We propose a novel hue-correction scheme for multi-exposure image fusion (MEF). Various MEF methods have so far been studied to generate higher-quality images. However, there are few MEF methods considering hue distortion unlike other fields of image processing, due to a lack of a reference image that has correct hue. In the proposed scheme, we generate an HDR image as a reference for hue correction, from input multi-exposure images. After that, hue distortion in images fused by an MEF method is removed by using hue information of the HDR one, on the basis of the constant-hue plane in the RGB color space. In simulations, the proposed scheme is demonstrated to be effective to correct hue-distortion caused by conventional MEF methods. Experimental results also show that the proposed scheme can generate high-quality images, regardless of exposure conditions of input multi-exposure images.

A quadratic extension field (QEF) defined by F1 = Fp[α]/(α2+1) is typically used for a supersingular isogeny Diffie-Hellman (SIDH). However, there exist other attractive QEFs Fi that result in a competitive or rather efficient performing the SIDH comparing with that of F1. To exploit these QEFs without a time-consuming computation of the initial setting, the authors propose to convert existing parameter sets defined over F1 to Fi by using an isomorphic map F1 → Fi.

In this paper, we consider the collaborative editing of two-dimensional (2D) data such as handwritten letters and illustrations. In contrast to the editing of 1D data, which is generally realized by the combination of insertion/deletion of characters, overriding of strokes can have a specific meaning in editing 2D data. In other words, the appearance of the resulting picture depends on the reflection order of strokes to the shared canvas in addition of the absolute coordinate of the strokes. We propose a Peer-to-Peer (P2P) collaborative drawing system consisting of several nodes with replica canvas, in which the consistency among replica canvases is maintained through data channel of WebRTC. The system supports three editing modes concerned with the reflection order of strokes generated by different users. The result of experiments indicates that the proposed system realizes a short latency of around 120 ms, which is a half of a cloud-based system implemented with Firebase Realtime Database. In addition, it realizes a smooth drawing of pictures on remote canvases with a refresh rate of 12 fps.

High dynamic range (HDR) imaging refers to digital image processing that modifies the range of color and contrast to enhance image visibility. To create an HDR image, two or more images that include various information are needed. In order to convert low dynamic range (LDR) images to HDR images, we consider the possibility of using a generative adversarial network (GAN) as an appropriate deep neural network. Deep learning requires a great deal of data in order to build a module, but once the module is created, it is convenient to use. In this paper, we propose a weight map for local luminance based on learning to reconstruct locally tone-mapped images.

An active gate driver IC generates arbitrary switching waveform is proposed to reduce the switching loss, the voltage overshoot, and the electromagnetic interference (EMI) by optimizing the switching pattern. However, it is hard to find optimal switching pattern because the switching pattern has huge possible combinations. In this paper, the method to estimate the switching loss and the voltage overshoot from the switching pattern with neural network (NN) is proposed. The implemented NN model obtains reasonable learning results for data-sets.

Effective user accommodation will be more and more important in passive optical networks (PONs) in the next decade since the number of subscribers has been leveling off as well and it is becoming more difficult for network operators to keep sufficient numbers of maintenance workers. Drastically reducing the number of small-scale communication buildings while keeping the number of accommodated users is one of the most attractive solutions to meet this situation. To achieve this, we propose two types of long-reach repeater-free upstream transmission configurations for PON systems; (i) one utilizes a semiconductor optical amplifier (SOA) as a pre-amplifier and (ii) the other utilizes distributed Raman amplification (DRA) in addition to the SOA. Our simulations assuming 10G-EPON specifications and transmission experiments on a 10G-EPON prototype confirm that configuration (i) can add a 17km trunk fiber to a normal PON system with 10km access reach and 1 : 64 split (total 27km reach), while configuration (ii) can further expand the trunk fiber distance to 37km (total 47km reach). Network operators can select these configurations depending on their service areas.

We aim at HDR imaging with simple processing while preventing spatial resolution degradation in multiple-exposure-time image sensor where the exposure time is controlled for each pixel. The contributions are the proposal of image interpolation by motion area detection and pixel adaptive weighting method by overexposure and motion blur detection.

With increasing technology scaling and the use of lower voltages, more research interest is being shown in variability-tolerant analog front end design. In this paper, we describe an adaptive amplitude control transmitter that is operated using differential signaling to reduce the temperature variability effect. It enables low power, low voltage operation by synergy between adaptive amplitude control and Vth temperature variation control. It is suitable for high-speed interface applications, particularly cable interfaces. By installing an aggressor circuit to estimate transmitter jitter and changing its frequency and activation rate, we were able to analyze the effects of the interface block on the input buffer and thence on the entire system. We also report a detailed estimation of the receiver clock-data recovery (CDR) operation for transmitter jitter estimation. These investigations provide suggestions for widening the eye opening of the transmitter.

This paper proposes an ultra-low quiescent current low-dropout regulator (LDO) with a flipped voltage follower (FVF)-based load transient enhanced circuit for wireless sensor network (WSN). Some characteristics of an FVF are low output impedance, low voltage operation, and simple circuit configuration [1]. In this paper, we focus on the characteristics of low output impedance and low quiescent current. A load transient enhanced circuit based on an FVF circuit configuration for an LDO was designed in this study. The proposed LDO, including the new circuit, was fabricated in a 0.6 µm CMOS process. The designed LDO achieved an undershoot of 75 mV under experimental conditions of a large load transient of 100 µA to 10 mA and a current slew rate (SR) of 1 µs. The quiescent current consumed by the LDO at no load operation was 204 nA.

Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain environments. However, high performance is not the sole metric for practical use such as in a game AI or autonomous driving. A highly efficient agent performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. Imitation learning reproduces the behavior of a human expert and builds a human-like agent. However, its performance is limited to the expert's. In this study, we propose a training scheme to construct a human-like and efficient agent via mixing reinforcement and imitation learning for discrete and continuous action space problems. The proposed hybrid agent achieves a higher performance than a strict imitation learning agent and exhibits more human-like behavior, which is measured via a human sensitivity test.

The brain processes numerous information related to traffic scenes for appropriate perception, judgment, and operation in vehicle driving. Here, the strategy for perception, judgment, and operation is individually different for each driver, and this difference is thought to be arise from experience of driving. In the present work, we measure and analyze human brain activity (EEG: Electroencephalogram) related to visual perception during vehicle driving to clarify the relationship between experience of driving and brain activity. As a result, more experts generate α activities than beginners, and also confirm that the β activities is reduced than beginners. These results firstly indicate that experience of driving is reflected into the activation pattern of EEG.

In IoT systems, data acquired by many sensors are required. However, since sensor operation depends on the actual environment, it is important to ensure sensor redundancy to improve system reliability in IoT systems. To evaluate the safety of the system, it is important to estimate the achievement probability of the function based on the sensing probability. In this research, we proposed a method to automatically generate a PRISM model from the sensor configuration of the target system and calculate and verify the function achievement probability in the assumed environment. By designing and evaluating iteratively until the target achievement probability is reached, the reliability of the system can be estimated at the initial design phase. This method reduces the possibility that the lack of reliability will be found after implementation and the redesign accompanying it will occur.

In this paper, we propose an interactive system for controlling the pressure while cutting paper with a knife. The purpose is to improve the cutting skill of novices learning the art of paper-cutting. Our system supports skill improvement for novices by measuring and evaluating their cutting pressure in real-time. In this study, we use a knife with a blade attached to a stylus with a pressure sensor, which can measure the pressure, coordinates, and cutting time. We have developed a similar support system using a stylus and a tablet device. This system allows the user to experience the pressure of experts through tracing. Paper-cutting is created by cutting paper with a knife. The practice system in this paper provides practice in an environment more akin to the production of paper cutting. In the first experiment, we observed differences in cutting ability by comparing cutting pressures between novices and experts. As a result, we confirmed that novices cut paper at a higher pressure than experts. We developed a practice system that guides the novices on controlling the pressure by providing information on the cutting pressure values of experts. This system shows the difference in pressure between novices and experts using a synchronous display of color and sound. Using these functions, novices learn to adjust their cutting pressure according to that of experts. Determining the right cutting pressure is a critical skill in the art of paper-cutting, and we aim to improve the same with our system. In the second experiment, we tested the effect of the practice system on the knife device. We compared the changes in cutting pressure with and without our system, the practice methods used in the workshop, and the previously developed stylus-based support system. As a result, we confirmed that practicing with the knife device had a better effect on the novice's skill in controlling cutting pressure than other practice methods.

Locally repairable codes (LRCs) with locality r and availability t are a class of codes which can recover data from erasures by accessing other t disjoint repair groups, that every group contain at most r other code symbols. This letter will investigate constructions of LRCs derived from cyclic codes and generalized quadrangle. On the one hand, two classes of cyclic LRC with given locality m-1 and availability em are proposed via trace function. Our LRCs have the same locality, availability, minimum distance and code rate, but have short length and low dimension. On the other hand, an LRC with $(2,(p+1)lfloorrac{s}{2} floor)$ is presented based on sets of points in PG(k, q) which form generalized quadrangles with order (s, p). For k=3, 4, 5, LRCs with r=2 and different t are determined.

Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.

A unified decision scheme for the classification and localization of cable faults is proposed based on a two-step procedure. Having basis in the time domain reflectometry (TDR), the proposed scheme is capable of determining not only the locations but also types of faults in a cable without an excessive additional computational burden compared to other TDR-based schemes. Results from simulation and experiments with measured real data demonstrate that the proposed scheme exhibits a higher rate of correct decision than the conventional schemes in localizing and classifying faults over a wide range of the location of faults.

Periodic sequences, used as keys in cryptosystems, plays an important role in cryptography. Such periodic sequences should possess high linear complexity to resist B-M algorithm. Sequences constructed by cyclotomic cosets have been widely studied in the past few years. In this paper, the linear complexity of n-periodic cyclotomic sequences of order 2 and 4 over 𝔽p has been calculated, where n and p are two distinct odd primes. The conclusions reveal that the presented sequences have high linear complexity in many cases, which indicates that the sequences can resist the linear attack.

This paper analyzes the system-level performance of Storage Class Memory (SCM)/NAND flash hybrid solid-state drives (SSDs) and SCM/NAND flash/NAND flash tri-hybrid SSDs in difference types of NAND flash memory. There are several types of NAND flash memory, i.e. 2-dimensional (2D) or 3-dimensional (3D), charge-trap type (CT) and floating-gate type (FG) and multi-level cell (MLC) or triple-level cell (TLC). In this paper, the following four types of NAND flash memory are analyzed: 1) 3D CT TLC, 2) 3D FG TLC, 3) 2D FG TLC, and 4) 2D FG MLC NAND flash. Regardless of read- and write-intensive workloads, SCM/NAND flash hybrid SSD with low cost 3D CT TLC NAND flash achieves the best performance that is 20% higher than that with higher cost 2D FG MLC NAND flash. The performance improvement of 3D CT TLC NAND flash can be obtained by the short write latency. On the other hand, in case of tri-hybrid SSD, SCM/3D CT TLC/3D CT TLC NAND flash tri-hybrid SSD improves the performance 102% compared to SCM/2D FG MLC/3D CT TLC NAND flash tri-hybrid SSD. In addition, SCM/2D FG MLC/2D FG MLC NAND flash tri-hybrid SSD shows 49% lower performance than SCM/2D FG MLC/3D CT TLC NAND flash tri-hybrid SSD. Tri-hybrid SSD flash with 3D CT TLC NAND flash is the best performance in tri-hybrid SSD thanks to larger block size and word-line (WL) write. Therefore, in 3D CT TLC NAND flash based SSDs, higher cost MLC NAND flash is not necessary for hybrid SSD and tri-hybrid SSD for data center applications.

In this article, we propose a method to identify the link layer home network topology, motivated by applications to cost reduction of support centers. If the topology of home networks can be identified automatically and efficiently, it is easier for operators of support centers to identify fault points. We use MAC address forwarding tables (AFTs) which can be collected from network devices. There are a couple of existing methods for identifying a network topology using AFTs, but they are insufficient for our purpose; they are not applicable to some specific network topologies that are typical in home networks. The advantage of our method is that it can handle such topologies. We also implemented these three methods and compared their running times. The result showed that, despite its wide applicability, our method is the fastest among the three.

Utilization of local memory from real-time embedded systems to high performance systems with multi-core processors has become an important factor for satisfying hard deadline constraints. However, challenges lie in the area of efficiently managing the memory hierarchy, such as decomposing large data into small blocks to fit onto local memory and transferring blocks for reuse and replacement. To address this issue, this paper presents a compiler optimization method that automatically manage local memory of multi-core processors. The method selects and maps multi-dimensional data onto software specified memory blocks called Adjustable Blocks. These blocks are hierarchically divisible with varying sizes defined by the features of the input application. Moreover, the method introduces mapping structures called Template Arrays to maintain the indices of the decomposed multi-dimensional data. The proposed work is implemented on the OSCAR automatic parallelizing compiler and evaluations were performed on the Renesas RP2 8-core processor. Experimental results from NAS Parallel Benchmark, SPEC benchmark, and multimedia applications show the effectiveness of the method, obtaining maximum speed-ups of 20.44 with 8 cores utilizing local memory from single core sequential versions that use off-chip memory.