Search

Quantum cryptography is a major ingredient of the future quantum internet that promises various secure communication tasks. Quantum conference key agreement (CKA) is an important cryptographic primitive of quantum cryptography, which provides the conference key among multiple users simultaneously. However, quantum CKA is currently far from practical application due to the low conference key rate. Here, we propose a quantum CKA protocol of three users with information-theoretic security. Our protocol only requires phase-randomized weak coherent sources and threshold single-photon detectors, and is anticipated to be experimentally demonstrated over 600 km under current technology. Our scheme can be widely implemented in the approaching large-scale quantum network., Comment: 6 pages, 3 figures, accepted by IEEE Access

Clock Skew Scheduling has become a common practice in state-of-the-art FPGAs with the introduction of delay chains on the clock path in the hardware of both Xilinx and Intel® FPGAs, as well as clock skew scheduling algorithms in the CAD tools. Ideally, globally optimal solutions are sought to find the best solution across the entire design. However, using Mixed-Integer Linear Programming (MILP) to find such optimal solutions has a large and sometimes unrealistic runtime cost, especially for bigger designs. Besides, a high runtime does not necessarily correlate to improved performance. We present, in this paper, techniques to reduce the runtime of mixed-integer linear programming approaches to clock skew scheduling, and provide alternatives that can achieve optimal or near-optimal performance gain with a fraction of the MILP runtime cost.

Decision tree is one of the models that are often used in classification. Pruning is necessary for decision tree in order to prevent overfitting. With the advent of the era of big data, there is one well-known folklore that, using decision tree pruning algorithms, growing trees with increasingly larger amounts of training data will result in larger tree size even when accuracy does not increase. This paper discusses issues in pruning a scalable classifier and presents the design of a new parallel algorithm based on error-based pruning. The improved error-based pruning (IEBP) uses k-fold cross validation and t-test to choose the optimal value for the certainty factor, which controls the pruning, instead of using the default one. Different experiments have been made to compare the behavior of the behavior of IEBP with the behavior of error-based pruning and reduced-error pruning. The Experimental results support the conclusion that varying the certainty factor allows significantly smaller trees to be obtained with no or minimal accuracy loss on large data sets.

A modified version of Neural Decision Tree (NDT) which was initially proposed by Biau is presented in this paper with significantly extended statistical consistency theoretical results. Numerical experiments are performed to investigate several key features of this alternative NDT. Our experimental results demonstrate the attractiveness of NDT over the traditional artificial neural networks by adopting a strategically selected network structure with appropriate initial weights. Moreover, our results indicate that in all cases we have tested, NDT provides higher classification accuracy than a traditional neural network with a comparable degree of freedom. Empirical evidence also suggests that NDT is more stable and robust with respect to hyper-parameters commonly involved in the training of neural networks.

A method based on stratified measurement and inverse fitting is proposed to measure and analyze the intensity of power in solid medium aiming at the problem of power ultrasonic intensity measurement in solid. The thickness of the typical solid medium is determined according to the sound pressure distribution on the acoustic axis of the simulated sound field, and the surface acoustic field is measured separately to obtain the change of the sound intensity in the radial direction. Then the measurements of different thicknesses are fitted based on a fractional linear fit algorithm. Finally, the distribution of the power ultrasonic field in the solid is obtained by using the fitting equation. This paper also compares the experimental measurement results with the COMSOL simulation results. The two have a high degree of similarity in the trend of change, indicating that the measurement scheme is feasible.

Autonomous Vehicles (AVs) have great potential and can improve transportation efficiency and safety through minimal manual intervention and optimized traffic control systems. Advances in artificial intelligence and real-time data processing technology have promoted the development of practical AVs. AV manufacturers are trying to understand the potential factors that may affect consumers' acceptance of autonomous vehicles. However, there is very little research on autonomous vehicles and consumers. In order to understand these factors, this research will use UTAUT 2, as a research framework to predict consumer intentions and behaviors. This research will first review the literature, invite experts to define and evaluate appropriate criteria and dimensions, and use the ANFIS is used to derive the decision rules, and the weights of the corresponding rules are compared. The resulting analysis can be used as a basis for predicting consumer acceptance of AVs in the future.

The purpose of Industry 4.0 is to make the automation for industry and the current trends of technological innovations to use or produce equipment with IT technologies. This research uses AutomationML language as the main structure of Asset Administration Shell (AAS), which refers to the relevant specifications and standards of OPC to build and combine OPC UA (Open Process Control Unified Architecture) to realizes the information and communication layer in the six functions of RAMI4.0.

Object detection obtains huge improvement after adopting deep learning technique. However, deep learning technique requires extremely high computation complexity and heavy DRAM (Dynamic Random Access Memory) bandwidth requirements, which blocks the deployment over various kinds of platforms. This paper provides a distance-aware technique for object detection that can adjust the required computation complexity and DRAM access amount according to several different searching distances. According to our analysis, the perception system can save up to 34.2% and 21.5% of computation complexity and DRAM access amount, respectively, for detecting near field objects when compared to the full range detection, without sacrificing any detection accuracy.

The wavelength-routed optical network-on-chip (WRONoC) is a promising solution for signal transmission in modern system-on-chip (SoC) designs. Previous works do not handle three main issues for WRONoCs: correlations between the topological structure and physical layout, trade-offs between the maximum insertion loss and wavelength power, and a fully automated flow to generate predictable designs. As a result, the insertion loss estimation is inaccurate, and thus only suboptimal results are obtained. To remedy these disadvantages, we present a fully automated topological structure and physical layout codesign flow to minimize the maximum insertion loss and the wavelength power simultaneously with a significant speedup. Experimental results show that our codesign flow significantly outperforms state-of-the-art works in the maximum insertion loss, wavelength power, and runtimes.

As the VLSI technology continues to scale down, combined with increasing demands for large bandwidth and low-power consumption, the optical interconnections with Wavelength Division Multiplexing (WDM) become an attractive alternative for on-chip signal transmission. Previous WDM-aware optical routing works consist of two main drawbacks: they are based mainly on heuristics or restricted integer linear programming to handle optical routing, and the addressed types of transmission loss and WDM overheads are incomplete. As a result, no performance guarantees can be achieved on their WDM clustering results, and/or their computations are too time-consuming. To remedy these disadvantages, we present a polynomial-time provably good WDM-aware clustering algorithm and a new WDM-aware optical routing flow to minimize the transmission loss and the WDM overheads with a significant speedup. The proposed WDM-aware clustering algorithm guarantees to find an optimal solution for 1-, 2-, and 3-path clustering, and has the constant performance bound 3 for most cases of 4-path clustering. Experimental results based on the ISPD 2007 and 2019 contest benchmarks and a real optical design show that our optical router significantly outperforms published works in wirelength, transmission loss, wavelength power, and runtimes.

Inspired by the success of semiconductor-chip-based MEMs rotation sensors, there has long been interest in the possible realization of chip-based optical gyroscopes. Such devices could potentially be lightweight and rugged, while featuring some of the sensitivity advantages of Sagnac-based sensing devices. However, the performance of integrated-optical gyroscopes has lagged behind MEMS devices on account of difficult-to-achieve requirements for low-optical-loss chip-based waveguides and optical resonators. Here, a chip-based ring laser gyroscope is described. Its sensitivity is high enough to measure the Earth's rotation. The physical principles of its operation and its current performance will be reviewed.

Mangrove forests are an important wetland ecosystem that provides numerous valuable resources to the marine environment and people. Unfortunately, mangrove forests are already one of threatened environments globally mainly by human disturbance, urban development and tourism activities. The change of wetland mangroves has become an important ecological indicator of the impact on coastal areas. Understanding the factors controlling wetland structure is vital to conservation and management. The issue of mangrove change is valued by ecologists, but it is difficult to survey the area through traditional methods. In this study, multi-temporal FORMOSAT-2 and FORMOSAT-5 satellite images with a resolution of 4 metres were used to detect mangrove cover and changes. Changes of the mangroves over the study period showed that the mangroves have a slight growth trend from 2016 to 2019, and the changes are located at the edge of channel and mangrove forest. Using NDVI, the areas of mangroves were estimated at 10.5-ha., 10.6-ha. and 10.62-ha. in 2016, 2017 and 2019, respectively. The overall Kappa coefficients are between 0.86-0.94. Finally, the technique of satellite remote sensor can be a quick assessment of mangroves, which provides meaningful information for wetland management.

In recent years, one of the popular method for mining evolving data, is to convert it to a network, in fact an adjacency matrix, where data units are nodes and their relations/similarity are the link weights. The network is then partitioned into communities to explore information hidden in the data. For last 50 years, various graph partitioning algorithms are proposed. Depending on the application, the optimization objective of the partitioning and the resulting clusters are different. Different algorithms, based on Linear Algebra, heuristics with different greedy optimization criterion, agglomerative algorithms, are proposed to meet different optimization criterion suitable for target network and application.In this work we proposed a genetic algorithm (GA) based dynamic clustering algorithm. Genetic algorithm (GA) for graph clustering, with fitness function as the modularity index, is already proposed, in our previous works. In this work, we propose a multimodal GA. The algorithm starts with modularity index (Q) as the optimization criterion. Once converged, we add another term in the fitness function which will balance the cardinalities of the partitions. The parameters are computed based on the partitions after the first stage of convergence. We continue to run the Genetic search with modified fitness function until a second convergence is achieved. In all our experiments, not only could we achieve better balance of the size of different clusters, in many experiments it actually improved the modularity index. For a highly modular graph, with Q ≥ 0.7, most of the algorithms produce the same result. When the optimum modularity index of the graph is low, GA with only modularity index as optimization criterion usually converges in local minimum. With the proposed modification, we could always find clustering with an improved Q value. We run popular partitioning algorithms on known real-world networks and found that the proposed algorithm could find better partitioning, closest to reality.

Purpose: The purpose of this paper is to explore the self–categorization theory of sports consumer behaviors. This research proposed a conceptual model delineating the social psychology of basketball fans during watching basketball games. Methods: An questionnaire with a convenience sample is designed for surveying fans of Chinese Taipei Basketball team in the FIBA Asia Basketball World Cup qualification. Structural equation modeling, including confirmatory factor analysis (CFA) and path analysis validated and confirmed the relationships between constructs in the model. Findings: Results indicated that Taiwanese fan identification is completely mediated in two factors: team image and supporter loyalty. There is a significantly negative predicting effect of correlation between fan identification and supporter satisfaction. However, the result still had correlation to the cultural difference with countries as research in the past. The factor of fan identification influenced fans' satisfaction towards the team they support. Discussion: The research concluded with suggestions for Taiwanese sports consumers to clarify their self-categorization and interpreted the factor that influenced fans' support to the team. The research also indicated that the variation in sports consumers from different countries and cultures affected fans' satisfaction level of their support. The questionnaire of this research was adopted by cross-sectional study approach. The result only came from a single sports event and therefore, it cannot be applied in other sports field. Longitudinal survey was suggested in the future research for professional basketball games with a view to deeply understanding the process of fans' self-categorization.

Spatial audio is an essential medium to audiences for 3D visual and auditory experience. However, the recording devices and techniques are expensive or inaccessible to the general public. In this work, we propose a self-supervised audio spatialization network that can generate spatial audio given the corresponding video and monaural audio. To enhance spatialization performance, we use an auxiliary classifier to classify ground-truth videos and those with audio where the left and right channels are swapped. We collect a large-scale video dataset with spatial audio to validate the proposed method. Experimental results demonstrate the effectiveness of the proposed model on the audio spatialization task., Comment: ICIP 2019

With the increasing demand for Internet access on the public transport, enabling network support on trains becomes an important topic. Trains can move at high speeds and for long distances, so their networks need to attach to different points of the Internet during mobility. In order to prevent user devices from experiencing any interruption of Internet access, it calls for network mobility (NEMO) support to handover the train's network from one access network to another. Though the network mobility protocols have been stipulated in IETF standards, only the default IPv6 NEMO is supported in commercial off-the-shelf (COTS) routers. It causes an issue to support mobility for mobile IPv4 wireless networks, which most networks and devices are using. Moreover, we discover that an intuitive solution, NAT-PT (Network Address Translator-Protocol Translator) for the IPv4/IPv6 conversion, is not applicable to addressing the issue. To this end, we propose a practical solution that uses COTS routers to support the IPv4 network mobility. On top of the IPv6 NEMO protocol, it leverages other three techniques: IPv4 tunneling, IPv4 over IPv6 tunnel, and network address translation (NAT). It allows user devices on a train to access the Internet via the train's mobile router, an access router on the ground, and the mobile router's home agent. Our prototype and evaluation results confirm the viability of the solution.

In this paper, triple-passband bandpass filter (BPF) with wide stopband is proposed. The filter is designed to have triple-passband at 1.8, 2.4 and 3.5 GHz. The 1.8 and 2.4 GHz are generated by controlling the impedance and length of the asymmetric stub-loaded uniform impedance resonators (UIRs) and 3.5 GHz is generated by shorted uniform impedance resonator (UIR). By using the asymmetric stub-loaded UIRs, the filter with closed passbands and frequency response of wide stopband having around −30 dB stopband from 3.7 to 12 GHz. The measured results are in favourable agreement with the fullwave electromagnetic (EM) simulation results.

It is expected that the concept of Internet of everything will be realized in 2020 because of the coming of the 5G wireless communication technology. Internet of Things (IoT) services in various fields require different types of network service features, such as mobility, security, bandwidth, latency, reliability and control strategies. In order to solve the complex requirements and provide customized services, a new network architecture is needed. To change the traditional control mode used in the traditional network architecture, the Software Defined Network (SDN) is proposed. First, SDN divides the network into the Control Plane and Data Plane and then delegates the network management authority to the controller of the control layer. This allows centralized control of connections of a large number of devices. Second, SDN can help realizing the network slicing in the aspect of network layer. With the network slicing technology proposed by 5G, it can cut the 5G network out of multiple virtual networks and each virtual network is to support the needs of diverse users. In this work, we design and develop a network slicing framework. The contributions of this article are two folds. First, through SDN technology, we develop to provide the corresponding end-to-end (E2E) network slicing for IoT applications with different requirements. Second, we develop a dynamic network slice resource scheduling and management method based on SDN to meet the services’ requirements with time-varying characteristics. This is usually observed in streaming and services with bursty traffic. A prototyping system is completed. The effectiveness of the system is demonstrated by using an electronic fence application as a use case.

Detection of reverse-conduction in GaN-based switched-mode power converters is essential to reduce power loss and to protect the circuit from severe undershoot voltage. In this paper, an active clamping SenseFET circuit is proposed to measure the duration that the low-side GaN HEMT is in reverse-conduction and with protection from the high voltage at the switching node. The output of the SenseFET clamping circuit is processed by a custom designed gate driver IC. This IC is fabricated using TSMC's $0.18\ \boldsymbol{\mu} \mathbf{m}$ BCD GEN2 process for driving e-mode GaN power HEMTs with an on-chip closed-loop dead-time correction circuit. The one-step correction mode can optimize the dead-times for both the turn-on and turn-off edges in one switching cycle for switching frequencies of up to 10 MHz with 0.32 ns precision. This allow the power converters to maintain optimal conversion efficiency over the full output current range.

Heat dissipation capability of helical slow-wave structure is an important factor that affects output power, operating stability and reliability of travelling-wave tube (TWT). As diamond material has superbly high thermal conductivity, use of it in fabricating helical slow-wave structure is able to improve heat dissipation capability of the component to some extent. By means of theoretical calculation, simulation & emulation and laboratory test, this paper studies influences of diamond-film-deposited support rod, diamond-film-deposited helix and diamond support rod on heat dissipation capability of slow-wave structure. With a contrast study of experiment and simulation data, the computer simulation is closely correlated to experimental test, which improves the accuracy of computer simulation research and offers important references for application of diamond material in slow-wave structure.

Important contributions of this work are: (1) Earthquake aftershock prediction, both location and time of occurrence, using seismographic data only; (2) Representing seismographic data as a directed weighted graph and analyzing it to extract relevant data for localized prediction; (3) Validation of the data and prediction algorithm. The key contribution is to make aftershock prediction using seismographic data alone. Seismographic data units are represented as nodes of a graph, and a special relation between them depicts the directed weighted edge between two nodes. This special relation facilitates connecting main earthquake with aftershocks caused by it. Converting vast amount of seismographic data to corresponding connected graph, and efficiently analyzing the graph to find cluster of nodes as hubs, is the first step. Though the link weights are real, in this work we consider whether two nodes are connected or not, depending on the connection weight is above some threshold or not. Extracting data of a hub, to use it for aftershock prediction of that individual localized area, is proposed. Data Driven approach is better because it is universally applicable and does not require domain knowledge (here, geophysical and geological knowledge of the plate structure). The idea could be extended for mining any big data.

In this paper, gesture control for quadcopter is studied. Firstly, a basic quadcopter is assembled to analyze the flying mechanism. Secondly, PID controller is analyzed to adjust pitch, roll, and yaw of the aircraft. Thirdly, distinct gestures are captured by leap motion to control the flight of quadcopter, which includes takeoff, land, rotation, forward, backward, left, and right. From 50 experimental trials of different hand gestures, the quadcopter responds to the gesture control and achieves a stable flight.

High-resolution and wide-range depth maps are the key to enable novel light-field applications, such as digital refocusing, view synthesis, and 3D reconstruction. In this paper, we present an energy-efficient depth-estimation processor on FPGA to meet this purpose. There are two major contributions. First, image-guided depth inference and upsampling is adopted and implemented to provide accurate depth maps while lowering the working frequency from 215MHz to 54MHz. Second, octave search range sampling is proposed to efficiently allocate depth labels for wide-depth-range scenes to save computation and maintain accuracy. Finally, the implementation result on Xilinx ZC706 shows ASIC-comparable energy efficiency—95pJ/label—for Full-HD five-view light fields at 30fps.

In this paper, we presented the compact quad-band bandpass filter (BPF) using stepped impedance resonators (SIRs). The filter is designed to have quad-band at 1.2, 2.4, 3.5 and 5.2 GHz. The four pass bands are simultaneously generated by controlling the impedance and length ratios of the traditional and half wavelength stepped impedance resonators. The filter can provide the multi-path propagation to enhance the frequency response and achieving the compact circuit size. The measured results are in favourable agreement with the full-wave electromagnetic (EM) simulation results.

Formation of residual stress, as results of machining process, is a familiar fact. One question that needs to be raised is how to know the actual value of residual stress and its distribution in work. In this paper, we explore a detecting-system based on ultrasonic and Wi-Fi to measure residual stress in materials expediently. By using different frequencies and normal transducers, residual stresses in different depths and surface of a U-shaped block, designed with the states of opening and closing, are measured by cross-correlation algorithm about difference of times L CR wave spreading. The Finite Element method analyzing the U-shaped block provides an object contrast to verify the correctness of results detected by the detecting system. We evaluate significantly the actual value of residual stress by comparing a position existing residual stress with another position without residual stress. The second contribution of the paper is to build a system about calibrating the results of residual stress measured by ultrasonic.

This paper presents a control scheme for an even but fast velocity motion of a permanent-magnet synchronous motor. While confining the jounce for a gentle motion, the proposed scheme includes a time-optimal control law so as to achieve a fast response. The proposed scheme has been practically realized and experimentally compared with other conventional schemes.

The rapid aging of the global population has become a topic valued by all countries. At the same time, it also makes the issue of elderly care increasingly important. This paper focuses on elderly care institutions and proposes four care indicators. The four indicators are physiological function tracking, activity domain monitoring, fall prevention, and emergency help. This paper mainly uses smart clothes, BLE components and indoor positioning algorithm for smart elderly care system to collect the data from the residents in their daily life. After collecting the data, we will analyze the data to figure out the indicators we mention above. BLE components also have wearable detection, low power and signal loss warnings from preventing the device problem. Currently, this smart elderly care system is already imported to Taiwan's long-term care institutions.

Large-scale and loosely-coupled applications cannot be implemented directly on high-performance computing platforms. At the same time, the deployment and maintenance of high-performance computing and high-throughput computing will result in the waste of computing resources. In order to solve the problem that existing resource management systems cannot make high-throughput computing applications execute efficiently on high-performance computers, we propose, design and implement a high-throughput computing job execution framework Teno without modifying the existing configuration environment of Slurm on Tianhe-2. It uses Slurm to implement fine-grained resource scheduling through the idea of hierarchical scheduling, and optimizes the traditional Master-Worker model, thereby speeding up the high-throughput operation and increasing the effective utilization of cluster resources. Effective fault-tolerance mechanisms such as fault recovery and error retry are also implemented. Finally we design various experiments in Tianhe-2 to test and evaluate the key factors for high-throughput calculations of Teno, Slurm and HTCondor, and analyze in detail why the performance of Teno is over that of the other two.

Significant objects can be obtained from the gray or color images via image segmentation by referring to the clustering problem. Inspired by the search strategy of GSA and the rapid convergence of PSO, this study proposes a memetic particle gravitation optimization (MPGO) algorithm, which is a hybrid approach for performing image segmentation. The proposed MPGO algorithm is verified using six well-known images, including Lena, Baboon, F16, Goldhill, Pepper, and Sailboat. MPGO significantly outperforms k-means, PSO and GSA in measures of the PSNR values.

In this study, we investigate on the effects of deep learning based speech enhancement as a preprocessor to speaker diarization in quite challenging realistic environments involving the background noises, reverberations and overlapping speech. To improve the generalization capability, the advanced long short-term memory (LSTM) architecture with the novel design of hidden layers via densely connected progressive learning and output layer via multiple-target learning is proposed for preprocessing. We build the deep model using synthesized training data pairs generated from WSJO reading-style speech and more than 100 noise types. Surprisingly, this proposed preprocessor demonstrates a strong generalization capability to speaker di-arization with the realistic noisy speech in highly mismatched conditions, in terms of the speaking style, interferences, and the interaction between them. Tested on three challenging tasks, namely AMI, ADOS, and SeedLings, the state-of-the-art diarization system with the novel LSTM-based speech preprocessor can yield consistent and significant reductions of diarization error rate (DER) over the systems using unprocessed noisy speech and traditional enhancement methods.