Physical contact (PC) optical connectors realize long-term stability by maintaining contact with the optical fiber even during temperature fluctuations caused by the microscopic displacement of the ferrule endface. With multicore fiber (MCF) connectors, stable PC connection conditions need to be newly investigated because MCFs have cores other than at the center. In this work, we investigated the microscopic displacement of connected ferrule endfaces using the finite element method (FEM). As a result, by using MCF connectors with an apex offset, we found that the allowable fiber undercut where all the cores make contact is slightly smaller than that of single-mode fiber (SMF) connectors. Therefore, we propose a new equation for determining the allowable fiber undercut of MCF connectors. We also fabricated MCF connectors with an allowable fiber undercut and confirmed their reliability using the composite temperature/humidity cyclic test.

Annealing computation has recently attracted attention as it can efficiently solve combinatorial optimization problems using an Ising spin-glass model. Stochastic cellular automata annealing (SCA) is a promising algorithm that can realize fast spin-update by utilizing its parallel computing capability. However, in SCA, pinning effect control to suppress the spin-flip probability is essential, making escaping from local minima more difficult than serial spin-update algorithms, depending on the problem. This paper proposes a novel approach called APC-SCA (Autonomous Pinning effect Control SCA), where the pinning effect can be controlled autonomously by focusing on individual spin-flip. The evaluation results using max-cut, N-queen, and traveling salesman problems demonstrate that APC-SCA can obtain better solutions than the original SCA that uses pinning effect control pre-optimized by a grid search. Especially in solving traveling salesman problems, we confirm that the tour distance obtained by APC-SCA is up to 56.3% closer to the best-known compared to the conventional approach.

The aim of a computer-aided drawing therapy system in this work is to associate drawings which a client makes with the client's mental state in quantitative terms. A case study is conducted on experimental data which contain both pastel drawings and mental state scores obtained from the same client in a psychotherapy program. To perform such association through colors, we translate a drawing to a color feature by measuring its representative colors as primary color rates. A primary color rate of a color is defined from a psychological primary color in a way such that it shows a rate of emotional properties of the psychological primary color which is supposed to affect the color. To obtain several informative colors as representative ones of a drawing, we define two kinds of color: approximate colors extracted by color reduction, and area-averaged colors calculated from the approximate colors. A color analysis method for extracting representative colors from each drawing in a drawing sequence under the same conditions is presented. To estimate how closely a color feature is associated with a concurrent mental state, we propose a method of utilizing machine-learning classification. A practical way of building a classification model through training and validation on a very small dataset is presented. The classification accuracy reached by the model is considered as the degree of association of the color feature with the mental state scores given in the dataset. Experiments were carried out on given clinical data. Several kinds of color feature were compared in terms of the association with the same mental state. As a result, we found out a good color feature with the highest degree of association. Also, primary color rates proved more effective in representing colors in psychological terms than RGB components. The experimentals provide evidence that colors can be associated quantitatively with states of human mind.

In this paper, we apply two methods in machine learning, dropout and semi-supervised learning, to a recently proposed method called CSQ-SDL which uses deep neural networks for evaluating shift quality from time-series measurement data. When developing a new Automatic Transmission (AT), calibration takes place where many parameters of the AT are adjusted to realize pleasant driving experience in all situations that occur on all roads around the world. Calibration requires an expert to visually assess the shift quality from the time-series measurement data of the experiments each time the parameters are changed, which is iterative and time-consuming. The CSQ-SDL was developed to shorten time consumed by the visual assessment, and its effectiveness depends on acquiring a sufficient number of data points. In practice, however, data amounts are often insufficient. The methods proposed here can handle such cases. For the cases wherein only a small number of labeled data points is available, we propose a method that uses dropout. For those cases wherein the number of labeled data points is small but the number of unlabeled data is sufficient, we propose a method that uses semi-supervised learning. Experiments show that while the former gives moderate improvement, the latter offers a significant performance improvement.

Recent studies have shown that concurrent transmission with precise time synchronization enables reliable and efficient flooding for wireless networks. However, most of them require all nodes in the network to forward packets a fixed number of times to reach the destination, which leads to unnecessary energy consumption in both one-to-one and many-to-one communication scenarios. In this letter, we propose G1M address this issue by reducing redundant packet forwarding in concurrent transmissions. The evaluation of G1M shows that compared with LWB, the average energy consumption of one-to-one and many-to-one transmission is reduced by 37.89% and 25%, respectively.

In 2014, the above paper entitled ‘Quasi-Linear Support Vector Machine for Nonlinear Classification’ was published by Zhou, et al. [1]. They proposed a quasi-linear kernel function for support vector machine (SVM). However, in this letter, we point out that this proposed kernel function is a part of multiple kernel functions generated by well-known multiple kernel learning which is proposed by Bach, et al. [2] in 2004. Since then, there have been a lot of related papers on multiple kernel learning with several applications [3]. This letter verifies that the main kernel function proposed by Zhou, et al. [1] can be derived using multiple kernel learning algorithms [3]. In the kernel construction, Zhou, et al. [1] used Gaussian kernels, but the multiple kernel learning had already discussed the locality of additive Gaussian kernels or other kernels in the framework [4], [5]. Especially additive Gaussian or other kernels were discussed in tutorial at major international conference ECCV2012 [6]. The authors did not discuss these matters.

Advanced driver-assistance systems (ADAS) generally play an important role to support safe drive by detecting potential risk factors beforehand and informing the driver of them. However, if too many services in ADAS rely on visual-based technologies, the driver becomes increasingly burdened and exhausted especially on their eyes. The drivers should be back out of monitoring tasks other than significantly important ones in order to alleviate the burden of the driver as long as possible. In-vehicle auditory signals to assist the safe drive have been appealing as another approach to altering visual suggestions in recent years. In this paper, we developed an in-vehicle auditory signals evaluation platform in an existing driving simulator. In addition, using in-vehicle auditory signals, we have demonstrated that our developed platform has highlighted the possibility to partially switch from only visual-based tasks to mixing with auditory-based ones for alleviating the burden on drivers.

Recently, a lot of low-light image enhancement methods have been proposed. However, these methods have some problems such as causing fine details lost in bright regions and/or unnatural color tones. In this paper, we propose a new low-light image enhancement method to cope with these problems. In the proposed method, a pixel is represented by a convex combination of white, black, and pure color. Then, an equi-hue plane in RGB color space is represented as a triangle whose vertices correspond to white, black, and pure color. The visibility of low-light image is improved by applying a modified gamma transform to the combination coefficients on an equi-hue plane in RGB color space. The contrast of the image is enhanced by the histogram specification method using the histogram smoothed by a filter with a kernel determined based on a gamma distribution. In the experiments, the effectiveness of the proposed method is verified by the comparison with the state-of-the-art low-light image enhancement methods.

This paper presents a deep network based on unrolling the diffusion process with the morphological Laplacian. The diffusion process is an iterative algorithm that can solve the diffusion equation and represents time evolution with Laplacian. The diffusion process is applied to smoothing of images and has been extended with non-linear operators for various image processing tasks. In this study, we introduce the morphological Laplacian to the basic diffusion process and unwrap to deep networks. The morphological filters are non-linear operators with parameters that are referred to as structuring elements. The discrete Laplacian can be approximated with the morphological filters without multiplications. Owing to the non-linearity of the morphological filter with trainable structuring elements, the training uses error back propagation and the network of the morphology can be adapted to specific image processing applications. We introduce two extensions of the morphological Laplacian for deep networks. Since the morphological filters are realized with addition, max, and min, the error caused by the limited bit-length is not amplified. Consequently, the morphological parts of the network are implemented in unsigned 8-bit integer with single instruction multiple data set (SIMD) to achieve fast computation on small devices. We applied the proposed network to image completion and Gaussian denoising. The results and computational time are compared with other denoising algorithm and deep networks.

Recent character detectors have been modeled using deep neural networks and have achieved high performance in various tasks, such as text detection in natural scenes and character detection in historical documents. However, existing methods cannot achieve high detection accuracy for wooden slips because of their multi-scale character sizes and aspect ratios, high character density, and close character-to-character distance. In this study, we propose a new U-Net-based character detection and localization framework that learns character regions and boundaries between characters. The proposed method enhances the learning performance of character regions by simultaneously learning the vertical and horizontal boundaries between characters. Furthermore, by adding simple and low-cost post-processing using the learned regions of character boundaries, it is possible to more accurately detect the location of a group of characters in a close neighborhood. In this study, we construct a wooden slip dataset. Experiments demonstrated that the proposed method outperformed existing character detection methods, including state-of-the-art character detection methods for historical documents.

We propose a non-photorealistic rendering method for automatically generating point-light-style (PLS) images from photographic images using peripheral difference filters with different window sizes. The proposed method can express PLS patterns near the edges of photographic images as dots. To verify the effectiveness of the proposed method, experiments were conducted to visually confirm PLS images generated from various photographic images.

In this paper, a circuit based on a field programmable analog array (FPAA) is proposed for three types of chaotic spiking oscillator (CSO). The input/output conversion characteristics of a specific element in the FPAA can be defined by the user. By selecting the proper characteristics, three types of CSO are realized without changing the structure of the circuit itself. Chaotic attractors are observed in a hardware experiment. It is confirmed that the dynamics of the CSOs are consistent with numerical simulations.

Canonical decomposition for bipartite graphs, which was introduced by Fouquet, Giakoumakis, and Vanherpe (1999), is a decomposition scheme for bipartite graphs associated with modular decomposition. Weak-bisplit graphs are bipartite graphs totally decomposable (i.e., reducible to single vertices) by canonical decomposition. Canonical decomposition comprises series, parallel, and K+S decomposition. This paper studies a decomposition scheme comprising only parallel and K+S decomposition. We show that bipartite graphs totally decomposable by this decomposition are precisely P6-free chordal bipartite graphs. This characterization indicates that P6-free chordal bipartite graphs can be recognized in linear time using the recognition algorithm for weak-bisplit graphs presented by Giakoumakis and Vanherpe (2003).

We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].

This paper summarizes recent reports on the internet's energy consumption and the internet's benefits on climate actions. It discusses energy-efficiency and the need for a common standard for evaluating the climate impact of future communication technologies and suggests a model that can be adapted to different internet applications such as streaming, online reading and downloading. The two main approaches today are based on how much data is transmitted or how much time the data is under way. The paper concludes that there is a need for a standardized method to estimate energy consumption and CO2 emission related to internet services. This standard should include a method for energy-optimizing future networks, where every Wh will be scrutinized.

Various optical fiber connectors have been developed during the 40 years since optical fiber communications systems were first put into practical use. This paper describes the key technologies for optical connectors and recent technical issues.

We have proposed autonomous network diagnosis platform for operation of future large capacity and virtualized network, including 5G and beyond 5G services. As for the one candidate of information collection and analyzing function blocks in the platform, we proposed novel optical sensing techniques that utilized tapped raw signal data acquired from digital coherent optical receivers. The raw signal data is captured before various digital signal processing for demodulation. Therefore, it contains various waveform deformation and/or noise as it experiences through transmission fibers. In this paper, we examined to detect two possible failures in transmission lines including fiber bending and optical filter shift by analyzing the above-mentioned raw signal data with the help of machine learning. For the purpose, we have implemented Docker container applications in WhiteBox Cassini to acquire real-time raw signal data. We generated CNN model for the detections in off-line processing and used them for real-time detections. We have confirmed successful detection of optical fiber bend and/or optical filter shift in real-time with high accuracy. Also, we evaluated their tolerance against ASE noise and invented novel approach to improve detection accuracy. In addition to that, we succeeded to detect them even in the situation of simultaneous occurrence of those failures.

Quadrature phase-shift keying (QPSK) and 16-quadrature amplitude modulation (16QAM) formats are deployed in inter-data center networks where high transmission capacity and spectral efficiency are required. However, in intra-data center networks, a four-level pulse amplitude modulation (PAM4) format is deployed to satisfy the requirements for a simple and low-cost transceiver configuration. For the seamless and effective connection of such heterogeneous networks without an optical-electrical-optical conversion, an all-optical modulation format conversion technique is required. In this paper, we propose all-optical PAM4 to QPSK and 16QAM modulation format conversions using silicon-rich nitride waveguides. The successful conversions from 50-Gbps-class PAM4 signals to 50-Gbps-class QPSK and 100-Gbps-class 16QAM signals are demonstrated via numerical simulations.

We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.

Multi-band WDM transmission beyond the C+L-band is a promising technology for achieving larger capacity transmission by a limited number of installed fibers. In addition to the C- and L-band, we can expect to use the S-band as the next band. Although the development of optical components for new bands, particularly transceivers, entails resource dispersion, which is one of the barriers to the realization of multi-band systems, wavelength conversion by transparent all-optical signal processing enables new wavelength bandtransmission using existing components. Therefore, we proposed a transmission system including a new wavelength band such as the S-band and made it possible to use a transceiver for the existing band by performing the whole-band wavelength conversion without using a transceiver for the new band. As a preliminary verification to demonstrate multi-band WDM transmission including S-band, we investigated the application of a novel wavelength converter between C-band and S-band, which consists of periodically poled lithium niobate waveguide, to the proposed system. We first characterized the conversion efficiency and noise figure of the wavelength converter and estimated the transmission performance of the system through the wavelength converter. Using the evaluated wavelength converters and test signals of 64 channels arranged in the C-band at 75-GHz intervals, we constructed an experimental setup for S-band transmission through an 80-km standard single-mode fiber. We then demonstrated error-free transmission of real-time 400-Gb/s DP-16QAM signals after forward error correction decoding. From the experimental results, it was clarified that the wavelength converter which realizes the uniform lossless conversion covering the whole C-band effectively achieves the S-band WDM transmission, and it was verified that the capacity improvement of the multi-band WDM system including the S-band can be expected by applying it in combination with the C+L-band WDM system.