This paper presents a printed circuit board (PCB) integrated multi-layered strip line tandem coupler, which used simple compensating ground through-hole (GTH) elements. The GTH elements on one end of the coupled line can generate additional capacitance between the signal line and the ground, which effectively compensates for the parasitic capacitance around the crossed signal lines on the opposite end of the coupled line. It has been experimentally demonstrated that the proposed coupler fabricated for the X-band is effective to improve both the reflection and the isolation characteristics.

With the increase of the number of wireless sensing or metering devices, the collection of sensing data using wireless communication becomes an important part of a smart grid system. Cognitive radio technology can be used to facilitate the deployment of smart grid systems. In this paper, we propose a data collection and dissemination framework for cognitive radio smart grid systems to fully utilize wireless resources while maintaining a reliably connected and efficient topology for each channel. In the proposed framework, each sensor node selects a channel considering the primary user (PU) channel utilization and network connectivity. In this way, the data collection and dissemination can be performed with a high reliability and short delay while avoiding a harmful effect on primary users. We use computer simulations to evaluate the proposed framework.

This paper presents an experimental investigation on the RF characteristics of a 3W-class cryogenically-cooled receiver amplifier employing a gallium-nitride high electron mobility transistor (GaN HEMT) with a blue light for mobile base stations. In general, a cryogenically-cooled receiver amplifier using a GaN HEMT exhibits unstable DC characteristics similar to those found in the current collapse phenomenon because the GaN HEMT loses thermal energy at cryogenic temperatures. The fabricated cryogenically-cooled receiver amplifier achieves stable DC characteristics by injecting blue light into the GaN HEMT instead of thermal energy. Experimental results show that the amplifier achieves fine stable DC characteristics for deviation in the drain-source current from 42% to 5% and RF characteristics for a maximum power added efficiency from 58% to 68% without and with the blue light at 60,K. The fabricated amplifier is effective in reducing the power consumption at cryogenic temperatures. To the best of our knowledge, this paper is the first report regarding RF characteristics of a cryogenically-cooled receiver amplifier using a blue light for mobile base stations.

A simple, low reflection, and highly-efficient pilot-plant scale microwave irradiation reactor for woody biomass pretreatment was fabricated. Pretreatment is an essential process for effective bioethanol production. The fabricated reactor consists of 8 microwave irradiators which are attached to a metal pipe. The woody biomass mixture which contains water and organic acid flows through the metal pipe and is heated by microwaves at a total power of 12,kW. To design the microwave irradiators, we used a 3D Finite Element Method (FEM) simulator, which was based on the measured complex permittivity data of the woody biomass mixture. The simulation results showed that the reflection coefficient $|S_{11}|$ from the reactor was less than -30,dB when the woody biomass mixture temperature was between 30$^{circ}$C and 90$^{circ}$C. Finally, we experimentally confirmed that the fabricated irradiation reactor yielded a microwave absorption efficiency of 79%.

Anomaly detection has several practical applications in different areas, including intrusion detection, image processing, and behavior analysis among others. Several approaches have been developed for this task such as detection by classification, nearest neighbor approach, and clustering. This paper proposes alternative clustering algorithms for the task of anomaly detection. By employing a weighted kernel extension of the least squares fitting of linear manifolds, we develop fuzzy clustering algorithms for kernel manifolds. Experimental results show that the proposed algorithms achieve promising performances compared to hard clustering techniques.

Cloud computing, a novel distributed paradigm to provide powerful computing capabilities, is usually adopted by developers and researchers to execute complicated IoT applications such as complex workflows. In this scenario, it is fundamentally important to make an effective and efficient workflow application scheduling and execution by fully utilizing the advantages of the cloud (as virtualization and elastic services). However, in the current stage, there is relatively few research for workflow scheduling in cloud environment, where they usually just bring the traditional methods directly into cloud. Without considering the features of cloud, it may raise two kinds of problems: (1) The traditional methods mainly focus on static resource provision, which will cause the waste of resources; (2) They usually ignore the performance fluctuation of virtual machines on the physical machines, therefore it will lead to the estimation error of task execution time. To address these problems, a novel mechanism which can estimate the probability distribution of subtask execution time based on background VM load series over physical machines is proposed. An elastic performance fluctuations-aware stochastic scheduling algorithm is introduced in this paper. The experiments show that our proposed algorithm can outperform the existing algorithms in several metrics and can relieve the influence of performance fluctuations brought by the dynamic nature of cloud.

Energy harvesting technology was introduced into wireless sensor networks (WSNs) to solve the problem of the short lifetimes of sensor nodes. The technology gives sensor nodes the ability to convert environmental energy into electricity. Sufficient electrical energy can lengthen the lifetime and improve the quality of service of a WSN. This paper proposes a novel use of mutual information to evaluate data transmission behavior in the energy harvesting WSNs. Data at a sink for a node deteriorates over time until the next periodic transmission from the node is received. In this paper, we suggest an optimized intermittent transmission method for WSNs that harvest energy. Our method overcomes the problem of information deterioration without increasing energy cost. We show that by using spatial correlation between different sensor nodes, our proposed method can mitigate information deterioration significantly at the sink.

Reversible logic is becoming more and more popular due to the fact that many novel technologies such as quantum computing, low power CMOS circuit design or quantum optical computing are becoming more and more realistic. In quantum computing, reversible computing is the main venue for the realization and design of classical functions and circuits. We present a new approach to synthesis of reversible circuits using Kronecker Functional Lattice Diagrams (KFLD). Unlike many of contemporary algorithms for synthesis of reversible functions that use n×n Toffoli gates, our method synthesizes functions using 3×3 Toffoli gates, Feynman gates and NOT gates. This reduces the quantum cost of the designed circuit but adds additional ancilla bits. The resulting circuits are always regular in a 4-neighbor model and all connections are predictable. Consequently resulting circuits can be directly mapped in to a quantum device such as quantum FPGA [14]. This is a significant advantage of our method, as it allows us to design optimum circuits for a given quantum technology.

This paper presents an efficient multi-service allocation scheme for the digital television terrestrial broadcasting systems in which the fixed service is modulated by orthogonal frequency division multiplexing and quadrature amplitude modulation (OFDM/QAM) with larger FFT size and the added mobile service is modulated by OFDM and offset quadrature amplitude modulation (OQAM) with smaller FFT size. The two different types of services share one 8MHz broadcasting channel. The isotropic orthogonal transform algorithm (IOTA) is chosen as the shaping filter for OQAM because of its isotropic convergence in time and frequency domain and the proper FFT size is selected to maximum the transmission capacity under mobile environment. The corresponding transceiver architecture is also proposed and analyzed. Simulations show that the newly added mobile service generates much less out-of-band interference to the fixed service and has a better performance under fast fading wireless channels.

Researchers have developed several social-based routing protocols for delay tolerant networks (DTNs) over the past few years. Two main routing metrics to support social-based routing in DTNs are centrality and similarity metrics. These two metrics help packets decide how to travel through the network to achieve short delay or low drop rate. This study presents a new routing scheme called Community-Relevance based Opportunistic routing (CROP). CROP uses a different message forwarding approach in DTNs by combining community structure with a new centrality metric called community relevance. One fundamental change in this approach is that community relevance values do not represent the importance of communities themselves. Instead, they are computed for each community-community relationship individually, which means that the level of importance of one community depends on the packet's destination community. The study also compares CROP with other routing algorithms such as BubbleRap and SimBet. Simulation results show that CROP achieves an average delivery ratio improvement of at least 30% and can distribute packets more fairly within the network.

This paper gives a survey of intelligent computational techniques in medical and health care system. First, we briefly describe diagnosable techniques in medical image processing. Next, we demonstrate two ultrasonic surgery support systems for orthopedic and rectum cancer surgeons. In them, intelligent computational technique plays a primary role. Third, computational techniques are introduced in human health care system. Usually, this goal is not to apply clinical treatment but to home use to pay consciousness to health. In it, a simple ECG and respiration meter are introduced with a mat sheet which detects heart rate and respiration. Finally, a medical big data application is introduced, that is, body weight prediction is shown based on autoregressive model. Thus, we show that intelligent computing is effective and essential in modern medical and health care system.

Despite the increasing use of mobile computing, exploiting its full potential is difficult due to its inherent characteristics such as error-prone transmission channels, diverse node capabilities, frequent disconnections and mobility. Mobile Cloud Computing (MCC) is a paradigm that is aimed at overcoming previous problems through integrating mobile devices with cloud computing. Mobile devices, in the traditional client-server architecture of MCC, offload their tasks to the cloud to utilize the computation and storage resources of data centers. However, along with the development of hardware and software technologies in mobile devices, researchers have begun to take into consideration local resource sharing among mobile devices themselves. This is defined as the cooperation based architecture of MCC. Analogous to the conventional terminology, the resource platforms that are comprised of surrounding surrogate mobile devices are called local resource clouds. Some researchers have recently verified the feasibility and benefits of this strategy. However, existing work has neglected an important issue with this approach, i.e., how to construct local resource clouds in dynamic mobile wireless networks. This paper presents the concept and design of a local resource cloud that is both energy and time efficient. Along with theoretical models and formal definitions of problems, an efficient heuristic algorithm with low computational complexity is also presented. The results from simulations demonstrate the effectiveness of the proposed models and method.

This paper propose a novel method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that were generated by different people is not easy because they often contain private information. The authors split the roles of servers into publishing pseudonyms and collecting answers. Splitting these roles, different entities can more easily join as pseudonym servers than in previous secure multi-party computation methods and there is less chance of collusion between servers. Thus, our method enables data holders to protect themselves against malicious attacks from data users. We also estimated a typical problem that occurred with our method and added a pseudonym availability confirmation protocol to prevent the problem. We report our evaluation of the effectiveness of our method through implementation and experimentation and discuss how we incorporated the WebSocket protocol and MySQL Memoty Storage Engine to remove the bottleneck and improve the implementation style. Finally, we explain how our method can obtain averages, variances, and correlation from 5000 data holders within 50 seconds.

Since electromagnetic (EM) noise resulting from an arc discharge disturbs other electric devices, parameters on electromagnetic compatibility, as well as lifetime and reliability, are important properties for electrical contacts. To clarify the characteristics and the mechanism of the generation of the EM noise, the arc column and voltage fluctuations generated by slowly breaking contacts with external direct current (DC) magnetic field, up to 20,mT, was investigated experimentally using Ag$_{90.7{ m wt%}}$SnO$_{2,9.3{ m wt}%}$ material. Firstly the motion of the arc column is measured by high-speed camera. Secondary, the distribution of the motion of the arc and contact voltage are discussed. It was revealed that the contact voltage fluctuation in the arc duration is related to the arc column motion.

Curve extension is a useful function in shape modeling for cyberworlds, while a Ball B-spline Curve (BBSC) has its advantages in representing freeform tubular objects. In this paper, an extension algorithm for the BBSC with G2-continuity is investigated. We apply the extending method of B-Spline curves to the skeleton of the BBSC through generalizing a minimal strain energy method from 2D to 3D. And the initial value of the G2-continuity parameter for the skeleton is selected by minimizing the approximate energy function which is a problem with O(1) time complexity. The corresponding radius function of the control ball points is determined through applying the G2-continuity conditions for the skeleton to the scalar function. In order to ensure the radii of the control ball points are positive, we make a decision about the range of the G2-continuity parameter for the radius and then determine it by minimizing the strain energy in the affected area. Some experiments comparing our method with other methods are given. And at the same time, we present the advantage of our method in modeling flexibility from the aspects of the skeleton and radius. The results illustrate our method for extending the BBSC is effective.

In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.

We discuss a new high performance computing service (HPCS) platform that has been developed to provide domain-neutral computing service under the governmental support from “EDucation-research Integration through Simulation On the Net” (EDISON) project. With a first focus on technical features, we not only present in-depth explanations of the implementation details, but also describe the strengths of the EDISON platform against the successful nanoHUB.org gateway. To validate the performance and utility of the platform, we provide benchmarking results for the resource virtualization framework, and prove the stability and promptness of the EDISON platform in processing simulation requests by analyzing several statistical datasets obtained from a three-month trial service in the initiative area of computational nanoelectronics. We firmly believe that this work provides a good opportunity for understanding the science gateway project ongoing for the first time in Republic of Korea, and that the technical details presented here can be served as an useful guideline for any potential designs of HPCS platforms.

We propose a novel IDDQ outlier screening flow through a two-phase approach: a clustering-based filtering and an estimation-based current-threshold determination. In the proposed flow, a clustering technique first filters out chips that have high IDDQ current. Then, in the current-threshold determination phase, device-parameters of the unfiltered chips are estimated based on measured IDDQ currents through Bayesian inference. The estimated device-parameters will further be used to determine a statistical leakage current distribution for each test pattern and to calculate a and suitable current-threshold. Numerical experiments using a virtual wafer show that our proposed technique is 14 times more accurate than the neighbor nearest residual (NNR) method and can achieve 80% of the test escape in the case of small leakage faults whose ratios of leakage fault sizes to the nominal IDDQ current are above 40%.

The rendering mechanism plays an indispensable role in browser-based Web application. It generates active webpages dynamically and provides human-readable layout through template engines, which are used as a standard programming model to separate the business logic and data computations from the webpage presentation. The client-side rendering mechanism, owing to the advances of rich application technologies, has been widely adopted. The adoption of client side rendering brings not only various merits but also new problems. In this paper, we propose and construct “pagelet”, a segment-based template engine for developing flexible and extensible Web applications. By presenting principles, practice and usage experience of pagelet, we conduct a comprehensive analysis of possible advantages and disadvantages brought by client-side rendering mechanism from the viewpoints of both developers and end-users.

The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.