High-performance Network Over Coax, or HINOC for short, is a broadband access technology that can achieve bidirectional transmission for high-speed Internet service through a coaxial medium. In HINOC access networks, buffer management scheme can improve the fairness of buffer usage among different output ports and the overall loss performance. To provide different services to multiple priority classes while reducing the overall packet loss rate and ensuring fairness among the output ports, this study proposes a QoS optimization method for access networks. A backpressure-based queue threshold control scheme is used to minimize the weighted average packet loss rate among multiple priorities. A theoretical analysis is performed to examine the performance of the proposed scheme, and optimal system parameters are provided. Software simulation shows that the proposed method can improve the average packet loss rate by about 20% to 40% compared with existing buffer management schemes. Besides, FPGA evaluation reveals that the proposed method can be implemented in practical hardware and performs well in access networks.

In recent years, autonomous driving technologies are being developed for vehicles and personal mobility devices including golf carts and autonomous wheelchairs for various use cases, not only outside areas but inside areas like shopping malls, hospitals and airpots. The main purpose of developing these autonomous vehicles is to avoid the traffic accidents caused by human errors, to assist people with walking, and to improve human comfort by relieving them from driving. Most relevant research focuses on the efficiency and safety of autonomous driving, however, in order to use by the widespread of people in the society, it is important to consider passenger comfort inside vehicles as well as safety and efficiency. Therefore, in this work, we emphasize the importance of considering passenger comfort in designing the control loop of autonomous navigation for the concept of comfortable intelligence in the future autonomous mobility. Moreover, passenger characteristics, in terms of ride comfort in an autonomous vehicle, have not been investigated with regard to safety and comfort, depending on each passenger's driving experience, habits, knowledge, personality, and preference. There are still few studies on the optimization of autonomous driving control reflecting passenger characteristics and different stress factors during the ride. In this study, passenger stress characteristics with different stress factors were objectively analyzed using physiological indices (heart rate and galvanic skin response sensors) during autonomous wheelchair usages. Two different experimental results from 12 participants suggest that there are always at least two types of passengers: one who experiences stress and the other who does not, depending on the stress factors considered. Moreover, with regard to the classification result for the stress reduction method, there are two types of passenger groups, for whom the solution method is, respectively, either effective or ineffective.

With the evolution of autonomous distributed systems such as smart cities, autonomous vehicles, smart control and scheduling systems there is an increased need for approaches to manage the execution of services to deliver real-time performance. As Cloud-hosted services are increasingly used to provide intelligence and analytic functionality to Internet of Things (IoT) systems, Quality of Service (QoS) techniques must be used to guarantee the timely service delivery. This paper reviews state-of-the-art QoS and Cloud techniques for real-time service delivery and data analysis. A review of straggler mitigation and a classification of real-time QoS techniques is provided. Then a mathematical framework is presented capturing the relationship between the host execution environment and the executing service allowing the response-times to predicted throughout execution. The framework is shown experimentally to reduce the number of QoS violations by 21% and provides alerts during the first 14ms provide alerts for 94% of future violations.

With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.

The locally one-dimensional finite-difference time-domain (FDTD) method in cylindrical coordinates is extended to a frequency-dependent version. The fundamental scheme is utilized to perform matrix-operator-free formulations in the right-hand sides. For the analysis of surface plasmon polaritons propagating along a plasmonic grating, the computation time is significantly reduced to less than 10%, compared with the explicit cylindrical FDTD method.

A plasmonic black pole (PBP) consisting of a series of touching spherical metal surfaces is analyzed using the finite-difference time-domain (FDTD) method with the periodic boundary condition. First, the wavelength characteristics of the PBP are studied under the assumption that the PBP is omnidirectionally illuminated. It is found that partial truncation of each metal sphere reduces the reflectivity over a wide wavelength range. Next, we consider the case where the PBP is illuminated with a cylindrical wave from a specific direction. It is shown that an absorptivity of more than 80% is obtained over a wavelength range of λ=500 nm to 1000 nm. Calculation regarding the Poynting vector distribution also shows that the incident wave is bent and absorbed towards the center axis of the PBP.

Operations, such as text entry and zooming, are simple and frequently used on mobile touch devices. However, these operations are far from being perfectly supported. In this paper, we present our prototype, BackAssist, which takes advantage of back-of-device input to augment front-of-device touch interaction. Furthermore, we present the results of a user study to evaluate whether users can master the back-of-device control of BackAssist or not. The results show that the back-of-device control can be easily grasped and used by ordinary smart phone users. Finally, we present two BackAssist supported applications - a virtual keyboard application and a map application. Users who tried out the two applications give positive feedback to the BackAssist supported augmentation.

Reinforcement learning has been used to adaptive service composition. However, traditional algorithms are not suitable for large-scale service composition. Based on Q-Learning algorithm, a multi-task oriented algorithm named multi-Q learning is proposed to realize subtask-assistance strategy for large-scale and adaptive service composition. Differ from previous studies that focus on one task, we take the relationship between multiple service composition tasks into account. We decompose complex service composition task into multiple subtasks according to the graph theory. Different tasks with the same subtasks can assist each other to improve their learning speed. The results of experiments show that our algorithm could obtain faster learning speed obviously than traditional Q-learning algorithm. Compared with multi-agent Q-learning, our algorithm also has faster convergence speed. Moreover, for all involved service composition tasks that have the same subtasks between each other, our algorithm can improve their speed of learning optimal policy simultaneously in real-time.

Predicting the service performance of Internet applications is important in service selection, especially for video services. In order to design a predictor for forecasting video service performance in third-party application, two famous service providers in China, Iqiyi and Letv, are monitored and analyzed. The study highlights that the measured performance in the observation period is time-series data, and it has strong autocorrelation, which means it is predictable. In order to combine the temporal information and map the measured data to a proper feature space, the authors propose a predictor based on a Conditional Restricted Boltzmann Machine (CRBM), which can capture the potential temporal relationship of the historical information. Meanwhile, the measured data of different sources are combined to enhance the training process, which can enlarge the training size and avoid the over-fit problem. Experiments show that combining the measured results from different resolutions for a video can raise prediction performance, and the CRBM algorithm shows better prediction ability and more stable performance than the baseline algorithms.

In recent years, since Turbo and LDPC codes are very close to the Shannon limit, a great deal of attention has been placed on the capacity of AWGN and fading channels with arbitrary inputs. However, no closed-form solution has been developed due to the complicated Gaussian integrations. In this paper, we investigate the capacity of AWGN and fading channels with BPSK/QPSK modulation. First, a simple series representation with fast-convergence for the capacity of AWGN is developed. Further, based on the series expression, the capacity of fading channels including Rayleigh, Nakagami and Rice fading can be obtained through some special functions. Numerical results verify the accuracy and convergence speed of the proposed expressions for the capacity of AWGN and fading channels.

Public safety networks need to more effectively meet the increasing demands for images or videos to be shared among first responders and incident commanders. Long term evolution (LTE) networks are considered to be candidates to achieve such broadband services. Capital expenditures in deploying base stations need to be decreased to introduce LTE for public safety. However, out-of-coverage areas tend to occur in cell edge areas or inside buildings because the cell areas of base stations for public safety networks are larger than those for commercial networks. The 3rd Generation Partnership Program (3GPP) in Release 13 has investigated device-to-device (D2D) based relay communication as a means to fill out-of-coverage areas in public safety LTE (PS-LTE). This paper proposes a relay selection scheme based on effective path throughput from an out-of-coverage terminal to a base station via an in-coverage relay terminal, which enables the optimal relay terminal to be selected. System level simulation results assuming on radii of 20km or less revealed that the proposed scheme could provide better user ratios that satisfied the throughput requirements for video transmission than the scheme standardized in 3GPP. Additionally, an evaluation that replicates actual group of fire-fighters indicated that the proposed scheme enabled 90% of out-of-coverage users to achieve the required throughput, i.e., 1.0Mbps, to transmit video images.

We have studied on thermally assisted nano-structured transistors made of superconductor ultra-thin films. These transistors potentially work as interface devices for Josephson-CMOS (complementary metal oxide semiconductor) hybrid memory systems, because they can generate a high output voltage of sub-V enough to drive a CMOS transistor. In addition, our superconductor transistors are formed with very fine lines down to several tens of nm in widths, leading to very small foot print enabling us to make large capacity hybrid memories. Our superconductor transistors are made with niobium titanium nitride (NbTiN) thin films deposited on thermally-oxidized silicon substrates, on which other superconductor circuits or semiconductor circuits can be formed. The NbTiN thickness dependence of the critical temperature and of resistivity suggest thermally activated vortex or anti-vortex behavior in pseudo-two-dimensional superconducting films plays an important role for the operating principle of the transistors. To show the potential that the transistors can drive MOS transistors, we analyzed the driving ability of the superconductor transistors with HSPICE simulation. We also showed the turn-on behavior of a MOS transistor used for readout of a CMOS memory cell experimentally. These results showed the high potential of superconductor transistors for Josephson-CMOS hybrid memories.

To prevent constraints or defects of a single sensor from malfunctions, this paper proposes a fire detection system based on the Dempster-Shafer theory with multi-sensor technology. The proposed system operates in three stages: measurement, data reception and alarm activation, where an Arduino is tasked with measuring and interpreting the readings from three types of sensors. Sensors under consideration involve smoke, light and temperature detection. All the measured data are wirelessly transmitted to the backend Raspberry Pi for subsequent processing. Within the system, the Raspberry Pi is used to determine the probability of fire events using the Dempster-Shafer theory. We investigate moderate settings of the conflict coefficient and how it plays an essential role in ensuring the plausibility of the system's deduced results. Furthermore, a MySQL database with a web server is deployed on the Raspberry Pi for backlog and data analysis purposes. In addition, the system provides three notification services, including web browsing, smartphone APP, and short message service. For validation, we collected the statistics from field tests conducted in a controllable and safe environment by emulating fire events happening during both daytime and nighttime. Each experiment undergoes the No-fire, On-fire and Post-fire phases. Experimental results show an accuracy of up to 98% in both the No-fire and On-fire phases during the daytime and an accuracy of 97% during the nighttime under reasonable conditions. When we take the three phases into account, the accuracy in the daytime and nighttime increase to 97% and 89%, respectively. Field tests validate the efficiency and accuracy of the proposed system.

In this study, we propose a deep neural generative model for predicting daily stock price movements given news articles. Approaches involving conventional technical analysis have been investigated to identify certain patterns in past price movements, which in turn helps to predict future price movements. However, the financial market is highly sensitive to specific events, including corporate buyouts, product releases, and the like. Therefore, recent research has focused on modeling relationships between these events that appear in the news articles and future price movements; however, a very large number of news articles are published daily, each article containing rich information, which results in overfitting to past price movements used for parameter adjustment. Given the above, we propose a model based on a generative model of news articles that includes price movement as a condition, thereby avoiding excessive overfitting thanks to the nature of the generative model. We evaluate our proposed model using historical price movements of Nikkei 225 and Standard & Poor's 500 Stock Index, confirming that our model predicts future price movements better than such conventional classifiers as support vector machines and multilayer perceptrons. Further, our proposed model extracts significant words from news articles that are directly related to future stock price movements.

The widespread application of mobile electronic devices has triggered a boom in energy consumption, especially in user equipment (UE). In this paper, we investigate the energy-efficiency (EE) of a UE experiencing the worst channel conditions, which is termed worst-EE. Due to the limited battery of the mobile equipment, worst-EE is a suitable metric for EE fairness optimization in the uplink transmissions of orthogonal frequency division multiple access (OFDMA) networks. More specifically, we determine the optimal power and sub-carrier allocation to maximize the worst-EE with respect to UEs' transmit power, sub-carriers and statistical quality-of-service (QoS). In order to maximize the worst-EE, we formulate a max-min power and sub-carrier allocation problem, which involves nonconvex fractional mixed integer nonlinear programming, i.e., NP-hard to solve. To solve the problem, we first relax the allocation of sub-carriers, formulate the upper bound problem on the original one and prove the quasi-concave property of objective function. With the aid of the Powell-Hestenes-Rockfellar (PHR) approach, we propose a fairness EE sub-carrier and power allocation algorithm. Finally, simulation results demonstrate the advantages of the proposed algorithm.

Creating new content based on existing original work is becoming popular especially among amateur creators. Such new content is called derivative work and can be transformed into the next new derivative work. Such derivative work creation is called “N-th order derivative creation.” Although derivative creation is popular, the reason an individual derivative work was created is not observable. To infer the factors that trigger derivative work creation, we have proposed a model that incorporates three factors: (1) original work's attractiveness, (2) original work's popularity, and (3) derivative work's popularity. Based on this model, in this paper, we describe a public web service for browsing derivation factors called Songrium Derivation Factor Analysis. Our service is implemented by applying our model to original works and derivative works uploaded to a video sharing service. Songrium Derivation Factor Analysis provides various visualization functions: Original Works Map, Derivation Tree, Popularity Influence Transition Graph, Creator Distribution Map, and Creator Profile. By displaying such information when users browse and watch videos, we aim to enable them to find new content and understand the N-th order derivative creation activity at a deeper level.

Device-to-device (D2D) networks are expected to play a number of roles, such as increasing frequency spectrum efficiency and improving throughput at hot-spots. In this paper, our interest is on the potential of D2D on reducing delivery latency. To enable fast D2D network forming, quick device discovery is essential. For quickening device discovery, we propose a method of defining and using common channel and group channels so as to avoid the channel scan uncertainty faced by the conventional method. Rules for using the common channel and group channels are designed. We evaluate and compare the discovery performance of the proposed method with conventional method by using the superframe structure defined in IEEE 802.15.8 and a general discovery procedure. IEEE 802.15.8 is a standard under development for fully distributed D2D communications. A Netlogo simulator is used to perform step by step MAC simulations. The simulation results verify the effectiveness of the proposed method.

The centralized controller of SDN enables a global topology view of the underlying network. It is possible for the SDN controller to achieve globally optimized resource composition and utilization, including optimized end-to-end paths. Currently, resource composition in SDN arena is usually conducted in an imperative manner where composition logics are explicitly specified in high level programming languages. It requires strong programming and OpenFlow backgrounds. This paper proposes declarative path composition, namely Compass, which offers a human-friendly user interface similar to natural language. Borrowing methodologies from Semantic Web, Compass models and stores SDN resources using OWL and RDF, respectively, to foster the virtualized and unified management of the network resources regardless of the concrete controller platform. Besides, path composition is conducted in a declarative manner where the user merely specifies the composition goal in the SPARQL query language instead of explicitly specifying concrete composition details in programming languages. Composed paths are also reused based on similarity matching, to reduce the chance of time-consuming path composition. The experiment results reflect the applicability of Compass in path composition and reuse.

Major cloud service providers, including Amazon and Microsoft, have started employing field-programmable gate arrays (FPGAs) to build high-performance and low-power-consumption cloud capability. However, utilizing an FPGA-enabled cloud is still challenging because of two main reasons. First, the introduction of software and hardware co-design leads to high development complexity. Second, FPGA virtualization and accelerator scheduling techniques are not fully researched for cluster deployment. In this paper, we propose an open-source FPGA-as-a-service (FaaS) platform, the hCODE, to simplify the design, management and deployment of FPGA accelerators at cluster scale. The proposed platform implements a Shell-and-IP design pattern and an open accelerator repository to reduce design and management costs of FPGA projects. Efficient FPGA virtualization and accelerator scheduling techniques are proposed to deploy accelerators on the FPGA-enabled cluster easily. With the proposed hCODE, hardware designers and accelerator users can be organized on one platform to efficiently build open-hardware ecosystem.

This paper describes analytical results obtained for floor penetration loss characteristics and their frequency dependency by measurements in multiple frequency bands, including those above 6GHz, in an indoor office environment. Measurement and analysis results confirm that the floor penetration loss depends on two dominant components: the transmission path through floors, and the path traveling through the outside building. We also clarify that these dominant paths have different path loss characteristics and frequency dependency. The transmission path through floors rapidly attenuates with large inter-floor offsets and in high frequency bands. On the other hand, the path traveling through outside of the building attenuates monotonically as the frequency increases. Therefore, the transmission path is dominant at short inter-floor offsets and low frequencies, and the path traveling through the outside is dominant at high number of floors or high frequency. Finally, we clarify that the floor penetration loss depends on the frequency dependency of the dominant path on the basis of the path loss characteristics of each dominant path.