Machine learning is used in various fields and demand for implementations is increasing. Within machine learning, a Random Forest is a multi-class classifier with high-performance classification, achieved using bagging and feature selection, and is capable of high-speed training and classification. However, as a type of ensemble learning, Random Forest determines classifications using the majority of multiple trees; so many decision trees must be built. Performance increases with the number of decision trees, requiring memory, and decreases if the number of decision trees is decreased. Because of this, the algorithm is not well suited to implementation on small-scale hardware as an embedded system. As such, we have proposed Boosted Random Forest, which introduces a boosting algorithm into the Random Forest learning method to produce high-performance decision trees that are smaller. When evaluated using databases from the UCI Machine learning Repository, Boosted Random Forest achieved performance as good or better than ordinary Random Forest, while able to reduce memory use by 47%. Thus, it is suitable for implementing Random Forests on embedded hardware with limited memory.

In this paper, a new approach is proposed for extracting the spatio-temporal patterns from a location-based social networking system (SNS) such as Foursquare. The proposed approach consists of the following procedures. First, the spatio-temporal behaviors of users in SNS are approximated as a probabilistic distribution by using a diffusion-type formula. Since the SNS datasets generally consist of sparse check-in's of users at some time points and locations, it is difficult to investigate the spatio-temporal patterns on a wide range of time and space scales. The proposed method can estimate such wide range patterns by smoothing the sparse datasets by a diffusion-type formula. It is crucial in this method to estimate robustly the scale parameter by giving a prior generative model on check-in's of users. The robust estimation enables the method to extract appropriate patterns even in small local areas. Next, the covariance matrix among the time points is calculated from the estimated distribution. Then, the principal eigenfunctions are approximately extracted as the spatio-temporal patterns by principal component analysis (PCA). The distribution is a mixture of various patterns, some of which are regular ones with a periodic cycle and some of which are irregular ones corresponding to transient events. Though it is generally difficult to separate such complicated mixtures, the experiments on an actual Foursquare dataset showed that the proposed method can extract many plausible and interesting spatio-temporal patterns.

In this letter we present an imperceptible and robust watermarking algorithm that uses a cryptographic hash function in the authentication application of digital medical imaging. In the proposed scheme we combine discrete Fourier transform (DFT) and local image masking to detect the watermark after a geometrical distortion and improve its imperceptibility. The image quality is measured by metrics currently used in digital image processing, such as VSNR, SSIM and PSNR.

Potential games form a class of non-cooperative games where the convergent of unilateral improvement dynamics is guaranteed in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.

In this letter, a robust algorithm for jointly finding an estimate of the start of the frame and transmission mode is proposed in a digital audio broadcasting (DAB) system. In doing so, the use of differential-correlation based joint detection is proposed, which considers not only the height of correlation peak but also its plateau. We show via simulations that the proposed detection algorithm is capable of robustly detecting the start of a frame and its mode against the variation of signal-to-noise ratio, providing a performance advantage over the conventional algorithm.

This letter deals with the consensus problem of multi-agent systems, which are composed of feedforward nonlinear systems under a directed network with a communication time delay. In order to solve this problem, a new consensus protocol with a low gain parameter is proposed. Moreover, it is shown that under some sufficient conditions, the proposed protocol can solve the consensus problem of nonlinear multi-agent systems even in the presence of an arbitrarily large communication delay. An illustrative example is presented to verify the validity of the proposed approach.

We propose a novel sparse representation-based direction-of-arrival (DOA) estimation method. In contrast to those that approximate l0-norm minimization by l1-norm minimization, our method designs a reweighted l1 norm to substitute the l0 norm. The capability of the reweighted l1 norm to bridge the gap between the l0- and l1-norm minimization is then justified. In addition, an array covariance vector without redundancy is utilized to extend the aperture. It is proved that the degree of freedom is increased as such. The simulation results show that the proposed method performs much better than l1-type methods when the signal-to-noise ratio (SNR) is low and when the number of snapshots is small.

We demonstrated that load balancing using actual subscriber extension numbers was practical and effective against traffic congestion after a disaster based on actual data. We investigated the ratios of the same subscriber extension numbers in each prefecture and found that most of them were located almost evenly all over the country without being concentrated in a particular area. The ratio of every number except for the fourth-last digit in the last group of four numbers in a telephone number was used almost equally and located almost evenly all over the country. Tolerance against overload in the last, second-, and third-last single digits stays close to that in the ideal situation if we assume that each session initiation protocol server has a capacity in accordance with the ratio of each number on every single digit in the last group of four numbers in Japan. Although tolerance against overload in double-, triple-, and quadruple-digit numbers does not stay close to that in the ideal situation, it still remains sufficiently high in the case of double- and triple-digit numbers. Although tolerance against overload in the quadruple-digit numbers becomes low, disaster congestion is still not likely to occur in almost half of the area of Japan (23 out of 47 prefectures).

This paper proposes the concept of adaptive multi-rate (AMR), which jointly employs switching between two links and adaptive rate on each link, for hybrid free-space optical/radio-frequency (FSO/RF) systems. Moreover, we present the cross-layer design of AMR switching, which is based on both the physical and link layers with an automatic-repeat request (ARQ) scheme. We develop an analytical framework based on a Markov chain model for system performance analysis. System performance metrics, including frame-error rate, goodput and link switching probability, are analytically studied over fading channels. Numerical results quantitatively show how the proposal significantly outperforms conventional ones with physical layer-based design and/or fixed-rate switching operation.

We demonstrated generation of arbitrarily patterned optical pulse trains and frequency tunable terahertz (THz) pulses by spectral synthesis of optical combs generated by a Mach-Zehnder-modulator-based flat comb generator (MZ-FCG). In our approach, THz pulses were generated by photomixing of a multi-tone signal, which is elongated pulse train, and a single-tone signal. Both signals were extracted from a comb signal by using optical tunable bandpass filters. In the case of optical pulse train generation, the MZ-FCG generated comb signals with 10 GHz-spacing and 330 GHz-width, which was converted to a 2.85 ps-width pulse train by chirp compensation using a single-mode fiber. By combining the MZ-FCG with a pulse picker composed of a 40 Gbps intensity modulator, divided pulse trains and arbitrarily bit sequences were successfully generated. The single-mode light was extracted by an optical bandpass filter and the band-controlled pulse train was extracted by an optical bandpass filter. By photomixing them, a THz pulse was successfully generated. In the case of THz pulse generation, by photomixing a single-tone and a multi-tone signals extracted by tunable bandpass filters, THz pulses with a center frequency of 300 GHz was successfully generated. Furthermore, frequency tunability of the center frequency was also demonstrated.

Non-orthogonal multiple access (NOMA) utilizing the power domain and advanced receiver has been considered as one promising multiple access technology for further cellular enhancements toward the 5th generation (5G) mobile communications system. Most of the existing investigations into NOMA focus on the combination of NOMA with orthogonal frequency division multiple access (OFDMA) for either downlink or uplink. In this paper, we investigate NOMA for uplink with single carrier-frequency division multiple access (SC-FDMA) being used. Differently from OFDMA, SC-FDMA requires consecutive resource allocation to a user equipment (UE) in order to achieve low peak to average power ratio (PAPR) transmission by the UE. Therefore, sophisticated designs of scheduling algorithm for NOMA with SC-FDMA are needed. To this end, this paper investigates the key issues of uplink NOMA scheduling such as UE grouping method and resource widening strategy. Because the optimal schemes have high computational complexity, novel schemes with low computational complexity are proposed for practical usage for uplink resource allocation of NOMA with SC-FDMA. On the basis of the proposed scheduling schemes, the performance of NOMA is investigated by system-level simulations in order to provide insights into the suitability of using NOMA for uplink radio access. Key issues impacting NOMA performance are evaluated and analyzed, such as scheduling granularity, UE number and the combination with fractional frequency reuse (FFR). Simulation results verify the effectiveness of the proposed algorithms and show that NOMA is a promising radio access technology for 5G systems.

Due to the fact that natural images are approximately sparse in Discrete Cosine Transform (DCT) or wavelet basis, the Compressive Sensing (CS) can be employed to decode both the host image and watermark with zero error, despite not knowing the host image. In this paper, Limited-Random Sequence (LRS) matrix is utilized to implement the blind CS detection, which benefits from zero error and lower complexity. The performance in Bit Error Rate (BER) and error-free detection probability confirms the validity and efficiency of the proposed scheme.

Passive SONAR receives a mixed form of signal that is a combination of continuous and discrete line-component spectrum signals. The conventional algorithms, DEMON and LOFAR, respectively target each type of signal, but do not consider the other type of signal also present in the practical environment. Thus when features from two types of signals are presented at the same time, analysis results may cause confusion. In this paper, we propose an integrated analysis algorithm for underwater signals using the modulation spectrogram domain. The proposed domain presents the visual difference between the different types of signal, and therefore can prevent confusion that would otherwise be feasible. Moreover, the proposed algorithm is more efficient than multiband DEMON in terms of computation complexity, while in colored ambient noise environment, it has similar performance to conventional DEMON and LOFAR. We prove the validity of the proposed algorithm through the relevant experiments with synthesized signal and actual underwater recordings.

Considering worse-case channel uncertainties, we investigate the robust energy efficient (EE) beamforming design problem in a K-user multiple-input-single-output (MISO) interference channel. Our objective is to maximize the worse-case sum EE under individual transmit power constraints. In general, this fractional programming problem is NP-hard for the optimal solution. To obtain an insight into the problem, we first transform the original problem into its lower bound problem with max-min and fractional form by exploiting the relationship between the user rate and the minimum mean square error (MMSE) and using the min-max inequality. To make it tractable, we transform the problem of fractional form into a subtractive form by using the Dinkelbach transformation, and then propose an iterative algorithm using Lagrangian duality, which leads to the locally optimal solution. Simulation results demonstrate that our proposed robust EE beamforming scheme outperforms the conventional algorithm.

In these days, recognizing a user personality is an important issue in order to support various personalized services. Besides the conventional phone usage such as call logs, SMS logs and application usages, smart phones can gather the behavior of users by polling various embedded sensors such as GPS sensors. In this paper, we focus on how to predict user attitude based on GPS log data by applying location clustering techniques and extracting features from the location clusters. Through the evaluation with one month-long GPS log data, it is observed that the location-based features, such as number of clusters and coverage of clusters, are correlated with user attitude to some extent. Especially, when SVM is used as a classifier for predicting the dichotomy of user attitudes of MBTI, over 90% F-measure is achieved.

Harn and Lin proposed an algorithm to detect and identify cheaters in Shamir's secret sharing scheme in the journal Designs, Codes and Cryptography, 2009. In particular, their algorithm for cheater identification is inefficient. We point out that some of their conditions for cheater detection and identification essentially follow from those on error detection/correction of Reed-Solomon codes, which have efficient decoding algorithms, while some other presented conditions turn out to be incorrect. The extended and improved version of the above mentioned scheme was recently presented at the conference International Computer Symposium 2012 (and the journal version appeared in the journal IET Information Security). The new scheme, which is ideal (i.e. the share size is equal to that of the secret), attempts to identify cheaters from minimal number of shares (i.e. the threshold of them). We show that the proposed cheater identification is impossible using the arguments from coding theory.

We propose simple but efficient encapsulation architecture. In the architecture, clients can better decode Extensible Markup Language (XML) based service information for TV contents with schema digest. Our experimental results show the superiority of the proposed architecture by comparing the compression ratios and decoding times of the proposed architecture and the existing architectures.

Local governments usually designed disaster response plan by themselves in order to overcome disasters. In previous research, we developed the effective analysis method for disaster response which is “BFD (Business Flow Diagram)”. In this research, in order to improve effect of BFD analysis, we designed and developed WBS Manager focusing on the process of WBS development which is a part of BFD analysis, because WBS development is fundamental process of BFD method. Especially we developed WBS Manager as web-based application, and implemented it to actual studies at local governments in planning their disaster response operations. In this paper, we introduced the overview of WBS Manager.

Many previous studies on image quality assessment of 3D still images or video clips have been conducted. In particular, it is important to know the region in which assessors are interested or on which they focus in images or video clips, as represented by the ROI (Region of Interest). For multi-view 3D images, it is obvious that there are a number of viewpoints; however, it is not clear whether assessors focus on objects or background regions. It is also not clear on what assessors focus depending on whether the background region is colored or gray scale. Furthermore, while case studies on coded degradation in 2D or binocular stereoscopic videos have been conducted, no such case studies on multi-view 3D videos exist, and therefore, no results are available for coded degradation according to the object or background region in multi-view 3D images. In addition, in the case where the background region is gray scale or not, it was not revealed that there were affection for gaze point environment of assessors and subjective image quality. In this study, we conducted experiments on the subjective evaluation of the assessor in the case of coded degradation by JPEG coding of the background or object or both in 3D CG images using an eight viewpoint parallax barrier method. Then, we analyzed the results statistically and classified the evaluation scores using an SVM.

In this letter, a local pattern coding scheme is proposed to reduce the dimensionality of feature vectors in the local ternary pattern. The proposed method encodes the ternary patterns into a binary pattern by clustering similar ternary patterns. The experimental results show that the proposed method outperforms the previous methods.