In recent years, single face image super-resolution (SR) using deep neural networks have been well developed. However, most of the face images captured by the camera in a real scene are from different views of the same person, and the existing traditional multi-frame image SR requires alignment between images. Due to multi-view face images contain texture information from different views, which can be used as effective prior information, how to use this prior information from multi-views to reconstruct frontal face images is challenging. In order to effectively solve the above problems, we propose a novel face SR network based on multi-view face images, which focus on obtaining more texture information from multi-view face images to help the reconstruction of frontal face images. And in this network, we also propose a texture attention mechanism to transfer high-precision texture compensation information to the frontal face image to obtain better visual effects. We conduct subjective and objective evaluations, and the experimental results show the great potential of using multi-view face images SR. The comparison with other state-of-the-art deep learning SR methods proves that the proposed method has excellent performance.

This paper reports the results of a new test on what types of failure cause falls in the stock prices of telecommunication service providers. This analysis of stock price is complementary to our previous one on market share. A clear result of our new test is that the type of failure causing falls in stock price is different from the type causing decline in market share. Specifically, the previous study identified frequent failures as causes of decline in market share, while the current study indicates large failures affecting many users as causes of falls in stock price. Together, these analyses give important information for reliability designs of telecommunications networks.

This paper proposes a pulse-width modulated (PWM) signaling[1] to send clock and data over a pair of channels for in-vehicle network where a closed chain of point-to-point (P2P) interconnection between electronic control units (ECU) has been established. To improve detection speed and margin of proposed receiver, we also proposed a novel clock and data recovery (CDR) scheme with 0.5 unit-interval (UI) tuning range and a PWM generator utilizing 10 equally-spaced phases. The feasibility of proposed system has been proved by successfully detecting 1.25 Gb/s data delivered via 3 ECUs and inter-channels in 180 nm CMOS technology. Compared to previous study, the proposed system achieved better efficiency in terms of power, cost, and reliability.

Locally repairable codes (LRCs) with availability have received considerable attention in recent years since they are able to solve many problems in distributed storage systems such as repairing multiple node failures and managing hot data. Constructing LRCs with locality r and availability t (also called (r, t)-LRCs) with new parameters becomes an interesting research subject in coding theory. The objective of this paper is to propose two generic constructions of cyclic (r, t)-LRCs via linearized polynomials over finite fields. These two constructions include two earlier ones of cyclic LRCs from trace functions and truncated trace functions as special cases and lead to LRCs with new parameters that can not be produced by earlier ones.

In this study, we devised a biofuel cell (BFC) by impregnating sheet-like cellulose nanofiber (CNF) with liquid fuel (fructose) and sandwiching it with the electrodes, making the structure simple and compact. CNF was considered as a suitable material for BFC because it is biocompatible, has a large specific surface area, and exhibits excellent properties as a catalyst and an adsorbent. In this BFC device, graphene-coated carbon fiber woven cloth (GCFC) was used as the material for preparing the electrodes, and the amount of enzyme modification on the surface of each electrode was enhanced. Further, as the distance between the electrodes was same as the thickness of the sheet-shaped CNF, it facilitated the exchange of protons between the electrodes. Moreover, the cathode, which requires an oxidation reaction, was exposed to the atmosphere to enhance the oxygen uptake. The maximum power density of the CNF-type BFC was recorded as 114.5 µW/cm2 at a voltage of 293 mV. This is more than 1.5 times higher than that of the liquid-fuel-type BFC. When measured after 24 h, the maximum power density was recorded as 44.9 µW/cm2 at 236 mV, and the output was maintained at 39% of that observed at the beginning of the measurement. However, it is not the case with general BFCs, where the power generation after 24 h is less than 5%. Therefore, the CNF-type BFCs have a longer lifespan and are fuel efficient.

Recent IoT devices require extremely low standby power consumption, while a certain performance is needed during the active time, and Coarse-Grained Reconfigurable Arrays (CGRAs) have received attention because of their high energy efficiency. For further reduction of the standby energy consumption of CGRAs, the leakage power for their configuration memory must be reduced. Although the power gating is a common technique, the lost data in flip-flops and memory must be retrieved after the wake-up. Recovering everything requires numerous state transitions and considerable overhead both on its execution time and energy. To address the problem, Non-volatile Cool Mega Array (NVCMA), a CGRA providing non-volatile flip-flops (NVFFs) with spin transfer torque type non-volatile memory (NVM) technology has been developed. However, in general, non-volatile memory technologies have problems with reliability. Some NVFFs are stacked-at-0/1, and cannot store the data in a certain possibility. To improve the chip yield, we propose a mapping algorithm to avoid faulty processing elements of the CGRA caused by the erroneous configuration data. Next, we also propose a method to add an error-correcting code (ECC) mechanism to NVFFs for the configuration and constant memory. The proposed method was applied to NVCMA to evaluate the availability rate and reduction of write time. By using both methods, the average availability ratio of 94.2% was achieved, while the average availability ratio of the nine applications was 0.056% when the probability of failure of the FF was 0.01. The energy for storing data becomes about 2.3 times because of the hardware overhead of ECC but the proposed method can save 8.6% of the writing power on average.

Loading test vectors and unloading test responses in shift mode during scan testing cause many scan flip-flops to switch simultaneously. The resulting shift switching activity around scan flip-flops can cause excessive local IR-drop that can change the states of some scan flip-flops, leading to test data corruption. A common approach solving this problem is partial-shift, in which multiple scan chains are formed and only one group of the scan chains is shifted at a time. However, previous methods based on this approach use random grouping, which may reduce global shift switching activity, but may not be optimized to reduce local shift switching activity, resulting in remaining high risk of test data corruption even when partial-shift is applied. This paper proposes novel algorithms (one optimal and one heuristic) to group scan chains, focusing on reducing local shift switching activity around scan flip-flops, thus reducing the risk of test data corruption. Experimental results on all large ITC'99 benchmark circuits demonstrate the effectiveness of the proposed optimal and heuristic algorithms as well as the scalability of the heuristic algorithm.

In this study, a novel decoupling method using parasitic elements (PEs) connected by a bridge line (BL) for two planar inverted-F antennas (PIFAs) is proposed. The proposed method is developed from a well-known decoupling method that uses a BL to directly connect antenna elements. When antenna elements are connected directly by a BL, strong mutual coupling can be reduced, but the resonant frequency shifts to a different frequency. Hence, to shift the resonant frequency toward the desired frequency, the original size of the antenna elements must be adjusted. This is disadvantageous if the method is applied in cases where the design conditions render it difficult to connect the antennas directly or adjust the original antenna size. Therefore, to easily reduce mutual coupling in such a case, a decoupling method that does not require both connecting antennas directly and adjusting the original antenna size is necessitated. This study demonstrates that using PEs connected by a BL reduces the mutual coupling from -6.6 to -14.1dB, and that the resonant frequency is maintained at the desired frequency (2.0GHz) without having to adjust the original PIFAs size. In addition, impedance matching can be adjusted to the desired frequency, resulting in an improved total antenna efficiency from 77.4% to 94.6%. This method is expected to be a simple and effective approach for reducing the mutual coupling between larger numbers of PIFA elements in the future.

In this paper, the effective Long Range (LoRa) based wireless sensor network is designed and implemented to provide the remote data sensing functions for the planned smart agricultural recycling rapid processing factory. The proposed wireless sensor network transmits the sensing data from various sensors, which measure the values of moisture, viscosity, pH, and electrical conductivity of agricultural organic wastes for the production and circulation of organic fertilizers. In the proposed wireless sensor network design, the LoRa transceiver module is used to provide data transmission functions at the sensor node, and the embedded platform by Raspberry Pi module is applied to support the gateway function. To design the cloud data server, the MySQL methodology is applied for the database management system with Apache software. The proposed wireless sensor network for data communication between the sensor node and the gateway supports a simple one-way data transmission scheme and three half-duplex two-way data communication schemes. By experiments, for the one-way data transmission scheme under the condition of sending one packet data every five seconds, the packet data loss rate approaches 0% when 1000 packet data is transmitted. For the proposed two-way data communication schemes, under the condition of sending one packet data every thirty seconds, the average packet data loss rates without and with the data-received confirmation at the gateway side can be 3.7% and 0%, respectively.

In this paper, we used the data set of domain names Global Top 1M provided by Alexa to analyze the effectiveness of Fallback in ECN. For the same test server, we first negotiate a connection with Not-ECN-Capable, and then negotiate a connection with ECN-Capable, if the sender does not receive the response to ECN-Capable negotiation from the receiver by the end of retransmission timeout, it will enter the Fallback state, and switch to negotiating a connection with Not-ECN-Capable. By extracting the header fields of the TCP/IP packets, we confirmed that in most regions, connectivity will be slightly improved after Fallback is enabled and Fallback has a positive effect on the total time of the whole access process. Meanwhile, we provided the updated information about the characteristics related to ECN with Fallback in different regions by considering the geographical region distribution of all targeted servers.

An n-variate Farlie-Gumbel-Morgenstern (FGM) copula consists of 2n - n - 1 parameters that express multivariate dependence among random variables. Motivated by the dependence structure of the n-variate FGM copula, we derive the exact range of the n-variate FGM copula's parameter. The exact range of the parameter is given by a closed-form expression under the condition that all parameters take the same value. Moreover, under the same condition, we reveal that the n-variate FGM copula becomes the independence copula for n → ∞. This result contributes to the dependence modeling such as reliability analysis considering dependent failure occurrence.

An olfactory display is a device to present smells. Temporal characteristics of three types of olfactory displays such as one based upon high-speed switching of solenoid valves, desktop-type one based on SAW atomizer and wearable-type one based on SAW atomizer were evaluated using three odorants with different volatilities. The sensory test revealed that the olfactory displays based on SAW atomizer had the presentation speeds faster than that of solenoid valves switching. Especially, the wearable one had an excellent temporal characteristic. These results largely depend on the difference in the odor delivery method. The data obtained in this study provides basic knowledge when we make olfactory contents.

This paper proposes an optimization model under uncertain traffic demands to design the backup network to minimize the total capacity of a backup network to protect the primary network from multiple link failures, where the probability of link failure is specified. The hose uncertainty is adopted to express uncertain traffic demands. The probabilistic survivability guarantee is provided by determining both primary and backup network routing, simultaneously. Robust optimization is introduced to provide probabilistic survivability guarantees for different link capacities in the primary network model under the hose uncertainty. Robust optimization in the proposed model handles two uncertain items: uncertain failed primary link with different capacities and uncertain traffic demands. We formulate an optimization problem for the proposed model. Since it is difficult to directly solve it, we introduce a heuristic approach for the proposed model. By using the heuristic approach, we investigate how the probability of link failure affects both primary and backup network routing. Numerical results show that the proposed model yields a backup network with lower total capacity requirements than the conventional model for the link failure probabilities examined in this paper. The results indicate that the proposed model reduces the total capacity of the backup network compared to the conventional model under the hose uncertainty. The proposed model shares more effectively the backup resources to protect primary links by determining routing in both primary and backup networks.

This paper proposes a high-quality computed tomography (CT) image reconstruction method from low-dose X-ray projection data. A state-of-the-art method, proposed by Xu et al., exploits dictionary learning for image patches. This method generates an overcomplete dictionary from patches of standard-dose CT images and reconstructs low-dose CT images by minimizing the sum of a data fidelity and a regularization term based on sparse representations with the dictionary. However, this method does not take characteristics of each patch, such as textures or edges, into account. In this paper, we propose to classify all patches into several classes and utilize an individual dictionary with an individual regularization parameter for each class. Furthermore, for fast computation, we introduce the orthogonality to column vectors of each dictionary. Since similar patches are collected in the same cluster, accuracy degradation by the orthogonality hardly occurs. Our simulations show that the proposed method outperforms the state-of-the-art in terms of both accuracy and speed.

We studied an acoustic anomaly detection system for equipments, where the outlier detection method based on recorded sounds is used. In a real environment, the SNR of the target sound against background noise is low, and there is the problem that it is necessary to catch slight changes in sound buried in noise. In this paper, we propose a system in which a sound source extraction process is provided at the preliminary stage of the outlier detection process. In the proposed system, nonnegative matrix factorization based on generalized Gaussian distribution (GGD-NMF) is used as a sound source extraction process. We evaluated the improvement of the anomaly detection performance in a low-SNR environment. In this experiment, SNR capable of detecting an anomaly was greatly improved by providing GGD-NMF for preprocessing.

A differential on chip oscillator (OCO) is proposed in this paper for low supply voltage, high frequency accuracy and fast startup. The differential architecture helps the OCO achieve a good power supply rejection ratio (PSRR) without using a regulator so as to make the OCO suitable for a low power supply voltage of 1.38V. A reference voltage generator is also developed to generate two output voltages lower than Vbe for low supply voltage operation. The output frequency is locked to 48MHz by a frequency-locked loop (FLL) and a 3.3-ppm/°C temperature coefficient of frequency is realized by the differential voltage ratio adjusting (differential VRA) technique. The startup time is only 1.47μs because the differential OCO is not necessary to charge a big capacitor for ripple reduction.

In this paper, we present a novel method for vehicle detection based on the Faster R-CNN frame. We integrate MobileNet into Faster R-CNN structure. First, the MobileNet is used as the base network to generate the feature map. In order to retain the more information of vehicle objects, a fusion strategy is applied to multi-layer features to generate a fused feature map. The fused feature map is then shared by region proposal network (RPN) and Fast R-CNN. In the RPN system, we employ a novel dimension cluster method to predict the anchor sizes, instead of choosing the properties of anchors manually. Our detection method improves the detection accuracy and saves computation resources. The results show that our proposed method respectively achieves 85.21% and 91.16% on the mean average precision (mAP) for DIOR dataset and UA-DETRAC dataset, which are respectively 1.32% and 1.49% improvement than Faster R-CNN (ResNet152). Also, since less operations and parameters are required in the base network, our method costs the storage size of 42.52MB, which is far less than 214.89MB of Faster R-CNN(ResNet50).

The phaseless antenna measurement technique is advantageous for high-frequency near-field measurements in which the uncertainty of the measured phase is a problem. In the phaseless measurement, which is expected to be used in the frequency band with a short wavelength, a slight positional deviation error of the probe greatly deteriorates the measurement result. This paper proposes a phase retrieval method that can compensate the measurement errors caused by misalignment of a probe and its jig. And this paper proposes a far-field estimation method by phase resurrection that incorporated the compensation techniques. We find that the positioning errors are due to the random errors occurring at each measurement point because of minute vibrations of the probe; in addition, we determine that the stationary depth errors occurring at each measurement surface as errors caused by improper setting of the probe jig. The random positioning error is eliminated by adding a low-pass filter in wavenumber space, and the depth positioning error is iteratively compensated on the basis of the relative residual obtained in each plane. The validity of the proposed method is demonstrated by estimating the far-field patterns using the results from numerical simulations, and is also demonstrated using measurement data with probe-positioning error. The proposed method can reduce the probe-positioning error and improve the far-field estimation accuracy by more over than 10 dB.

In this paper, we propose a robust output feedback control method for nonlinear systems with uncertain time-varying parameters associated with diagonal terms and there are additional external disturbances. First, we provide a new practical guidance of obtaining a compact set which contains the allowed time-varying parameters by utilizing a Lyapunov equation and matrix inequalities. Then, we show that all system states and observer errors of the controlled system remain bounded by the proposed controller. Moreover, we show that the ultimate bounds of some system states and observer errors can be made (arbitrarily) small by adjusting a gain-scaling factor depending on the system nonlinearity. With an application example, we illustrate the effectiveness of our control scheme over the existing one.

This paper presents a low profile high-efficiency transmitarray (TA) antenna based on a hybrid frequency selective surface (FSS). The hybrid FSS consists of two types of unit cells that have different incident angles and TE/TM polarization. This design minimizes the performance degradation caused by the oblique incident angle when designing a low profile TA antenna. In addition, the set of transmission phases to minimize transmission loss is selected by employing the optimal output phase reference. To verify its feasibility, a low profile TA (focal length/diameter of FSS =0.24) antenna that employs a unit patch antenna with a low gain and wide beamwidth as a feed antenna without an additional structure is designed. The simulated and measured results are in good agreement. In particular, the high simulated and measured aperture efficiencies of 42.7% and 41.9%, respectively, are obtained at 10GHz, respectively.