predict the inpainting quality. The greatest advantage of the proposed method is that the same inpainting quality prediction scheme can be used in the above two different situations, and their results have common trends. Experimental results show that the inpainting quality predicted by the proposed method can be successfully used as a universal quality measure.

The Long Term Evolution (LTE) of mobile communication standard was designed by the 3rd generation partnership project (3GPP) to serve the requirements. Nowadays, the combining of the orthogonal frequency division multiplexing (OFDM) and the multiple input multiple output (MIMO) is supported in LTE system. The MIMO-OFDM is considered to improve data rate and channel capacity without additional bandwidth. Because the receivers get all transmission signals from all transmitters at the same time, many detection schemes have been developed for accurate estimation and low complexity. Among the detection schemes, the QR decomposition with M algorithm (QRD-M) achieves optimal error performance with low complexity. Nevertheless, the conventional QRD-M has high complexity for implementation. To overcome the problem, this letter proposes the low complexity QRD-M detection scheme in MIMO-OFDM systems. The proposed scheme has two elements which decide layer value and the limited candidates. The two elements are defined by the number of transmit antennas and the cardinality of modulation set respectively. From simulation results, the proposed scheme has the same error performance with the conventional QRD-M and very lower complexity than the conventional QRD-M.

In this letter, an effective algorithm is proposed to improve the performance of relay enhanced cellular networks, which is to allocate appropriate resources to each access point with quality of service constraint. First we derive the ergodic rate for backhaul link based on a poison point process model, and then allocate resources to each link according to the quality of service requirements and ergodic rate of links. Numerical results show that the proposed algorithm can not only improve system throughput but also improve the rate distribution of user equipment.

This paper deals with a novel, interesting problem of detecting frauds in comparison-shopping services (CSS). In CSS, there exist frauds who perform excessive clicks on a target item. They aim at making the item look very popular and subsequently ranked high in the search and recommendation results. As a result, frauds may distort the quality of recommendations and searches. We propose an approach of detecting such frauds by analyzing click behaviors of users in CSS. We evaluate the effectiveness of the proposed approach on a real-world clickstream dataset.

In this paper, a novel approach is proposed for stereo vision-based ground plane detection at superpixel-level, which is implemented by employing a Disparity Texture Map in a convolution neural network architecture. In particular, the Disparity Texture Map is calculated with a new Local Disparity Texture Descriptor (LDTD). The experimental results demonstrate our superior performance in KITTI dataset.

Descriptor aggregation techniques such as the Fisher vector and vector of locally aggregated descriptors (VLAD) are used in most image retrieval frameworks. It takes some time to extract local descriptors, and the geometric verification requires storage if a real-valued descriptor such as SIFT is used. Moreover, if we apply binary descriptors to such a framework, the performance of image retrieval is not better than if we use a real-valued descriptor. Our approach tackles these issues by using a dual representation descriptor that has advantages of being both a real-valued and a binary descriptor. The real value of the dual representation descriptor is aggregated into a VLAD in order to achieve high accuracy in the image retrieval, and the binary one is used to find correspondences in the geometric verification stage in order to reduce the amount of storage needed. We implemented a dual representation descriptor extracted in semi-real time by using the CARD descriptor. We evaluated the accuracy of our image retrieval framework including the geometric verification on three datasets (holidays, ukbench and Stanford mobile visual search). The results indicate that our framework is as accurate as the framework that uses SIFT. In addition, the experiments show that the image retrieval speed and storage requirements of our framework are as efficient as those of a framework that uses ORB.

Constant composition codes (CCCs) are a special class of constant-weight codes. They include permutation codes as a subclass. The study and constructions of CCCs with parameters meeting certain bounds have been an interesting research subject in coding theory. A bridge from zero difference balanced (ZDB) functions to CCCs with parameters meeting the Luo-Fu-Vinck-Chen bound has been established by Ding (IEEE Trans. Information Theory 54(12) (2008) 5766-5770). This provides a new approach for obtaining optimal CCCs. The objective of this letter is to construct two classes of ZDB functions whose parameters not covered in the literature, and then obtain two classes of optimal CCCs meeting the Luo-Fu-Vinck-Chen bound from these new ZDB functions.

We build a privacy-preserving system of linear regression protecting both input data secrecy and output privacy. Our system achieves those goals simultaneously via a novel combination of homomorphic encryption and differential privacy dedicated to linear regression and its variants (ridge, LASSO). Our system is proved scalable over cloud servers, and its efficiency is extensively checked by careful experiments.

In the case of drive-by download attacks, most malicious web sites identify the software environment of the clients and change their behavior. Then we cannot always obtain sufficient information appropriate to the client organization by automatic dynamic analysis in open services. It is required to prepare for expected incidents caused by re-accessing same malicious web sites from the other client in the organization. To authors' knowledge, there is no study of utilizing analysis results of malicious web sites for digital forensic on the incident and hedging the risk of expected incident in the organization. In this paper, we propose a system for evaluating the impact of accessing malicious web sites by using the results of multi-environment analysis. Furthermore, we report the results of evaluating malicious web sites by the multi-environment analysis system, and show how to utilize analysis results for forensic analysis and risk hedge based on actual cases of analyzing malicious web sites.

People's body movements in daily face-to-face communication influence each other. For instance, during a heated debate, the participants use more gestures and other body movements, while in a calm discussion they use fewer gestures. This “coevolution” of interpersonal body movements occurs on multiple time scales, like minutes or hours. However, the multi-time-scale coevolution in daily communication is not clear yet. In this paper, we explore the minute-to-minute coevolution of interpersonal body movements in daily communication and investigate the characteristics of this coevolution. We present quantitative data on upper-body movements from thousand test subjects from seven organizations gathered over several months via wearable sensors. The device we employed measured upper-body movements with an accelerometer and the duration of face-to-face communication with an infrared ray sensor on a minute-by-minute basis. We defined a coevolution measure between two people as the number of per-minute changes of their body movement and compared the indices for face-to-face and non-face-to-face situations. We found that on average, the amount of people's body movements changed correspondingly for face-to-face communication and that the average rate of coevolution in the case of face-to-face communication was 3-4% higher than in the case of non-face-to-face situation. These results reveal minute-to-minute coevolution of upper-body movements between people in daily communication. The finding suggests that the coevolution of body movement arises in multiple time scales.

Constant weight codes have mathematical interest and practical applications such as coding for bandwidth-efficient channels and construction of spherical codes for modulation. In this letter, by using difference balanced functions with d-form property, we constructed a class of constant composition code with new parameters, which achieves the equal sign of generalized Johnson bound.

This paper aims to propose a Fallback Control System isolated from cyber-attacks against networked control systems. The fallback control system implements maintaining functionality after an incident. Since cyber-attacks tamper with the communication contents of the networked control systems, the fallback control system is installed in a control target side. The fallback control system detects the incident without the communication contents on field network. This system detects an incident based on a bilinear observer and a switched Lyapunov function. When an incident is detected, normal operation is switched to fallback operation automatically. In this paper, a practical experiment with Ball-Sorter simulating a simple defective discriminator as a part of Factory Automation systems is shown. Assumed cyber-attacks against Ball-Sorter are Man In The Middle attack and Denial of Service attack.

Anisotropic dielectrics and ferromagnetic materials are widely used in dispersion-engineered metamaterials. For example, nonreciprocal magnetic photonic crystals (MPhCs) are periodic structures whose unit cell is composed of two misaligned anisotropic dielectric layers and one ferromagnetic layer and they have extraordinary characteristics such as wave slowdown and field amplitude increase. We develop an unconditionally stable complex-envelop alternating-direction-implicit finite-difference time-domain method (CE-ADI-FDTD) suitable for the transient analysis of anisotropic dielectrics and ferromagnetic materials. In the proposed algorithm, the perfectly-matched-layer (PML) is straightforwardly incorporated in Maxwell's curl equations. Numerical examples show that the proposed PML-CE-ADI-FDTD method can reduce the CPU time significantly for the transient analysis of anisotropic dielectrics and ferromagnetic materials while maintaining computational accuracy.

Community detection is a pivotal task in data mining, and users' emotional behaviors have an important impact on today's society. So it is very significant for society management or marketing strategies to detect emotional communities in social networks. Based on the emotional homophily of users in social networks, it could confirm that users would like to gather together to form communities according to emotional similarity. This paper exploits multivariate emotional behaviors of users to measure users' emotional similarity, then takes advantage of users' emotional similarity as edge weight to remodel an emotional network and detect communities. The detailed process of detecting emotional communities is as follows: 1) an emotional network is constructed and emotional homophily in experimental dataset is verified; 2) both CNM and BGLL algorithms are employed to detect emotional communities in emotional network, and emotional characters of each community are analyzed; 3) in order to verify the superiority of emotional network for detecting emotional communities, 1 unweighted network and 3 other weighted and undirected networks are constructed as comparison. Comparison experiments indicate that the emotional network is more suitable for detecting emotional communities, the users' emotional behaviors are more similar and denser in identical communities of emotional network than the contrastive networks' communities.

We surveyed employees who use personal devices for work. Residual analysis for cross-tabulation was carried out for three groups classified based on company rules and regulations established for mobile work. We show that the behavior of employees working with personal devices to process business data changes due to the presence or absence of the company rules and regulations.

This paper proposes a method for human pose estimation in still images. The proposed method achieves occlusion-aware appearance modeling. Appearance modeling with less accurate appearance data is problematic because it adversely affects the entire training process. The proposed method evaluates the effectiveness of mitigating the influence of occluded body parts in training sample images. In order to improve occlusion evaluation by a discriminatively-trained model, occlusion images are synthesized and employed with non-occlusion images for discriminative modeling. The score of this discriminative model is used for weighting each sample in the training process. Experimental results demonstrate that our approach improves the performance of human pose estimation in contrast to base models.

In the present paper, we propose a novel high-resolution analog-to-digital converter (ADC) for low-power biomedical analog front-ends, which we call the successive stochastic approximation ADC. The proposed ADC uses a stochastic flash ADC (SF-ADC) to realize a digitally controlled variable-threshold comparator in a successive-approximation-register ADC (SAR-ADC), which can correct errors originating from the internal digital-to-analog converter in the SAR-ADC. For the residual error after SAR-ADC operation, which can be smaller than thermal noise, the SF-ADC uses the statistical characteristics of noise to achieve high resolution. The SF-ADC output for the residual signal is combined with the SAR-ADC output to obtain high-precision output data using the supervised machine learning method.