This paper theoretically presents that a terahertz (THz) oscillator using a resonant tunneling diode (RTD) and a rectangular cavity, which has previously been proposed, can radiate high output power by the impedance matching between RTD and load through metal-insulator-metal (MIM) capacitors. Based on an established equivalent-circuit model, an equation for output power has been deduced. By changing MIM capacitors, a matching point can be derived for various sizes of rectangular-cavity resonator. Simulation results show that high output power is possible by long cavity. For example, a high output power of 5 mW is expected at 1 THz.

As scientific and technological resources are experiencing information overload, it is quite expensive to find resources that users are interested in exactly. The personalized recommendation system is a good candidate to solve this problem, but data sparseness and the cold starting problem still prevent the application of the recommendation system. Sparse data affects the quality of the similarity measurement and consequently the quality of the recommender system. In this paper, we propose a matrix factorization recommendation algorithm based on similarity calculation(SCMF), which introduces potential similarity relationships to solve the problem of data sparseness. A penalty factor is adopted in the latent item similarity matrix calculation to capture more real relationships furthermore. We compared our approach with other 6 recommendation algorithms and conducted experiments on 5 public data sets. According to the experimental results, the recommendation precision can improve by 2% to 9% versus the traditional best algorithm. As for sparse data sets, the prediction accuracy can also improve by 0.17% to 18%. Besides, our approach was applied to patent resource exploitation provided by the wanfang patents retrieval system. Experimental results show that our method performs better than commonly used algorithms, especially under the cold starting condition.

To address slow response and scattered resources in patent service, this paper proposes a one-stop service business model based on scientific and technological resource bundle. The proposed one-step model is composed of a project model, a resource bundle model and a service product model through Web Service integration. This paper describes the patent resource bundle model from the aspects of content and context, and designs the configuration of patent service products and patent resource bundle. The model is then applied to the patent service of the Yangtze River Delta urban agglomeration in China, and the monthly agent volume increased by 38.8%, and the average response time decreased by 14.3%. Besides, it is conducive to improve user satisfaction and resource sharing efficiency of urban agglomeration.

Multi-FPGA systems have been receiving a lot of attention as a low cost and energy efficient system for Multi-access Edge Computing (MEC). For such purpose, a bare-metal multi-FPGA system called FiC (Flow-in-Cloud) is under development. In this paper, we introduce the FiC multi FPGA cluster which is applied partial reconfiguration (PR) FPGA design flow to support online user defined accelerator replacement while executing FPGA interconnection network and its low-level multiple FPGA management software called remote PR manager. With the remote PR manager, the user can define the FiC FPGA cluster setup by JSON and control the cluster from user application with the cooperation of simple cluster management tool / library called ficmgr on the client host and REST API service provider called ficwww on Raspberry Pi 3 (RPi3) on each node. According to the evaluation results with a prototype FiC FPGA cluster system with 12 nodes, using with online application replacement by PR and on-the-fly FPGA bitstream compression, the time for FPGA bitstream distribution was reduced to 1/17 and the total cluster setup time was reduced by 21∼57% than compared to cluster setup with full configuration FPGA bitstream.

A neural network that outputs reconstructed images based on projection data containing scattered X-rays is presented, and the proposed scheme exhibits better accuracy than conventional computed tomography (CT), in which the scatter information is removed. In medical X-ray CT, it is a common practice to remove scattered X-rays using a collimator placed in front of the detector. In this study, the scattered X-rays were assumed to have useful information, and a method was devised to utilize this information effectively using a neural network. Therefore, we generated 70,000 projection data by Monte Carlo simulations using a cube comprising 216 (6 × 6 × 6) smaller cubes having random density parameters as the target object. For each projection simulation, the densities of the smaller cubes were reset to different values, and detectors were deployed around the target object to capture the scattered X-rays from all directions. Then, a neural network was trained using these projection data to output the densities of the smaller cubes. We confirmed through numerical evaluations that the neural-network approach that utilized scattered X-rays reconstructed images with higher accuracy than did the conventional method, in which the scattered X-rays were removed. The results of this study suggest that utilizing the scattered X-ray information can help significantly reduce patient dosing during imaging.

There are many knowledge entities in sci-tech intelligence resources. Extracting these knowledge entities is of great importance for building knowledge networks, exploring the relationship between knowledge, and optimizing search engines. Many existing methods, which are mainly based on rules and traditional machine learning, require significant human involvement, but still suffer from unsatisfactory extraction accuracy. This paper proposes a novel approach for knowledge entity extraction based on BiLSTM and conditional random field (CRF).A BiLSTM neural network to obtain the context information of sentences, and CRF is then employed to integrate global label information to achieve optimal labels. This approach does not require the manual construction of features, and outperforms conventional methods. In the experiments presented in this paper, the titles and abstracts of 20,000 items in the existing sci-tech literature are processed, of which 50,243 items are used to build benchmark datasets. Based on these datasets, comparative experiments are conducted to evaluate the effectiveness of the proposed approach. Knowledge entities are extracted and corresponding knowledge networks are established with a further elaboration on the correlation of two different types of knowledge entities. The proposed research has the potential to improve the quality of sci-tech information services.

Resource sharing is to ensure required resources available for their demanders. However, due to the lack of proper sharing model, the current sharing rate of the scientific and technological resources is low, impeding technological innovation and value chain development. Here we propose a novel method to share scientific and technological resources by storing resources as nodes and correlations as links to form a complex network. We present a few-shot relational learning model to solve the cold-start and long-tail problems that are induced by newly added resources. Experimentally, using NELL-One and Wiki-One datasets, our one-shot results outperform the baseline framework - metaR by 40.2% and 4.1% on MRR in Pre-Train setting. We also show two practical applications, a resource graph and a resource map, to demonstrate how the complex network helps resource sharing.

This paper reviews the evolutionary process that reduced the transmission loss of silica optical fibers from the report of 20dB/km by Corning in 1970 to the current record-low loss. At an early stage, the main effort was to remove impurities especially hydroxy groups for fibers with GeO2-SiO2 core, resulting in the loss of 0.20dB/km in 1980. In order to suppress Rayleigh scattering due to composition fluctuation, pure-silica-core fibers were developed, and the loss of 0.154dB/km was achieved in 1986. As the residual main factor of the loss, Rayleigh scattering due to density fluctuation was actively investigated by utilizing IR and Raman spectroscopy in the 1990s and early 2000s. Now, ultra-low-loss fibers with the loss of 0.150dB/km are commercially available in trans-oceanic submarine cable systems.

Robustness of complex networks is an essential subject for improving their performance when vertices or links are removed due to potential threats. In recent years, significant advancements have been achieved in this field by many researchers. In this paper we show an overview from a novel statistic perspective. We present a brief review about complex networks at first including 2 primary network models, 12 popular attack strategies and the most convincing network robustness metrics. Then, we focus on the correlations of 12 attack strategies with each other, and the difference of the correlations from one network model to the other. We are also curious about the robustness of networks when vertices are removed according to different attack strategies and the difference of robustness from one network model to the other. Our aim is to observe the correlation mechanism of centralities for distinct network models, and compare the network robustness when different centralities are applied as attacking directors to distinct network models. What inspires us is that maybe we can find a paradigm that combines several high-destructive attack strategies to find the optimal strategy based on the deep learning framework.

We propose online feedback path modeling with a pre-inverse type active noise control (PIANC) system to track the fluctuation stably in the feedback path. The conventional active noise control (ANC) system with online feedback path modeling (FBPM) filter bases filtered-x least mean square (FxLMS) algorithm. In the FxLMS algorithm, the error of FBPM influences a control filter, which generates an anti-noise, and secondary path modeling (SPM) filter. The control filter diverges when the error is too large. Therefore, it is difficult for the FxLMS algorithm to track the feedback path without divergence. On the other hand, the proposed approach converges stably because the FBPM filter's error does not influence a control filter on the PIANC system. Thus, the proposed method can reduce noise while tracking the feedback path. This paper verified the effectiveness of the proposed method by convergence analysis, computer simulation, and implementation of a digital signal processor.

This paper describes a novel 4K 120fps (frames per second) real-time HEVC (High Efficiency Video Coding) encoder for high-frame-rate video encoding and transmission. Motion portrayal problems such as motion blur and jerkiness may occur in video scenes containing fast-moving objects or quick camera panning. A high-frame-rate solves such problems and provides a more immersive viewing experience that can express even the fast-moving scenes without discomfort. It can also be used in remote operation for scenes with high motion, such as VAR (Video Assistant Referee) systems in sports. Real-time encoding of high-frame-rate videos with low latency and temporal scalability is required for providing such high-frame-rate video services. The proposed encoder achieves full 4K/120fps real-time encoding, which is twice the current 4K service frame rate of 60fps, by multichip configuration with two encoder LSI. Exchange of reference picture data near a spatially divided slice boundary provides cross-chip motion estimation, and maintains the coding efficiency. The encoder supports temporal-scalable coding mode, in which it output stream with temporal scalability transmitted over one or two transmission paths. The encoder also supports the other mode, low-delay coding mode, in which it achieves 21.8msec low-latency processing through motion vector restriction. Evaluation of the proposed encoder's multichip configuration shows that the BD-bitrate (the average rate of bitrate increase), compared to simple slice division without inter-chip transfer, is -2.86% at minimum and -2.41% on average in temporal-scalable coding mode. The proposed encoder system will open the door to the next generation of high-frame-rate UHDTV (ultra-high-definition television) services.

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.

Concept map has been widely used as an interactive media to deliver contents in learning. Incorporating concept maps into collaborative learning could promote more interactive and meaningful learning environments. Furthermore, delivering concept maps in a digital form, such as in Kit-Build concept map, could improve learning interaction further. Collaborative learning with Kit-Build concept map has been shown to have positive effects on students' understanding. The way students compose their concept maps while discussing with others is presumed to affect their learning. However, supporting collaborative learning in an online setting is formidable to keep the interaction meaningful and fluid. This study proposed a new approach of real-time collaborative learning with Kit-Build concept map. This study also investigated how concept map recomposition with Kit-Build concept map could help students collaboratively learn EFL reading comprehension from a distance by comparing it with the traditional open-ended concept mapping approach. The learning effect and students' conversation during collaboration with the proposed online Kit-Build concept map system were investigated. Comparative analysis with a traditional collaborative concept mapping approach is also presented. The results suggested that collaborative learning with Kit-Build concept map yielded better outcomes and more meaningful discussion than the traditional open-end concept mapping.

In this paper, the distributed cooperative communication of unmanned aerial vehicles (UAVs) is studied, where the condition number (CN) and the inner product (InP) are used to measure the quality of communication links. By optimizing the relative position of UAVs, large channel capacity and stable communication links can be obtained. Using the spherical wave model under the line of sight (LOS) channel, CN expression of the channel matrix is derived when there are Nt transmitters and two receivers in the system. In order to maximize channel capacity, we derive the UAVs position constraint equation (UAVs-PCE), and the constraint between BS elements distance and carrier wavelength is analyzed. The result shows there is an area where no matter how the UAVs' positions are adjusted, the CN is still very large. Then a special scenario is considered where UAVs form a rectangular lattice array, and the optimal constraint between communication distance and UAVs distance is derived. After that, we derive the InP of channel matrix and the gradient expression of InP with respect to UAVs' position. The particle swarm optimization (PSO) algorithm is used to minimize the CN and the gradient descent (GD) algorithm is used to minimize the InP by optimizing UAVs' position iteratively. Both of the two algorithms present great potentials for optimizing the CN and InP respectively. Furthermore, a hybrid algorithm named PSO-GD combining the advantage of the two algorithms is proposed to maximize the communication capacity with lower complexity. Simulations show that PSO-GD is more efficient than PSO and GD. PSO helps GD to break away from local extremum and provides better positions for GD, and GD can converge to an optimal solution quickly by using the gradient information based on the better positions. Simulations also reveal that a better channel can be obtained when those parameters satisfy the UAVs position constraint equation (UAVs-PCE), meanwhile, theory analysis also explains the abnormal phenomena in simulations.

In this study, two modification methods that employ graphene-coated carbon fiber woven fabric (GCFC) as an electrode and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid (ABTS) as a mediator were used to evaluate cathode performance. In addition, a prototype of an atmosphere-exposed ascorbic-acid enzyme biofuel cell (AAEBFC) consisting of an improved GCFC cathode and ABTS was evaluated. No modification was made in the anode region, and only the cathode region was coated with the enzyme of bilirubin oxidase (BOD). As a result of implementing an ABTS-modified cathode in the AAEBFC, an output of 721μW/cm2 was obtained at 0.189V. When the gel thickness was changed, an output of 1200μW/cm2 was obtained at 0.17V. To the best of our knowledge, this is currently the highest reported output for an AAEBFC fueled by ascorbic acid.

We previously developed a new terahertz (THz) wave detection method that utilizes the effect of nonlinear optical (NLO) polymers. The new method provided us with a gapless detection, a wide detection bandwidth, and a simpler optical geometry in the THz wave detection. In this paper, polarization dependences in THz wave detection by the Stark effect were investigated. The projection model was employed to analyze the polarization dependences and the consistency with experiments was observed qualitatively, surely supporting the use of the first-order Stark effect in this method. The relations between THz wave detection by the Stark effect and Stark spectroscopy or electroabsorption spectroscopy are also discussed.

The optical properties of new tricyanopyrroline (TCP)-based chromophores with a benzyloxy group bound to aminobenzene donor unit were characterized by hyper-Rayleigh scattering (HRS), absorption spectrum, and 1H-NMR measurements, and the influence of the benzyloxy group on TCP-based chromophores was discussed based on the data. A positive effect of NLO properties was found in TCP-based NLO chromophores with a benzyloxy group compared with benchmark NLO chromophores without the benzyloxy group, suggesting an influence of intra-molecular hydrogen bond. Furthermore, we propose a formation of double intra-molecular hydrogen bonds in the TCP chromophore with monoene as the π-conjugation bridge and aminobenzene with a benzyloxy group as the donor unit.

It is found that the electrical resistance-length characteristic in an electroactive supercoiled polymer artificial muscle strongly depends on the temperature. This may come from the thermal expansion of coils in the artificial muscle, which increases the contact area of neighboring coils and results in a lower electrical resistance at a higher temperature. On the other hand, the electrical resistance-length characteristic collected during electrical driving seriously deviates from those collected at constant temperatures. Inhomogeneous heating during electrical driving seems to be a key for the deviation.

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.

With the development of the Internet of Vehicles, License plate detection technology is widely used, e.g., smart city and edge senor monitor. However, traditional license plate detection methods are based on the license plate edge detection, only suitable for limited situation, such as, wealthy light and favorable camera's angle. Fortunately, deep learning networks represented by YOLOv3 can solve the problem, relying on strict condition. Although YOLOv3 make it better to detect large targets, its low performance in detecting small targets and lack of the real-time interactively. Motivated by this, we present a faster and lightweight YOLOv3 model for multi-vehicle or under-illuminated images scenario. Generally, our model can serves as a guideline for optimizing neural network in multi-vehicle scenario.