The image-to-image translation aims to learn a mapping between the source and target domains. For improving visual quality, the majority of previous works adopt multi-stage techniques to refine coarse results in a progressive manner. In this work, we present a novel approach for generating plausible details by only introducing a group of intermediate supervisions without cascading multiple stages. Specifically, we propose a Laplacian Pyramid Transformation Generative Adversarial Network (LapTransGAN) to simultaneously transform components in different frequencies from the source domain to the target domain within only one stage. Hierarchical perceptual and gradient penalization are utilized for learning consistent semantic structures and details at each pyramid level. The proposed model is evaluated based on various metrics, including the similarity in feature maps, reconstruction quality, segmentation accuracy, similarity in details, and qualitative appearances. Our experiments show that LapTransGAN can achieve a much better quantitative performance than both the supervised pix2pix model and the unsupervised CycleGAN model. Comprehensive ablation experiments are conducted to study the contribution of each component.

Text detection is a crucial pre-processing step in optical character recognition (OCR) for the accurate recognition of text, including both fonts and handwritten characters, in documents. While current deep learning-based text detection tools can detect text regions with high accuracy, they often treat multiple lines of text as a single region. To perform line-based character recognition, it is necessary to divide the text into individual lines, which requires a line detection technique. This paper focuses on the development of a new approach to single-line detection in OCR that is based on the existing Character Region Awareness For Text detection (CRAFT) model and incorporates a deep neural network specialized in line segmentation. However, this new method may still detect multiple lines as a single text region when multi-line text with narrow spacing is present. To address this, we also introduce a post-processing algorithm to detect single text regions using the output of the single-line segmentation. Our proposed method successfully detects single lines, even in multi-line text with narrow line spacing, and hence improves the accuracy of OCR.

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.

Person authentication using biometric information has recently become popular among researchers. User management based on biometrics is more reliable than that using conventional methods. To secure private information, it is necessary to build continuous authentication-based user management systems. Brain waves are suitable biometric modalities for continuous authentication. This study is based on biometric authentication using brain waves evoked by invisible visual stimuli. Invisible visual stimulation is considered over visual stimulation to overcome the obstacles faced by a user when using a system. Invisible stimuli are confirmed by changing the intensity of the image and presenting high-speed stimulation. To ensure invisibility, stimuli of different intensities were tested, and the stimuli with an intensity of 5% was confirmed to be invisible. To improve the verification performance, a continuous wavelet transform was introduced over the Fourier transform because it extracts both time and frequency information from the brain wave. The scalogram obtained by the wavelet transform was used as an individual feature and for synchronizing the template and test data. Furthermore, to improve the synchronization performance, the waveband was split based on the power distribution of the scalogram. A performance evaluation using 20 subjects showed an equal error rate of 3.8%.

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.

In this paper, multi-input multi-output (MIMO) signal detection with random walk along a gradient descent direction using an intermediate search point is presented. As a low complexity MIMO signal detection schemes, a gradient descent algorithm with Metropolis-Hastings (MH) methods has been proposed. Random walk along a gradient descent direction speeds up the MH based search using the gradient of a least-squares cost function. However, the gradient vector may be discarded through QAM constellation quantization in some cases. For further performance improvement, this paper proposes an improved search scheme in which the gradient vector is stored for the next search iteration to generate an intermediate search point. The performance of the proposed scheme improves with higher order modulation symbols as compared with that of a conventional scheme. Numerical results obtained through computer simulation show that a bit error rate (BER) performance improves by 5dB at a BER of 10-3 for 64QAM symbols in a 16×16 MIMO system.

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.

We propose a fine structure imaging for the surface and its inside of solid material such as coated drill bits with TiN (Titanium Nitride). We call this method i-MSE (innovative MSE) since the fine structure is visualized with a local mechanical strength (the local erosion rate) which is obtained from a set of erosion depth profiles measured with Micro Slurry-jet Erosion test (MSE). The local erosion rate at any sampling point is estimated from the depth profile using a sliding window regression and for the rest of the 2-dimensional points it is interpolated with the mean value coordinate technique. The interpolated rate is converted to a 2D image (i-MSE image) with a color map. The i-MSE image can distinguish layers if the testing material surface is composed of coats which have different resistance to erosion (erosive wear), while microscopic image such as SEM (Scanning Electron Microscope) and a calotest just provides appearance information, not physical characteristics. Experiments for some layered specimens show that i-MSE can be an effective tool to visualize the structure and to evaluate the mechanical characteristics for the surface and the inside of solid material.

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.

For massive multiple-input multiple-output (MIMO) communication systems, simple linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance with reduced computational complexity. However, such linear detectors always involve complicated matrix inversion, which will suffer from high computational overhead in the practical implementation. Due to the massive parallel-processing and efficient hardware-implementation nature, the neural network has become a promising approach to signal processing for the future wireless communications. In this paper, we first propose an efficient neural network to calculate the pseudo-inverses for any type of matrices based on the improved Newton's method, termed as the PINN. Through detailed analysis and derivation, the linear massive MIMO detectors are mapped on PINNs, which can take full advantage of the research achievements of neural networks in both algorithms and hardwares. Furthermore, an improved limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is studied as the learning algorithm of PINNs to achieve a better performance/complexity trade-off. Simulation results finally validate the efficiency of the proposed scheme.

Most quantum circuits require SWAP gate insertion to run on quantum hardware with limited qubit connectivity. A promising SWAP gate insertion method for blocks of commuting two-qubit gates is a predetermined swap strategy which applies layers of SWAP gates simultaneously executable on the coupling map. A good initial mapping for the swap strategy reduces the number of required swap gates. However, even when a circuit consists of commuting gates, e.g., as in the Quantum Approximate Optimization Algorithm (QAOA) or trotterized simulations of Ising Hamiltonians, finding a good initial mapping is a hard problem. We present a SAT-based approach to find good initial mappings for circuits with commuting gates transpiled to the hardware with swap strategies. Our method achieves a 65% reduction in gate count for random three-regular graphs with 500 nodes. In addition, we present a heuristic approach that combines the SAT formulation with a clustering algorithm to reduce large problems to a manageable size. This approach reduces the number of swap layers by 25% compared to both a trivial and random initial mapping for a random three-regular graph with 1000 nodes. Good initial mappings will therefore enable the study of quantum algorithms, such as QAOA and Ising Hamiltonian simulation applied to sparse problems, on noisy quantum hardware with several hundreds of qubits.

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.

In 5th generation (5G) and Beyond 5G mobile communication systems, it is expected that numerous antennas will be densely deployed to realize ultra-broadband communication and uniform coverage. However, as the number of antennas increases, total power consumption of all antennas will also increase, which leads to a negative impact on the environment and operating costs of telecommunication operators. Thus, it is necessary to simplify an antenna structure to suppress the power consumption of each antenna. On the other hand, as a way to realize ultra-broadband communication, millimeter waves will be utilized because they can transmit signals with a broader bandwidth than lower frequencies. However, since millimeter waves have a large propagation loss, a propagation distance is shorter than that of low frequencies. Therefore, in order to extend the propagation distance, it is necessary to increase an equivalent isotropic radiated power by beamforming with phased array antenna. In this paper, a phased antenna array module in combined with analog radio over fiber (A-RoF) technology for 40-GHz millimeter wave is developed and evaluated for the first time. An 8×8 phased array antenna for 40-GHz millimeter wave with integrated photodiodes and RF chains has been developed, and end-to-end transmission experiment including 20km A-RoF transmission and 3-m over-the-air transmission from the developed phased array antenna has been conducted. The results showed that the 40-GHz RF signal after the end-to-end transmission satisfied the criteria of 3GPP signal quality requirements within ±50 degrees of main beam direction.

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.