Web browsing services are expanding as smartphones are becoming increasingly popular worldwide. To provide customers with appropriate quality of web-browsing services, quality design and in-service quality management on the basis of quality of experience (QoE) is important. We propose a web-browsing QoE estimation model. The most important QoE factor for web-browsing is the waiting time for a web page to load. Next, the variation in the communication quality based on a mobile network should be considered. We developed a subjective quality assessment test to clarify QoE characteristics in terms of waiting time using 20 different types of web pages and constructed a web-page QoE estimation model. We then conducted a subjective quality assessment test of web-browsing to clarify the relationship between web-page QoE and web-browsing QoE for three web sites. We obtained the following two QoE characteristics. First, the main factor influencing web-browsing QoE is the average web-page QoE. Second, when web-page QoE variation occurs, a decrease in web-page QoE with a huge amplitude causes the web-browsing QoE to decrease. We used these characteristics in constructing our web-browsing QoE estimation model. The verification test results using non-training data indicate the accuracy of the model. We also show that our findings are applicable to web-browsing quality design and solving management issues on the basis of QoE.

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.

Novel design approaches for microwave active circuits using composite right-/left-handed (CRLH) transmission line (TL) stubs are presented. We show that, by modifying the dispersion diagram of the CRLH TL stub, the frequency band or the harmonic tuning capability can be enhanced in such a way that it would have been difficult or impractical if done using conventional micro-strip line stubs. The frequency response of the CRLH TL stub can be controlled almost arbitrarily while at the same time reducing the stub length significantly, because the dispersion curve in the left-handed region and in the right-handed region is controlled independently. As a proof of concept, a triple-band rectifier, single-band and dual-band harmonic tuning circuits for class-F amplifiers are demonstrated.

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.

An operating system is an essential piece of software that manages hardware and software resources. Thus, attacks on an operating system kernel using kernel rootkits pose a particularly serious threat. Detecting an attack is difficult when the operating system kernel is infected with a kernel rootkit. For this reason, handling an attack will be delayed causing an increase in the amount of damage done to a computer system. In this paper, we propose Kernel Rootkits Guard (KRGuard), which is a new method to detect kernel rootkits that monitors branch records in the kernel space. Since many kernel rootkits make branches that differ from the usual branches in the kernel space, KRGuard can detect these differences by using the hardware features of commodity processors. Our evaluation shows that KRGuard can detect kernel rootkits that involve new branches in the system call handler processing with small overhead.

This letter proposes a relay selection scheme in order to improve a performance in the wireless cooperative communication system. The cooperative communication uses the relays in order to obtain a improved performance. The relay selection scheme has a great influence on the performance of the wireless cooperative communication. Because the diversity gain is affected by the superposition of the channels, a superposition of the channels is important in the wireless cooperative communication. The constructive superposition of the channels can improve the performance of the wireless cooperative communication. Because the conventional schemes do not consider the superposition of the channels, the conventional schemes are not suitable for the cooperative communication using the multiple relays. The new scheme considers the superposition of channels and selects the relays that can achieve the constructive superposition. Therefore, the new scheme can provide the improved performance by using the phase information between channels. The simulation results show that the bit error performance of the proposed scheme is better than the conventional schemes.

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.

A single-dimensional interface which enables users to obtain diverse localizations of audio sources is proposed. In many conventional interfaces for arranging audio sources, there are multiple arrangement parameters, some of which allow users to control positions of audio sources. However, it is difficult for users who are unfamiliar with these systems to optimize the arrangement parameters since the number of possible settings is huge. We propose a simple, single-dimensional interface for adjusting arrangement parameters, allowing users to sample several diverse audio source arrangements and easily find their preferred auditory localizations. To select subsets of arrangement parameters from all of the possible choices, auditory-localization space vectors (ASVs) are defined to represent the auditory localization of each arrangement parameter. By selecting subsets of ASVs which are approximately orthogonal, we can choose arrangement parameters which will produce diverse auditory localizations. Experimental evaluations were conducted using music composed of three audio sources. Subjective evaluations confirmed that novice users can obtain diverse localizations using the proposed interface.

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.

To suppress intercell interference for three-cell half-duplex relay systems, joint interference suppression and multiuser detection (MUD) schemes that estimate weight coefficients by the recursive least-squares (RLS) algorithm have been proposed but show much worse bit error rate (BER) performance than maximum likelihood detection (MLD). To improve the BER performance, this paper proposes a joint interference suppression and MUD scheme that estimates the weight coefficients by eigenvalue decomposition. The proposed scheme carries the same advantages as the conventional RLS based schemes; it does not need channel state information (CSI) feedback while incurring much less amount of computational complexity than MLD. In addition, it needs to know only two out of three preambles used in the system. Computer simulations of orthogonal frequency-division multiplexing (OFDM) transmission under three-cell and frequency selective fading conditions are conducted. It is shown that the eigendecomposition-based scheme overwhelmingly outperforms the conventional RLS-based scheme although requiring higher computational complexity.

We developed a system called DELTA that supports the students' use of backward chaining (BC) to prove the congruence of two triangles. DELTA is designed as an interactive learning environment and supports the use of BC by providing hints and a function to automatically check the proofs inputted by the students. DELTA also has coloring, marking, and highlighting functions to support students' attempts to prove the congruence of two triangles. We evaluated the efficacy of DELTA with 36 students in the second grade of a junior high school in Japan. We found that (1) the mean number of problems, which the experimental group (EG) completely solved, was statistically higher than that of the control group on the post-test; (2) the EG effectively used the BC strategy to solve problems; and (3) the students' attempt to use both the forward chaining strategy and the BC strategy led to solving the problems completely.

Multi-label classification is an appealing and challenging supervised learning problem, where multiple labels, rather than a single label, are associated with an unseen test instance. To remove possible noises in labels and features of high-dimensionality, multi-label dimension reduction has attracted more and more attentions in recent years. The existing methods usually suffer from several problems, such as ignoring label outliers and label correlations. In addition, most of them emphasize on conducting dimension reduction in an unsupervised or supervised way, therefore, unable to utilize the label information or a large amount of unlabeled data to improve the performance. In order to cope with these problems, we propose a novel method termed Robust sEmi-supervised multi-lAbel DimEnsion Reduction, shortly READER. From the viewpoint of empirical risk minimization, READER selects most discriminative features for all the labels in a semi-supervised way. Specifically, the ℓ2,1-norm induced loss function and regularization term make READER robust to the outliers in the data points. READER finds a feature subspace so as to keep originally neighbor instances close and embeds labels into a low-dimensional latent space nonlinearly. To optimize the objective function, an efficient algorithm is developed with convergence property. Extensive empirical studies on real-world datasets demonstrate the superior performance of the proposed method.

An ultra-compact InGaAs photodetector (PD) is demonstrated based on a photonic crystal (PhC) waveguide to meet the demand for a photoreceiver for future dense photonic integration. Although the PhC-PD has a length of only 1.7µm and a capacitance of less than 1fF, a high responsivity of 1A/W was observed both theoretically and experimentally. This low capacitance PD allows us to expect a resistor-loaded receiver to be realized that requires no electrical amplifiers. We fabricated a resistor-loaded PhC-PD for light-to-voltage conversion, and demonstrated a kV/W efficiency with a GHz bandwidth without using amplifiers. This will lead to a photoreceiver with an ultralow energy consumption of less than 1fJ/bit, which is a step along the road to achieving a dense photonic network and processor on a chip.

In order to meet various requirements for the 5th generation mobile communication, a high SHF wideband massive-MIMO system has been widely studied which offers wide system bandwidth and high spectral efficiency. A hybrid beamforming configuration which combines analog beamforming by APAA (Active Phased Array Antenna) and digital MIMO signal processing is one of the promising approaches for reducing the complexity and power consumption of the high SHF wideband massive-MIMO system. In order to realize the hybrid beamforming configuration in high SHF band, small size, low power consumption and precise beam forming over the wide-band frequency range are strongly required for RF frontend which constitutes analog beam former. In this paper, a compact RF frontend module for high SHF wideband 5G small cell base station is proposed. This RF frontend module is prototyped. Various key components of the RF frontend module are fabricated in 15GHz band, and measured results show that high RF performances are able to meet the requirements of RF frontend.

A 15GHz-band 4-channel transmit/receive RF core-chip is presented for high SHF wide-band massive MIMO in 5G. In order to realize small RF frontend for 5G base stations, both 6bit phase shifters (PS) and 0.25 dB resolution variable gain amplifiers (VGA) are integrated in TX and RX paths of 4-channels on the chip. A PS calibration technique is applied to compensate the error of 6bit PS caused by process variations. A common gate current steering topology with tail current control is used for VGA to enhance the gain control accuracy. The 15GHz-band RF core-chip fabricated in 65 nm CMOS process achieved phase control error of 1.9deg. rms., and amplitude control error of 0.23 dB. rms.

We newly propose the first lateral mode selective active multimode interferometer laser diode. The design principle is to arrange identical propagation path of different lateral mode. Thanks to multimode waveguide structure, 0th mode and 1st order mode has individual propagation path within one device. Individual lasing of fundamental mode as well as first mode was confirmed successfully.

Detection of cavities in X-ray astronomical images has become a field of interest, since the flourishing studies on black holes and the Active Galactic Nuclei (AGN). In this paper, an approach is proposed to detect cavities in X-ray astronomical images using our newly designed Granular Convolutional Neural Network (GCNN) based classifiers. The raw data are firstly preprocessed to obtain images of the observed objects, i.e., galaxies or galaxy clusters. In each image, pixels are classified into three categories, (1) the faint backgrounds (BKG), (2) the cavity regions (CAV), and (3) the bright central gas regions (CNT). And the sample sets are then generated by dividing large images into subimages with a window size according to the cavities' scale. Since the number of BKG samples are far more than the other types, to achieve balanced training sets, samples from the major class are split into subsets, i.e., granule. Then a group of three-convolutional-layer granular CNN networks without subsampling layers are designed as the classifiers, and trained with the labeled granular sample sets. Finally, the trained GCNN classifiers are applied to new observations, so as to estimate the cavity regions with a voting strategy and locate them with elliptical profiles on the raw observation images. Experiments and applications of our approach are demonstrated on 40 X-ray astronomical observations retrieved from chandra Data Archive (CDA). Comparisons among our approach, the β-model fitting and the Unsharp Masking (UM) methods were also performed, which prove our approach was more accurate and robust.

In data stream analysis, detecting the concept drift accurately is important to maintain the classification performance. Most drift detection methods assume that the class labels become available immediately after a data sample arrives. However, it is unrealistic to attempt to acquire all of the labels when processing the data streams, as labeling costs are high and much time is needed. In this paper, we propose a concept drift detection method under the assumption that there is limited access or no access to class labels. The proposed method detects concept drift on unlabeled data streams based on the class label information which is predicted by a classifier or a virtual classifier. Experimental results on synthetic and real streaming data show that the proposed method is competent to detect the concept drift on unlabeled data stream.

In this paper, an improved lattice reduction (LR)-aided soft-output multiple-input multiple-output (MIMO) detector is proposed. Conventional LR-aided soft-output MIMO detectors involve the empty set problem (ESP), in which an entry with a particular bit in the candidate list might not exist. To overcome the performance degradation resulting from this ESP, a post-processing algorithm that modifies the candidate list is proposed. The proposed algorithm efficiently resolves the ESP by utilizing the near-orthogonality of the lattice-reduced system model so that the bit error rate (BER) performance is enhanced. In addition, as the complexity of the candidate list generation is reduced with the aid of the post-processing algorithm, the overall complexity is also reduced. Simulation results and the complexity comparisons demonstrate that our proposed method lowers the required Eb/No by 4-5 dB at the BER of 10-5 and the complexity by 13%-55%, compared to the conventional method.

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.